Abstract
Complete characterization of the genetic effects on gene expression is needed to elucidate tissue biology and the etiology of complex traits. In the present study, we analyzed 2,344 subcutaneous adipose tissue samples and identified 34,774 conditionally distinct expression quantitative trait locus (eQTL) signals at 18,476 genes. Over half of eQTL genes exhibited at least two eQTL signals. Compared with primary eQTL signals, nonprimary eQTL signals had lower effect sizes, lower minor allele frequencies and less promoter enrichment; they corresponded to genes with higher heritability and higher tolerance for loss of function. Colocalization of eQTLs with genome-wide association study (GWAS) signals for 28 cardiometabolic traits identified 1,835 genes. Inclusion of nonprimary eQTL signals increased discovery of colocalized GWAS–eQTL signals by 46%. Furthermore, 21 genes with ≥2 colocalized GWAS–eQTL signals showed a mediating gene dosage effect on the GWAS trait. Thus, expanded eQTL identification reveals more mechanisms underlying complex traits and improves understanding of the complexity of gene expression regulation.
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Data availability
The AdipoExpress meta-analysis results are available via Zenodo at https://zenodo.org/records/13845120 (ref. 87). Results include full marginal eQTL summary statistics for all individuals and only European-ancestry individuals, men and women, along with the conditional AB1 eQTL summary statistics for each signal. Colocalized GWAS–eQTL signals can be downloaded from Zenodo at https://zenodo.org/records/13845120 (ref. 87) and visualized at https://adipose.colocus.app. METSIM genotypes and gene expression data are available at dbGaP phs000743. FUSION genotypes and gene expression data are available at dbGaP phs001048. GTEx whole-genome sequencing and gene expression data are available at dbGaP phs000424.v8.p2. TwinsUK RNA-seq data are available in the European Genome–phenome Archive under accession EGAS00001000805. TwinsUK genotypes are available upon application to the TwinsUK Resource Executive Committee. For information on how to apply, see https://twinsuk.ac.uk/resources-for-researchers/access-our-data. GWAS datasets were accessed according to the links and publications listed in Supplementary Table 18. The following databases and datasets were used to perform analyses for this manuscript: GENCODE v.19 (https://www.gencodegenes.org/human/release_19.html); Cis-BP v.2.00 (http://cisbp2.ccbr.utoronto.ca/index.php); Human Protein Atlas (proteinatlas.org); UKBB (application 25953; http://www.ukbiobank.ac.uk); STARNET (PMID: 27540175); eQTLGen (http://www.eqtlgen.org); gnomAD (https://gnomad.broadinstitute.org/downloads#v2); NIH Roadmap Epigenomics chromatin states (https://egg2.wustl.edu/roadmap/web_portal/chr_state_learning.html); and ATAC peaks (PMID: 34699533). Source data are provided with this paper.
Code availability
Analyses described in this manuscript were conducted using the following freely available software: Fastx-toolkit v.0.0.14; Cutadapt v.1.18; STAR v.2.4.0.1, v.2.4.2a and v.2.7.3a; vcftools v.0.1.15; QTLtools v.1.1; QoRTs v.1.3.6; R v.4.0.3, v.4.1.0 and v.4.1.3; edgeR v.3.36.0; ggplot2 v.3.4.0; PEER v.1.3; APEX v.0.2; apex2R; PLINK v.1.90b3; METAL v.2011-03-25; LocusZoom v.1.4; SusieR v.0.12.35; GCTA v.1.94.1; coloc v.5.1.0.1; swiss v.1.1.1; bedtools v.2.3.0; SMR v.1.3.1; MRLocus v.0.0.26; and GARFIELD v.2.
