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Untargeted Metabolome- and Transcriptome-Wide Association Study Suggests Causal Genes Modulating Metabolite Concentrations in Urine
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  • Reyhan Sönmez Flitman*
    Reyhan Sönmez Flitman
    Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
    Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
    *Email: [email protected]
    More by Reyhan Sönmez Flitman
    Orcidhttps://orcid.org/0000-0002-4556-4075
  • Bita Khalili
    Bita Khalili
    Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
    Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
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    Orcidhttps://orcid.org/0000-0001-5630-1812
  • Zoltan Kutalik
    Zoltan Kutalik
    Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
    University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland
    Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
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  • Rico Rueedi
    Rico Rueedi
    Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
    Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
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  • Anneke Brümmer
    Anneke Brümmer
    Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
    Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
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    Orcidhttps://orcid.org/0000-0002-3576-0750
  • Sven Bergmann*
    Sven Bergmann
    Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
    Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
    Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town 7700, South Africa
    *Email: [email protected]
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Journal of Proteome Research

Cite this: J. Proteome Res. 2021, 20, 11, 5103–5114
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https://doi.org/10.1021/acs.jproteome.1c00585
Published October 26, 2021

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Abstract

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Gene products can affect the concentrations of small molecules (aka “metabolites”), and conversely, some metabolites can modulate the concentrations of gene transcripts. While many specific instances of this interplay have been revealed, a global approach to systematically uncover human gene-metabolite interactions is still lacking. We performed a metabolome- and transcriptome-wide association study to identify genes influencing the human metabolome using untargeted metabolome features, extracted from 1H nuclear magnetic resonance spectroscopy (NMR) of urine samples, and gene expression levels, quantified from RNA-Seq of lymphoblastoid cell lines (LCL) from 555 healthy individuals. We identified 20 study-wide significant associations corresponding to 15 genes, of which 5 associations (with 2 genes) were confirmed with follow-up NMR data. Using metabomatching, we identified the metabolites corresponding to metabolome features associated with the genes, namely, N-acetylated compounds with ALMS1 and trimethylamine (TMA) with HPS1. Finally, Mendelian randomization analysis supported a potential causal link between the expression of genes in both the ALMS1- and HPS1-loci and their associated metabolite concentrations. In the case of HPS1, we additionally observed that TMA concentration likely exhibits a reverse causal effect on HPS1 expression levels, indicating a negative feedback loop. Our study highlights how the integration of metabolomics, gene expression, and genetic data can pinpoint causal genes modulating metabolite concentrations.

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Introduction

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Genome-wide association studies (GWAS) have identified thousands of common variants that are associated with complex traits, (1) but the regulatory mechanisms behind these associations mostly remain poorly understood. Pinpointing causal variants is difficult since the lead variants associated with a trait are often in high linkage disequilibrium (LD) with other variants in the same region with only a slightly lower association signal. Such associated LD blocks typically contain several genes or functional elements, preventing the accurate identification of causal genes. Furthermore, some trait associated variants fall into intergenic regions of the genome with no obvious functional role at all.
A number of studies reported that trait associated genetic variants are significantly enriched in expression quantitative trait loci (eQTLs), suggesting that many trait associated variants affect the phenotype by altering gene expression. (2−5) There is also a growing body of literature highlighting the more pronounced effects of genetic variants on molecular traits compared to phenotypic traits. (6−9) This is not surprising since molecular traits representing fundamental biological processes such as gene expression and metabolism are intermediates in the causal chain from genotype to phenotype.
With high-throughput measurements becoming more accessible and widespread, integration of molecular traits into association studies has become a central challenge in the field. Such synthesis allows investigating the interplay between different organizational layers of a biological system. Despite metabolism and gene expression regulation both being fundamental biological processes that are commonly studied as molecular phenotypes, there are very few studies in humans that focus on the interplay between them. Several studies investigated the relationship between serum metabolites and whole blood gene expression in humans, (10−12) but to the best of our knowledge, no transcriptome- and metabolome-wide association study has been performed using urine metabolome data of healthy human subjects. Most metabolome- and genome-wide association studies (mGWAS) reporting metabolite quantitative trait loci (mQTL) use targeted approaches where the concentrations of a limited number of metabolites are estimated from the metabolome data generated by mass spectrometry or NMR spectroscopy. This targeted approach is limited to the number of known quantifiable metabolites in the biofluid under study.
In the current study, we adopted an untargeted approach, making use of the entire metabolomic data captured by binned urine 1H NMR spectra as our molecular traits. We use RNAseq data from lymphoblastoid cell lines (LCLs). LCLs have been widely used in genomic studies and proven their usefulness as surrogates of primary tissues for studying both gene expression variation among individuals and the genetic architecture underlying regulatory variation of gene expression. (13−16) While LCLs partially reflect the genetic variance of gene expression in primary tissues affecting the urine metabolome, they do have the advantage of not being influenced by immediate environmental factors such as recent changes in the diet or exposure to drugs. Our transcriptome and metabolome data was taken from 555 healthy individuals that were part of the Cohort Lausannoise (CoLaus). (17) In addition, using a different NMR platform, metabolomic profiles were generated for a subset of 315 individuals from follow-up urine samples taken after 5 years. We identified several associations between expression levels and urine metabolome features that were partly validated with the follow-up data, allowing us to refine previous links between the corresponding genes and metabolites.

Materials and Methods

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Study Samples

CoLaus (Cohorte Lausannoise) is a population-based cross-sectional study of 6188 healthy participants residing in Lausanne, Switzerland. (17) Recruitment to the cohort was done on the basis of a simple, non-stratified random selection of the entire Lausanne population aged 35 to 75 in 2003. While all participants were genotyped, a random subset of 1000 participants was selected for NMR spectroscopy of baseline urine samples. For a random subset of 555 participants (limited by available funding and sample preparation success rate), transcriptome analysis by RNA sequencing of lymphoblastoid cells was performed (see below). The mean age of these subjects with transcriptome and metabolome profiles was 55 (min = 35, max = 75) and 53% of them were women. For a further subset of 315 participants NMR spectroscopy was performed on urine samples taken at a follow-up 5 years after the baseline.

Metabolomics Data

Baseline urinary metabolic profiles were generated using one-dimensional proton nuclear magnetic resonance (NMR) spectroscopy. NMR spectra were acquired at 300 K on a Bruker 16.4 T Avance II 700 MHz NMR spectrometer (Bruker Biospin, Rheinstetten, Germany) using a standard 1H detection pulse sequence with water suppression. The spectra were referenced to the TSP signal and phase and baseline corrected. We binned the spectra into chemical shift increments of 0.005 ppm, obtaining metabolome profiles of 2200 metabolome features, of which 1276 remain after filtering for missing values. (18) Lastly, the data set was log10-transformed and standardised first across features (thereby normalizing the concentration of each sample) then across samples (thereby making intensities comparable). We used the z-score as a standardization method. We decided to use this statistical normalization approach, as opposed to normalizing to maximum or total creatinine levels, because it resulted in similar associations with transcriptome data, but with higher significance.
The follow-up data were acquired with an Avance III HD 600 NMR spectrometer. Spectra were referenced to the TSP signal and phase and baseline corrected. We binned the chemical shifts into 0.005 ppm bins. After removing water and urea spectral regions (4.55–5.00 ppm and 5.5–6.1 ppm), the data set was log10-transformed and standardised as described above. Our final metabolic data set includes 1289 features.
PCA plots for baseline and follow-up metabolomics data and a comparison between the baseline and follow-up urine NMR data are shown in Figure S1.

Gene Expression Data

Total RNA was extracted from Epstein–Barr-virus-transformed lymphoblastoid cell lines (LCLs) by following the Illumina TruSeq v2 RNA Sample Preparation protocol (Illumina, Inc., San Diego, CA) by the Department of Genetic Medicine and Development at the University of Geneva. Next, mRNA sequencing was performed on the Illumina HiSeq2000 platform producing 49 bp paired-end reads. On average, 17.5 million RNA-Seq reads were sequenced per sample, and its distribution is shown in Figure S2. Paired-end reads were mapped to human genome assembly GRCh37 (hg19) with GEMTools using GENCODE v15 as gene annotation. (19) The reads were then filtered for concordant orientation of the two ends and a minimum quality score of 150 while allowing 5 mismatches at both ends. Gene level read counts were quantified with an in-house script. This resulted in expression profiles of 45,470 genes for 555 individuals, which were quantified as RPKM (reads per kilobase per million reads) values. The number of genes with RPKM > 0 was on average 23,000 in each sample (Figure S2). We transformed RPKM values by applying log-transformation [log2(1 + RPKM)] and then standardization (i.e., z-scoring) across samples to make genes comparable. For our analysis, we excluded genes on sex chromosomes as well as on the mitochondrial chromosome, resulting in 43,614 genes to use in the association analysis.

Genotypic Data

Genotyping was performed by using the Affymetrix GeneChip Human Mapping 500 K array set, and the imputation was carried out for HapMap II SNPs. Further details of genotype calling and the imputation can be found in Rueedi et al. (18)

Association Analysis

All statistical analyses were performed using Matlab. (20) Urine metabolome features were transformed to be normally distributed, conserving their rank in order to remove strong outlier effects.
We used a linear regression model for each pair of (transformed) metabolome feature as the response variable and gene expression level as the explanatory variable. The model also included the following common confounding factors: age, sex, the first four principal components of the genotypic data (correcting for population stratification), and the first 50 principal components of the gene expression data (correcting for potential batch effects). We tested 1276 metabolome features for association with the expression of 19,123 protein coding and 24,491 non-coding genes. We decided to not apply any a priori exclusion criteria to remove genes from the analysis, as any such criterion would be arbitrary. Instead, for genes with significant associations, we evaluated the distribution of RPKM values to ensure close to normal distribution for accurate regression estimations. In particular, we kept genes if the maximum RPKM was larger than 1 or if RPKM values were larger than 0 for ≥5% of samples.
We applied a nominal Bonferroni threshold for multiple testing pmax = 0.05/(125 × 1109) = 3.6 × 10–7 by taking into account the effective number of independent tests which we estimated to be 125 for metabolome features and 1109 for genes (i.e., the number of principal components explaining more than 95% of the data, as proposed by Gao et al. (21)). Associations with p values below pmax were considered significant.

Metabomatching

Metabomatching is a method to identify metabolites underlying associations of SNPs with metabolome features. (18,22) It compares the association profile of a given variable with all metabolome features across the full ppm range, so-called pseudospectrum, with NMR spectra of pure metabolites available in public databases such as HMDB. (23)
While metabomatching was originally developed to use SNP-metabolome associations, we recently showed that it can also identify metabolites based on co-varying features in NMR data. (24) In the present study, we use metabomatching to identify metabolites that are associated with gene expression.

Mendelian Randomization

We performed Mendelian randomization (MR) analysis (25,26) to assess the causal relationship between gene expression and metabolite concentration, using SNPs as instrumental variables (IVs), gene expression as exposure, and metabolome features as the outcome (or vice versa in the case of testing for reverse causality). For the MR analysis, we used mQTL and eQTL summary statistics from studies with greater statistical power, i.e., from untargeted mQTL study by Raffler et al. (27) (n = 3861) and from the largest blood eQTL database eQTLGen Consortium (28) (n = 31,684).
Causal effects were estimated by using the Wald method where the effect of a given genetic variant on the outcome is divided by the effect of the same genetic variant on the exposure. (29) Next, ratio estimates from different instruments (SNPs) were combined using the inverse variance weighted method (IVW) to calculate the causal estimate. (30)
We selected significant SNPs from relevant eQTL studies as our IVs. To detect independent SNPs, we used a stepwise pruning approach where we first selected the strongest lead eQTL and then pruned the rest of the SNPs in a stepwise manner if they were correlated with the lead SNP (r2 > 0.2). We repeated the pruning process with the next available SNP until there were no SNPs left to prune. We used Cochran’s Q test to determine heterogeneity among the candidate IVs. (31) Heterogeneous SNPs were removed in a stepwise manner from the model until the model did not show any more signs of heterogeneity (Cochran’s Q statistic p value >0.05/# of original instruments). We applied four different meta-analysis (MR inference) methods to evaluate the significance of the causal estimates: Inverse variance weighted, maximum-likelihood, weighted median, and MR-Egger. The latter two methods are robust methods that have more relaxed MR assumptions, can tolerate the violation of the exclusion-restriction assumption for some instruments, and are thus less sensitive to heterogeneity among IVs. When using these robust MR methods, we did not remove any heterogeneous instruments. For all MR analysis, we used the Mendelian randomization package implemented in R. (32)

Results

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Association Analysis Identifies 20 Significant Metabolome- and Transcriptome-Wide Associations

We performed an untargeted metabolome- and transcriptome- wide association study by pairwise linear regression of each of 1276 metabolome features (as response variable) onto each of log-transformed expression levels of each of 43,614 genes (as explanatory variable) quantified in 555 healthy subjects (see Materials and Methods). Metabolome features resulted from binning raw urinary NMR spectra with a bin-size of 0.005 ppm, and rank-normalizing each bin passing quality control (see Materials and Methods). Gene expression levels were quantified as RPKM from RNAseq on lymphoblastoid cell lines derived for each subject (see Materials and Methods).
The QQ-plot of all pairwise associations (Figure 1) is well calibrated, with four highly significant associations (FDR < 0.05) (involving the ALMS1 and HPS1 genes). Applying an adjusted Bonferroni threshold of 3.6 × 10–7 to account for the effective number of independent variables (see Materials and Methods), we identified 20 additional less significant (“suggestive”) feature-gene associations. These 24 association pairs involved 19 unique genes and 21 unique features. As we did not apply any a priori exclusion criteria to remove genes from the analysis, we inspected the expression value distributions of these 19 significant genes in order to identify cases in which the small p value may be due to a problematic distribution of the expression values (see Materials and Methods). This resulted in four genes to be removed, corresponding to four significant association pairs (Figure S3). The remaining 20 gene-feature associations are listed in Table 1 and their distributions are presented in Figure S4.

Figure 1

Figure 1. QQ-plot of −log10 (p values) of metabolome- and transcriptome-wide association analysis. The highly significant associations (FDR < 0.05) with ALMS1 expression are ranked 1st and 2nd and with HPS1 expression 3rd and 4th.

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Table 1. 20 Study-Wide Significant Associations from Metabolome- and Transcriptome-Wide Association Analysisa
genesmetaboliteassociationpublished as mGWAS
ensembl gene IDChrgene symbolfeature(s)effect sizep valuebody fluid
ENSG000001161272ALMS12.0375, 2.0325, 2.0275, 2.04250.72, 0.69, 0.45, 0.411.1 × 10–20, 2.0 × 10–19, 7.8 × 10–09, 1.2 × 10–07serum, (9,33) urine (27,34)
ENSG0000010752110HPS12.8575, 2.8725–0.38, −0.371.1 × 10–10, 5.6 × 10–10serum, (9,33) urine (27,34)
ENSG0000014908911APIP2.7925–0.334.7 × 10–09serum, (9,33) urine (27)
ENSG000002560291RP11-190A12.73.09250.269.7 × 10–09serum (9)
ENSG0000010060314SNW18.1275–0.381.5 × 10–08serum (33)
ENSG000001630162ALMS1P2.0325, 2.03750.27, 0.272.5 × 10–08, 2.7 × 10–08serum, (9,33) urine (27)
ENSG000002192576RP11-14I4.22.3275–0.255.7 × 10–08 
ENSG000002593571RP11-316M1.127.78750.336.1 × 10–08 
ENSG000001635203FBLN25.43750.299.5 × 10–08serum (9,33)
ENSG000002264308USP17L72.7075–0.241.8 × 10–07 
ENSG0000021935512RPL31P522.8675–0.232.1 × 10–07 
ENSG0000026679517RP11-744K17.97.27250.262.2 × 10–07serum (9)
ENSG0000015059310PDCD45.40750.422.2 × 10–07serum, (9,33) urine (27)
ENSG0000026680518RP11-61L19.15.35250.242.7 × 10–07 
ENSG000002543969RP11-56F10.33.09250.233.6 × 10–07 
a

20 study-wide significant associations involving 15 unique genes and 17 unique features. Associations are grouped by genes and sorted by the lowest association p value for each gene.

Identification of Metabolites Corresponding to Metabolome Features Associated with Gene Expression

To identify metabolites underlying these significant associations between gene expression levels and metabolome features, we used metabomatching, (18,22) which has been previously established as an effective tool for prioritizing candidate metabolites underlying profiles of SNP-metabolome feature associations, so-called pseudospectra. (18,27) In this study, we used profiles of metabolome features that were associated significantly with one of the 15 identified genes as input to metabomatching.
We found that the pseudospectrum of the strongest associating gene, ALMS1, consists of four neighboring features at 2.0375 ppm (p value = 1 × 10–20), 2.0325 ppm (p value = 2 × 10–19), 2.0275 ppm (p value = 8 × 10–9), and 2.0425 ppm (p value = 1 × 10–7). A peak in this region is a typical feature of N-acetylated compounds (NACs) in their 1H NMR spectra (35) but is also found in NMR spectra of other compounds (see Figure 2A). Although NAC was not ranked first by metabomatching, it is the only compound with the strongest peak at 2.0375 ppm, and the association with ALMS1 expression at the strongest peaks of other compounds is not very significant. We therefore believe that ALMS1 is likely associated with one or several NACs. To investigate if we could pinpoint a specific NAC, we built a library consisting of all NAC proton NMR spectra from HMDB and the Biological Magnetic Resonance Data Bank (BMRB). Metabomatching gave similar scores to many of these compounds (Figure S5). For the first ranked compound, acetylglycine, we would have expected a strong association of ALMS1 expression with its other peak at ∼3.7 ppm, which is not the case. In fact, the secondary strong association signal in the pseudospectrum of ALMS1 is at ∼1.7 ppm and this matches a resonance in the NMR spectra of N(alpha)-acetyl-dl-ornithine and N(alpha)-acetyllysine, making these two compounds likely candidates. N-acetyl-l-aspartate (NAA) is a further possible candidate due to matches of its NMR features at around 7.92 and 2.71 ppm (Figure S5) with an association signal at these positions in the ALMS1 pseudospectrum (p value = 4 × 10–3 and 4 × 10–2, respectively).

Figure 2

Figure 2. Metabomatching (22) results for pseudospectra derived from gene expression - metabolome feature associations for ALMS1 (A) and HPS1 (B). Upper panels show the features in each pseudospectrum, color-coded according to the direction of the effect (positive in blue and negative in orange). Lower panels show the highest ranking candidate metabolites with their reference NMR spectra (color coded to indicate their relative peak intensities). Leading features allowing metabolite identification are in (A) at 2.04 ppm, which matches well with the highest intensity peak of the NAA spectrum and in (B) at 2.87 ppm, which matches well with the TMA singlet.

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The pseudospectrum of ALMS1P (ALMS1 pseudogene) similarly points to NAC (including N-acetylneuraminate and NAA) as likely matching compounds (Figure S6), although with less significant association p values (see Table 1).
The reference spectrum of NAA in the Urinary Metabolome Database (UMDB) that we used for metabomatching was recorded in water. In order to verify that the peaks of this spectrum are comparable to those of NAA in urine, we spiked NAA into pooled urine samples from our collection at a concentration of 10 mM and recorded its 1H NMR spectrum. Inspecting the 5 multiplet regions of NAA, we concluded that the NAA peak positions are very similar in both solvents (Figure S7).
Two associations, which are the third and fourth strongest in this study, are between HPS1 expression (second strongest associating gene) and two neighboring metabolome features at 2.8575 ppm (p value = 1 × 10–10) and 2.8725 ppm (p value = 6 × 10–10), respectively. This pseudospectrum matched well with the trimethylamine (TMA) NMR spectrum (Figure 2B). Among the top three metabolites suggested by metabomatching, trimethylamine (TMA) is the most plausible metabolite driving the association pattern because all the other metabolites have secondary peaks for which we have no significant association signal, while TMA has just a single peak in the 2.86 ppm region.
For the third strongest associating gene, APIP, metabomatching suggested asparagine as a metabolite (Figure S8), and the pseudospectrum of SNW1 (5th strongest associating gene) matched well with the single peak compound adenine (Figure S9). For the remaining 10 significantly associating genes, which were associated with only one metabolome feature, metabomatching was not able to identify a corresponding metabolite.

Validation of Significant Metabolome–Transcriptome Associations Using Follow-up Urine NMR Data

To the best of our knowledge, there is no independent data set with urine NMR spectra and gene expression data of LCLs of sufficient sample size for proper out-of-sample replication of our results. However, we generated additional NMR spectra from urine samples collected from a subset of 315 CoLaus subjects in a follow-up study conducted 5 years after the baseline data collection. We note that the follow-up NMR data are not independent from the baseline data, yet they were obtained from physically different samples collected at a significantly later time and processed with a different NMR spectrometer and facility. As for the expression data, we only have those from LCLs derived from blood taken at the baseline, so we could only test whether the associations we observed between baseline metabolomics and baseline transcriptomics measurements would persist as associations between follow-up metabolomics and baseline transcriptomics data.
As baseline and follow-up urine NMR data were each processed and binned individually, the features did not correspond one-to-one between the studies (see Materials and Methods). Thus, to validate the association of genes with relevant features, we tested the two nearest features to every top feature associated with a gene in the baseline data set. We used the Benjamini–Hochberg (BH) procedure (36) to detect significant replications; more specifically, we considered replications validated if the p value was smaller than 0.05/(2 features x rank of the association in the discovery) for any one of the two tested features. In the follow-up, both ALMS1 and HPS1 gene expression levels were associated significantly with features corresponding to those of the discovery study (Table S1). The third most significantly associating gene with the baseline metabolome data, APIP, yielded an association in the follow-up data with p value = 9.8 × 10–3, which is just above its BH threshold of 0.005. All other genes did not show any significant association with representative features in the follow-up study.

mGWAS for NAC and TMA Indicates Numerous Significant SNPs in the ALMS1 and HPS1 Gene Loci

To get a broader overview of the genetic influences on metabolome features in the NAC and TMA NMR peak regions, we performed an untargeted metabolome- and genome-wide association study using data from 826 individuals of the CoLaus cohort, for whom baseline urinary NMR spectra were available (similar to Rueedi et al. (18)). SNPs that are significantly associated with the NAC metabolome feature at 2.0375 ppm are correlated with each other (r2 > 0.8) and lie within an LD block containing nine genes, including ALMS1, ALMS1-IT1, NAT8, and ALMS1P (Figure 3A). Out of these nine genes, ALMS1 and ALMS1P have the most significant association results with the 2.0375 ppm feature and with the NAA feature at 7.9225 ppm (Figure 3B). However, we note that the expression levels in LCLs differ between genes; in particular, NAT8 has RPKM = 0 in most samples, which could impact its association results. Similarly, SNPs that associate with the TMA metabolome feature at 2.8725 ppm are also highly correlated and lie in a locus surrounding the HPS1/PYROXD2 genes (Figure 4A). Even though the SNPs with the most significant association with feature 2.8725 are physically located closer to PYROXD2 rather than HPS1, the expression level of PYROXD2 does not show a significant association with this feature (Figure 4B). Notably here, HPSE2, MIR1287, and MIR4685 are very lowly expressed (RPKM = 0 in most samples).

Figure 3

Figure 3. SNP - metabolome feature and SNP - gene expression associations in ALMS1/NAT8 locus. (A) LocusZoom plot for ALMS1/NAT8 locus, where the SNPs are associated with metabolome feature at 2.0375 ppm, LD colored with respect to lead mQTL. (B) Bar plot shows −log10 transformed p values from associating expression values of nine genes in the locus with the five NAA features.

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Figure 4

Figure 4. SNP - metabolome feature and SNP - gene expression associations in HPS1/PYROXD2 locus. (A) LocusZoom plot for HPS1/PYROXD2 locus, showing the association significance of SNP with the metabolome feature at 2.8725 ppm. Colors indicate the correlation (LD) to the lead QTL. (B) Bar plot shows −log10 transformed p values from associating expression values of seven genes in the locus with the same feature.

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To further evaluate a possible regulation of NAC and TMA by other genes suggested by published mGWAS studies, we investigated the pseudospectra of these using metabomatching. In particular, we investigated genes that either were the target of an eQTL SNP that is a mQTL of NAC/TMA or were within 500 kb of ALMS1/HPS1. However, none of these candidate genes (14 for the ALMS1-NAC and 6 for the HPS1-TMA association pair) produced a pseudospectrum containing even a single nominally significant signal pointing to these metabolites (data not shown).
Inspecting published mGWAS in humans, (37) we found that the SNPs in both ALMS1 and HPS1 loci have been previously reported to associate with a number of metabolic traits (Table 2). The ALMS1 locus has been associated with N-acetylated compounds, while the HPS1 locus has been associated with various metabolites including trimethylamine and dimethylamine. (18,27,34) In mGWAS studies, the most likely candidate genes are usually inferred based on their physical proximity to the lead mQTL and, if available, their functional annotation. Indeed, published mGWAS studies were not able to distinguish between NAT8 or ALMS1 and HPS1 or PYROXD2 as genes affecting NAC and TMA, respectively. In contrast, our association study using gene expression data from LCLs clearly favors ALMS1 and HPS1 as the relevant genes. However, for NAT8, which is mostly not expressed in LCLs, conclusions on its association with NAC in functionally more relevant tissues are not possible.
Table 2. Previously Reported mGWAS Results for the ALMS1/NAT8 and HPS1/PYROXD2 Locia
referenceplatformbiofluidlocusmetabolite
Nicholson et al. 2011 (34)MS + NMRurine + plasmaALMS1, NAT8N-acetylated compounds
Montoliu et al. 2013 (44)NMRurineALMS1N-acetylated compounds
Rueedi et al. 2014 (18)NMRurineALMS12.0375 (suggested as N-acetylated compounds)
Raffler et al. 2015 (27)NMRurineNAT82.031 (suggested as N-acetyl-l-aspartate)
Suhre et al. 2011 (38)MSserumNAT8N-acetylornithine
Yu et al. 2014 (39)MSserumNAT8N-acetylornithine
Shin et al. 2014 (33)MSserumNAT8N-acetyllysine, unknown compounds
Nicholson et al. 2011 (34)MS + NMRurine + plasmaHPS1, PYROXD2trimethylamine (urine), dimethylamine (plasma),
Rueedi et al. 2014 (18)NMRurinePYROXD2trimethylamine, unknown compound, 2.8575, 1.8025
Raffler et al. 2015 (27)NMRurinePYROXD22.854 (suggested as trimethylamine)
Raffler et al. 2013 (40)NMRplasmaPYROXD22.757
Rhee et al. 2013 (41)MSplasmaHPS1asymmetric dimethylarginine
Krumsiek et al. 2012 (42)MSserumHPS1, PYROXD2multiple compounds, unknown compounds
Hong et al. 2013 (43)MSserumHPS1caprolactam
Shin et al. 2014 (33)MSserumPYROXD2unknown compounds
a

MS: mass spectrometry; numbers in metabolite section refer to NMR spectral shift positions in ppm. Reported genes are mostly based on proximity to the mQTL or based on gene function.