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Acknowledgements
We thank the METSIM, FUSION and TwinsUK study investigators and participants for providing the subcutaneous adipose tissue samples and genotypes that made the present study possible. We thank E. Hill-Burns for analyses performed in revision. The present study was supported by the following NIH grants: R01DK093757, R01DK072193 and U01DK105561 (to K.L.M.), R01DK132775 (to P.P., K.L.M. and L.J.S.), R01DK062370 (to M.B. and L.J.S.), R01HL170604 and R01HG010505 (to P.P.), R01HG009976 (to M.B.), UM1DK126185 (to K.L.M. and S.C.J.P.), ZIAHG000024 (to F.S.C.), F31HL154730 and T32GM007092 (to S.M.B.), F31HL146121 (to K.W.C.) and T32HL129982 (to K.A.B.). The present study was also supported by opportunity pool funds from the Accelerating Medical Partnerships Type 2 Diabetes consortium (grant U01DK105554/UM1DK105554 to K.L.M. and K.S.S.), the Academy of Finland (grant 321428 to M.L.), Sigrid Juselius Foundation, Finnish Foundation for Cardiovascular Research and Centre of Excellence of Cardiovascular and Metabolic Diseases (grant 0245896-3 to M.L.), Medical Research Council (grants MR/M004422/1 and MR/R023131/1 to K.S.S.), National Institute for Health Research (NIHR) Biomedical Research Centre (to M.T. and Y.R.), King’s China Scholarship Council PhD scholarship (to X.Y. and D.W.) and the University of North Carolina Global Partnership Initiative (to S.M.B.). TwinsUK is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation, Zoe and the NIHR Clinical Research Network and Biomedical Research Centre based at the Guy’s and St Thomas’s NHS Foundation Trust in partnership with King’s College London. This project utilized the King’s Computational Research, Engineering and Technology Environment88. This research has been conducted using the UKBB resource under application 25953. The GTEx Project was supported by the Common Fund of the Office of the Director of the NIH (commonfund.nih.gov/GTEx). Additional funds were provided by the National Cancer Institute, National Human Genome Research Institute, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Institute of Mental Health and National Institute of Neurological Disorders and Stroke. Donors were enrolled at biospecimen source sites funded by NCI/Leidos Biomedical Research subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171) and Science Care (X10S172). The Laboratory, Data Analysis, and Coordinating Center was funded through a contract (HHSN268201000029C) to the Broad Institute. Biorepository operations were funded through a Leidos Biomedical Research subcontract to Van Andel Research Institute (10ST1035). Additional data repository and project management were provided by Leidos Biomedical Research (HHSN261200800001E). The Brain Bank supported supplements to University of Miami grant DA006227. Statistical methods development grants were made to the University of Geneva (grants MH090941 and MH101814), the University of Chicago (grants MH090951, MH090937, MH101825 and MH101820), the University of North Carolina—Chapel Hill (grant MH090936), North Carolina State University (grant MH101819), Harvard University (grant MH090948), Stanford University (grant MH101782), Washington University (grant MH101810) and the University of Pennsylvania (grant MH101822). The datasets used for the analyses described in the present study were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession phs000424.v8.p2.
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S.M.B., J.S.E.-S.M., L.G., K.L.M., K.S.S. and L.J.S. conceived and designed the study. S.M.B., J.S.E.-S.M. and L.G. generated data. S.M.B., J.S.E.-S.M., L.G., K.A.B., D.W., A.U.J., R.W., K.W.C., M.T., S.V. and M.I.L. performed analyses. H.M.S., A.L.R., T.A.L., A.O., L.F.S., N.N., M.R.E., T.Y., L.L.B., C.K.R., Y.R., X.Y., S.C.J.P., J.K., P.P., J.T., F.S.C., M.B., H.A.K. and M.L. provided resources. S.M.B., J.S.E.-S.M., L.G., K.L.M., K.S.S. and L.J.S. interpreted results and wrote the manuscript. All co-authors provided critical feedback and approved the manuscript.
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Extended data
Extended Data Fig. 1 Comparison of effect sizes in sex-stratified meta-analyses.