A likely genetically driven relationship between ALMS1 and NAC concentrations measured in the baseline data set, through a shared eQTL and mQTL (SNP rs7566315), is illustrated in Figure 5.

Figure 5

Figure 5. Scatter plot of the mQTL effect of SNP (rs7566315) on NAC and its eQTL effect on ALMS1 gene expression. Each point represents a study sample. NAC concentration is approximated by the feature at 2.0375 ppm that is log10 transformed after feature- and sample-wise z-scoring (y axis). ALMS1 expression is z-scored after log2 transforming RPKM+1 values (x axis). Color code represents the genotype of rs7566315 (legend) that is an eQTL of ALMS1 and mQTL of NAA.

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Mendelian Randomization Analysis Suggests ALMS1 Expression in Blood and Confirms NAT8 Expression in Other Tissues to Causally Effect NAC Concentration

To assess a causal relationship between gene expression and metabolite concentration, we performed MR analysis using SNPs as instrumental variables (IVs) selected based on summary statistics from the eQTLGen Consortium (28) and Raffler et al. (27) for eQTL and untargeted mQTL results, respectively. We investigated both the causal effect of the gene expression on the metabolite concentration and vice versa for the ALMS1-NAC and HPS1-TMA gene-metabolite pairs.
We first investigated the causal effect of the ALMS1 gene on NAC concentration reflected by the NMR peak intensity at 2.0308 ppm. (34,44) We considered 86 SNPs that were significant eQTLs (FDR < 0.05) in eQTLGen and that were also identified as mQTLs by Raffler et al. After stepwise pruning (see Materials and Methods), 14 independent SNPs remained as candidate IVs. Next, we performed Cochran’s Q test to detect heterogeneity among these SNPs and removed a further three, resulting in 11 SNPs as potentially valid IVs to use in the MR analysis (see Materials and Methods). Our causal effect estimates given by four meta-analysis methods (inverse variance weighted, weighted median, MR-Egger, maximum-likelihood; see Materials and Methods) are reported in Table 3A. All methods agree on the ALMS1 expression level being causal for NAC concentrations, indicated by p values < 2 × 10–16.
Table 3. MR Results for Testing a Causal Link between ALMS1 Expression and Concentration of N-Acetylated Compoundsa
  methodcausal effect size estimatestd. error95% CIp valueCochran’s Q-statistic p value
AALMS1 → NACinverse variance weighted0.9670.0610.847–1.087<2 × 10–160.2323
 weighted median1.1110.0750.965–1.257<2 × 10–16NA
 MR - Egger0.9940.0920.812–1.175<2 × 10–160.1776
 maximum-likelihood0.9990.0650.872–1.126<2 × 10–160.249
        
BNAC → ALMS1inverse variance weighted–0.0150.264–0.532–0.5020.9550.7443
 weighted median0.1220.321–0.507–0.7510.704NA
 MR - Egger1.4951.976–2.377–5.3680.4490.7256
 maximum-likelihood–0.0150.266–0.535–0.5050.9550.7443
        
CALMS1 → NACinverse variance weighted0.7960.1830.437–1.155<2 × 10–160.1902
(NAT8 related SNPs removed)weighted median0.6680.2420.193–1.1420.006NA
 MR - Egger1.9120.7040.532–3.2910.0070.4144
 maximum-likelihood0.8050.1850.444–1.167< 2 × 10–160.2199
a

MR results for testing (A) causal effect of ALMS1 gene expression levels on N-acetylated compounds (ALMS1 → NAC), (B) causal effect of N-acetylated compounds on ALMS1 gene expression levels (NAC → ALMS1), (C) causal effect of ALMS1 gene expression levels on N-acetylated compounds (ALMS1 → NAC) when NAT8-related SNPs were removed from the instrument set.

As NAT8 has a known N-acetyltransferase activity and was previously suggested to be involved with NAC concentration (see Table 2), we performed an additional MR analysis with ALMS1 as the exposure where we removed the possible pleiotropic effect of the NAT8 gene. To achieve this, we excluded 10 out of 14 original candidate IVs that were also significant eQTLs of NAT8 or were in LD (R-squared > 0.1) with these NAT8 eQTLs (as there were no NAT8 eQTLs in eQTLGen, these were taken from GTEx consortium (v8), considering all available tissues (2) and using a significance cutoff of p value < 10–7). Without these NAT8-related SNPs, the MR results remained significantly causal (Table 3C), indicating that the causal effect of ALMS1 on NAC may not be driven by the pleiotropic effect of NAT8.
Finally, we performed an additional sensitivity analysis by lowering the R-squared threshold in the stepwise LD pruning process to 0.05, resulting in more strictly independent IVs for MR analysis. The significant causal effect of ALMS1 on NAC persisted with more strictly independent instruments, indicated by all four meta-analysis methods (see Table S2, top panel). However, when removing NAT8-related eQTLs, only one meta-analysis method (weighted-median) indicated a significant causal effect of ALMS1 on NAC (see Table S2, bottom panel). We also performed an MR analysis with NAT8 as the exposure, but due to the low number of valid IVs (only two remained after removing two with heterogeneity), we could only use robust MR analysis methods. Both weighted median and MR-Egger indicated a significant causal effect of NAT8 expression on NAC (beta = 0.36 and 0.63, respectively; p values < 10–16). When excluding ALMS1-related SNPs, only two IVs remained and thus we could not test if the causal effect of NAT8 is independent of ALMS1 expression.
For the completeness of the analysis, we also tested a causal effect of NAC on the ALMS1 gene expression level. IVs were selected among the SNPs that were reported as significant mQTLs for the 2.03 ppm feature (p value < 1 × 10–6) in Raffler et al. (27) Among the cis-eQTLs of ALMS1 from eQTLGen, we observed strong heterogeneity between their expected and observed effects. To overcome this problem, we sought to use also trans-eQTLs of ALMS1; however, none of the candidate IVs were measured in the trans-eQTL study of eQTLGen. As an alternative, we performed an association study between the candidate IVs and ALMS1 gene expression level as measured for our 555 CoLaus individuals and used these trans-eQTL results. Overall, we identified 26 overlapping significant mQTLs and trans-eQTLs, corresponding to six independent SNPs. Two of the six candidate IVs were removed due to pleiotropic effects resulting in four SNPs to be used as potentially valid IVs in the MR analysis (see Materials and Methods). None of the four meta-analysis methods (Table 3B) gave a significant causal effect estimate, indicating that we have no evidence for NAC concentration causally affecting ALMS1 gene expression.
In conclusion, our MR analysis using SNPs selected from eQTLs in blood tissue suggests a causal effect of ALMS1 expression levels on NAC, which appears independent from NAT8 gene expression (in various GTEx tissues). However, we also detected a significant causal effect of NAT8 expression (in GTex tissues) on NAC concentration.

Mendelian Randomization Analysis Suggests HPS1 as Causal Gene Modulating TMA Concentration and Indicates a Reverse Causal Effect between Both

We performed a similar MR analysis to test a causal relationship between HPS1 gene expression and TMA concentration. As there were no targeted summary statistics for TMA concentration, we used the NMR peak intensity at 2.8541 ppm from Raffler et al. (27) as a proxy for TMA concentration. This position is in agreement with the singlet peak position range from 2.79 to 2.99 ppm, according to HMDB. Among the 77 SNPs that were reported as significant eQTLs for HPS1 (FDR < 0.05) in eQTLGen and measured by Raffler et al., only six could be used as valid IVs that were independent and did not exhibit heterogeneity (see Materials and Methods). Causal effects estimated by four different meta-analysis methods were all significant (p value < 0.005; see Table 4A), suggesting that HPS1 gene expression has a causal effect on TMA concentration.
Table 4. MR Results for Testing a Causal Link between HPS1 Expression and TMA Concentrationa
  methodcausal effect size estimatestd. error95% CIp valueCochran’s Q-statistic p value
AHPS1 → TMAinverse variance weighted0.2660.0940.082–0.4500.0050.0803
 weighted median0.3110.0720.170–0.453<2 × 10–16NA
 MR - Egger0.370.1260.123–0.6170.0030.0852
 maximum-likelihood0.2670.0940.083–0.4520.0040.0829
        
BTMA → HPS1inverse variance weighted–0.0890.012–0.113 to –0.065<2 × 10–160.0958
 weighted median–0.090.011–0.111 to –0.068<2 × 10–16NA
 MR - Egger–0.0860.013–0.111 to –0.061<2 × 10–160.0758
 maximum-likelihood–0.090.012–0.114 to –0.066<2 × 10–160.1258
        
CHPS1 → TMAinverse variance weighted     
(PYROXD2 related SNPs removed)weighted median1.0790.1210.842–1.315<2 × 10–16NA
 MR - Egger1.7050.3051.107–2.303<2 × 10–160.5575
 maximum-likelihood     
a

MR results for testing (A) causal effect of HPS1 gene expression levels on TMA (HPS1 → TMA), (B) causal effect of TMA on HPS1 gene expression levels (TMA → HPS1), (C) causal effect of HPS1 gene expression levels on TMA (HPS1 → TMA) when PYROXD2-related SNPs were removed from the instrument set.

We performed an additional MR analysis removing the possible pleiotropic effect of the other candidate gene in the locus, PYROXD2, as this gene was previously suggested to be involved in modulating TMA (see Table 2). For this, we removed from the original 77 candidate IVs 54 SNPs that were also significant eQTLs of PYROXD2 (eQTLGen FDR < 0.05) or were in LD with these eQTLs (R-squared >0.1). Stepwise pruning with R-squared thresholds of 0.2 or 0.05 both resulted in 3 SNPs to be used in MR analysis. These 3 SNPs had heterogeneity; however, due to the low number of instruments, we could not further remove any of them. Therefore, we relied on robust MR methods. Both robust MR methods (weighted median and MR-Egger) indicated a significant causal effect of HPS1 on TMA (p value < 2 × 10–16) with estimated causal effect sizes larger than 1.0 (Table 4C). This suggests that the causal effect of HPS1 on TMA is not driven by a pleiotropic effect of PYROXD2. We also attempted an MR analysis testing the causal effect of PYROXD2 on TMA. However, when HPS1-related SNPs were discarded from the candidate IVs, there were no SNPs left for the analysis.
Next, we performed a sensitivity analysis using an R-squared threshold of 0.05 in stepwise LD pruning, which showed a persisting causal effect of HPS1 on TMA, when all HPS1 eQTLs were used as candidate SNPs in the MR analysis (p value < 2 × 10–16 for all four meta-analysis methods; see Table S3, upper panel), and when PYROXD2-related eQTLs were removed from the candidate SNPs (p value < 2 × 10–16 for robust meta-analysis methods; see Table S3, bottom panel).
Finally, we tested the causal effect in the reverse direction, from TMA concentration to HPS1 gene expression. From 87 significant mQTLs in Raffler et al. (27) that were also measured in eQTLGen, 18 SNPs remained to be used as IVs in the MR analysis after stepwise pruning and removing the SNPs showing heterogeneity (see Materials and Methods). All four meta-analysis methods agreed on TMA concentration being causal for HPS1 expression (p value < 2 × 10–16; Table 4B). While the estimated causal effect size of HPS1 on TMA ranged between 0.27 and 0.37 for different methods, the causal effect size of TMA on HPS1 was around −0.09, pointing to the existence of a negative feedback loop.

Discussion

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In this study, we present a metabolome- and transcriptome-wide association study using RNA-seq from LCLs and NMR urine profiles from 555 subjects of the CoLaus cohort. This is the first study performed on untargeted urine metabolome data from healthy individuals. We identified two genes, ALMS1 and HPS1, whose association with NMR features are highly significant, and 13 additional genes that are associated with metabolome features with lower but still significant p values (see Table 1). Among these 15 genes, 9 are in loci with SNPs that have been previously reported as mQTLs by mGWAS. This shows that our approach can identify likely candidates of metabolically relevant genes, despite a limited sample size.
To search for metabolite candidates underlying gene expression-metabolome feature associations, we used our metabomatching tool that ranks metabolites with known NMR peaks based on their match to a given pseudospectrum. (22) We consider NAC as the most likely compounds to be associated with ALMS1 expression, even though NACs were not ranked top by metabomatching. Similarly, for HPS1, we consider TMA, which also did not rank top, as the most likely compound underlying the association with the NMR feature at ∼2.86 ppm. The reason for this was in both cases that the most significant association peaks of ALMS1 and HPS1 matched best with the highest peaks in the reference spectra of NAC and TMA, respectively. Thus, metabomatching was very useful in prioritizing candidate metabolites, but its scoring could be improved by weighting the association signal based on the relative peak height of the reference spectrum, which currently is only visualized.
Our top hit ALMS1 as well as the second strongest association involving HPS1 had previously been implicated by mGWAS linking their loci to compound families. However, in both cases, the reported mQTLs were also in proximity to other genes, leaving the causal gene-metabolite association ambiguous. Specifically, the locus associated through mGWAS with N-acetylated compounds (NAC) includes two genes, ALMS1 and NAT8, (18,27,34,44) and the latter seemed to be an appealing candidate due to its known N-acetyltransferase activity. Yet, our association study linked NAC concentrations to ALMS1 and not NAT8 expression in LCLs. This is probably due to the fact that expression of NAT8 in LCLs is very low, so one would need to test its expression in tissues such as liver or kidney, where it is known to be highly expressed, which is however not feasible for a cohort study like CoLaus. A metabolic role of ALMS1 is supported through its known role in Alström syndrome characterized by metabolic deficits (53) and kidney health disorder phenotypes. (45) Interestingly, in the mGWAS reported by Montoliu et al. using data from a Brazilian cohort, the authors observed the association between NAC and the SNPs located in ALMS1/NAT8 locus with stronger SNP associations in the ALMS1 gene rather than NAT8. (44) They argued that the high ethnic diversity of their study population might have been responsible for breaking down the linkage disequilibrium in the ALMS1/NAT8 region of the genome, resulting in a stronger association for SNPs close to or within the ALMS1 gene compared to other studies.
Intriguingly, NAA, a likely NAC underlying the observed association, is the second most abundant metabolite in the brain and is involved in neural signaling by serving as a source of acetate for lipid and myelin synthesis in oligodendrocytes. (46) NAA can be detected in urine in low concentrations, (47) and it has a long history of being a surrogate marker of neural health and a broad measure of cognitive performance. (48,49) Recently, it has been shown that NAA correlates with neuropsychological performance measures. (50) The signals of SNPs in ALMS1 by GWAS with intellectual phenotypes such as a self-reported ability in mathematics (51,52) might therefore be due to its role in modulating NAA. This conjecture of course assumes that NAA levels in relevant brain tissues reflect those in urine and that the ALMS1 expression variation and in particular its genetic component, in LCLs or blood, can serve as a proxy for brain tissue.
As for HPS1, our second strongest associating gene, we note that mGWAS previously associated its locus with TMA levels. (18,27,34) Yet, most of these studies, including the aforementioned mGWAS using a Brazilian cohort, (44) considered the PYROXD2 gene, which is in the same locus, as the most likely modulator of TMA concentrations due to its known function as pyridine nucleotide-disulfide oxidoreductase. While we cannot rule out that PYROXD2 is involved in TMA metabolism, our study finds no evidence for association of PYROXD2 expression levels in LCLs with TMA concentration in urine, indicating that the mQTLs of this locus may act predominantly as eQTLs through HPS1.
In order to study the causal relationship between gene expression and metabolite concentrations, we conducted Mendelian randomization analyses. Our analyses supported a causal relationship between ALMS1 expression in blood and NAC concentration in urine that appears independent of NAT8 expression. Yet, our MR analysis also confirmed a causal effect of NAT8 expression in other tissues (than LCLs or blood) on NAC concentration in urine. For HPS1, we observed a causal effect of its expression level in blood on TMA concentration in urine that appears independent of PYROXD2 expression in addition to a negative feedback loop between HPS1 expression levels and TMA concentrations. However, the causal links identified through MR analysis using mQTLs from urine and eQTLs mostly from blood may not represent very well the actual effects in the metabolically relevant tissues. Thus, based on the available data, MR analysis cannot unambiguously pinpoint the causal gene in the ALMS1/NAT8- and HPS1/PYROXD2-loci, whose expression drives the respective metabolite concentrations.
We acknowledge that our study has several limitations: First, the transcriptome data from LCLs may only poorly reflect the expression of genes in more functionally relevant cells and tissues. Furthermore, the mRNA expression levels may not correlate well with the protein concentrations of the enzymes involved in the metabolic reactions, as these are additionally shaped by translation regulation, post-translational modifications, or decay. Second, the metabolome data from urine may depend on food intake and may only provide a poor proxy for the metabolite concentrations in other relevant biosamples. Thus, relating LCL expression levels with urine metabolite concentrations might not allow detecting all regulations that occur in more functionally relevant tissues. This is likely the case for NAT8, which is barely expressed in LCLs but has high expression levels in, e.g., kidney and liver. Finally, our study is limited by the sample size of studied CoLaus data and by the available eQTLs and mQTLs, and the LD reference panel, used for MR. More highly powered studies are needed to provide a more definite answer on some casualties.
Nevertheless, our study shows a proof of concept for how global association of sets of two or more distinct molecular traits observed in the same cohort can facilitate new discoveries of how different molecular entities affect each other. Such analyses, when applied to sizable datasets, will be instrumental in unravelling the complex regulatory relationships underlying human metabolism.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.1c00585.

  • Figure S1: Principal component analysis of baseline and follow-up metabolomics data; Figure S2: Overview of RNA-Seq read count and quantifiable genes in 555 individuals; Figure S3: Scatter plots of removed associations; Figure S4: Scatter plots of 20 study-wide significant metabolome feature - gene expression associations; Figure S5: Metabomatching figure showing the pseudospectrum derived from ALMS1 gene expression - metabolome features associations; Figure S6: Metabomatching figure showing the pseudospectrum derived from ALMS1P gene expression - metabolome features associations; Figure S7: NMR profiles of 3 different spike-in experiments; Figure S8: Metabomatching figure showing the pseudospectrum derived from APIP gene expression - metabolome feature associations; Figure S9: Metabomatching figure showing the pseudospectrum derived from SNW1 gene expression - metabolome feature associations; Table S1: Validation of all associations discovered in CoLaus baseline; Table S2: MR results of testing causal effect of ALMS1 gene expression levels on N-acetylated compounds, using R-squared threshold of 0.05; Table S3: MR results of testing causal effect of HPS1 gene expression levels on trimethylamine, using R-squared threshold of 0.05 (PDF)

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Author Information

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  • Corresponding Authors
    • Reyhan Sönmez Flitman - Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland;  Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland;  Orcidhttps://orcid.org/0000-0002-4556-4075;  Email: [email protected]
    • Sven Bergmann - Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland;  Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland;  Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town 7700, South Africa;  Email: [email protected]
  • Authors
    • Bita Khalili - Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland;  Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland;  Orcidhttps://orcid.org/0000-0001-5630-1812
    • Zoltan Kutalik - Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland;  University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland;  Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
    • Rico Rueedi - Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland;  Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
    • Anneke Brümmer - Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland;  Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland;  Orcidhttps://orcid.org/0000-0002-3576-0750
  • Author Contributions

    A.B. and S.B. are equal last authors.

    Author Contributions

    R.S.F., R.R., and S.B. designed the project. R.S.F. carried out the computational analysis and prepared the results, with help from A.B. B.K. and R.R. provided data and feedback on the metabolomics analysis. Z.K. designed and supervised the MR analysis. All authors discussed the results and provided feedback on the manuscript that was written by R.S.F., B.K., A.B., and S.B.

  • Notes
    The authors declare no competing financial interest.

    Processed transcriptome and metabolome data are accessible at Zenodo (www.zenodo.org, doi:10.5281/zenodo.5106119). This data set includes RPKM levels in LCLs for 45,484 genes and 555 individuals, binned and normalized NMR peak intensities for 1276 bins in 555 individuals from baseline urine samples, and 1289 bins in 315 individuals from follow-up urine samples.

Acknowledgments

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This work was supported by the Swiss National Science Foundation (grant FN 310030_152724/1) and the NIH (grant R03 CA211815).

References

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    We analyzed the mRNA levels for 36,778 transcript expression traits (probes) from 2,765 individuals to comprehensively investigate the genetic architecture and degree of missing heritability for gene expression in peripheral blood. We identified 11,204 cis and 3,791 trans independent expression quant. trait loci (eQTL) by using linear mixed models to perform genome-wide assocn. analyses. Furthermore, using information on both closely and distantly related individuals, heritability was estd. for all expression traits. Of the set of expressed probes (15,966), 10,580 (66%) had an estd. narrow-sense heritability (h2) greater than zero with a mean (median) value of 0.192 (0.142). Across these probes, on av. the proportion of genetic variance explained by all eQTL (h2COJO) was 31% (0.060/0.192), meaning that 69% is missing, with the sentinel SNP of the largest eQTL explaining 87% (0.052/0.060) of the variance attributed to all identified cis- and trans-eQTL. For the same set of probes, the genetic variance attributed to genome-wide common (MAF > 0.01) HapMap 3 SNPs (h2g) accounted for on av. 48% (0.093/0.192) of h2. Taken together, the evidence suggests that approx. half the genetic variance for gene expression is not tagged by common SNPs, and of the variance that is tagged by common SNPs, a large proportion can be attributed to identifiable eQTL of large effect, typically in cis. Finally, we present evidence that, compared with a meta-anal., using individual-level data results in an increase of approx. 50% in power to detect eQTL.
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  7. 7
    Montgomery, S. B.; Sammeth, M.; Gutierrez-Arcelus, M.; Lach, R. P.; Ingle, C.; Nisbett, J. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 2010, 464, 773,  DOI: 10.1038/nature08903
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    Transcriptome genetics using second generation sequencing in a Caucasian population
    Montgomery, Stephen B.; Sammeth, Micha; Gutierrez-Arcelus, Maria; Lach, Radoslaw P.; Ingle, Catherine; Nisbett, James; Guigo, Roderic; Dermitzakis, Emmanouil T.
    Nature (London, United Kingdom) (2010), 464 (7289), 773-777CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
    Gene expression is an important phenotype that informs about genetic and environmental effects on cellular state. Many studies have previously identified genetic variants for gene expression phenotypes using custom and com. available microarrays. Second generation sequencing technologies are now providing unprecedented access to the fine structure of the transcriptome. We have sequenced the mRNA fraction of the transcriptome in 60 extended HapMap individuals of European descent and have combined these data with genetic variants from the HapMap3 project. We have quantified exon abundance based on read depth and have also developed methods to quantify whole transcript abundance. We have found that approx. 10 million reads of sequencing can provide access to the same dynamic range as arrays with better quantification of alternative and highly abundant transcripts. Correlation with SNPs (small nucleotide polymorphisms) leads to a larger discovery of eQTLs (expression quant. trait loci) than with arrays. We also detect a substantial no. of variants that influence the structure of mature transcripts indicating variants responsible for alternative splicing. Finally, measures of allele-specific expression allowed the identification of rare eQTLs and allelic differences in transcript structure. This anal. shows that high throughput sequencing technologies reveal new properties of genetic effects on the transcriptome and allow the exploration of genetic effects in cellular processes.
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  8. 8
    Wright, F. A.; Sullivan, P. F.; Brooks, A. I.; Zou, F.; Sun, W.; Xia, K. Heritability and genomics of gene expression in peripheral blood. Nat. Genet. 2014, 46, 430,  DOI: 10.1038/ng.2951
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    Heritability and genomics of gene expression in peripheral blood
    Wright, Fred A.; Sullivan, Patrick F.; Brooks, Andrew I.; Zou, Fei; Sun, Wei; Xia, Kai; Madar, Vered; Jansen, Rick; Chung, Wonil; Zhou, Yi-Hui; Abdellaoui, Abdel; Batista, Sandra; Butler, Casey; Chen, Guanhua; Chen, Ting-Huei; D'Ambrosio, David; Gallins, Paul; Ha, Min Jin; Hottenga, Jouke Jan; Huang, Shunping; Kattenberg, Mathijs; Kochar, Jaspreet; Middeldorp, Christel M.; Qu, Ani; Shabalin, Andrey; Tischfield, Jay; Todd, Laura; Tzeng, Jung-Ying; van Grootheest, Gerard; Vink, Jacqueline M.; Wang, Qi; Wang, Wei; Wang, Weibo; Willemsen, Gonneke; Smit, Johannes H.; de Geus, Eco J.; Yin, Zhaoyu; Penninx, Brenda W. J. H.; Boomsma, Dorret I.
    Nature Genetics (2014), 46 (5), 430-437CODEN: NGENEC; ISSN:1061-4036. (Nature Publishing Group)
    We assessed gene expression profiles in 2752 twins, using a classic twin design to quantify expression heritability and quant. trait loci (eQTLs) in peripheral blood. The most highly heritable genes (∼777) were grouped into distinct expression clusters, enriched in gene-poor regions, assocd. with specific gene function or ontol. classes, and strongly assocd. with disease designation. The design enabled a comparison of twin-based heritability to ests. based on dizygotic identity-by-descent sharing and distant genetic relatedness. Consideration of sampling variation suggests that previous heritability ests. have been upwardly biased. Genotyping of 2494 twins enabled powerful identification of eQTLs, which we further examd. in a replication set of 1895 unrelated subjects. A large no. of non-redundant local eQTLs (6756) met replication criteria, whereas a relatively small no. of distant eQTLs (165) met quality control and replication stds. Our results provide a new resource toward understanding the genetic control of transcription.
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  9. 9
    Suhre, K.; Wallaschofski, H.; Raffler, J.; Friedrich, N.; Haring, R.; Michael, K. A genome-wide association study of metabolic traits in human urine. Nat. Genet. 2011, 43, 565– 569,  DOI: 10.1038/ng.837
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    9
    A genome-wide association study of metabolic traits in human urine
    Suhre, Karsten; Wallaschofski, Henri; Raffler, Johannes; Friedrich, Nele; Haring, Robin; Michael, Kathrin; Wasner, Christina; Krebs, Alexander; Kronenberg, Florian; Chang, David; Meisinger, Christa; Wichmann, H.-Erich; Hoffmann, Wolfgang; Voelzke, Henry; Voelker, Uwe; Teumer, Alexander; Biffar, Reiner; Kocher, Thomas; Felix, Stephan B.; Illig, Thomas; Kroemer, Heyo K.; Gieger, Christian; Roemisch-Margl, Werner; Nauck, Matthias
    Nature Genetics (2011), 43 (6), 565-569CODEN: NGENEC; ISSN:1061-4036. (Nature Publishing Group)
    We present a genome-wide assocn. study of metabolic traits in human urine, designed to investigate the detoxification capacity of the human body. Using NMR spectroscopy, we tested for assocns. between 59 metabolites in urine from 862 male participants in the population-based SHIP study. We replicated the results using 1,039 addnl. samples of the same study, including a 5-yr follow-up, and 992 samples from the independent KORA study. We report five loci with joint P values of assocn. from 3.2 × 10-19 to 2.1 × 10-182. Variants at three of these loci have previously been linked with important clin. outcomes: SLC7A9 is a risk locus for chronic kidney disease, NAT2 for coronary artery disease and genotype-dependent response to drug toxicity, and SLC6A20 for iminoglycinuria. Moreover, we identify rs37369 in AGXT2 as the genetic basis of hyper-β-aminoisobutyric aciduria.
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  10. 10
    Bartel, J.; Krumsiek, J.; Schramm, K.; Adamski, J.; Gieger, C.; Herder, C. The Human Blood Metabolome-Transcriptome Interface. PLoS Genet. 2015, 11, e1005274,  DOI: 10.1371/journal.pgen.1005274
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    10
    The human blood metabolome-transcriptome interface
    Bartel, Joerg; Krumsiek, Jan; Schramm, Katharina; Adamski, Jerzy; Gieger, Christian; Herder, Christian; Carstensen, Maren; Peters, Annette; Rathmann, Wolfgang; Roden, Michael; Strauch, Konstantin; Suhre, Karsten; Kastenmueller, Gabi; Prokisch, Holger; Theis, Fabian J.
    PLoS Genetics (2015), 11 (6), e1005274/1-e1005274/32CODEN: PGLEB5; ISSN:1553-7404. (Public Library of Science)
    Biol. systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous anal. of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying mol. mechanisms on a physiol. scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based anal. identified 1,109 significant assocns. between 522 transcripts and 114 metabolites summarized in an integrated network, the 'human blood metabolometranscriptome interface' (BMTI). Bidirectional causality anal. using Mendelian randomization did not yield any statistically significant causal assocns. between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metab. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biol. layers at a pathway level. Using a transcription factor binding site enrichment anal., this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into mol. mechanisms assocd. to intermediate clin. traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the mol. mechanisms underlying both normal physiol. and disease.
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  11. 11
    Burkhardt, R.; Kirsten, H.; Beutner, F.; Holdt, L. M.; Gross, A.; Teren, A. Integration of Genome-Wide SNP Data and Gene-Expression Profiles Reveals Six Novel Loci and Regulatory Mechanisms for Amino Acids and Acylcarnitines in Whole Blood. PLoS Genet. 2015, 11, e1005510,  DOI: 10.1371/journal.pgen.1005510
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    11
    Integration of genome-wide SNP data and gene-expression profiles reveals six novel loci and regulatory mechanisms for amino acids and acylcarnitines in whole blood
    Burkhardt, Ralph; Kirsten, Holger; Beutner, Frank; Holdt, Lesca M.; Gross, Arnd; Teren, Andrej; Toenjes, Anke; Becker, Susen; Krohn, Knut; Kovacs, Peter; Stumvoll, Michael; Teupser, Daniel; Thiery, Joachim; Ceglarek, Uta; Scholz, Markus
    PLoS Genetics (2015), 11 (9), e1005510/1-e1005510/25CODEN: PGLEB5; ISSN:1553-7404. (Public Library of Science)
    Profiling amino acids and acylcarnitines in whole blood spots is a powerful tool in the lab. diagnosis of several inborn errors of metab. Emerging data suggests that altered blood levels of amino acids and acylcarnitines are also assocd. with common metabolic diseases in adults. Thus, the identification of common genetic determinants for blood metabolites might shed light on pathways contributing to human physiol. and common diseases. We applied a targeted mass-spectrometry-based method to analyze whole blood concns. of 96 amino acids, acylcarnitines and pathway assocd. metabolite ratios in a Central European cohort of 2,107 adults and performed genome-wide assocn. (GWA) to identify genetic modifiers of metabolite concns. We discovered and replicated six novel loci assocd. with blood levels of total acylcarnitine, arginine (both on chromosome 6; rs12210538, rs17657775), propionylcarnitine (chromosome 10; rs12779637), 2-hydroxyisovalerylcarnitine (chromosome 21; rs1571700), stearoylcarnitine (chromosome 1; rs3811444), and aspartic acid traits (chromosome 8; rs750472). Based on an integrative anal. of expression quant. trait loci in blood mononuclear cells and correlations between gene expressions and metabolite levels, we provide evidence for putative causative genes: SLC22A16 for total acylcarnitines, ARG1 for arginine, HLCS for 2-hydroxyisovalerylcarnitine, JAM3 for stearoylcarnitine via a trans-effect at chromosome 1, and PPP1R16A for aspartic acid traits. Further, we report replication and provide addnl. functional evidence for ten loci that have previously been published for metabolites measured in plasma, serum or urine. In conclusion, our integrative anal. of SNP, gene-expression and metabolite data points to novel genetic factors that may be involved in the regulation of human metab. At several loci, we provide evidence for metabolite regulation via gene-expression and obsd. overlaps with GWAS loci for common diseases. These results form a strong rationale for subsequent functional and disease-related studies.
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  12. 12
    Inouye, M.; Kettunen, J.; Soininen, P.; Silander, K.; Ripatti, S.; Kumpula, L. S.; Hämäläinen, E.; Jousilahti, P.; Kangas, A. J.; Männistö, S.; Savolainen, M. J.; Jula, A.; Leiviskä, J.; Palotie, A.; Salomaa, V.; Perola, M.; Ala-Korpela, M.; Peltonen, L. Metabonomic, transcriptomic, and genomic variation of a population cohort. Mol. Syst. Biol. 2010, 6, 441,  DOI: 10.1038/msb.2010.93
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    12
    Metabonomic, transcriptomic, and genomic variation of a population cohort
    Inouye Michael; Kettunen Johannes; Soininen Pasi; Silander Kaisa; Ripatti Samuli; Kumpula Linda S; Hamalainen Eija; Jousilahti Pekka; Kangas Antti J; Mannisto Satu; Savolainen Markku J; Jula Antti; Leiviska Jaana; Palotie Aarno; Salomaa Veikko; Perola Markus; Ala-Korpela Mika; Peltonen Leena
    Molecular systems biology (2010), 6 (), 441 ISSN:.
    Comprehensive characterization of human tissues promises novel insights into the biological architecture of human diseases and traits. We assessed metabonomic, transcriptomic, and genomic variation for a large population-based cohort from the capital region of Finland. Network analyses identified a set of highly correlated genes, the lipid-leukocyte (LL) module, as having a prominent role in over 80 serum metabolites (of 134 measures quantified), including lipoprotein subclasses, lipids, and amino acids. Concurrent association with immune response markers suggested the LL module as a possible link between inflammation, metabolism, and adiposity. Further, genomic variation was used to generate a directed network and infer LL module's largely reactive nature to metabolites. Finally, gene co-expression in circulating leukocytes was shown to be dependent on serum metabolite concentrations, providing evidence for the hypothesis that the coherence of molecular networks themselves is conditional on environmental factors. These findings show the importance and opportunity of systematic molecular investigation of human population samples. To facilitate and encourage this investigation, the metabonomic, transcriptomic, and genomic data used in this study have been made available as a resource for the research community.
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  13. 13
    Bullaughey, K.; Chavarria, C. I.; Coop, G.; Gilad, Y. Expression quantitative trait loci detected in cell lines are often present in primary tissues. Hum. Mol. Genet. 2009, 18, 4296– 4303,  DOI: 10.1093/hmg/ddp382
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    13
    Expression quantitative trait loci detected in cell lines are often present in primary tissues
    Bullaughey, Kevin; Chavarria, Claudia I.; Coop, Graham; Gilad, Yoav
    Human Molecular Genetics (2009), 18 (22), 4296-4303CODEN: HMGEE5; ISSN:0964-6906. (Oxford University Press)
    Expression quant. trait loci (eQTL) mapping is a powerful tool for identifying genetic regulatory variation. However, at present, most eQTLs in humans were identified using gene expression data from cell lines, and it remains unknown whether these eQTLs also have a regulatory function in other expression contexts, such as human primary tissues. Here we investigate this question using a targeted strategy. Specifically, we selected a subset of large-effect eQTLs identified in the HapMap lymphoblastoid cell lines, and examd. the assocn. of these eQTLs with gene expression levels across individuals in five human primary tissues (heart, kidney, liver, lung and testes). We show that genotypes at the eQTLs we selected are often predictive of variation in gene expression levels in one or more of the five primary tissues. The genotype effects in the primary tissues are consistently in the same direction as the effects inferred in the cell lines. Addnl., a no. of the eQTLs we tested are found in more than one of the tissues. Our results indicate that functional studies in cell lines may uncover a substantial amt. of genetic variation that affects gene expression levels in human primary tissues.
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    Çalışkan, M.; Cusanovich, D. A.; Ober, C.; Gilad, Y. The effects of EBV transformation on gene expression levels and methylation profiles. Hum. Mol. Genet. 2011, 20, 1643– 1652,  DOI: 10.1093/hmg/ddr041
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    14
    The effects of EBV transformation on gene expression levels and methylation profiles
    Caliskan, Minal; Cusanovich, Darren A.; Ober, Carole; Gilad, Yoav
    Human Molecular Genetics (2011), 20 (8), 1643-1652CODEN: HMGEE5; ISSN:0964-6906. (Oxford University Press)
    Epstein-Barr virus (EBV) transformed lymphoblastoid cell lines (LCLs) provide a conveniently accessible and renewable resource for functional genomic studies in humans. The ability to accumulate multidimensional data pertaining to the same individual cell lines, from complete genomic sequences to detailed gene regulatory profiles, further enhances the utility of LCLs as a model system. A lingering concern, however, is that the changes assocd. with EBV transformation of B cells reduce the usefulness of LCLs as a surrogate model for primary tissues. To evaluate the validity of this concern, the authors compared global gene expression and methylation profiles between CD20+ primary B cells sampled from six individuals and six independent replicates of transformed LCLs derived from each sample. These data allowed the authors to obtain a detailed catalog of the genes and pathways whose regulation is affected by EBV transformation. The expression levels and promoter methylation profiles of more than half of the studied genes were affected by the EBV transformation, including enrichments of genes involved in transcription regulation, cell cycle and immune response. However, most of the differences in gene expression levels between LCLs and B cells are of small magnitude, and that LCLs can often recapitulate the naturally occurring gene expression variation in primary B cells. Thus, these observations suggest that inference of the genetic architecture that underlies regulatory variation in LCLs can typically be generalized to primary B cells. In contrast, inference based on functional studies in LCLs may be more limited to the cell lines.
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    Dimas, A. S.; Deutsch, S.; Stranger, B. E.; Montgomery, S. B.; Borel, C.; Attar-Cohen, H. Common regulatory variation impacts gene expression in a cell type–dependent manner. Science 2009, 325, 1246– 1250,  DOI: 10.1126/science.1174148
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    15
    Common Regulatory Variation Impacts Gene Expression in a Cell Type-Dependent Manner
    Dimas, Antigone S.; Deutsch, Samuel; Stranger, Barbara E.; Montgomery, Stephen B.; Borel, Christelle; Attar-Cohen, Homa; Ingle, Catherine; Beazley, Claude; Gutierrez Arcelus, Maria; Sekowska, Magdalena; Gagnebin, Marilyne; Nisbett, James; Deloukas, Panos; Dermitzakis, Emmanouil T.; Antonarakis, Stylianos E.
    Science (Washington, DC, United States) (2009), 325 (5945), 1246-1250CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
    Studies correlating genetic variation to gene expression facilitate the interpretation of common human phenotypes and disease. As functional variants may be operating in a tissue-dependent manner, we performed gene expression profiling and assocn. with genetic variants (single-nucleotide polymorphisms) on three cell types of 75 individuals. We detected cell type-specific genetic effects, with 69 to 80% of regulatory variants operating in a cell type-specific manner, and identified multiple expressive quant. trait loci (eQTLs) per gene, unique or shared among cell types and pos. correlated with the no. of transcripts per gene. Cell type-specific eQTLs were found at larger distances from genes and at lower effect size, similar to known enhancers. These data suggest that the complete regulatory variant repertoire can only be uncovered in the context of cell-type specificity.
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    Ding, J.; Gudjonsson, J. E.; Liang, L.; Stuart, P. E.; Li, Y.; Chen, W. Gene expression in skin and lymphoblastoid cells: Refined statistical method reveals extensive overlap in cis-eQTL signals. Am. J. Hum. Genet. 2010, 87, 779– 789,  DOI: 10.1016/j.ajhg.2010.10.024
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    16
    Gene expression in skin and lymphoblastoid cells: refined statistical method reveals extensive overlap in cis-eQTL signals
    Ding, Jun; Gudjonsson, Johann E.; Liang, Liming; Stuart, Philip E.; Li, Yun; Chen, Wei; Weichenthal, Michael; Ellinghaus, Eva; Franke, Andre; Cookson, William; Nair, Rajan P.; Elder, James T.; Abecasis, Goncalo R.
    American Journal of Human Genetics (2010), 87 (6), 779-789CODEN: AJHGAG; ISSN:0002-9297. (Cell Press)
    Psoriasis, an immune-mediated, inflammatory disease of the skin and joints, provides an ideal system for expression quant. trait locus (eQTL) anal., because it has a strong genetic basis and disease-relevant tissue (skin) is readily accessible. To better understand the role of genetic variants regulating cutaneous gene expression, we identified 841 cis-acting eQTLs using RNA extd. from skin biopsies of 53 psoriatic individuals and 57 healthy controls. We found substantial overlap between cis-eQTLs of normal control, uninvolved psoriatic, and lesional psoriatic skin. Consistent with recent studies and with the idea that control of gene expression can mediate relationships between genetic variants and disease risk, we found that eQTL SNPs are more likely to be assocd. with psoriasis than are randomly selected SNPs. To explore the tissue specificity of these eQTLs and hence to quantify the benefits of studying eQTLs in different tissues, we developed a refined statistical method for estg. eQTL overlap and used it to compare skin eQTLs to a published panel of lymphoblastoid cell line (LCL) eQTLs. Our method accounts for the fact that most eQTL studies are likely to miss some true eQTLs as a result of power limitations and shows that ∼70% of cis-eQTLs in LCLs are shared with skin, as compared with the naive est. of <50% sharing. Our results provide a useful method for estg. the overlap between various eQTL studies and provide a catalog of cis-eQTLs in skin that can facilitate efforts to understand the functional impact of identified susceptibility variants on psoriasis and other skin traits.
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    Firmann, M.; Mayor, V.; Vidal, P. M.; Bochud, M.; Pécoud, A.; Hayoz, D. The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. BMC Cardiovasc. Disord. 2008, 8, 6,  DOI: 10.1186/1471-2261-8-6
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    17
    The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome
    Firmann Mathieu; Mayor Vladimir; Vidal Pedro Marques; Bochud Murielle; Pecoud Alain; Hayoz Daniel; Paccaud Fred; Preisig Martin; Song Kijoung S; Yuan Xin; Danoff Theodore M; Stirnadel Heide A; Waterworth Dawn; Mooser Vincent; Waeber Gerard; Vollenweider Peter
    BMC cardiovascular disorders (2008), 8 (), 6 ISSN:.
    BACKGROUND: Cardiovascular diseases and their associated risk factors remain the main cause of mortality in western societies. In order to assess the prevalence of cardiovascular risk factors (CVRFs) in the Caucasian population of Lausanne, Switzerland, we conducted a population-based study (Colaus Study). A secondary aim of the CoLaus study will be to determine new genetic determinants associated with CVRFs. METHODS: Single-center, cross-sectional study including a random sample of 6,188 extensively phenotyped Caucasian subjects (3,251 women and 2,937 men) aged 35 to 75 years living in Lausanne, and genotyped using the 500 K Affymetrix chip technology. RESULTS: Obesity (body mass index > or = 30 kg/m2), smoking, hypertension (blood pressure > or = 140/90 mmHg and/or treatment), dyslipidemia (high LDL-cholesterol and/or low HDL-cholesterol and/or high triglyceride levels) and diabetes (fasting plasma glucose > or = 7 mmol/l and/or treatment) were present in 947 (15.7%), 1673 (27.0%), 2268 (36.7%), 2113 (34.2%) and 407 (6.6%) of the participants, respectively, and the prevalence was higher in men than in women. In both genders, the prevalence of obesity, hypertension and diabetes increased with age. CONCLUSION: The prevalence of major CVRFs is high in the Lausanne population in particular in men. We anticipate that given its size, the depth of the phenotypic analysis and the availability of dense genome-wide genetic data, the CoLaus Study will be a unique resource to investigate not only the epidemiology of isolated, or aggregated CVRFs like the metabolic syndrome, but can also serve as a discovery set, as well as replication set, to identify novel genes associated with these conditions.
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    Rueedi, R.; Ledda, M.; Nicholls, A. W.; Salek, R. M.; Marques-Vidal, P.; Morya, E. Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links. PLoS Genet. 2014, 10, e1004132,  DOI: 10.1371/journal.pgen.1004132
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    18
    Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links
    Rueedi, Rico; Ledda, Mirko; Nicholls, Andrew W.; Salek, Reza M.; Marques-Vidal, Pedro; Morya, Edgard; Sameshima, Koichi; Montoliu, Ivan; Da Silva, Laeticia; Collino, Sebastiano; Martin, Francois-Pierre; Rezzi, Serge; Steinbeck, Christoph; Waterworth, Dawn M.; Waeber, Gerard; Vollenweider, Peter; Beckmann, Jacques S.; Le Coutre, Johannes; Mooser, Vincent; Bergmann, Sven; Genick, Ulrich K.; Kutalik, Zoltan
    PLoS Genetics (2014), 10 (2), e1004132/1-e1004132/10, 10 pp.CODEN: PGLEB5; ISSN:1553-7404. (Public Library of Science)
    Metabolic traits are mol. phenotypes that can drive clin. phenotypes and may predict disease progression. Here, we report results from a metabolome- and genome-wide assocn. study on 1H-NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compd. identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5 × 10-8) and independent assocns. between single nucleotide polymorphisms (SNP) and metabolome features. Fifty-six of these assocns. replicated in the TasteSensomics cohort, comprising 601 individuals from S~ao Paulo of vastly diverse ethnic background. They correspond to eleven gene-metabolite assocns., six of which had been previously identified in the urine metabolome and three in the serum metabolome. Our key novel findings are the assocns. of two SNPs with NMR spectral signatures pointing to fucose (rs492602, P = 6.9 × 10-44) and lysine (rs8101881, P = 1.2 × 10-33), resp. Fine-mapping of the first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been assocd. with Crohn's disease. This implicates fucose as a potential prognostic disease marker, for which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene, rare mutations of which have been linked to severe kidney damage. The replication of previous assocns. and our new discoveries demonstrate the potential of untargeted metabolomics GWAS to robustly identify mol. disease markers.
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    Marco-Sola, S.; Sammeth, M.; Guigó, R.; Ribeca, P. The GEM mapper: fast, accurate and versatile alignment by filtration. Nat. Methods 2012, 9, 1185,  DOI: 10.1038/nmeth.2221
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    The GEM mapper: fast, accurate and versatile alignment by filtration
    Marco-Sola, Santiago; Sammeth, Michael; Guigo, Roderic; Ribeca, Paolo
    Nature Methods (2012), 9 (12), 1185-1188CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)
    Because of ever-increasing throughput requirements of sequencing data, most existing short-read aligners have been designed to focus on speed at the expense of accuracy. The Genome Multitool (GEM) mapper can leverage string matching by filtration to search the alignment space more efficiently, simultaneously delivering precision (performing fully tunable exhaustive searches that return all existing matches, including gapped ones) and speed (being several times faster than comparable state-of-the-art tools).
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    MATLAB 8.5.0.197613 (R2015a); The MathWorks Inc.: Natick, Massachusetts, 2015.
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    Gao, X.; Starmer, J.; Martin, E. R. A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet. Epidemiol. 2008, 32, 361– 369,  DOI: 10.1002/gepi.20310
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    A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms
    Gao Xiaoyi; Starmer Joshua; Martin Eden R
    Genetic epidemiology (2008), 32 (4), 361-9 ISSN:0741-0395.
    Multiple testing is a challenging issue in genetic association studies using large numbers of single nucleotide polymorphism (SNP) markers, many of which exhibit linkage disequilibrium (LD). Failure to adjust for multiple testing appropriately may produce excessive false positives or overlook true positive signals. The Bonferroni method of adjusting for multiple comparisons is easy to compute, but is well known to be conservative in the presence of LD. On the other hand, permutation-based corrections can correctly account for LD among SNPs, but are computationally intensive. In this work, we propose a new multiple testing correction method for association studies using SNP markers. We show that it is simple, fast and more accurate than the recently developed methods and is comparable to permutation-based corrections using both simulated and real data. We also demonstrate how it might be used in whole-genome association studies to control type I error. The efficiency and accuracy of the proposed method make it an attractive choice for multiple testing adjustment when there is high intermarker LD in the SNP data set.
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    Rueedi, R.; Mallol, R.; Raffler, J.; Lamparter, D.; Friedrich, N.; Vollenweider, P. Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy. PLoS Comput. Biol. 2017, 13, e1005839,  DOI: 10.1371/journal.pcbi.1005839
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    Metabomatching: using genetic association to identify metabolites in proton NMR spectroscopy
    Rueedi, Rico; Mallol, Roger; Raffler, Johannes; Lamparter, David; Friedrich, Nele; Vollenweider, Peter; Waeber, Gerard; Kastenmuller, Gabi; Kutalik, Zoltan; Bergmann, Sven
    PLoS Computational Biology (2017), 13 (12), e1005839/1-e1005839/17CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
    A metabolome-wide genome-wide assocn. study aims to discover the effects of genetic variants on metabolome phenotypes. Most mGWASes use as phenotypes concns. of limited sets of metabolites that can be identified and quantified from spectral information. In contrast, in an untargeted mGWAS both identification and quantification are forgone and, instead, all measured metabolome features are tested for assocn. with genetic variants. While the untargeted approach does not discard data that may have eluded identification, the interpretation of assocd. features remains a challenge. To address this issue, we developed metabomatching to identify the metabolites underlying significant assocns. obsd. in untargeted mGWASes on proton NMR metabolome data. Metabomatching capitalizes on genetic spiking, the concept that because metabolome features assocd. with a genetic variant tend to correspond to the peaks of the NMR spectrum of the underlying metabolite, genetic assocn. can allow for identification. Applied to the untargeted mGWASes in the SHIP and CoLaus cohorts and using 180 ref. NMR spectra of the urine metabolome database, metabomatching successfully identified the underlying metabolite in 14 of 19, and 8 of 9 assocns., resp. The accuracy and efficiency of our method make it a strong contender for facilitating or complementing metabolomics analyses in large cohorts, where the availability of genetic, or other data, enables our approach, but targeted quantification is limited.
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    Wishart, D. S.; Feunang, Y. D.; Marcu, A.; Guo, A. C.; Liang, K.; Vázquez-Fresno, R. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2018, 46, D608– D617,  DOI: 10.1093/nar/gkx1089
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    HMDB 4.0: the human metabolome database for 2018
    Wishart, David S.; Feunang, Yannick Djoumbou; Marcu, Ana; Guo, An Chi; Liang, Kevin; Vazquez-Fresno, Rosa; Sajed, Tanvir; Johnson, Daniel; Li, Carin; Karu, Naama; Sayeeda, Zinat; Lo, Elvis; Assempour, Nazanin; Berjanskii, Mark; Singhal, Sandeep; Arndt, David; Liang, Yonjie; Badran, Hasan; Grant, Jason; Serra-Cayuela, Arnau; Liu, Yifeng; Mandal, Rupa; Neveu, Vanessa; Pon, Allison; Knox, Craig; Wilson, Michael; Manach, Claudine; Scalbert, Augustin
    Nucleic Acids Research (2018), 46 (D1), D608-D617CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)
    A review. The Human Metabolome Database or HMDB (www. hmdb.ca) is a web-enabled metabolomic database contg. comprehensive information about human metabolites along with their biol. roles, physiol. concns., disease assocns., chem. reactions, metabolic pathways, and ref. spectra. First described in 2007, the HMDB is now considered the std. metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web stds. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the no. of fully annotated metabolites has increased by nearly threefold, the no. of exptl. spectra has grown by almost fourfold and the no. of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB's chem. taxonomy, chem. ontol., spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC-MS ref. spectral data as well as predicted (physiol. feasible) metabolite structures to facilitate novel metabolite identification. Addnl. information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmaco-metabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochem., clin. chem., clin. genetics, medicine, and metabolomics science.
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    Khalili, B.; Tomasoni, M.; Mattei, M.; Parera, R. M.; Sonmez, R.; Krefl, D. Automated analysis of large-scale NMR data generates metabolomic signatures and links them to candidate metabolites. J. Proteome Res. 2019, 613935,  DOI: 10.1021/acs.jproteome.9b00295
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    Burgess, S.; Small, D. S.; Thompson, S. G. A review of instrumental variable estimators for Mendelian randomization. Stat. Methods Med. Res. 2017, 26, 2333– 2355,  DOI: 10.1177/0962280215597579
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    A review of instrumental variable estimators for Mendelian randomization
    Burgess Stephen; Thompson Simon G; Small Dylan S
    Statistical methods in medical research (2017), 26 (5), 2333-2355 ISSN:.
    Instrumental variable analysis is an approach for obtaining causal inferences on the effect of an exposure (risk factor) on an outcome from observational data. It has gained in popularity over the past decade with the use of genetic variants as instrumental variables, known as Mendelian randomization. An instrumental variable is associated with the exposure, but not associated with any confounder of the exposure-outcome association, nor is there any causal pathway from the instrumental variable to the outcome other than via the exposure. Under the assumption that a single instrumental variable or a set of instrumental variables for the exposure is available, the causal effect of the exposure on the outcome can be estimated. There are several methods available for instrumental variable estimation; we consider the ratio method, two-stage methods, likelihood-based methods, and semi-parametric methods. Techniques for obtaining statistical inferences and confidence intervals are presented. The statistical properties of estimates from these methods are compared, and practical advice is given about choosing a suitable analysis method. In particular, bias and coverage properties of estimators are considered, especially with weak instruments. Settings particularly relevant to Mendelian randomization are prioritized in the paper, notably the scenario of a continuous exposure and a continuous or binary outcome.
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    Davey Smith, G.; Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?. Int. J. Epidemiol. 2003, 32, 1– 22,  DOI: 10.1093/ije/dyg070
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    Raffler, J.; Friedrich, N.; Arnold, M.; Kacprowski, T.; Rueedi, R.; Altmaier, E. Genome-wide association study with targeted and non-targeted NMR metabolomics identifies 15 novel loci of urinary human metabolic individuality. PLoS Genet. 2015, 11, e1005487,  DOI: 10.1371/journal.pgen.1005487
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    Genome-wide association study with targeted and non-targeted nmr metabolomics identifies 15 novel loci of urinary human metabolic individuality
    Raffler, Johannes; Friedrich, Nele; Arnold, Matthias; Kacprowski, Tim; Rueedi, Rico; Altmaier, Elisabeth; Bergmann, Sven; Budde, Kathrin; Gieger, Christian; Homuth, Georg; Pietzner, Maik; Roemisch-Margl, Werner; Strauch, Konstantin; Voelzke, Henry; Waldenberger, Melanie; Wallaschofski, Henri; Nauck, Matthias; Voelker, Uwe; Kastenmueller, Gabi; Suhre, Karsten
    PLoS Genetics (2015), 11 (9), e1005487/1-e1005487/28CODEN: PGLEB5; ISSN:1553-7404. (Public Library of Science)
    Genome-wide assocn. studies with metabolic traits (mGWAS) uncovered many genetic variants that influence human metab. These genetically influenced metabotypes (GIMs) contribute to our metabolic individuality, our capacity to respond to environmental challenges, and our susceptibility to specific diseases. While metabolic homeostasis in blood is a well investigated topic in large mGWAS with over 150 known loci, metabolic detoxification through urinary excretion has only been addressed by few small mGWAS with only 11 assocd. loci so far. Here we report the largest mGWAS to date, combining targeted and non-targeted 1H NMR anal. of urine samples from 3,861 participants of the SHIP-0 cohort and 1,691 subjects of the KORA F4 cohort. We identified and replicated 22 loci with significant assocns. with urinary traits, 15 of which are new (HIBCH, CPS1, AGXT, XYLB, TKT, ETNPPL, SLC6A19, DMGDH, SLC36A2, GLDC, SLC6A13, ACSM3, SLC5A11, PNMT, SLC13A3). Two-thirds of the urinary loci also have a metabolite assocn. in blood. For all but one of the 6 loci where significant assocns. target the same metabolite in blood and urine, the genetic effects have the same direction in both fluids. In contrast, for the SLC5A11 locus, we found increased levels of myo-inositol in urine whereas mGWAS in blood reported decreased levels for the same genetic variant. This might indicate less effective re-absorption of myo-inositol in the kidneys of carriers. In summary, our study more than doubles the no. of known loci that influence urinary phenotypes. It thus allows novel insights into the relationship between blood homeostasis and its regulation through excretion. The newly discovered loci also include variants previously linked to chronic kidney disease (CPS1, SLC6A13), pulmonary hypertension (CPS1), and ischemic stroke (XYLB). By establishing connections from gene to disease via metabolic traits our results provide novel hypotheses about mol. mechanisms involved in the etiol. of diseases.
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    Võsa, U.; Claringbould, A.; Westra, H.-J.; Bonder, M. J.; Deelen, P.; Zeng, B.; Kirsten, H.; Saha, A.; Kreuzhuber, R.; Brugge, H. Large-scale cis-and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 2021, 53, 1300– 1310,  DOI: 10.1038/s41588-021-00913-z
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    Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression
    Vosa, Urmo; Claringbould, Annique; Westra, Harm-Jan; Bonder, Marc Jan; Deelen, Patrick; Zeng, Biao; Kirsten, Holger; Saha, Ashis; Kreuzhuber, Roman; Yazar, Seyhan; Brugge, Harm; Oelen, Roy; de Vries, Dylan H.; van der Wijst, Monique G. P.; Kasela, Silva; Pervjakova, Natalia; Alves, Isabel; Fave, Marie-Julie; Agbessi, Mawusse; Christiansen, Mark W.; Jansen, Rick; Seppala, Ilkka; Tong, Lin; Teumer, Alexander; Schramm, Katharina; Hemani, Gibran; Verlouw, Joost; Yaghootkar, Hanieh; Sonmez Flitman, Reyhan; Brown, Andrew; Kukushkina, Viktorija; Kalnapenkis, Anette; Rueger, Sina; Porcu, Eleonora; Kronberg, Jaanika; Kettunen, Johannes; Lee, Bernett; Zhang, Futao; Qi, Ting; Hernandez, Jose Alquicira; Arindrarto, Wibowo; Beutner, Frank; Dmitrieva, Julia; Elansary, Mahmoud; Fairfax, Benjamin P.; Georges, Michel; Heijmans, Bastiaan T.; Hewitt, Alex W.; Kahonen, Mika; Kim, Yungil; Knight, Julian C.; Kovacs, Peter; Krohn, Knut; Li, Shuang; Loeffler, Markus; Marigorta, Urko M.; Mei, Hailang; Momozawa, Yukihide; Muller-Nurasyid, Martina; Nauck, Matthias; Nivard, Michel G.; Penninx, Brenda W. J. H.; Pritchard, Jonathan K.; Raitakari, Olli T.; Rotzschke, Olaf; Slagboom, Eline P.; Stehouwer, Coen D. A.; Stumvoll, Michael; Sullivan, Patrick; 't Hoen, Peter A. C.; Thiery, Joachim; Tonjes, Anke; van Dongen, Jenny; van Iterson, Maarten; Veldink, Jan H.; Volker, Uwe; Warmerdam, Robert; Wijmenga, Cisca; Swertz, Morris; Andiappan, Anand; Montgomery, Grant W.; Ripatti, Samuli; Perola, Markus; Kutalik, Zoltan; Dermitzakis, Emmanouil; Bergmann, Sven; Frayling, Timothy; van Meurs, Joyce; Prokisch, Holger; Ahsan, Habibul; Pierce, Brandon L.; Lehtimaki, Terho; Boomsma, Dorret I.; Psaty, Bruce M.; Gharib, Sina A.; Awadalla, Philip; Milani, Lili; Ouwehand, Willem H.; Downes, Kate; Stegle, Oliver; Battle, Alexis; Visscher, Peter M.; Yang, Jian; Scholz, Markus; Powell, Joseph; Gibson, Greg; Esko, Tonu; Franke, Lude
    Nature Genetics (2021), 53 (9), 1300-1310CODEN: NGENEC; ISSN:1061-4036. (Nature Portfolio)
    Trait-assocd. genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quant. trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-assocd. variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type compn. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.
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    Wald, A. The fitting of straight lines if both variables are subject to error. Ann. Math. Stat. 1940, 11, 284– 300,  DOI: 10.1214/aoms/1177731868
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    Hartung, J.; Knapp, G.; Sinha, B. K.; Sinha, B. K. Statistical meta-analysis with applications; Wiley: New York, 2008.
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    Greco, M. F. D.; Minelli, C.; Sheehan, N. A.; Thompson, J. R. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat. Med. 2015, 34, 2926– 2940,  DOI: 10.1002/sim.6522
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    Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome
    Greco M Fabiola Del; Minelli Cosetta; Sheehan Nuala A; Thompson John R
    Statistics in medicine (2015), 34 (21), 2926-40 ISSN:.
    Mendelian randomisation (MR) estimates causal effects of modifiable phenotypes on an outcome by using genetic variants as instrumental variables, but its validity relies on the assumption of no pleiotropy, that is, genes influence the outcome only through the given phenotype. Excluding pleiotropy is difficult, but the use of multiple instruments can indirectly address the issue: if all genes represent valid instruments, their MR estimates should vary only by chance. The Sargan test detects pleiotropy when individual phenotype, outcome and genotype data are measured in the same subjects. We propose an alternative approach to be used when only summary genetic data are available or data on gene-phenotype and gene-outcome come from different subjects. The presence of pleiotropy is investigated using the between-instrument heterogeneity Q test (together with the I(2) index) in a meta-analysis of MR Wald estimates, derived separately from each instrument. For a continuous outcome, we evaluate the approach through simulations and illustrate it using published data. For the scenario where all data come from the same subjects, we compare it with the Sargan test. The Q test tends to be conservative in small samples. Its power increases with the degree of pleiotropy and the sample size, as does the precision of the I(2) index, in which case results are similar to those of the Sargan test. In MR studies with large sample sizes based on summary data, the between-instrument Q test represents a useful tool to explore the presence of heterogeneity due to pleiotropy or other causes.
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    Yavorska, O. O.; Burgess, S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol. 2017, 46, 1734– 1739,  DOI: 10.1093/ije/dyx034
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    MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data
    Yavorska Olena O; Burgess Stephen
    International journal of epidemiology (2017), 46 (6), 1734-1739 ISSN:.
    MendelianRandomization is a software package for the R open-source software environment that performs Mendelian randomization analyses using summarized data. The core functionality is to implement the inverse-variance weighted, MR-Egger and weighted median methods for multiple genetic variants. Several options are available to the user, such as the use of robust regression, fixed- or random-effects models and the penalization of weights for genetic variants with heterogeneous causal estimates. Extensions to these methods, such as allowing for variants to be correlated, can be chosen if appropriate. Graphical commands allow summarized data to be displayed in an interactive graph, or the plotting of causal estimates from multiple methods, for comparison. Although the main method of data entry is directly by the user, there is also an option for allowing summarized data to be incorporated from the PhenoScanner database of genotype-phenotype associations. We hope to develop this feature in future versions of the package. The R software environment is available for download from [https://www.r-project.org/]. The MendelianRandomization package can be downloaded from the Comprehensive R Archive Network (CRAN) within R, or directly from [https://cran.r-project.org/web/packages/MendelianRandomization/]. Both R and the MendelianRandomization package are released under GNU General Public Licenses (GPL-2 GPL-3).
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    Shin, S.-Y.; Fauman, E. B.; Petersen, A.-K.; Krumsiek, J.; Santos, R.; Huang, J. An atlas of genetic influences on human blood metabolites. Nat. Genet. 2014, 46, 543,  DOI: 10.1038/ng.2982
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    An atlas of genetic influences on human blood metabolites
    Shin, So-Youn; Fauman, Eric B.; Petersen, Ann-Kristin; Krumsiek, Jan; Santos, Rita; Huang, Jie; Arnold, Matthias; Erte, Idil; Forgetta, Vincenzo; Yang, Tsun-Po; Walter, Klaudia; Menni, Cristina; Chen, Lu; Vasquez, Louella; Valdes, Ana M.; Hyde, Craig L.; Wang, Vicky; Ziemek, Daniel; Roberts, Phoebe; Xi, Li; Grundberg, Elin; Waldenberger, Melanie; Richards, J. Brent; Mohney, Robert P.; Milburn, Michael V.; John, Sally L.; Trimmer, Jeff; Theis, Fabian J.; Overington, John P.; Suhre, Karsten; Brosnan, M. Julia; Gieger, Christian; Kastenmuller, Gabi; Spector, Tim D.; Soranzo, Nicole
    Nature Genetics (2014), 46 (6), 543-550CODEN: NGENEC; ISSN:1061-4036. (Nature Publishing Group)
    Genome-wide assocn. scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metab. and complex disease. Here the authors report the most comprehensive exploration of genetic loci influencing human metab. thus far, comprising 7,824 adult individuals from 2 European population studies. The authors report genome-wide significant assocns. at 145 metabolic loci and their biochem. connectivity with more than 400 metabolites in human blood. The authors extensively characterize the resulting in vivo blueprint of metab. in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metab. and pharmacol. targets. The authors further developed a database and web-based resources for data mining and results visualization. The authors' findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.
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    Nicholson, G.; Rantalainen, M.; Li, J. V.; Maher, A. D.; Malmodin, D.; Ahmadi, K. R. A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection. PLoS Genet. 2011, 7, e1002270,  DOI: 10.1371/journal.pgen.1002270
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    A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection
    Nicholson, George; Rantalainen, Mattias; Li, Jia V.; Maher, Anthony D.; Malmodin, Daniel; Ahmadi, Kourosh R.; Faber, Johan H.; Barrett, Amy; Min, Josine L.; Rayner, N. William; Toft, Henrik; Krestyaninova, Maria; Viksna, Juris; Neogi, Sudeshna Guha; Dumas, Marc-Emmanuel; Sarkans, Ugis; Donnelly, Peter; Illig, Thomas; Adamski, Jerzy; Suhre, Karsten; Allen, Maxine; Zondervan, Krina T.; Spector, Tim D.; Nicholson, Jeremy K.; Lindon, John C.; Baunsgaard, Dorrit; Holmes, Elaine; McCarthy, Mark I.; Holmes, Chris C.
    PLoS Genetics (2011), 7 (9), e1002270CODEN: PGLEB5; ISSN:1553-7404. (Public Library of Science)
    We have performed a metabolite quant. trait locus (mQTL) study of the 1H NMR spectroscopy metabolome in humans, building on recent targeted knowledge of genetic drivers of metabolic regulation. Urine and plasma samples were collected from two cohorts of individuals of European descent, with one cohort comprised of female twins donating samples longitudinally. Sample metabolite concns. were quantified by 1H NMR and tested for assocn. with genome-wide single-nucleotide polymorphisms (SNPs). 4 Metabolites' concns. exhibited significant, replicable assocn. with SNP variation (8.6 × 10-11 < p < 2.8 × 10-23). 3 Of these, trimethylamine, 3-amino-isobutyrate, and an N-acetylated compd., were measured in urine. The other, dimethylamine, was measured in plasma. Trimethylamine and dimethylamine mapped to a single genetic region (hence we report a total of three implicated genomic regions). 2 Of the 3 hit regions lie within haplotype blocks (at 2p13.1 and 10q24.2) that carry the genetic signature of strong, recent, pos. selection in European populations. Genes NAT8 and PYROXD2, both with relatively uncharacterized functional roles, are good candidates for mediating the corresponding mQTL assocns. The studies longitudinal twin design allowed detailed variance-components anal. of the sources of population variation in metabolite levels. The mQTLs explained 40%-64% of biol. population variation in the corresponding metabolites' concns. These effect sizes are stronger than those reported in a recent, targeted mQTL study of metabolites in serum using the targeted-metabolomics Biocrates platform. By re-analyzing our plasma samples using the Biocrates platform, we replicated the mQTL findings of the previous study and discovered a previously uncharacterized yet substantial familial component of variation in metabolite levels in addn. to the heritability contribution from the corresponding mQTL effects.
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  35. 35
    Engelke, U. F.; Liebrand-van Sambeek, M. L.; De Jong, J. G.; Leroy, J. G.; Morava, E.; Smeitink, J. A. N-acetylated metabolites in urine: proton nuclear magnetic resonance spectroscopic study on patients with inborn errors of metabolism. Clin. Chem. 2004, 50, 58– 66,  DOI: 10.1373/clinchem.2003.020214
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    N-acetylated metabolites in urine: Proton nuclear magnetic resonance spectroscopic study on patients with inborn errors of metabolism
    Engelke, Udo F. H.; Liebrand-van Sambeek, Maria L. F.; de Jong, Jan G. N.; Leroy, Jules G.; Morava, Eva; Smeitink, Jan A. M.; Wevers, Ron A.
    Clinical Chemistry (Washington, DC, United States) (2004), 50 (1), 58-66CODEN: CLCHAU; ISSN:0009-9147. (American Association for Clinical Chemistry)
    There is no comprehensive anal. technique to analyze N-acetylated metabolites in urine. Many of these compds. are involved in inborn errors of metab. In the present study, the authors examd. the potential of proton NMR (1H-NMR) spectroscopy as a tool to identify and quantify N-acetylated metabolites in urine of patients with various inborn errors of metab. The authors performed 1H-NMR spectroscopy on a 500 MHz spectrometer. Using a combination of 1- and 2-dimensional correlation spectroscopy (COSY) 1H-NMR spectra, the authors were able to assign and quantify resonances of characteristic N-acetylated compds. products in urine of patients with 13 inborn errors of metab. The disease-specific N-acetylated metabolites were excreted at concns. >100 μmol/mmol of creatinine in the patients' urine. In control urine samples, the concn. of individual N-acetyl-contg. compds. was <40 μmol/mmol of creatinine. The combination of one- and two-dimensional COSY NMR spectroscopy led to the correct diagnosis of nine different inborn errors of metab. No abnormalities were obsd. in the spectra of urine from patients with GM1- or GM2-gangliosidosis. The authors also detd. the 1H-NMR characteristics of N-acetylated metabolites that may be relevant to human metab. 1H-NMR spectroscopy may be used to identify and quantify N-acetylated metabolites of diagnostic importance for the field of inborn errors of metab.
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    Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Stat. Soc.: B 1995, 57, 289– 300,  DOI: 10.1111/j.2517-6161.1995.tb02031.x
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    Kastenmüller, G.; Raffler, J.; Gieger, C.; Suhre, K. Genetics of human metabolism: an update. Hum. Mol. Genet. 2015, 24, R93– R101,  DOI: 10.1093/hmg/ddv263
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    Genetics of human metabolism: an update
    Kastenmueller, Gabi; Raffler, Johannes; Gieger, Christian; Suhre, Karsten
    Human Molecular Genetics (2015), 24 (Rev. Issue 1), R93-R101CODEN: HMGEE5; ISSN:0964-6906. (Oxford University Press)
    A review. Genome-wide assocn. studies with metabolomics (mGWAS) identify genetically influenced metabotypes (GIMs), their ensemble defining the heritable part of every human's metabolic individuality. Knowledge of genetic variation in metab. has many applications of biomedical and pharmaceutical interests, including the functional understanding of genetic assocns. with clin. end points, design of strategies to correct dysregulations in metabolic disorders and the identification of genetic effect modifiers of metabolic disease biomarkers. Furthermore, it has been shown that GIMs provide testable hypotheses for functional genomics and metabolomics and for the identification of novel gene functions and metabolite identities. mGWAS with growing sample sizes and increasingly complex metabolic trait panels are being conducted, allowing for more comprehensive and systems-based downstream analyses. The generated large datasets of genetic assocns. can now be mined by the biomedical research community and provide valuable resources for hypothesis-driven studies. In this review, we provide a brief summary of the key aspects of mGWAS, followed by an update of recently published mGWAS. We then discuss new approaches of integrating and exploring mGWAS results and finish by presenting selected applications of GIMs in recent studies.
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    Suhre, K.; Shin, S. Y.; Petersen, A. K.; Mohney, R. P.; Meredith, D.; Wägele, B. Human metabolic individuality in biomedical and pharmaceutical research. Nature 2011, 477, 54– 60,  DOI: 10.1038/nature10354
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    Human metabolic individuality in biomedical and pharmaceutical research
    Suhre, Karsten; Shin, So-Youn; Petersen, Ann-Kristin; Mohney, Robert P.; Meredith, David; Waegele, Brigitte; Altmaier, Elisabeth; Deloukas, Panos; Erdmann, Jeanette; Grundberg, Elin; Hammond, Christopher J.; Hrabe de Angelis, Martin; Kastenmueller, Gabi; Koettgen, Anna; Kronenberg, Florian; Mangino, Massimo; Meisinger, Christa; Meitinger, Thomas; Mewes, Hans-Werner; Milburn, Michael V.; Prehn, Cornelia; Raffler, Johannes; Ried, Janina S.; Roemisch-Margl, Werner; Samani, Nilesh J.; Small, Kerrin S.; Wichmann, H.-Erich; Zhai, Guang-Ju; Illig, Thomas; Spector, Tim D.; Adamski, Jerzy; Soranzo, Nicole; Gieger, Christian
    Nature (London, United Kingdom) (2011), 477 (7362), 54-60CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
    A review based on author's and other researchers studies. Genome-wide assocn. studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biol. processes is often lacking. Assocns. with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive anal. of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci assocd. with blood metabolite concns., of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our assocns. provide new functional insights for many disease-related assocns. that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.
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    Yu, B.; Zheng, Y.; Alexander, D.; Morrison, A. C.; Coresh, J.; Boerwinkle, E. Genetic determinants influencing human serum metabolome among African Americans. PLoS Genet. 2014, 10, e1004212  DOI: 10.1371/journal.pgen.1004212
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    Genetic determinants influencing human serum metabolome among African Americans
    Yu, Bing; Zheng, Yan; Alexander, Danny; Morrison, Alanna C.; Coresh, Josef; Boerwinkle, Eric
    PLoS Genetics (2014), 10 (3), e1004212/1-e1004212/8, 8 pp.CODEN: PGLEB5; ISSN:1553-7404. (Public Library of Science)
    Phenotypes proximal to gene action generally reflect larger genetic effect sizes than those that are distant. The human metabolome, a result of multiple cellular and biol. processes, are functional intermediate phenotypes proximal to gene action. Here, we present a genome-wide assocn. study of 308 untargeted metabolite levels among African Americans from the Atherosclerosis Risk in Communities (ARIC) Study. Nineteen significant common variant-metabolite assocns. were identified, including 13 novel loci (p<1.6 × 1010). These loci were assocd. with 7-50 % of the difference in metabolite levels per allele, and the variance explained ranged from 4% to 20%. Fourteen genes were identified within the nineteen loci, and four of them contained non-synonymous substitutions in four enzyme-encoding genes (KLKB1, SIAE, CPS1, and NAT8); the other significant loci consist of eight other enzyme-encoding genes (ACE, GATM, ACY3, ACSM2B, THEM4, ADH4, UGT1A, TREH), a transporter gene (SLC6A13) and a polycystin protein gene (PKD2L1). In addn., four potential disease-assocd. paths were identified, including two direct longitudinal predictive relationships: NAT8 with N-acetylornithine, N-acetyl-1-methylhistidine and incident chronic kidney disease, and TREH with trehalose and incident diabetes. These results highlight the value of using endophenotypes proximal to gene function to discover new insights into biol. and disease pathol.
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    Raffler, J.; Römisch-Margl, W.; Petersen, A. K.; Pagel, P.; Blöchl, F.; Hengstenberg, C. Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma. Genome Med. 2013, 5, 13,  DOI: 10.1186/gm417
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    Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma
    Raffler, Johannes; Roemisch-Margl, Werner; Petersen, Ann-Kristin; Pagel, Philipp; Bloechl, Florian; Hengstenberg, Christian; Illig, Thomas; Meisinger, Christa; Stark, Klaus; Wichmann, H.-Erich; Adamski, Jerzy; Gieger, Christian; Kastenmueller, Gabi; Suhre, Karsten
    Genome Medicine (2013), 5 (2), 13CODEN: GMEECG; ISSN:1756-994X. (BioMed Central Ltd.)
    NMR spectroscopy provides robust readouts of many metabolic parameters in one expt. However, identification of clin. relevant markers in 1H NMR spectra is a major challenge. Assocn. of NMR-derived quantities with genetic variants can uncover biol. relevant metabolic traits. Using NMR data of plasma samples from 1,757 individuals from the KORA study together with 655,658 genetic variants, we show that ratios between NMR intensities at two chem. shift positions can provide informative and robust biomarkers. We report seven loci of genetic assocn. with NMR-derived traits (APOA1, CETP, CPS1, GCKR, FADS1, LIPC, PYROXD2) and characterize these traits biochem. using mass spectrometry. These ratios may now be used in clin. studies.
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    Rhee, E. P.; Ho, J. E.; Chen, M. H.; Shen, D.; Cheng, S.; Larson, M. G. A genome-wide association study of the human metabolome in a community-based cohort. Cell Metab. 2013, 18, 130– 143,  DOI: 10.1016/j.cmet.2013.06.013
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    A Genome-wide Association Study of the Human Metabolome in a Community-Based Cohort
    Rhee, Eugene P.; Ho, Jennifer E.; Chen, Ming-Huei; Shen, Dongxiao; Cheng, Susan; Larson, Martin G.; Ghorbani, Anahita; Shi, Xu; Helenius, Iiro T.; O'Donnell, Christopher J.; Souza, Amanda L.; Deik, Amy; Pierce, Kerry A.; Bullock, Kevin; Walford, Geoffrey A.; Vasan, Ramachandran S.; Florez, Jose C.; Clish, Clary; Yeh, J.-R. Joanna; Wang, Thomas J.; Gerszten, Robert E.
    Cell Metabolism (2013), 18 (1), 130-143CODEN: CMEEB5; ISSN:1550-4131. (Elsevier Inc.)
    Because metabolites are hypothesized to play key roles as markers and effectors of cardiometabolic diseases, recent studies have sought to annotate the genetic determinants of circulating metabolite levels. We report a genome-wide assocn. study (GWAS) of 217 plasma metabolites, including >100 not measured in prior GWAS, in 2076 participants of the Framingham Heart Study (FHS). For the majority of analytes, we find that estd. heritability explains >20% of interindividual variation, and that variation attributable to heritable factors is greater than that attributable to clin. factors. Further, we identify 31 genetic loci assocd. with plasma metabolites, including 23 that have not previously been reported. Importantly, we include GWAS results for all surveyed metabolites and demonstrate how this information highlights a role for AGXT2 in cholesterol ester and triacylglycerol metab. Thus, our study outlines the relative contributions of inherited and clin. factors on the plasma metabolome and provides a resource for metab. research.
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    Krumsiek, J.; Suhre, K.; Evans, A. M.; Mitchell, M. W.; Mohney, R. P.; Milburn, M. V. Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet. 2012, 8, e1003005,  DOI: 10.1371/journal.pgen.1003005
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    Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information
    Krumsiek, Jan; Suhre, Karsten; Evans, Anne M.; Mitchell, Matthew W.; Mohney, Robert P.; Milburn, Michael V.; Waegele, Brigitte; Roemisch-Margl, Werner; Illig, Thomas; Adamski, Jerzy; Gieger, Christian; Theis, Fabian J.; Kastenmueller, Gabi
    PLoS Genetics (2012), 8 (10), e1003005CODEN: PGLEB5; ISSN:1553-7404. (Public Library of Science)
    Recent genome-wide assocn. studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amt. of the mols. currently quantified by modern metabolomics techniques are chem. unidentified. The identification of these "unknown metabolites" is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the anal. Here we present a systems-level approach that combines genome-wide assocn. anal. and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype-metabotype assocns. for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic assocns., metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochem. identities of 106 unknown metabolites. As a proof of principle, we exptl. confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different exptl. platforms.
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    Hong, M. G.; Karlsson, R.; Magnusson, P. K.; Lewis, M. R.; Isaacs, W.; Zheng, L. S. A Genome-Wide Assessment of Variability in Human Serum Metabolism. Hum. Mutat. 2013, 34, 515– 524,  DOI: 10.1002/humu.22267
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    A Genome-Wide Assessment of Variability in Human Serum Metabolism
    Hong, Mun-Gwan; Karlsson, Robert; Magnusson, Patrik K. E.; Lewis, Matthew R.; Isaacs, William; Zheng, Lilly S.; Xu, Jianfeng; Groenberg, Henrik; Ingelsson, Erik; Pawitan, Yudi; Broeckling, Corey; Prenni, Jessica E.; Wiklund, Fredrik; Prince, Jonathan A.
    Human Mutation (2013), 34 (3), 515-524CODEN: HUMUE3; ISSN:1059-7794. (Wiley-Liss, Inc.)
    The study of the genetic regulation of metab. in human serum samples can contribute to a better understanding of the intermediate biol. steps that lead from polymorphism to disease. Here, we conducted a genome-wide assocn. study (GWAS) to discover metabolic quant. trait loci (mQTLs) utilizing samples from a study of prostate cancer in Swedish men, consisting of 402 individuals (214 cases and 188 controls) in a discovery set and 489 case-only samples in a replication set. A global nontargeted metabolite profiling approach was utilized resulting in the detection of 6,138 mol. features followed by targeted identification of assocd. metabolites. Seven replicating loci were identified (PYROXD2, FADS1, PON1, CYP4F2, UGT1A8, ACADL, and LIPC) with assocd. sequence variants contributing significantly to trait variance for one or more metabolites (P = 10-13-10-91). Regional mQTL enrichment analyses implicated two loci that included FADS1 and a novel locus near PDGFC. Biol. pathway anal. implicated ACADM, ACADS, ACAD8, ACAD10, ACAD11, and ACOXL, reflecting significant enrichment of genes with acyl-CoA dehydrogenase activity. mQTL SNPs and mQTL-harboring genes were over-represented across GWASs conducted to date, suggesting that these data may have utility in tracing the mol. basis of some complex disease assocns.
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    Montoliu, I.; Genick, U.; Ledda, M.; Collino, S.; Martin, F.-P.; le Coutre, J. Current status on genome–metabolome-wide associations: an opportunity in nutrition research. Gene Nutr. 2013, 8, 19,  DOI: 10.1007/s12263-012-0313-7
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    Chambers, J. C.; Zhang, W.; Lord, G. M.; Van Der Harst, P.; Lawlor, D. A.; Sehmi, J. S. Genetic loci influencing kidney function and chronic kidney disease. Nat. Genet. 2010, 42, 373– 375,  DOI: 10.1038/ng.566
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    Genetic loci influencing kidney function and chronic kidney disease
    Chambers, John C.; Zhang, Weihua; Lord, Graham M.; van der Harst, Pim; Lawlor, Debbie A.; Sehmi, Joban S.; Gale, Daniel P.; Wass, Mark N.; Ahmadi, Kourosh R.; Bakker, Stephan J. L.; Beckmann, Jacqui; Bilo, Henk J. G.; Bochud, Murielle; Brown, Morris J.; Caulfield, Mark J.; Connell, John M. C.; Cook, H. Terence; Cotlarciuc, Ioana; Smith, George Davey; de Silva, Ranil; Deng, Guohong; Devuyst, Olivier; Dikkeschei, Lambert D.; Dimkovic, Nada; Dockrell, Mark; Dominiczak, Anna; Ebrahim, Shah; Eggermann, Thomas; Farrall, Martin; Ferrucci, Luigi; Floege, Jurgen; Forouhi, Nita G.; Gansevoort, Ron T.; Han, Xijin; Hedblad, Bo; van der Heide, Jaap J. Homan; Hepkema, Bouke G.; Hernandez-Fuentes, Maria; Hypponen, Elina; Johnson, Toby; de Jong, Paul E.; Kleefstra, Nanne; Lagou, Vasiliki; Lapsley, Marta; Li, Yun; Loos, Ruth J. F.; Luan, Jian'an; Luttropp, Karin; Marechal, Celine; Melander, Olle; Munroe, Patricia B.; Nordfors, Louise; Parsa, Afshin; Peltonen, Leena; Penninx, Brenda W.; Perucha, Esperanza; Pouta, Anneli; Prokopenko, Inga; Roderick, Paul J.; Ruokonen, Aimo; Samani, Nilesh J.; Sanna, Serena; Schalling, Martin; Schlessinger, David; Schlieper, Georg; Seelen, Marc A. J.; Shuldiner, Alan R.; Sjoegren, Marketa; Smit, Johannes H.; Snieder, Harold; Soranzo, Nicole; Spector, Timothy D.; Stenvinkel, Peter; Sternberg, Michael J. E.; Swaminathan, Ramasamyiyer; Tanaka, Toshiko; Ubink-Veltmaat, Lielith J.; Uda, Manuela; Vollenweider, Peter; Wallace, Chris; Waterworth, Dawn; Zerres, Klaus; Waeber, Gerard; Wareham, Nicholas J.; Maxwell, Patrick H.; McCarthy, Mark I.; Jarvelin, Marjo-Riitta; Mooser, Vincent; Abecasis, Goncalo R.; Lightstone, Liz; Scott, James; Navis, Gerjan; Elliott, Paul; Kooner, Jaspal S.
    Nature Genetics (2010), 42 (5), 373-375CODEN: NGENEC; ISSN:1061-4036. (Nature Publishing Group)
    Using genome-wide assocn., we identify common variants at 2p12-p13, 6q26, 17q23 and 19q13 assocd. with serum creatinine, a marker of kidney function (P = 10-10 to 10-15). Of these, rs10206899 (near NAT8, 2p12-p13) and rs4805834 (near SLC7A9, 19q13) were also assocd. with chronic kidney disease (P = 5.0 × 10-5 and P = 3.6 × 10-4, resp.). Our findings provide insight into metabolic, solute and drug-transport pathways underlying susceptibility to chronic kidney disease.
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    Simmons, M. L.; Frondoza, C. G.; Coyle, J. T. Immunocytochemical localization of N-acetyl-aspartate with monoclonal antibodies. Neuroscience 1991, 45, 37– 45,  DOI: 10.1016/0306-4522(91)90101-S
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    Immunocytochemical localization of N-acetyl-aspartate with monoclonal antibodies
    Simmons, M. L.; Frondoza, C. G.; Coyle, J. T.
    Neuroscience (Oxford, United Kingdom) (1991), 45 (1), 37-45CODEN: NRSCDN; ISSN:0306-4522.
    N-Acetyl-aspartate is found in high concns. in all areas of the brain, but is undetectable in non-neuronal tissue. In order to characterize the cellular localization of N-acetyl-aspartate in brain, highly specific monoclonal antibodies against N-acetyl-aspartate were produced by fusing spleen lymphocytes obtained from mice immunized with N-acetyl-aspartate conjugated to thyroglobulin by carbodiimide with P3/x63-Ag8.653 mouse myeloma cells. Clones were selected which secrete IgG2a(k) antibodies highly specific for conjugated N-acetyl-aspartate. Only 3-6% cross-reactivity with conjugated N-acetyl-aspartate-glutamate was obsd. at high antibody concns., whereas no cross-reactivity (<1%) was obsd. with conjugated N-acetyl-glutamate or aspartate. Preincubation of the antibodies with 0.5 mg/mL conjugated N-acetyl-aspartate blocked immunoreactivity more than 90%, while preincubation with conjugated N-acetyl-aspartate-glutamate and free N-acetyl-aspartate had no effect. Immunocytochem. staining has shown that N-acetyl-aspartate-like immunoreactivity is localized in neurons, which are widely distributed throughout the brain. The immunoreactive neurons exhibited intense staining of the perikarya, proximal dendrites and axons. No consistent pattern of distribution of immunoreactivity was obsd. with regard to primary neurotransmitter characteristics of stained neurons although neurons with long projections or extensive arbors, such as pyramidal cells in cortex, locus ceruleus, motor neurons and Purkinje cells, stained much more intensively than local circuit neurons.
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    Masaharu, M.; Hideo, M.; Mutsuhiko, M.; Yasuo, K. N-acetyl-l-aspartic acid, N-acetyl-α-l-aspartyl-l-glutamic acid and β-citryl-l-glutamic acid in human urine. Clin. Chim. Acta 1982, 120, 119– 126,  DOI: 10.1016/0009-8981(82)90082-1
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    N-acetyl aspartate--a neuronal marker?
    Barker P B
    Annals of neurology (2001), 49 (4), 423-4 ISSN:0364-5134.
    There is no expanded citation for this reference.
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    Jung, R. E.; Brooks, W. M.; Yeo, R. A.; Chiulli, S. J.; Weers, D. C.; Sibbitt, W. L., Jr. Biochemical markers of intelligence: a proton MR spectroscopy study of normal human brain. Proc. R. Soc. London, Ser. B 1999, 266, 1375– 1379,  DOI: 10.1098/rspb.1999.0790
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    Biochemical markers of intelligence: a proton MR spectroscopy study of normal human brain
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    Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function
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    A review. Alstrom syndrome (AS) is characterised by metabolic deficits, retinal dystrophy, sensorineural hearing loss, dilated cardiomyopathy and multi-organ fibrosis. Elucidating the function of the mutated gene, ALMS1, is crit. for the development of specific treatments and may uncover pathways relevant to a range of other disorders including common forms of obesity and type 2 diabetes. Interest in ALMS1 is heightened by the recent discovery of its involvement in neonatal cardiomyocyte cell cycle arrest, a process with potential relevance to regenerative medicine. ALMS1 encodes a ∼ 0.5 megadalton protein that localises to the base of centrioles. Some studies have suggested a role for this protein in maintaining centriole-nucleated sensory organelles termed primary cilia, and AS is now considered to belong to the growing class of human genetic disorders linked to ciliary dysfunction (ciliopathies). However, mechanistic details are lacking, and recent studies have implicated ALMS1 in several processes including endosomal trafficking, actin organization, maintenance of centrosome cohesion and transcription. In line with a more complex picture, multiple isoforms of the protein likely exist and non-centrosomal sites of localisation have been reported. This review outlines the evidence for both ciliary and extra-ciliary functions of ALMS1.
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  • Abstract

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    Figure 1

    Figure 1. QQ-plot of −log10 (p values) of metabolome- and transcriptome-wide association analysis. The highly significant associations (FDR < 0.05) with ALMS1 expression are ranked 1st and 2nd and with HPS1 expression 3rd and 4th.

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    Figure 2

    Figure 2. Metabomatching (22) results for pseudospectra derived from gene expression - metabolome feature associations for ALMS1 (A) and HPS1 (B). Upper panels show the features in each pseudospectrum, color-coded according to the direction of the effect (positive in blue and negative in orange). Lower panels show the highest ranking candidate metabolites with their reference NMR spectra (color coded to indicate their relative peak intensities). Leading features allowing metabolite identification are in (A) at 2.04 ppm, which matches well with the highest intensity peak of the NAA spectrum and in (B) at 2.87 ppm, which matches well with the TMA singlet.

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    Figure 3

    Figure 3. SNP - metabolome feature and SNP - gene expression associations in ALMS1/NAT8 locus. (A) LocusZoom plot for ALMS1/NAT8 locus, where the SNPs are associated with metabolome feature at 2.0375 ppm, LD colored with respect to lead mQTL. (B) Bar plot shows −log10 transformed p values from associating expression values of nine genes in the locus with the five NAA features.

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    Figure 4

    Figure 4. SNP - metabolome feature and SNP - gene expression associations in HPS1/PYROXD2 locus. (A) LocusZoom plot for HPS1/PYROXD2 locus, showing the association significance of SNP with the metabolome feature at 2.8725 ppm. Colors indicate the correlation (LD) to the lead QTL. (B) Bar plot shows −log10 transformed p values from associating expression values of seven genes in the locus with the same feature.

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    Figure 5

    Figure 5. Scatter plot of the mQTL effect of SNP (rs7566315) on NAC and its eQTL effect on ALMS1 gene expression. Each point represents a study sample. NAC concentration is approximated by the feature at 2.0375 ppm that is log10 transformed after feature- and sample-wise z-scoring (y axis). ALMS1 expression is z-scored after log2 transforming RPKM+1 values (x axis). Color code represents the genotype of rs7566315 (legend) that is an eQTL of ALMS1 and mQTL of NAA.

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      Genome-wide assocn. studies have identified many noncoding variants assocd. with common diseases and traits. We show that these variants are concd. in regulatory DNA marked by DNase I hypersensitive sites (DHSs). Eighty-eight percent of such DHSs are active during fetal development and are enriched in variants assocd. with gestational exposure-related phenotypes. We identified distant gene targets for hundreds of variant-contg. DHSs that may explain phenotype assocns. Disease-assocd. variants systematically perturb transcription factor recognition sequences, frequently alter allelic chromatin states, and form regulatory networks. We also demonstrated tissue-selective enrichment of more weakly disease-assocd. variants within DHSs and the de novo identification of pathogenic cell types for Crohn's disease, multiple sclerosis, and an ECG trait, without prior knowledge of physiol. mechanisms. Our results suggest pervasive involvement of regulatory DNA variation in common human disease and provide pathogenic insights into diverse disorders.
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    4. 4
      Nicolae, D. L.; Gamazon, E.; Zhang, W.; Duan, S.; Dolan, M. E.; Cox, N. J. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 2010, 6, e1000888,  DOI: 10.1371/journal.pgen.1000888
      There is no corresponding record for this reference.
    5. 5
      Ward, L. D.; Kellis, M. Interpreting non-coding variation in complex disease genetics. Nat. Biotechnol. 2012, 30, 1095,  DOI: 10.1038/nbt.2422
      5
      Interpreting noncoding genetic variation in complex traits and human disease
      Ward, Lucas D.; Kellis, Manolis
      Nature Biotechnology (2012), 30 (11), 1095-1106CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
      A review. Assocn. studies provide genome-wide information about the genetic basis of complex disease, but medical research has focused primarily on protein-coding variants, owing to the difficulty of interpreting noncoding mutations. This picture has changed with advances in the systematic annotation of functional noncoding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs and mol. quant. trait loci all provide complementary information about the function of noncoding sequences. These functional maps can help with prioritizing variants on risk haplotypes, filtering mutations encountered in the clinic and performing systems-level analyses to reveal processes underlying disease assocns. Advances in predictive modeling can enable data-set integration to reveal pathways shared across loci and alleles, and richer regulatory models can guide the search for epistatic interactions. Lastly, new massively parallel reporter expts. can systematically validate regulatory predictions. Ultimately, advances in regulatory and systems genomics can help unleash the value of whole-genome sequencing for personalized genomic risk assessment, diagnosis and treatment.
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    6. 6
      Lloyd-Jones, L. R.; Holloway, A.; McRae, A.; Yang, J.; Small, K.; Zhao, J. The genetic architecture of gene expression in peripheral blood. Am. J. Hum. Genet. 2017, 100, 228– 237,  DOI: 10.1016/j.ajhg.2016.12.008
      6
      The Genetic Architecture of Gene Expression in Peripheral Blood
      Lloyd-Jones, Luke R.; Holloway, Alexander; McRae, Allan; Yang, Jian; Small, Kerrin; Zeng, Biao; Bakshi, Andrew; Metspalu, Andres; Dermitzakis, Manolis; Gibson, Greg; Spector, Tim; Montgomery, Grant; Esko, Tonu; Visscher, Peter M.; Powell, Joseph E.
      American Journal of Human Genetics (2017), 100 (2), 228-237CODEN: AJHGAG; ISSN:0002-9297. (Cell Press)
      We analyzed the mRNA levels for 36,778 transcript expression traits (probes) from 2,765 individuals to comprehensively investigate the genetic architecture and degree of missing heritability for gene expression in peripheral blood. We identified 11,204 cis and 3,791 trans independent expression quant. trait loci (eQTL) by using linear mixed models to perform genome-wide assocn. analyses. Furthermore, using information on both closely and distantly related individuals, heritability was estd. for all expression traits. Of the set of expressed probes (15,966), 10,580 (66%) had an estd. narrow-sense heritability (h2) greater than zero with a mean (median) value of 0.192 (0.142). Across these probes, on av. the proportion of genetic variance explained by all eQTL (h2COJO) was 31% (0.060/0.192), meaning that 69% is missing, with the sentinel SNP of the largest eQTL explaining 87% (0.052/0.060) of the variance attributed to all identified cis- and trans-eQTL. For the same set of probes, the genetic variance attributed to genome-wide common (MAF > 0.01) HapMap 3 SNPs (h2g) accounted for on av. 48% (0.093/0.192) of h2. Taken together, the evidence suggests that approx. half the genetic variance for gene expression is not tagged by common SNPs, and of the variance that is tagged by common SNPs, a large proportion can be attributed to identifiable eQTL of large effect, typically in cis. Finally, we present evidence that, compared with a meta-anal., using individual-level data results in an increase of approx. 50% in power to detect eQTL.
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    7. 7
      Montgomery, S. B.; Sammeth, M.; Gutierrez-Arcelus, M.; Lach, R. P.; Ingle, C.; Nisbett, J. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 2010, 464, 773,  DOI: 10.1038/nature08903
      7
      Transcriptome genetics using second generation sequencing in a Caucasian population
      Montgomery, Stephen B.; Sammeth, Micha; Gutierrez-Arcelus, Maria; Lach, Radoslaw P.; Ingle, Catherine; Nisbett, James; Guigo, Roderic; Dermitzakis, Emmanouil T.
      Nature (London, United Kingdom) (2010), 464 (7289), 773-777CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
      Gene expression is an important phenotype that informs about genetic and environmental effects on cellular state. Many studies have previously identified genetic variants for gene expression phenotypes using custom and com. available microarrays. Second generation sequencing technologies are now providing unprecedented access to the fine structure of the transcriptome. We have sequenced the mRNA fraction of the transcriptome in 60 extended HapMap individuals of European descent and have combined these data with genetic variants from the HapMap3 project. We have quantified exon abundance based on read depth and have also developed methods to quantify whole transcript abundance. We have found that approx. 10 million reads of sequencing can provide access to the same dynamic range as arrays with better quantification of alternative and highly abundant transcripts. Correlation with SNPs (small nucleotide polymorphisms) leads to a larger discovery of eQTLs (expression quant. trait loci) than with arrays. We also detect a substantial no. of variants that influence the structure of mature transcripts indicating variants responsible for alternative splicing. Finally, measures of allele-specific expression allowed the identification of rare eQTLs and allelic differences in transcript structure. This anal. shows that high throughput sequencing technologies reveal new properties of genetic effects on the transcriptome and allow the exploration of genetic effects in cellular processes.
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    8. 8
      Wright, F. A.; Sullivan, P. F.; Brooks, A. I.; Zou, F.; Sun, W.; Xia, K. Heritability and genomics of gene expression in peripheral blood. Nat. Genet. 2014, 46, 430,  DOI: 10.1038/ng.2951
      8
      Heritability and genomics of gene expression in peripheral blood
      Wright, Fred A.; Sullivan, Patrick F.; Brooks, Andrew I.; Zou, Fei; Sun, Wei; Xia, Kai; Madar, Vered; Jansen, Rick; Chung, Wonil; Zhou, Yi-Hui; Abdellaoui, Abdel; Batista, Sandra; Butler, Casey; Chen, Guanhua; Chen, Ting-Huei; D'Ambrosio, David; Gallins, Paul; Ha, Min Jin; Hottenga, Jouke Jan; Huang, Shunping; Kattenberg, Mathijs; Kochar, Jaspreet; Middeldorp, Christel M.; Qu, Ani; Shabalin, Andrey; Tischfield, Jay; Todd, Laura; Tzeng, Jung-Ying; van Grootheest, Gerard; Vink, Jacqueline M.; Wang, Qi; Wang, Wei; Wang, Weibo; Willemsen, Gonneke; Smit, Johannes H.; de Geus, Eco J.; Yin, Zhaoyu; Penninx, Brenda W. J. H.; Boomsma, Dorret I.
      Nature Genetics (2014), 46 (5), 430-437CODEN: NGENEC; ISSN:1061-4036. (Nature Publishing Group)
      We assessed gene expression profiles in 2752 twins, using a classic twin design to quantify expression heritability and quant. trait loci (eQTLs) in peripheral blood. The most highly heritable genes (∼777) were grouped into distinct expression clusters, enriched in gene-poor regions, assocd. with specific gene function or ontol. classes, and strongly assocd. with disease designation. The design enabled a comparison of twin-based heritability to ests. based on dizygotic identity-by-descent sharing and distant genetic relatedness. Consideration of sampling variation suggests that previous heritability ests. have been upwardly biased. Genotyping of 2494 twins enabled powerful identification of eQTLs, which we further examd. in a replication set of 1895 unrelated subjects. A large no. of non-redundant local eQTLs (6756) met replication criteria, whereas a relatively small no. of distant eQTLs (165) met quality control and replication stds. Our results provide a new resource toward understanding the genetic control of transcription.
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    9. 9
      Suhre, K.; Wallaschofski, H.; Raffler, J.; Friedrich, N.; Haring, R.; Michael, K. A genome-wide association study of metabolic traits in human urine. Nat. Genet. 2011, 43, 565– 569,  DOI: 10.1038/ng.837
      9
      A genome-wide association study of metabolic traits in human urine
      Suhre, Karsten; Wallaschofski, Henri; Raffler, Johannes; Friedrich, Nele; Haring, Robin; Michael, Kathrin; Wasner, Christina; Krebs, Alexander; Kronenberg, Florian; Chang, David; Meisinger, Christa; Wichmann, H.-Erich; Hoffmann, Wolfgang; Voelzke, Henry; Voelker, Uwe; Teumer, Alexander; Biffar, Reiner; Kocher, Thomas; Felix, Stephan B.; Illig, Thomas; Kroemer, Heyo K.; Gieger, Christian; Roemisch-Margl, Werner; Nauck, Matthias
      Nature Genetics (2011), 43 (6), 565-569CODEN: NGENEC; ISSN:1061-4036. (Nature Publishing Group)
      We present a genome-wide assocn. study of metabolic traits in human urine, designed to investigate the detoxification capacity of the human body. Using NMR spectroscopy, we tested for assocns. between 59 metabolites in urine from 862 male participants in the population-based SHIP study. We replicated the results using 1,039 addnl. samples of the same study, including a 5-yr follow-up, and 992 samples from the independent KORA study. We report five loci with joint P values of assocn. from 3.2 × 10-19 to 2.1 × 10-182. Variants at three of these loci have previously been linked with important clin. outcomes: SLC7A9 is a risk locus for chronic kidney disease, NAT2 for coronary artery disease and genotype-dependent response to drug toxicity, and SLC6A20 for iminoglycinuria. Moreover, we identify rs37369 in AGXT2 as the genetic basis of hyper-β-aminoisobutyric aciduria.
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    10. 10
      Bartel, J.; Krumsiek, J.; Schramm, K.; Adamski, J.; Gieger, C.; Herder, C. The Human Blood Metabolome-Transcriptome Interface. PLoS Genet. 2015, 11, e1005274,  DOI: 10.1371/journal.pgen.1005274
      10
      The human blood metabolome-transcriptome interface
      Bartel, Joerg; Krumsiek, Jan; Schramm, Katharina; Adamski, Jerzy; Gieger, Christian; Herder, Christian; Carstensen, Maren; Peters, Annette; Rathmann, Wolfgang; Roden, Michael; Strauch, Konstantin; Suhre, Karsten; Kastenmueller, Gabi; Prokisch, Holger; Theis, Fabian J.
      PLoS Genetics (2015), 11 (6), e1005274/1-e1005274/32CODEN: PGLEB5; ISSN:1553-7404. (Public Library of Science)
      Biol. systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous anal. of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying mol. mechanisms on a physiol. scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based anal. identified 1,109 significant assocns. between 522 transcripts and 114 metabolites summarized in an integrated network, the 'human blood metabolometranscriptome interface' (BMTI). Bidirectional causality anal. using Mendelian randomization did not yield any statistically significant causal assocns. between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metab. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biol. layers at a pathway level. Using a transcription factor binding site enrichment anal., this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into mol. mechanisms assocd. to intermediate clin. traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the mol. mechanisms underlying both normal physiol. and disease.
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    11. 11
      Burkhardt, R.; Kirsten, H.; Beutner, F.; Holdt, L. M.; Gross, A.; Teren, A. Integration of Genome-Wide SNP Data and Gene-Expression Profiles Reveals Six Novel Loci and Regulatory Mechanisms for Amino Acids and Acylcarnitines in Whole Blood. PLoS Genet. 2015, 11, e1005510,  DOI: 10.1371/journal.pgen.1005510
      11
      Integration of genome-wide SNP data and gene-expression profiles reveals six novel loci and regulatory mechanisms for amino acids and acylcarnitines in whole blood
      Burkhardt, Ralph; Kirsten, Holger; Beutner, Frank; Holdt, Lesca M.; Gross, Arnd; Teren, Andrej; Toenjes, Anke; Becker, Susen; Krohn, Knut; Kovacs, Peter; Stumvoll, Michael; Teupser, Daniel; Thiery, Joachim; Ceglarek, Uta; Scholz, Markus
      PLoS Genetics (2015), 11 (9), e1005510/1-e1005510/25CODEN: PGLEB5; ISSN:1553-7404. (Public Library of Science)
      Profiling amino acids and acylcarnitines in whole blood spots is a powerful tool in the lab. diagnosis of several inborn errors of metab. Emerging data suggests that altered blood levels of amino acids and acylcarnitines are also assocd. with common metabolic diseases in adults. Thus, the identification of common genetic determinants for blood metabolites might shed light on pathways contributing to human physiol. and common diseases. We applied a targeted mass-spectrometry-based method to analyze whole blood concns. of 96 amino acids, acylcarnitines and pathway assocd. metabolite ratios in a Central European cohort of 2,107 adults and performed genome-wide assocn. (GWA) to identify genetic modifiers of metabolite concns. We discovered and replicated six novel loci assocd. with blood levels of total acylcarnitine, arginine (both on chromosome 6; rs12210538, rs17657775), propionylcarnitine (chromosome 10; rs12779637), 2-hydroxyisovalerylcarnitine (chromosome 21; rs1571700), stearoylcarnitine (chromosome 1; rs3811444), and aspartic acid traits (chromosome 8; rs750472). Based on an integrative anal. of expression quant. trait loci in blood mononuclear cells and correlations between gene expressions and metabolite levels, we provide evidence for putative causative genes: SLC22A16 for total acylcarnitines, ARG1 for arginine, HLCS for 2-hydroxyisovalerylcarnitine, JAM3 for stearoylcarnitine via a trans-effect at chromosome 1, and PPP1R16A for aspartic acid traits. Further, we report replication and provide addnl. functional evidence for ten loci that have previously been published for metabolites measured in plasma, serum or urine. In conclusion, our integrative anal. of SNP, gene-expression and metabolite data points to novel genetic factors that may be involved in the regulation of human metab. At several loci, we provide evidence for metabolite regulation via gene-expression and obsd. overlaps with GWAS loci for common diseases. These results form a strong rationale for subsequent functional and disease-related studies.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XmtFGltrg%253D&md5=9f36470c050b23fa8b6425d998e68d91
    12. 12
      Inouye, M.; Kettunen, J.; Soininen, P.; Silander, K.; Ripatti, S.; Kumpula, L. S.; Hämäläinen, E.; Jousilahti, P.; Kangas, A. J.; Männistö, S.; Savolainen, M. J.; Jula, A.; Leiviskä, J.; Palotie, A.; Salomaa, V.; Perola, M.; Ala-Korpela, M.; Peltonen, L. Metabonomic, transcriptomic, and genomic variation of a population cohort. Mol. Syst. Biol. 2010, 6, 441,  DOI: 10.1038/msb.2010.93
      12
      Metabonomic, transcriptomic, and genomic variation of a population cohort
      Inouye Michael; Kettunen Johannes; Soininen Pasi; Silander Kaisa; Ripatti Samuli; Kumpula Linda S; Hamalainen Eija; Jousilahti Pekka; Kangas Antti J; Mannisto Satu; Savolainen Markku J; Jula Antti; Leiviska Jaana; Palotie Aarno; Salomaa Veikko; Perola Markus; Ala-Korpela Mika; Peltonen Leena
      Molecular systems biology (2010), 6 (), 441 ISSN:.
      Comprehensive characterization of human tissues promises novel insights into the biological architecture of human diseases and traits. We assessed metabonomic, transcriptomic, and genomic variation for a large population-based cohort from the capital region of Finland. Network analyses identified a set of highly correlated genes, the lipid-leukocyte (LL) module, as having a prominent role in over 80 serum metabolites (of 134 measures quantified), including lipoprotein subclasses, lipids, and amino acids. Concurrent association with immune response markers suggested the LL module as a possible link between inflammation, metabolism, and adiposity. Further, genomic variation was used to generate a directed network and infer LL module's largely reactive nature to metabolites. Finally, gene co-expression in circulating leukocytes was shown to be dependent on serum metabolite concentrations, providing evidence for the hypothesis that the coherence of molecular networks themselves is conditional on environmental factors. These findings show the importance and opportunity of systematic molecular investigation of human population samples. To facilitate and encourage this investigation, the metabonomic, transcriptomic, and genomic data used in this study have been made available as a resource for the research community.
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    13. 13
      Bullaughey, K.; Chavarria, C. I.; Coop, G.; Gilad, Y. Expression quantitative trait loci detected in cell lines are often present in primary tissues. Hum. Mol. Genet. 2009, 18, 4296– 4303,  DOI: 10.1093/hmg/ddp382
      13
      Expression quantitative trait loci detected in cell lines are often present in primary tissues
      Bullaughey, Kevin; Chavarria, Claudia I.; Coop, Graham; Gilad, Yoav
      Human Molecular Genetics (2009), 18 (22), 4296-4303CODEN: HMGEE5; ISSN:0964-6906. (Oxford University Press)
      Expression quant. trait loci (eQTL) mapping is a powerful tool for identifying genetic regulatory variation. However, at present, most eQTLs in humans were identified using gene expression data from cell lines, and it remains unknown whether these eQTLs also have a regulatory function in other expression contexts, such as human primary tissues. Here we investigate this question using a targeted strategy. Specifically, we selected a subset of large-effect eQTLs identified in the HapMap lymphoblastoid cell lines, and examd. the assocn. of these eQTLs with gene expression levels across individuals in five human primary tissues (heart, kidney, liver, lung and testes). We show that genotypes at the eQTLs we selected are often predictive of variation in gene expression levels in one or more of the five primary tissues. The genotype effects in the primary tissues are consistently in the same direction as the effects inferred in the cell lines. Addnl., a no. of the eQTLs we tested are found in more than one of the tissues. Our results indicate that functional studies in cell lines may uncover a substantial amt. of genetic variation that affects gene expression levels in human primary tissues.
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    14. 14
      Çalışkan, M.; Cusanovich, D. A.; Ober, C.; Gilad, Y. The effects of EBV transformation on gene expression levels and methylation profiles. Hum. Mol. Genet. 2011, 20, 1643– 1652,  DOI: 10.1093/hmg/ddr041
      14
      The effects of EBV transformation on gene expression levels and methylation profiles
      Caliskan, Minal; Cusanovich, Darren A.; Ober, Carole; Gilad, Yoav
      Human Molecular Genetics (2011), 20 (8), 1643-1652CODEN: HMGEE5; ISSN:0964-6906. (Oxford University Press)
      Epstein-Barr virus (EBV) transformed lymphoblastoid cell lines (LCLs) provide a conveniently accessible and renewable resource for functional genomic studies in humans. The ability to accumulate multidimensional data pertaining to the same individual cell lines, from complete genomic sequences to detailed gene regulatory profiles, further enhances the utility of LCLs as a model system. A lingering concern, however, is that the changes assocd. with EBV transformation of B cells reduce the usefulness of LCLs as a surrogate model for primary tissues. To evaluate the validity of this concern, the authors compared global gene expression and methylation profiles between CD20+ primary B cells sampled from six individuals and six independent replicates of transformed LCLs derived from each sample. These data allowed the authors to obtain a detailed catalog of the genes and pathways whose regulation is affected by EBV transformation. The expression levels and promoter methylation profiles of more than half of the studied genes were affected by the EBV transformation, including enrichments of genes involved in transcription regulation, cell cycle and immune response. However, most of the differences in gene expression levels between LCLs and B cells are of small magnitude, and that LCLs can often recapitulate the naturally occurring gene expression variation in primary B cells. Thus, these observations suggest that inference of the genetic architecture that underlies regulatory variation in LCLs can typically be generalized to primary B cells. In contrast, inference based on functional studies in LCLs may be more limited to the cell lines.
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    15. 15
      Dimas, A. S.; Deutsch, S.; Stranger, B. E.; Montgomery, S. B.; Borel, C.; Attar-Cohen, H. Common regulatory variation impacts gene expression in a cell type–dependent manner. Science 2009, 325, 1246– 1250,  DOI: 10.1126/science.1174148
      15
      Common Regulatory Variation Impacts Gene Expression in a Cell Type-Dependent Manner
      Dimas, Antigone S.; Deutsch, Samuel; Stranger, Barbara E.; Montgomery, Stephen B.; Borel, Christelle; Attar-Cohen, Homa; Ingle, Catherine; Beazley, Claude; Gutierrez Arcelus, Maria; Sekowska, Magdalena; Gagnebin, Marilyne; Nisbett, James; Deloukas, Panos; Dermitzakis, Emmanouil T.; Antonarakis, Stylianos E.
      Science (Washington, DC, United States) (2009), 325 (5945), 1246-1250CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      Studies correlating genetic variation to gene expression facilitate the interpretation of common human phenotypes and disease. As functional variants may be operating in a tissue-dependent manner, we performed gene expression profiling and assocn. with genetic variants (single-nucleotide polymorphisms) on three cell types of 75 individuals. We detected cell type-specific genetic effects, with 69 to 80% of regulatory variants operating in a cell type-specific manner, and identified multiple expressive quant. trait loci (eQTLs) per gene, unique or shared among cell types and pos. correlated with the no. of transcripts per gene. Cell type-specific eQTLs were found at larger distances from genes and at lower effect size, similar to known enhancers. These data suggest that the complete regulatory variant repertoire can only be uncovered in the context of cell-type specificity.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtVOmsrjM&md5=c1c8c0127f5791bbf889eb29caaf5a82
    16. 16
      Ding, J.; Gudjonsson, J. E.; Liang, L.; Stuart, P. E.; Li, Y.; Chen, W. Gene expression in skin and lymphoblastoid cells: Refined statistical method reveals extensive overlap in cis-eQTL signals. Am. J. Hum. Genet. 2010, 87, 779– 789,  DOI: 10.1016/j.ajhg.2010.10.024
      16
      Gene expression in skin and lymphoblastoid cells: refined statistical method reveals extensive overlap in cis-eQTL signals
      Ding, Jun; Gudjonsson, Johann E.; Liang, Liming; Stuart, Philip E.; Li, Yun; Chen, Wei; Weichenthal, Michael; Ellinghaus, Eva; Franke, Andre; Cookson, William; Nair, Rajan P.; Elder, James T.; Abecasis, Goncalo R.
      American Journal of Human Genetics (2010), 87 (6), 779-789CODEN: AJHGAG; ISSN:0002-9297. (Cell Press)
      Psoriasis, an immune-mediated, inflammatory disease of the skin and joints, provides an ideal system for expression quant. trait locus (eQTL) anal., because it has a strong genetic basis and disease-relevant tissue (skin) is readily accessible. To better understand the role of genetic variants regulating cutaneous gene expression, we identified 841 cis-acting eQTLs using RNA extd. from skin biopsies of 53 psoriatic individuals and 57 healthy controls. We found substantial overlap between cis-eQTLs of normal control, uninvolved psoriatic, and lesional psoriatic skin. Consistent with recent studies and with the idea that control of gene expression can mediate relationships between genetic variants and disease risk, we found that eQTL SNPs are more likely to be assocd. with psoriasis than are randomly selected SNPs. To explore the tissue specificity of these eQTLs and hence to quantify the benefits of studying eQTLs in different tissues, we developed a refined statistical method for estg. eQTL overlap and used it to compare skin eQTLs to a published panel of lymphoblastoid cell line (LCL) eQTLs. Our method accounts for the fact that most eQTL studies are likely to miss some true eQTLs as a result of power limitations and shows that ∼70% of cis-eQTLs in LCLs are shared with skin, as compared with the naive est. of <50% sharing. Our results provide a useful method for estg. the overlap between various eQTL studies and provide a catalog of cis-eQTLs in skin that can facilitate efforts to understand the functional impact of identified susceptibility variants on psoriasis and other skin traits.
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    17. 17
      Firmann, M.; Mayor, V.; Vidal, P. M.; Bochud, M.; Pécoud, A.; Hayoz, D. The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. BMC Cardiovasc. Disord. 2008, 8, 6,  DOI: 10.1186/1471-2261-8-6
      17
      The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome
      Firmann Mathieu; Mayor Vladimir; Vidal Pedro Marques; Bochud Murielle; Pecoud Alain; Hayoz Daniel; Paccaud Fred; Preisig Martin; Song Kijoung S; Yuan Xin; Danoff Theodore M; Stirnadel Heide A; Waterworth Dawn; Mooser Vincent; Waeber Gerard; Vollenweider Peter
      BMC cardiovascular disorders (2008), 8 (), 6 ISSN:.
      BACKGROUND: Cardiovascular diseases and their associated risk factors remain the main cause of mortality in western societies. In order to assess the prevalence of cardiovascular risk factors (CVRFs) in the Caucasian population of Lausanne, Switzerland, we conducted a population-based study (Colaus Study). A secondary aim of the CoLaus study will be to determine new genetic determinants associated with CVRFs. METHODS: Single-center, cross-sectional study including a random sample of 6,188 extensively phenotyped Caucasian subjects (3,251 women and 2,937 men) aged 35 to 75 years living in Lausanne, and genotyped using the 500 K Affymetrix chip technology. RESULTS: Obesity (body mass index > or = 30 kg/m2), smoking, hypertension (blood pressure > or = 140/90 mmHg and/or treatment), dyslipidemia (high LDL-cholesterol and/or low HDL-cholesterol and/or high triglyceride levels) and diabetes (fasting plasma glucose > or = 7 mmol/l and/or treatment) were present in 947 (15.7%), 1673 (27.0%), 2268 (36.7%), 2113 (34.2%) and 407 (6.6%) of the participants, respectively, and the prevalence was higher in men than in women. In both genders, the prevalence of obesity, hypertension and diabetes increased with age. CONCLUSION: The prevalence of major CVRFs is high in the Lausanne population in particular in men. We anticipate that given its size, the depth of the phenotypic analysis and the availability of dense genome-wide genetic data, the CoLaus Study will be a unique resource to investigate not only the epidemiology of isolated, or aggregated CVRFs like the metabolic syndrome, but can also serve as a discovery set, as well as replication set, to identify novel genes associated with these conditions.
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    18. 18
      Rueedi, R.; Ledda, M.; Nicholls, A. W.; Salek, R. M.; Marques-Vidal, P.; Morya, E. Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links. PLoS Genet. 2014, 10, e1004132,  DOI: 10.1371/journal.pgen.1004132
      18
      Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links
      Rueedi, Rico; Ledda, Mirko; Nicholls, Andrew W.; Salek, Reza M.; Marques-Vidal, Pedro; Morya, Edgard; Sameshima, Koichi; Montoliu, Ivan; Da Silva, Laeticia; Collino, Sebastiano; Martin, Francois-Pierre; Rezzi, Serge; Steinbeck, Christoph; Waterworth, Dawn M.; Waeber, Gerard; Vollenweider, Peter; Beckmann, Jacques S.; Le Coutre, Johannes; Mooser, Vincent; Bergmann, Sven; Genick, Ulrich K.; Kutalik, Zoltan
      PLoS Genetics (2014), 10 (2), e1004132/1-e1004132/10, 10 pp.CODEN: PGLEB5; ISSN:1553-7404. (Public Library of Science)
      Metabolic traits are mol. phenotypes that can drive clin. phenotypes and may predict disease progression. Here, we report results from a metabolome- and genome-wide assocn. study on 1H-NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compd. identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5 × 10-8) and independent assocns. between single nucleotide polymorphisms (SNP) and metabolome features. Fifty-six of these assocns. replicated in the TasteSensomics cohort, comprising 601 individuals from S~ao Paulo of vastly diverse ethnic background. They correspond to eleven gene-metabolite assocns., six of which had been previously identified in the urine metabolome and three in the serum metabolome. Our key novel findings are the assocns. of two SNPs with NMR spectral signatures pointing to fucose (rs492602, P = 6.9 × 10-44) and lysine (rs8101881, P = 1.2 × 10-33), resp. Fine-mapping of the first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been assocd. with Crohn's disease. This implicates fucose as a potential prognostic disease marker, for which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene, rare mutations of which have been linked to severe kidney damage. The replication of previous assocns. and our new discoveries demonstrate the potential of untargeted metabolomics GWAS to robustly identify mol. disease markers.
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    19. 19
      Marco-Sola, S.; Sammeth, M.; Guigó, R.; Ribeca, P. The GEM mapper: fast, accurate and versatile alignment by filtration. Nat. Methods 2012, 9, 1185,  DOI: 10.1038/nmeth.2221
      19
      The GEM mapper: fast, accurate and versatile alignment by filtration
      Marco-Sola, Santiago; Sammeth, Michael; Guigo, Roderic; Ribeca, Paolo
      Nature Methods (2012), 9 (12), 1185-1188CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)
      Because of ever-increasing throughput requirements of sequencing data, most existing short-read aligners have been designed to focus on speed at the expense of accuracy. The Genome Multitool (GEM) mapper can leverage string matching by filtration to search the alignment space more efficiently, simultaneously delivering precision (performing fully tunable exhaustive searches that return all existing matches, including gapped ones) and speed (being several times faster than comparable state-of-the-art tools).
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    20. 20
      MATLAB 8.5.0.197613 (R2015a); The MathWorks Inc.: Natick, Massachusetts, 2015.
      There is no corresponding record for this reference.
    21. 21
      Gao, X.; Starmer, J.; Martin, E. R. A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet. Epidemiol. 2008, 32, 361– 369,  DOI: 10.1002/gepi.20310
      21
      A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms
      Gao Xiaoyi; Starmer Joshua; Martin Eden R
      Genetic epidemiology (2008), 32 (4), 361-9 ISSN:0741-0395.
      Multiple testing is a challenging issue in genetic association studies using large numbers of single nucleotide polymorphism (SNP) markers, many of which exhibit linkage disequilibrium (LD). Failure to adjust for multiple testing appropriately may produce excessive false positives or overlook true positive signals. The Bonferroni method of adjusting for multiple comparisons is easy to compute, but is well known to be conservative in the presence of LD. On the other hand, permutation-based corrections can correctly account for LD among SNPs, but are computationally intensive. In this work, we propose a new multiple testing correction method for association studies using SNP markers. We show that it is simple, fast and more accurate than the recently developed methods and is comparable to permutation-based corrections using both simulated and real data. We also demonstrate how it might be used in whole-genome association studies to control type I error. The efficiency and accuracy of the proposed method make it an attractive choice for multiple testing adjustment when there is high intermarker LD in the SNP data set.
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    22. 22
      Rueedi, R.; Mallol, R.; Raffler, J.; Lamparter, D.; Friedrich, N.; Vollenweider, P. Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy. PLoS Comput. Biol. 2017, 13, e1005839,  DOI: 10.1371/journal.pcbi.1005839
      22
      Metabomatching: using genetic association to identify metabolites in proton NMR spectroscopy
      Rueedi, Rico; Mallol, Roger; Raffler, Johannes; Lamparter, David; Friedrich, Nele; Vollenweider, Peter; Waeber, Gerard; Kastenmuller, Gabi; Kutalik, Zoltan; Bergmann, Sven
      PLoS Computational Biology (2017), 13 (12), e1005839/1-e1005839/17CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
      A metabolome-wide genome-wide assocn. study aims to discover the effects of genetic variants on metabolome phenotypes. Most mGWASes use as phenotypes concns. of limited sets of metabolites that can be identified and quantified from spectral information. In contrast, in an untargeted mGWAS both identification and quantification are forgone and, instead, all measured metabolome features are tested for assocn. with genetic variants. While the untargeted approach does not discard data that may have eluded identification, the interpretation of assocd. features remains a challenge. To address this issue, we developed metabomatching to identify the metabolites underlying significant assocns. obsd. in untargeted mGWASes on proton NMR metabolome data. Metabomatching capitalizes on genetic spiking, the concept that because metabolome features assocd. with a genetic variant tend to correspond to the peaks of the NMR spectrum of the underlying metabolite, genetic assocn. can allow for identification. Applied to the untargeted mGWASes in the SHIP and CoLaus cohorts and using 180 ref. NMR spectra of the urine metabolome database, metabomatching successfully identified the underlying metabolite in 14 of 19, and 8 of 9 assocns., resp. The accuracy and efficiency of our method make it a strong contender for facilitating or complementing metabolomics analyses in large cohorts, where the availability of genetic, or other data, enables our approach, but targeted quantification is limited.
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    23. 23
      Wishart, D. S.; Feunang, Y. D.; Marcu, A.; Guo, A. C.; Liang, K.; Vázquez-Fresno, R. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2018, 46, D608– D617,  DOI: 10.1093/nar/gkx1089
      23
      HMDB 4.0: the human metabolome database for 2018
      Wishart, David S.; Feunang, Yannick Djoumbou; Marcu, Ana; Guo, An Chi; Liang, Kevin; Vazquez-Fresno, Rosa; Sajed, Tanvir; Johnson, Daniel; Li, Carin; Karu, Naama; Sayeeda, Zinat; Lo, Elvis; Assempour, Nazanin; Berjanskii, Mark; Singhal, Sandeep; Arndt, David; Liang, Yonjie; Badran, Hasan; Grant, Jason; Serra-Cayuela, Arnau; Liu, Yifeng; Mandal, Rupa; Neveu, Vanessa; Pon, Allison; Knox, Craig; Wilson, Michael; Manach, Claudine; Scalbert, Augustin
      Nucleic Acids Research (2018), 46 (D1), D608-D617CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)
      A review. The Human Metabolome Database or HMDB (www. hmdb.ca) is a web-enabled metabolomic database contg. comprehensive information about human metabolites along with their biol. roles, physiol. concns., disease assocns., chem. reactions, metabolic pathways, and ref. spectra. First described in 2007, the HMDB is now considered the std. metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web stds. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the no. of fully annotated metabolites has increased by nearly threefold, the no. of exptl. spectra has grown by almost fourfold and the no. of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB's chem. taxonomy, chem. ontol., spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC-MS ref. spectral data as well as predicted (physiol. feasible) metabolite structures to facilitate novel metabolite identification. Addnl. information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmaco-metabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochem., clin. chem., clin. genetics, medicine, and metabolomics science.
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    24. 24
      Khalili, B.; Tomasoni, M.; Mattei, M.; Parera, R. M.; Sonmez, R.; Krefl, D. Automated analysis of large-scale NMR data generates metabolomic signatures and links them to candidate metabolites. J. Proteome Res. 2019, 613935,  DOI: 10.1021/acs.jproteome.9b00295
      There is no corresponding record for this reference.
    25. 25
      Burgess, S.; Small, D. S.; Thompson, S. G. A review of instrumental variable estimators for Mendelian randomization. Stat. Methods Med. Res. 2017, 26, 2333– 2355,  DOI: 10.1177/0962280215597579
      25
      A review of instrumental variable estimators for Mendelian randomization
      Burgess Stephen; Thompson Simon G; Small Dylan S
      Statistical methods in medical research (2017), 26 (5), 2333-2355 ISSN:.
      Instrumental variable analysis is an approach for obtaining causal inferences on the effect of an exposure (risk factor) on an outcome from observational data. It has gained in popularity over the past decade with the use of genetic variants as instrumental variables, known as Mendelian randomization. An instrumental variable is associated with the exposure, but not associated with any confounder of the exposure-outcome association, nor is there any causal pathway from the instrumental variable to the outcome other than via the exposure. Under the assumption that a single instrumental variable or a set of instrumental variables for the exposure is available, the causal effect of the exposure on the outcome can be estimated. There are several methods available for instrumental variable estimation; we consider the ratio method, two-stage methods, likelihood-based methods, and semi-parametric methods. Techniques for obtaining statistical inferences and confidence intervals are presented. The statistical properties of estimates from these methods are compared, and practical advice is given about choosing a suitable analysis method. In particular, bias and coverage properties of estimators are considered, especially with weak instruments. Settings particularly relevant to Mendelian randomization are prioritized in the paper, notably the scenario of a continuous exposure and a continuous or binary outcome.
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    26. 26
      Davey Smith, G.; Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?. Int. J. Epidemiol. 2003, 32, 1– 22,  DOI: 10.1093/ije/dyg070
      There is no corresponding record for this reference.
    27. 27
      Raffler, J.; Friedrich, N.; Arnold, M.; Kacprowski, T.; Rueedi, R.; Altmaier, E. Genome-wide association study with targeted and non-targeted NMR metabolomics identifies 15 novel loci of urinary human metabolic individuality. PLoS Genet. 2015, 11, e1005487,  DOI: 10.1371/journal.pgen.1005487
      27
      Genome-wide association study with targeted and non-targeted nmr metabolomics identifies 15 novel loci of urinary human metabolic individuality
      Raffler, Johannes; Friedrich, Nele; Arnold, Matthias; Kacprowski, Tim; Rueedi, Rico; Altmaier, Elisabeth; Bergmann, Sven; Budde, Kathrin; Gieger, Christian; Homuth, Georg; Pietzner, Maik; Roemisch-Margl, Werner; Strauch, Konstantin; Voelzke, Henry; Waldenberger, Melanie; Wallaschofski, Henri; Nauck, Matthias; Voelker, Uwe; Kastenmueller, Gabi; Suhre, Karsten
      PLoS Genetics (2015), 11 (9), e1005487/1-e1005487/28CODEN: PGLEB5; ISSN:1553-7404. (Public Library of Science)
      Genome-wide assocn. studies with metabolic traits (mGWAS) uncovered many genetic variants that influence human metab. These genetically influenced metabotypes (GIMs) contribute to our metabolic individuality, our capacity to respond to environmental challenges, and our susceptibility to specific diseases. While metabolic homeostasis in blood is a well investigated topic in large mGWAS with over 150 known loci, metabolic detoxification through urinary excretion has only been addressed by few small mGWAS with only 11 assocd. loci so far. Here we report the largest mGWAS to date, combining targeted and non-targeted 1H NMR anal. of urine samples from 3,861 participants of the SHIP-0 cohort and 1,691 subjects of the KORA F4 cohort. We identified and replicated 22 loci with significant assocns. with urinary traits, 15 of which are new (HIBCH, CPS1, AGXT, XYLB, TKT, ETNPPL, SLC6A19, DMGDH, SLC36A2, GLDC, SLC6A13, ACSM3, SLC5A11, PNMT, SLC13A3). Two-thirds of the urinary loci also have a metabolite assocn. in blood. For all but one of the 6 loci where significant assocns. target the same metabolite in blood and urine, the genetic effects have the same direction in both fluids. In contrast, for the SLC5A11 locus, we found increased levels of myo-inositol in urine whereas mGWAS in blood reported decreased levels for the same genetic variant. This might indicate less effective re-absorption of myo-inositol in the kidneys of carriers. In summary, our study more than doubles the no. of known loci that influence urinary phenotypes. It thus allows novel insights into the relationship between blood homeostasis and its regulation through excretion. The newly discovered loci also include variants previously linked to chronic kidney disease (CPS1, SLC6A13), pulmonary hypertension (CPS1), and ischemic stroke (XYLB). By establishing connections from gene to disease via metabolic traits our results provide novel hypotheses about mol. mechanisms involved in the etiol. of diseases.
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    28. 28
      Võsa, U.; Claringbould, A.; Westra, H.-J.; Bonder, M. J.; Deelen, P.; Zeng, B.; Kirsten, H.; Saha, A.; Kreuzhuber, R.; Brugge, H. Large-scale cis-and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 2021, 53, 1300– 1310,  DOI: 10.1038/s41588-021-00913-z
      28
      Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression
      Vosa, Urmo; Claringbould, Annique; Westra, Harm-Jan; Bonder, Marc Jan; Deelen, Patrick; Zeng, Biao; Kirsten, Holger; Saha, Ashis; Kreuzhuber, Roman; Yazar, Seyhan; Brugge, Harm; Oelen, Roy; de Vries, Dylan H.; van der Wijst, Monique G. P.; Kasela, Silva; Pervjakova, Natalia; Alves, Isabel; Fave, Marie-Julie; Agbessi, Mawusse; Christiansen, Mark W.; Jansen, Rick; Seppala, Ilkka; Tong, Lin; Teumer, Alexander; Schramm, Katharina; Hemani, Gibran; Verlouw, Joost; Yaghootkar, Hanieh; Sonmez Flitman, Reyhan; Brown, Andrew; Kukushkina, Viktorija; Kalnapenkis, Anette; Rueger, Sina; Porcu, Eleonora; Kronberg, Jaanika; Kettunen, Johannes; Lee, Bernett; Zhang, Futao; Qi, Ting; Hernandez, Jose Alquicira; Arindrarto, Wibowo; Beutner, Frank; Dmitrieva, Julia; Elansary, Mahmoud; Fairfax, Benjamin P.; Georges, Michel; Heijmans, Bastiaan T.; Hewitt, Alex W.; Kahonen, Mika; Kim, Yungil; Knight, Julian C.; Kovacs, Peter; Krohn, Knut; Li, Shuang; Loeffler, Markus; Marigorta, Urko M.; Mei, Hailang; Momozawa, Yukihide; Muller-Nurasyid, Martina; Nauck, Matthias; Nivard, Michel G.; Penninx, Brenda W. J. H.; Pritchard, Jonathan K.; Raitakari, Olli T.; Rotzschke, Olaf; Slagboom, Eline P.; Stehouwer, Coen D. A.; Stumvoll, Michael; Sullivan, Patrick; 't Hoen, Peter A. C.; Thiery, Joachim; Tonjes, Anke; van Dongen, Jenny; van Iterson, Maarten; Veldink, Jan H.; Volker, Uwe; Warmerdam, Robert; Wijmenga, Cisca; Swertz, Morris; Andiappan, Anand; Montgomery, Grant W.; Ripatti, Samuli; Perola, Markus; Kutalik, Zoltan; Dermitzakis, Emmanouil; Bergmann, Sven; Frayling, Timothy; van Meurs, Joyce; Prokisch, Holger; Ahsan, Habibul; Pierce, Brandon L.; Lehtimaki, Terho; Boomsma, Dorret I.; Psaty, Bruce M.; Gharib, Sina A.; Awadalla, Philip; Milani, Lili; Ouwehand, Willem H.; Downes, Kate; Stegle, Oliver; Battle, Alexis; Visscher, Peter M.; Yang, Jian; Scholz, Markus; Powell, Joseph; Gibson, Greg; Esko, Tonu; Franke, Lude
      Nature Genetics (2021), 53 (9), 1300-1310CODEN: NGENEC; ISSN:1061-4036. (Nature Portfolio)
      Trait-assocd. genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quant. trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-assocd. variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type compn. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.
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    29. 29
      Wald, A. The fitting of straight lines if both variables are subject to error. Ann. Math. Stat. 1940, 11, 284– 300,  DOI: 10.1214/aoms/1177731868
      There is no corresponding record for this reference.
    30. 30
      Hartung, J.; Knapp, G.; Sinha, B. K.; Sinha, B. K. Statistical meta-analysis with applications; Wiley: New York, 2008.
      There is no corresponding record for this reference.
    31. 31
      Greco, M. F. D.; Minelli, C.; Sheehan, N. A.; Thompson, J. R. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat. Med. 2015, 34, 2926– 2940,  DOI: 10.1002/sim.6522
      31
      Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome
      Greco M Fabiola Del; Minelli Cosetta; Sheehan Nuala A; Thompson John R
      Statistics in medicine (2015), 34 (21), 2926-40 ISSN:.
      Mendelian randomisation (MR) estimates causal effects of modifiable phenotypes on an outcome by using genetic variants as instrumental variables, but its validity relies on the assumption of no pleiotropy, that is, genes influence the outcome only through the given phenotype. Excluding pleiotropy is difficult, but the use of multiple instruments can indirectly address the issue: if all genes represent valid instruments, their MR estimates should vary only by chance. The Sargan test detects pleiotropy when individual phenotype, outcome and genotype data are measured in the same subjects. We propose an alternative approach to be used when only summary genetic data are available or data on gene-phenotype and gene-outcome come from different subjects. The presence of pleiotropy is investigated using the between-instrument heterogeneity Q test (together with the I(2) index) in a meta-analysis of MR Wald estimates, derived separately from each instrument. For a continuous outcome, we evaluate the approach through simulations and illustrate it using published data. For the scenario where all data come from the same subjects, we compare it with the Sargan test. The Q test tends to be conservative in small samples. Its power increases with the degree of pleiotropy and the sample size, as does the precision of the I(2) index, in which case results are similar to those of the Sargan test. In MR studies with large sample sizes based on summary data, the between-instrument Q test represents a useful tool to explore the presence of heterogeneity due to pleiotropy or other causes.
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    32. 32
      Yavorska, O. O.; Burgess, S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol. 2017, 46, 1734– 1739,  DOI: 10.1093/ije/dyx034
      32
      MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data
      Yavorska Olena O; Burgess Stephen
      International journal of epidemiology (2017), 46 (6), 1734-1739 ISSN:.
      MendelianRandomization is a software package for the R open-source software environment that performs Mendelian randomization analyses using summarized data. The core functionality is to implement the inverse-variance weighted, MR-Egger and weighted median methods for multiple genetic variants. Several options are available to the user, such as the use of robust regression, fixed- or random-effects models and the penalization of weights for genetic variants with heterogeneous causal estimates. Extensions to these methods, such as allowing for variants to be correlated, can be chosen if appropriate. Graphical commands allow summarized data to be displayed in an interactive graph, or the plotting of causal estimates from multiple methods, for comparison. Although the main method of data entry is directly by the user, there is also an option for allowing summarized data to be incorporated from the PhenoScanner database of genotype-phenotype associations. We hope to develop this feature in future versions of the package. The R software environment is available for download from [https://www.r-project.org/]. The MendelianRandomization package can be downloaded from the Comprehensive R Archive Network (CRAN) within R, or directly from [https://cran.r-project.org/web/packages/MendelianRandomization/]. Both R and the MendelianRandomization package are released under GNU General Public Licenses (GPL-2 GPL-3).
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    33. 33
      Shin, S.-Y.; Fauman, E. B.; Petersen, A.-K.; Krumsiek, J.; Santos, R.; Huang, J. An atlas of genetic influences on human blood metabolites. Nat. Genet. 2014, 46, 543,  DOI: 10.1038/ng.2982
      33
      An atlas of genetic influences on human blood metabolites
      Shin, So-Youn; Fauman, Eric B.; Petersen, Ann-Kristin; Krumsiek, Jan; Santos, Rita; Huang, Jie; Arnold, Matthias; Erte, Idil; Forgetta, Vincenzo; Yang, Tsun-Po; Walter, Klaudia; Menni, Cristina; Chen, Lu; Vasquez, Louella; Valdes, Ana M.; Hyde, Craig L.; Wang, Vicky; Ziemek, Daniel; Roberts, Phoebe; Xi, Li; Grundberg, Elin; Waldenberger, Melanie; Richards, J. Brent; Mohney, Robert P.; Milburn, Michael V.; John, Sally L.; Trimmer, Jeff; Theis, Fabian J.; Overington, John P.; Suhre, Karsten; Brosnan, M. Julia; Gieger, Christian; Kastenmuller, Gabi; Spector, Tim D.; Soranzo, Nicole
      Nature Genetics (2014), 46 (6), 543-550CODEN: NGENEC; ISSN:1061-4036. (Nature Publishing Group)
      Genome-wide assocn. scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metab. and complex disease. Here the authors report the most comprehensive exploration of genetic loci influencing human metab. thus far, comprising 7,824 adult individuals from 2 European population studies. The authors report genome-wide significant assocns. at 145 metabolic loci and their biochem. connectivity with more than 400 metabolites in human blood. The authors extensively characterize the resulting in vivo blueprint of metab. in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metab. and pharmacol. targets. The authors further developed a database and web-based resources for data mining and results visualization. The authors' findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.
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    34. 34
      Nicholson, G.; Rantalainen, M.; Li, J. V.; Maher, A. D.; Malmodin, D.; Ahmadi, K. R. A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection. PLoS Genet. 2011, 7, e1002270,  DOI: 10.1371/journal.pgen.1002270
      34
      A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection
      Nicholson, George; Rantalainen, Mattias; Li, Jia V.; Maher, Anthony D.; Malmodin, Daniel; Ahmadi, Kourosh R.; Faber, Johan H.; Barrett, Amy; Min, Josine L.; Rayner, N. William; Toft, Henrik; Krestyaninova, Maria; Viksna, Juris; Neogi, Sudeshna Guha; Dumas, Marc-Emmanuel; Sarkans, Ugis; Donnelly, Peter; Illig, Thomas; Adamski, Jerzy; Suhre, Karsten; Allen, Maxine; Zondervan, Krina T.; Spector, Tim D.; Nicholson, Jeremy K.; Lindon, John C.; Baunsgaard, Dorrit; Holmes, Elaine; McCarthy, Mark I.; Holmes, Chris C.
      PLoS Genetics (2011), 7 (9), e1002270CODEN: PGLEB5; ISSN:1553-7404. (Public Library of Science)
      We have performed a metabolite quant. trait locus (mQTL) study of the 1H NMR spectroscopy metabolome in humans, building on recent targeted knowledge of genetic drivers of metabolic regulation. Urine and plasma samples were collected from two cohorts of individuals of European descent, with one cohort comprised of female twins donating samples longitudinally. Sample metabolite concns. were quantified by 1H NMR and tested for assocn. with genome-wide single-nucleotide polymorphisms (SNPs). 4 Metabolites' concns. exhibited significant, replicable assocn. with SNP variation (8.6 × 10-11 < p < 2.8 × 10-23). 3 Of these, trimethylamine, 3-amino-isobutyrate, and an N-acetylated compd., were measured in urine. The other, dimethylamine, was measured in plasma. Trimethylamine and dimethylamine mapped to a single genetic region (hence we report a total of three implicated genomic regions). 2 Of the 3 hit regions lie within haplotype blocks (at 2p13.1 and 10q24.2) that carry the genetic signature of strong, recent, pos. selection in European populations. Genes NAT8 and PYROXD2, both with relatively uncharacterized functional roles, are good candidates for mediating the corresponding mQTL assocns. The studies longitudinal twin design allowed detailed variance-components anal. of the sources of population variation in metabolite levels. The mQTLs explained 40%-64% of biol. population variation in the corresponding metabolites' concns. These effect sizes are stronger than those reported in a recent, targeted mQTL study of metabolites in serum using the targeted-metabolomics Biocrates platform. By re-analyzing our plasma samples using the Biocrates platform, we replicated the mQTL findings of the previous study and discovered a previously uncharacterized yet substantial familial component of variation in metabolite levels in addn. to the heritability contribution from the corresponding mQTL effects.
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    35. 35
      Engelke, U. F.; Liebrand-van Sambeek, M. L.; De Jong, J. G.; Leroy, J. G.; Morava, E.; Smeitink, J. A. N-acetylated metabolites in urine: proton nuclear magnetic resonance spectroscopic study on patients with inborn errors of metabolism. Clin. Chem. 2004, 50, 58– 66,  DOI: 10.1373/clinchem.2003.020214
      35
      N-acetylated metabolites in urine: Proton nuclear magnetic resonance spectroscopic study on patients with inborn errors of metabolism
      Engelke, Udo F. H.; Liebrand-van Sambeek, Maria L. F.; de Jong, Jan G. N.; Leroy, Jules G.; Morava, Eva; Smeitink, Jan A. M.; Wevers, Ron A.
      Clinical Chemistry (Washington, DC, United States) (2004), 50 (1), 58-66CODEN: CLCHAU; ISSN:0009-9147. (American Association for Clinical Chemistry)
      There is no comprehensive anal. technique to analyze N-acetylated metabolites in urine. Many of these compds. are involved in inborn errors of metab. In the present study, the authors examd. the potential of proton NMR (1H-NMR) spectroscopy as a tool to identify and quantify N-acetylated metabolites in urine of patients with various inborn errors of metab. The authors performed 1H-NMR spectroscopy on a 500 MHz spectrometer. Using a combination of 1- and 2-dimensional correlation spectroscopy (COSY) 1H-NMR spectra, the authors were able to assign and quantify resonances of characteristic N-acetylated compds. products in urine of patients with 13 inborn errors of metab. The disease-specific N-acetylated metabolites were excreted at concns. >100 μmol/mmol of creatinine in the patients' urine. In control urine samples, the concn. of individual N-acetyl-contg. compds. was <40 μmol/mmol of creatinine. The combination of one- and two-dimensional COSY NMR spectroscopy led to the correct diagnosis of nine different inborn errors of metab. No abnormalities were obsd. in the spectra of urine from patients with GM1- or GM2-gangliosidosis. The authors also detd. the 1H-NMR characteristics of N-acetylated metabolites that may be relevant to human metab. 1H-NMR spectroscopy may be used to identify and quantify N-acetylated metabolites of diagnostic importance for the field of inborn errors of metab.
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    36. 36
      Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Stat. Soc.: B 1995, 57, 289– 300,  DOI: 10.1111/j.2517-6161.1995.tb02031.x
      There is no corresponding record for this reference.
    37. 37
      Kastenmüller, G.; Raffler, J.; Gieger, C.; Suhre, K. Genetics of human metabolism: an update. Hum. Mol. Genet. 2015, 24, R93– R101,  DOI: 10.1093/hmg/ddv263
      37
      Genetics of human metabolism: an update
      Kastenmueller, Gabi; Raffler, Johannes; Gieger, Christian; Suhre, Karsten
      Human Molecular Genetics (2015), 24 (Rev. Issue 1), R93-R101CODEN: HMGEE5; ISSN:0964-6906. (Oxford University Press)
      A review. Genome-wide assocn. studies with metabolomics (mGWAS) identify genetically influenced metabotypes (GIMs), their ensemble defining the heritable part of every human's metabolic individuality. Knowledge of genetic variation in metab. has many applications of biomedical and pharmaceutical interests, including the functional understanding of genetic assocns. with clin. end points, design of strategies to correct dysregulations in metabolic disorders and the identification of genetic effect modifiers of metabolic disease biomarkers. Furthermore, it has been shown that GIMs provide testable hypotheses for functional genomics and metabolomics and for the identification of novel gene functions and metabolite identities. mGWAS with growing sample sizes and increasingly complex metabolic trait panels are being conducted, allowing for more comprehensive and systems-based downstream analyses. The generated large datasets of genetic assocns. can now be mined by the biomedical research community and provide valuable resources for hypothesis-driven studies. In this review, we provide a brief summary of the key aspects of mGWAS, followed by an update of recently published mGWAS. We then discuss new approaches of integrating and exploring mGWAS results and finish by presenting selected applications of GIMs in recent studies.
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    38. 38
      Suhre, K.; Shin, S. Y.; Petersen, A. K.; Mohney, R. P.; Meredith, D.; Wägele, B. Human metabolic individuality in biomedical and pharmaceutical research. Nature 2011, 477, 54– 60,  DOI: 10.1038/nature10354
      38
      Human metabolic individuality in biomedical and pharmaceutical research
      Suhre, Karsten; Shin, So-Youn; Petersen, Ann-Kristin; Mohney, Robert P.; Meredith, David; Waegele, Brigitte; Altmaier, Elisabeth; Deloukas, Panos; Erdmann, Jeanette; Grundberg, Elin; Hammond, Christopher J.; Hrabe de Angelis, Martin; Kastenmueller, Gabi; Koettgen, Anna; Kronenberg, Florian; Mangino, Massimo; Meisinger, Christa; Meitinger, Thomas; Mewes, Hans-Werner; Milburn, Michael V.; Prehn, Cornelia; Raffler, Johannes; Ried, Janina S.; Roemisch-Margl, Werner; Samani, Nilesh J.; Small, Kerrin S.; Wichmann, H.-Erich; Zhai, Guang-Ju; Illig, Thomas; Spector, Tim D.; Adamski, Jerzy; Soranzo, Nicole; Gieger, Christian
      Nature (London, United Kingdom) (2011), 477 (7362), 54-60CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
      A review based on author's and other researchers studies. Genome-wide assocn. studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biol. processes is often lacking. Assocns. with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive anal. of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci assocd. with blood metabolite concns., of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our assocns. provide new functional insights for many disease-related assocns. that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.
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    39. 39
      Yu, B.; Zheng, Y.; Alexander, D.; Morrison, A. C.; Coresh, J.; Boerwinkle, E. Genetic determinants influencing human serum metabolome among African Americans. PLoS Genet. 2014, 10, e1004212  DOI: 10.1371/journal.pgen.1004212
      39
      Genetic determinants influencing human serum metabolome among African Americans
      Yu, Bing; Zheng, Yan; Alexander, Danny; Morrison, Alanna C.; Coresh, Josef; Boerwinkle, Eric
      PLoS Genetics (2014), 10 (3), e1004212/1-e1004212/8, 8 pp.CODEN: PGLEB5; ISSN:1553-7404. (Public Library of Science)
      Phenotypes proximal to gene action generally reflect larger genetic effect sizes than those that are distant. The human metabolome, a result of multiple cellular and biol. processes, are functional intermediate phenotypes proximal to gene action. Here, we present a genome-wide assocn. study of 308 untargeted metabolite levels among African Americans from the Atherosclerosis Risk in Communities (ARIC) Study. Nineteen significant common variant-metabolite assocns. were identified, including 13 novel loci (p<1.6 × 1010). These loci were assocd. with 7-50 % of the difference in metabolite levels per allele, and the variance explained ranged from 4% to 20%. Fourteen genes were identified within the nineteen loci, and four of them contained non-synonymous substitutions in four enzyme-encoding genes (KLKB1, SIAE, CPS1, and NAT8); the other significant loci consist of eight other enzyme-encoding genes (ACE, GATM, ACY3, ACSM2B, THEM4, ADH4, UGT1A, TREH), a transporter gene (SLC6A13) and a polycystin protein gene (PKD2L1). In addn., four potential disease-assocd. paths were identified, including two direct longitudinal predictive relationships: NAT8 with N-acetylornithine, N-acetyl-1-methylhistidine and incident chronic kidney disease, and TREH with trehalose and incident diabetes. These results highlight the value of using endophenotypes proximal to gene function to discover new insights into biol. and disease pathol.
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    40. 40
      Raffler, J.; Römisch-Margl, W.; Petersen, A. K.; Pagel, P.; Blöchl, F.; Hengstenberg, C. Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma. Genome Med. 2013, 5, 13,  DOI: 10.1186/gm417
      40
      Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma
      Raffler, Johannes; Roemisch-Margl, Werner; Petersen, Ann-Kristin; Pagel, Philipp; Bloechl, Florian; Hengstenberg, Christian; Illig, Thomas; Meisinger, Christa; Stark, Klaus; Wichmann, H.-Erich; Adamski, Jerzy; Gieger, Christian; Kastenmueller, Gabi; Suhre, Karsten
      Genome Medicine (2013), 5 (2), 13CODEN: GMEECG; ISSN:1756-994X. (BioMed Central Ltd.)
      NMR spectroscopy provides robust readouts of many metabolic parameters in one expt. However, identification of clin. relevant markers in 1H NMR spectra is a major challenge. Assocn. of NMR-derived quantities with genetic variants can uncover biol. relevant metabolic traits. Using NMR data of plasma samples from 1,757 individuals from the KORA study together with 655,658 genetic variants, we show that ratios between NMR intensities at two chem. shift positions can provide informative and robust biomarkers. We report seven loci of genetic assocn. with NMR-derived traits (APOA1, CETP, CPS1, GCKR, FADS1, LIPC, PYROXD2) and characterize these traits biochem. using mass spectrometry. These ratios may now be used in clin. studies.
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    41. 41
      Rhee, E. P.; Ho, J. E.; Chen, M. H.; Shen, D.; Cheng, S.; Larson, M. G. A genome-wide association study of the human metabolome in a community-based cohort. Cell Metab. 2013, 18, 130– 143,  DOI: 10.1016/j.cmet.2013.06.013
      41
      A Genome-wide Association Study of the Human Metabolome in a Community-Based Cohort
      Rhee, Eugene P.; Ho, Jennifer E.; Chen, Ming-Huei; Shen, Dongxiao; Cheng, Susan; Larson, Martin G.; Ghorbani, Anahita; Shi, Xu; Helenius, Iiro T.; O'Donnell, Christopher J.; Souza, Amanda L.; Deik, Amy; Pierce, Kerry A.; Bullock, Kevin; Walford, Geoffrey A.; Vasan, Ramachandran S.; Florez, Jose C.; Clish, Clary; Yeh, J.-R. Joanna; Wang, Thomas J.; Gerszten, Robert E.
      Cell Metabolism (2013), 18 (1), 130-143CODEN: CMEEB5; ISSN:1550-4131. (Elsevier Inc.)
      Because metabolites are hypothesized to play key roles as markers and effectors of cardiometabolic diseases, recent studies have sought to annotate the genetic determinants of circulating metabolite levels. We report a genome-wide assocn. study (GWAS) of 217 plasma metabolites, including >100 not measured in prior GWAS, in 2076 participants of the Framingham Heart Study (FHS). For the majority of analytes, we find that estd. heritability explains >20% of interindividual variation, and that variation attributable to heritable factors is greater than that attributable to clin. factors. Further, we identify 31 genetic loci assocd. with plasma metabolites, including 23 that have not previously been reported. Importantly, we include GWAS results for all surveyed metabolites and demonstrate how this information highlights a role for AGXT2 in cholesterol ester and triacylglycerol metab. Thus, our study outlines the relative contributions of inherited and clin. factors on the plasma metabolome and provides a resource for metab. research.
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    42. 42
      Krumsiek, J.; Suhre, K.; Evans, A. M.; Mitchell, M. W.; Mohney, R. P.; Milburn, M. V. Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet. 2012, 8, e1003005,  DOI: 10.1371/journal.pgen.1003005
      42
      Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information
      Krumsiek, Jan; Suhre, Karsten; Evans, Anne M.; Mitchell, Matthew W.; Mohney, Robert P.; Milburn, Michael V.; Waegele, Brigitte; Roemisch-Margl, Werner; Illig, Thomas; Adamski, Jerzy; Gieger, Christian; Theis, Fabian J.; Kastenmueller, Gabi
      PLoS Genetics (2012), 8 (10), e1003005CODEN: PGLEB5; ISSN:1553-7404. (Public Library of Science)
      Recent genome-wide assocn. studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amt. of the mols. currently quantified by modern metabolomics techniques are chem. unidentified. The identification of these "unknown metabolites" is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the anal. Here we present a systems-level approach that combines genome-wide assocn. anal. and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype-metabotype assocns. for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic assocns., metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochem. identities of 106 unknown metabolites. As a proof of principle, we exptl. confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different exptl. platforms.
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    43. 43
      Hong, M. G.; Karlsson, R.; Magnusson, P. K.; Lewis, M. R.; Isaacs, W.; Zheng, L. S. A Genome-Wide Assessment of Variability in Human Serum Metabolism. Hum. Mutat. 2013, 34, 515– 524,  DOI: 10.1002/humu.22267
      43
      A Genome-Wide Assessment of Variability in Human Serum Metabolism
      Hong, Mun-Gwan; Karlsson, Robert; Magnusson, Patrik K. E.; Lewis, Matthew R.; Isaacs, William; Zheng, Lilly S.; Xu, Jianfeng; Groenberg, Henrik; Ingelsson, Erik; Pawitan, Yudi; Broeckling, Corey; Prenni, Jessica E.; Wiklund, Fredrik; Prince, Jonathan A.
      Human Mutation (2013), 34 (3), 515-524CODEN: HUMUE3; ISSN:1059-7794. (Wiley-Liss, Inc.)
      The study of the genetic regulation of metab. in human serum samples can contribute to a better understanding of the intermediate biol. steps that lead from polymorphism to disease. Here, we conducted a genome-wide assocn. study (GWAS) to discover metabolic quant. trait loci (mQTLs) utilizing samples from a study of prostate cancer in Swedish men, consisting of 402 individuals (214 cases and 188 controls) in a discovery set and 489 case-only samples in a replication set. A global nontargeted metabolite profiling approach was utilized resulting in the detection of 6,138 mol. features followed by targeted identification of assocd. metabolites. Seven replicating loci were identified (PYROXD2, FADS1, PON1, CYP4F2, UGT1A8, ACADL, and LIPC) with assocd. sequence variants contributing significantly to trait variance for one or more metabolites (P = 10-13-10-91). Regional mQTL enrichment analyses implicated two loci that included FADS1 and a novel locus near PDGFC. Biol. pathway anal. implicated ACADM, ACADS, ACAD8, ACAD10, ACAD11, and ACOXL, reflecting significant enrichment of genes with acyl-CoA dehydrogenase activity. mQTL SNPs and mQTL-harboring genes were over-represented across GWASs conducted to date, suggesting that these data may have utility in tracing the mol. basis of some complex disease assocns.
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    44. 44
      Montoliu, I.; Genick, U.; Ledda, M.; Collino, S.; Martin, F.-P.; le Coutre, J. Current status on genome–metabolome-wide associations: an opportunity in nutrition research. Gene Nutr. 2013, 8, 19,  DOI: 10.1007/s12263-012-0313-7
      There is no corresponding record for this reference.
    45. 45
      Chambers, J. C.; Zhang, W.; Lord, G. M.; Van Der Harst, P.; Lawlor, D. A.; Sehmi, J. S. Genetic loci influencing kidney function and chronic kidney disease. Nat. Genet. 2010, 42, 373– 375,  DOI: 10.1038/ng.566
      46
      Genetic loci influencing kidney function and chronic kidney disease
      Chambers, John C.; Zhang, Weihua; Lord, Graham M.; van der Harst, Pim; Lawlor, Debbie A.; Sehmi, Joban S.; Gale, Daniel P.; Wass, Mark N.; Ahmadi, Kourosh R.; Bakker, Stephan J. L.; Beckmann, Jacqui; Bilo, Henk J. G.; Bochud, Murielle; Brown, Morris J.; Caulfield, Mark J.; Connell, John M. C.; Cook, H. Terence; Cotlarciuc, Ioana; Smith, George Davey; de Silva, Ranil; Deng, Guohong; Devuyst, Olivier; Dikkeschei, Lambert D.; Dimkovic, Nada; Dockrell, Mark; Dominiczak, Anna; Ebrahim, Shah; Eggermann, Thomas; Farrall, Martin; Ferrucci, Luigi; Floege, Jurgen; Forouhi, Nita G.; Gansevoort, Ron T.; Han, Xijin; Hedblad, Bo; van der Heide, Jaap J. Homan; Hepkema, Bouke G.; Hernandez-Fuentes, Maria; Hypponen, Elina; Johnson, Toby; de Jong, Paul E.; Kleefstra, Nanne; Lagou, Vasiliki; Lapsley, Marta; Li, Yun; Loos, Ruth J. F.; Luan, Jian'an; Luttropp, Karin; Marechal, Celine; Melander, Olle; Munroe, Patricia B.; Nordfors, Louise; Parsa, Afshin; Peltonen, Leena; Penninx, Brenda W.; Perucha, Esperanza; Pouta, Anneli; Prokopenko, Inga; Roderick, Paul J.; Ruokonen, Aimo; Samani, Nilesh J.; Sanna, Serena; Schalling, Martin; Schlessinger, David; Schlieper, Georg; Seelen, Marc A. J.; Shuldiner, Alan R.; Sjoegren, Marketa; Smit, Johannes H.; Snieder, Harold; Soranzo, Nicole; Spector, Timothy D.; Stenvinkel, Peter; Sternberg, Michael J. E.; Swaminathan, Ramasamyiyer; Tanaka, Toshiko; Ubink-Veltmaat, Lielith J.; Uda, Manuela; Vollenweider, Peter; Wallace, Chris; Waterworth, Dawn; Zerres, Klaus; Waeber, Gerard; Wareham, Nicholas J.; Maxwell, Patrick H.; McCarthy, Mark I.; Jarvelin, Marjo-Riitta; Mooser, Vincent; Abecasis, Goncalo R.; Lightstone, Liz; Scott, James; Navis, Gerjan; Elliott, Paul; Kooner, Jaspal S.
      Nature Genetics (2010), 42 (5), 373-375CODEN: NGENEC; ISSN:1061-4036. (Nature Publishing Group)
      Using genome-wide assocn., we identify common variants at 2p12-p13, 6q26, 17q23 and 19q13 assocd. with serum creatinine, a marker of kidney function (P = 10-10 to 10-15). Of these, rs10206899 (near NAT8, 2p12-p13) and rs4805834 (near SLC7A9, 19q13) were also assocd. with chronic kidney disease (P = 5.0 × 10-5 and P = 3.6 × 10-4, resp.). Our findings provide insight into metabolic, solute and drug-transport pathways underlying susceptibility to chronic kidney disease.
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    46. 46
      Simmons, M. L.; Frondoza, C. G.; Coyle, J. T. Immunocytochemical localization of N-acetyl-aspartate with monoclonal antibodies. Neuroscience 1991, 45, 37– 45,  DOI: 10.1016/0306-4522(91)90101-S
      47
      Immunocytochemical localization of N-acetyl-aspartate with monoclonal antibodies
      Simmons, M. L.; Frondoza, C. G.; Coyle, J. T.
      Neuroscience (Oxford, United Kingdom) (1991), 45 (1), 37-45CODEN: NRSCDN; ISSN:0306-4522.
      N-Acetyl-aspartate is found in high concns. in all areas of the brain, but is undetectable in non-neuronal tissue. In order to characterize the cellular localization of N-acetyl-aspartate in brain, highly specific monoclonal antibodies against N-acetyl-aspartate were produced by fusing spleen lymphocytes obtained from mice immunized with N-acetyl-aspartate conjugated to thyroglobulin by carbodiimide with P3/x63-Ag8.653 mouse myeloma cells. Clones were selected which secrete IgG2a(k) antibodies highly specific for conjugated N-acetyl-aspartate. Only 3-6% cross-reactivity with conjugated N-acetyl-aspartate-glutamate was obsd. at high antibody concns., whereas no cross-reactivity (<1%) was obsd. with conjugated N-acetyl-glutamate or aspartate. Preincubation of the antibodies with 0.5 mg/mL conjugated N-acetyl-aspartate blocked immunoreactivity more than 90%, while preincubation with conjugated N-acetyl-aspartate-glutamate and free N-acetyl-aspartate had no effect. Immunocytochem. staining has shown that N-acetyl-aspartate-like immunoreactivity is localized in neurons, which are widely distributed throughout the brain. The immunoreactive neurons exhibited intense staining of the perikarya, proximal dendrites and axons. No consistent pattern of distribution of immunoreactivity was obsd. with regard to primary neurotransmitter characteristics of stained neurons although neurons with long projections or extensive arbors, such as pyramidal cells in cortex, locus ceruleus, motor neurons and Purkinje cells, stained much more intensively than local circuit neurons.
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    47. 47
      Masaharu, M.; Hideo, M.; Mutsuhiko, M.; Yasuo, K. N-acetyl-l-aspartic acid, N-acetyl-α-l-aspartyl-l-glutamic acid and β-citryl-l-glutamic acid in human urine. Clin. Chim. Acta 1982, 120, 119– 126,  DOI: 10.1016/0009-8981(82)90082-1
      There is no corresponding record for this reference.
    48. 48
      Barker, P. B. N-acetyl aspartate─a neuronal marker?. Ann. Neurol. 2001, 49, 423– 424,  DOI: 10.1002/ana.90
      49
      N-acetyl aspartate--a neuronal marker?
      Barker P B
      Annals of neurology (2001), 49 (4), 423-4 ISSN:0364-5134.
      There is no expanded citation for this reference.
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    49. 49
      Jung, R. E.; Brooks, W. M.; Yeo, R. A.; Chiulli, S. J.; Weers, D. C.; Sibbitt, W. L., Jr. Biochemical markers of intelligence: a proton MR spectroscopy study of normal human brain. Proc. R. Soc. London, Ser. B 1999, 266, 1375– 1379,  DOI: 10.1098/rspb.1999.0790
      50
      Biochemical markers of intelligence: a proton MR spectroscopy study of normal human brain
      Jung R E; Brooks W M; Yeo R A; Chiulli S J; Weers D C; Sibbitt W L Jr
      Proceedings. Biological sciences / The Royal Society (1999), 266 (1426), 1375-9 ISSN:0962-8452.
      Proton magnetic resonance spectroscopy (1H-MRS) offers a unique non-invasive approach to measurement of N-acetylaspartate (NAA) and choline (Cho), putative markers of neuronal and glial integrity. Previous studies revealed that these neurochemicals predict cognitive impairment in diseased subjects, although little is known about their relationship to cognitive functioning in healthy people. We measured the concentrations of NAA and Cho in the left occipitoparietal white matter of 26 healthy adults and compared them with intellectual performance assessed by the Wechsler Adult Intelligence Scale-3. We found that NAA (b = 0.6, p < 0.01) and Cho (b = -0.42, p < 0.01) were independently associated with the Full-Scale Intelligence Quotient. Together, these metabolites accounted for a large proportion of the variance in intelligence (r2 = 0.45). Possible mechanisms underlying these correlations, such as mitochondrial function and myelin turnover, are discussed. 1H-MRS is a sensitive new tool to assess the neuronal underpinnings of cognitive function non-invasively.
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    50. 50
      Patel, T.; Blyth, J. C.; Griffiths, G.; Kelly, D.; Talcott, J. B. Moderate relationships between NAA and cognitive ability in healthy adults: implications for cognitive spectroscopy. Front. Hum. Neurosci. 2014, 8, 39,  DOI: 10.3389/fnhum.2014.00039
      51
      Moderate relationships between NAA and cognitive ability in healthy adults: implications for cognitive spectroscopy
      Patel, Tulpesh; Blyth, Jacqueline C.; Griffiths, Gareth; Kelly, Deirdre; Talcott, Joel B.
      Frontiers in Human Neuroscience (2014), 8 (), 39/1-39/10, 10CODEN: FHNRAI; ISSN:1662-5161. (Frontiers Media S.A.)
      Background: Proton Magnetic Resonance Spectroscopy (1H-MRS)is a non-invasive imaging technique that enables quantification of neurochem. in vivo and thereby facilitates investigation of the biochem. underpinnings of human cognitive variability. Studies in the field of cognitive spectroscopy have commonly focused on relationships between measures of N-acetyl aspartate (NAA), a surrogate marker of neuronal health and function, and broad measures of cognitive performance, such as IQ. Methodol./Principal Findings: In this study, we used 1H-MRS to interrogate single-voxels in occipitoparietal and frontal cortex, in parallel with assessments of psychometric intelligence, in a sample of 40 healthy adult participants. We found correlations between NAA and IQ that were within the range reported in previous studies. However, the magnitude of these effects was significantly modulated by the stringency of data screening and the extent to which outlying values contributed to statistical analyses. Conclusions/Significance:1H-MRS offers a sensitive tool for assessing neurochem. non-invasively, yet the relationships between brain metabolites and broad aspects of human behavior such as IQ are subtle. We highlight the need to develop an increasingly rigorous anal. and interpretive framework for collecting and reporting data obtained from cognitive spectroscopy studies of this kind.
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    51. 51
      Davies, G.; Lam, M.; Harris, S. E.; Trampush, J. W.; Luciano, M.; Hill, W. D. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat. Commun. 2018, 9, 2098,  DOI: 10.1038/s41467-018-04362-x
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      Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function
      Davies Gail; Harris Sarah E; Luciano Michelle; Hill W David; Hagenaars Saskia P; Ritchie Stuart J; Marioni Riccardo E; Fawns-Ritchie Chloe; Liewald David C M; Okely Judith A; Corley Janie; Cox Simon R; Porteous David J; Starr John M; McIntosh Andrew M; Gale Catharine R; Deary Ian J; Lam Max; DeRosse Pamela; Harris Sarah E; Marioni Riccardo E; Porteous David J; Trampush Joey W; Trampush Joey W; Hagenaars Saskia P; Ahola-Olli Ari V; Rovio Suvi P; Raitakari Olli T; Ahola-Olli Ari V; Barnes Catriona L K; Campbell Harry; Joshi Peter K; Rudan Igor; Wilson James F; Bertram Lars; Liu Tian; Bis Joshua C; Psaty Bruce M; Burdick Katherine E; Roussos Panos; Burdick Katherine E; Burdick Katherine E; Christoforou Andrea; Djurovic Srdjan; Giddaluru Sudheer; Melle Ingrid; Le Hellard Stephanie; Steen Vidar M; Christoforou Andrea; Giddaluru Sudheer; Le Hellard Stephanie; Steen Vidar M; DeRosse Pamela; Yu Jin; Malhotra Anil; Lencz Todd; Djurovic Srdjan; Espeseth Thomas; Melle Ingrid; Andreassen Ole A; Sundet Kjetil; Espeseth Thomas; Sundet Kjetil; Reinvang Ivar; Giakoumaki Stella; Gustavson Daniel E; Dale Anders M; Kremen William S; Gustavson Daniel E; Kremen William S; Hayward Caroline; Vitart Veronique; Wilson James F; Hayward Caroline; Murray Alison D; Porteous David J; Hofer Edith; Petrovic Katja; Koini Marisa; Schmidt Reinhold; Hofer Edith; Ikram M Arfan; Terzikhan Natalie; van der Lee Sven J; Amin Najaf; Uitterlinden Andre; Wen K Hoyan; Adams Hieab H H; Ikram M Kamran; Van Duijn Cornelia M; Ikram M Arfan; Ikram M Arfan; Ikram M Kamran; Karlsson Robert; Karlsson Ida; Hagg Sara; Pedersen Nancy L; Knowles Emma; Glahn David C; Lahti Jari; Raikkonen Katri; Lahti Jari; Leber Markus; Ramirez Alfredo; Li Shuo; Yang Qiong; Mather Karen A; Thalamuthu Anbupalam; Assareh Amelia A; Brodaty Henry; Kochan Nicole A; Lee Teresa; Reppermund Simone; Sachdev Perminder S; Trollor Julian N; Morris Derek; Donohoe Gary; Oldmeadow Christopher; Palviainen Teemu; Loukola Anu; Kaprio Jaakko; Widen Elisabeth; Vuoksimaa Eero; Palotie Aarno; Payton Antony; Pazoki Raha; Dehghan Abbas; Evangelou Evangelos; Gao He; Tzoulaki Ioanna; Elliott Paul; Reynolds Chandra A; Sargurupremraj Muralidharan; Soumare Aicha; Tzourio Christophe; Debette Stephanie; Scholz Markus; Scholz Markus; Luck Tobias; Smith Jennifer A; Kardia Sharon L R; Zhao Wei; Smith Jennifer A; Ware Erin B; Faul Jessica D; Weir David R; Smith Albert V; Gudnason Vilmundur; Smith Albert V; Gudnason Vilmundur; Terzikhan Natalie; Trompet Stella; Noordam Raymond; Windham B Gwen; Mosley Thomas H Jr; Wright Margaret J; Hansell Narelle K; Wright Margaret J; Yang Jingyun; Bennett David A; Boyle Patricia A; Yang Jingyun; Bennett David A; Ames David; Ames David; Amouyel Philippe; Andreassen Ole A; Armstrong Nicola J; Attia John R; Holliday Elizabeth G; Scott Rodney J; Attix Deborah; Chiba-Falek Ornit; Attix Deborah; Avramopoulos Dimitrios; Avramopoulos Dimitrios; Arking Dan E; Bohmer Anne C; Ramirez Alfredo; Bohmer Anne C; Boyle Patricia A; Brodaty Henry; Cannon Tyrone D; Cirulli Elizabeth T; Congdon Eliza; Freimer Nelson A; London Edythe; Bilder Robert M; Conley Emily Drabant; Dale Anders M; Dale Anders M; Dale Anders M; Dehghan Abbas; Tzoulaki Ioanna; Elliott Paul; Dick Danielle; Dickinson Dwight; Eriksson Johan G; Evangelou Evangelos; Kaprio Jaakko; Eriksson Johan G; Eriksson Johan G; Eriksson Johan G; Ford Ian; Giegling Ina; Hartmann Annette M; Konte Bettina; Dan Rujescu; Gillespie Nathan A; Gordon Scott D; Martin Nicholas G; Montgomery Grant W; Gottesman Rebecca F; Gottesman Rebecca F; Griswold Michael E; Simino Jeannette; Harris Tamara B; Launer Lenore; Hatzimanolis Alex; Smyrnis Nikolaos; Stefanis Nikos C; Hatzimanolis Alex; Smyrnis Nikolaos; Stefanis Nikos C; Hatzimanolis Alex; Stefanis Nikos C; Heiss Gerardo; Kahonen Mika; Kahonen Mika; Kahonen Mika; Kleineidam Luca; Kleineidam Luca; Wagner Holger; Ramirez Alfredo; Kleineidam Luca; Wagner Michael; Kleineidam Luca; Wagner Michael; Knopman David S; Kochan Nicole A; Lee Teresa; Sachdev Perminder S; Kwok John B; Kwok John B; Lehtimaki Terho; Lyytikainen Leo-Pekka; Lehtimaki Terho; Lyytikainen Leo-Pekka; Li Shu-Chen; Liu Tian; Papenberg Goran; Lindenberger Ulman; Li Shu-Chen; Lill Christina M; Longstreth Will T Jr; Longstreth Will T Jr; Fitzpatrick Annette L; Lopez Oscar L; Luck Tobias; Riedel-Heller Steffi G; Lundervold Astri J; Lundervold Astri J; Lundquist Anders; Nyberg Lars; Lundquist Anders; Montgomery Grant W; Murray Alison D; Need Anna C; Nyberg Lars; Nyberg Lars; Ollier William; Papenberg Goran; Pattie Alison; Taylor Adele M; Polasek Ozren; Polasek Ozren; Poldrack Russell A; Psaty Bruce M; Psaty Bruce M; Reppermund Simone; Trollor Julian N; Rose Richard J; Rotter Jerome I; Taylor Kent D; Rotter Jerome I; Taylor Kent D; Roussos Panos; Roussos Panos; Saba Yasaman; Schmidt Helena; Sabb Fred W; Seshadri Sudha; Satizabal Claudia L; Seshadri Sudha; Satizabal Claudia L; Schmid Matthias; Weinhold Leonie; Scult Matthew A; Hariri Ahmad R; Slagboom P Eline; Stott David J; Straub Richard E; Weinberger Daniel R; Tzoulaki Ioanna; Tzourio Christophe; Uitterlinden Andre; Voineskos Aristotle N; Kaprio Jaakko; Vuoksimaa Eero; Adams Hieab H H; Bitsios Panos; Boerwinkle Eric; Bressler Jan; Boerwinkle Eric; Corvin Aiden; Gill Michael; De Jager Philip L; De Jager Philip L; Debette Stephanie; Fitzpatrick Annette L; Jukema J Wouter; Keller Matthew C; Palotie Aarno; Palotie Aarno; Pendleton Neil; Raitakari Olli T; Schofield Peter W; Schofield Peter R; Schofield Peter R; Starr John M; Turner Steven T; Villringer Arno; Villringer Arno; Malhotra Anil; Malhotra Anil; McIntosh Andrew M; Lencz Todd; Gale Catharine R; Seshadri Sudha
      Nature communications (2018), 9 (1), 2098 ISSN:.
      General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16-102) and find 148 genome-wide significant independent loci (P < 5 × 10(-8)) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.
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    52. 52
      Lee, J. J.; Wedow, R.; Okbay, A.; Kong, E.; Maghzian, O.; Zacher, M. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 2018, 50, 1112– 1121,  DOI: 10.1038/s41588-018-0147-3
      53
      Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals
      Lee, James J.; Wedow, Robbee; Okbay, Aysu; Kong, Edward; Maghzian, Omeed; Zacher, Meghan; Nguyen-Viet, Tuan Anh; Bowers, Peter; Sidorenko, Julia; Karlsson Linner, Richard; Fontana, Mark Alan; Kundu, Tushar; Lee, Chanwook; Li, Hui; Li, Ruoxi; Royer, Rebecca; Timshel, Pascal N.; Walters, Raymond K.; Willoughby, Emily A.; Yengo, Loic; 23andMe Research Team; Cogent; Social Science Genetic Association Consortium; Alver, Maris; Bao, Yanchun; Clark, David W.; Day, Felix R.; Furlotte, Nicholas A.; Joshi, Peter K.; Kemper, Kathryn E.; Kleinman, Aaron; Langenberg, Claudia; Magi, Reedik; Trampush, Joey W.; Verma, Shefali Setia; Wu, Yang; Lam, Max; Zhao, Jing Hua; Zheng, Zhili; Boardman, Jason D.; Campbell, Harry; Freese, Jeremy; Harris, Kathleen Mullan; Hayward, Caroline; Herd, Pamela; Kumari, Meena; Lencz, Todd; Luan, Jian'an; Malhotra, Anil K.; Metspalu, Andres; Milani, Lili; Ong, Ken K.; Perry, John R. B.; Porteous, David J.; Ritchie, Marylyn D.; Smart, Melissa C.; Smith, Blair H.; Tung, Joyce Y.; Wareham, Nicholas J.; Wilson, James F.; Beauchamp, Jonathan P.; Conley, Dalton C.; Esko, Tonu; Lehrer, Steven F.; Magnusson, Patrik K. E.; Oskarsson, Sven; Pers, Tune H.; Robinson, Matthew R.; Thom, Kevin; Watson, Chelsea; Chabris, Christopher F.; Meyer, Michelle N.; Laibson, David I.; Yang, Jian; Johannesson, Magnus; Koellinger, Philipp D.; Turley, Patrick; Visscher, Peter M.; Benjamin, Daniel J.; Cesarini, David
      Nature Genetics (2018), 50 (8), 1112-1121CODEN: NGENEC; ISSN:1061-4036. (Nature Research)
      Here we conducted a large-scale genetic assocn. anal. of educational attainment in a sample of approx. 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a sep. anal. of the X chromosome, we identify 10 independent genome-wide-significant SNPs and est. a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) anal. of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.
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    53. 53
      Hearn, T. ALMS1 and Alström syndrome: a recessive form of metabolic, neurosensory and cardiac deficits. J. Mol Med. 2019, 97, 1– 7,  DOI: 10.1007/s00109-018-1714-x
      54
      ALMS1 and Alstrom syndrome: a recessive form of metabolic, neurosensory and cardiac deficits
      Hearn, Tom
      Journal of Molecular Medicine (Heidelberg, Germany) (2019), 97 (1), 1-17CODEN: JMLME8; ISSN:0946-2716. (Springer)
      A review. Alstrom syndrome (AS) is characterised by metabolic deficits, retinal dystrophy, sensorineural hearing loss, dilated cardiomyopathy and multi-organ fibrosis. Elucidating the function of the mutated gene, ALMS1, is crit. for the development of specific treatments and may uncover pathways relevant to a range of other disorders including common forms of obesity and type 2 diabetes. Interest in ALMS1 is heightened by the recent discovery of its involvement in neonatal cardiomyocyte cell cycle arrest, a process with potential relevance to regenerative medicine. ALMS1 encodes a ∼ 0.5 megadalton protein that localises to the base of centrioles. Some studies have suggested a role for this protein in maintaining centriole-nucleated sensory organelles termed primary cilia, and AS is now considered to belong to the growing class of human genetic disorders linked to ciliary dysfunction (ciliopathies). However, mechanistic details are lacking, and recent studies have implicated ALMS1 in several processes including endosomal trafficking, actin organization, maintenance of centrosome cohesion and transcription. In line with a more complex picture, multiple isoforms of the protein likely exist and non-centrosomal sites of localisation have been reported. This review outlines the evidence for both ciliary and extra-ciliary functions of ALMS1.
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  • Supporting Information

    Supporting Information

    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.1c00585.

    • Figure S1: Principal component analysis of baseline and follow-up metabolomics data; Figure S2: Overview of RNA-Seq read count and quantifiable genes in 555 individuals; Figure S3: Scatter plots of removed associations; Figure S4: Scatter plots of 20 study-wide significant metabolome feature - gene expression associations; Figure S5: Metabomatching figure showing the pseudospectrum derived from ALMS1 gene expression - metabolome features associations; Figure S6: Metabomatching figure showing the pseudospectrum derived from ALMS1P gene expression - metabolome features associations; Figure S7: NMR profiles of 3 different spike-in experiments; Figure S8: Metabomatching figure showing the pseudospectrum derived from APIP gene expression - metabolome feature associations; Figure S9: Metabomatching figure showing the pseudospectrum derived from SNW1 gene expression - metabolome feature associations; Table S1: Validation of all associations discovered in CoLaus baseline; Table S2: MR results of testing causal effect of ALMS1 gene expression levels on N-acetylated compounds, using R-squared threshold of 0.05; Table S3: MR results of testing causal effect of HPS1 gene expression levels on trimethylamine, using R-squared threshold of 0.05 (PDF)


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