(A) Effect sizes of lead variants per gene identified in the female marginal eQTL meta-analysis looked up in the male marginal eQTL analysis. (B) Effect sizes of lead variants per gene identified in the male marginal eQTL meta-analysis looked up in the female marginal eQTL analysis. Each point is a variant-gene pair. The linear regression lines are shown in blue and the black diagonal line indicates slope = 1. R2 values are the Pearson correlations.
Extended Data Fig. 2 Overlap of adipose and STARNET eQTL signals.
Stacked bar charts show the proportion of significant adipose eQTL signals that overlapped with significant STARNET eQTL signals (FDR ≤ 0.05). Each bar shows eQTL signals from a different tissue in STARNET or at least one tissue (“Any tissue”) versus primary adipose eQTL signals. Gray indicates the proportion of primary adipose eQTL signals with an eQTL signal detected in STARNET (LD r2 ≥ 0.2), purple indicates the proportion of primary adipose eQTL signals with the same eQTL gene detected in STARNET but the signal differed (LD r2 < 0.2), and white indicates the primary adipose eQTL signals with a gene not reported in STARNET.
Extended Data Fig. 3 Overlap of adipose and blood eQTL signals.
Stacked bar charts show the proportion of significant adipose eQTL signals that overlapped with significant blood eQTL signals (FDR ≤ 0.05). The top bar shows all adipose eQTL signals, the middle bar shows only primary adipose eQTL signals, and the bottom bar shows non-primary adipose eQTL signals. Gray indicates the proportion of primary adipose eQTL signals with an eQTL signal detected in eQTLGen (LD r2 ≥ 0.2), purple indicates the proportion of primary adipose eQTL signals with the same eQTL gene detected in eQTLGen but the signal differed (LD r2 < 0.2), and white indicates the primary adipose eQTL signals with a gene not reported in eQTLGen.
Extended Data Fig. 4 Characteristics of eQTL signal variants.
Violin plots with inset boxplots of the (A) absolute value of the effect sizes of lead variants, (B) MAF, and (C) distance of the lead variants to the gene TSS for the indicated signals in order of discovery. The 5 sets of violin plots represent genes with exactly 1,2,3,4 or 5+ signals and include 9,148, 5,310, 2,309, 917, and 661 genes (or 1,209 signals for 5 + ), per set respectively. Boxplot center lines represent median values, limits represent upper and lower quartiles, whiskers represent 1.5x interquartile ranges, and black circles represent outliers. The black lines connect the median values of each signal group. In C, 163 points with a distance to TSS greater than 600 were excluded.
Extended Data Fig. 5 Heritability of genes with an eQTL grouped by gene expression.
(A) Boxplots of heritability in TwinsUK for genes with zero to five or more eQTL signals. Boxplots are split into subgroups based on the quartile of gene expression in TwinsUK. The number of genes with 0, 1, 2, 3, 4 and 5+ eQTL signals per expression quantile are Q1 (2,589, 1,819, 839, 288, 117, 87), Q2 (1,696, 1,830, 1,221, 590, 242, 159), Q3 (1,295, 1,995, 1,378, 635, 236, 199), and Q4 (1,160, 1,923, 1,465, 687, 295, 209), respectively. Lowly expressed genes are in quartile 1 shown in white, while highly expressed genes are in quartile 4 shown in darkest blue. Boxplot center lines represent the median value, box limits represent the upper and lower quartiles, whiskers represent the 1.5x interquartile range, and the black circles represent outliers. The black line connects the median of the boxplots. (B) Boxplots of heritability for genes separated by gene expression quartile. Numbers are the median heritability values. Boxplots as in A, with addition of the kernel density plots. Quartiles Q1-Q4 contain 5,738 or 5,739 genes.
Extended Data Fig. 6 pLI scores of eQTL genes with 1 through 5 or more signals.
(A) Proportion of genes with a pLI score ≥0.9 among all genes with a pLI score, by TwinUK gene expression quartile. Quartile 1 indicates the genes with the lowest expression and quartile 4 indicates genes with the highest expression. Expression quartiles were formed using all genes in TwinsUK, including those without pLI scores. The number of genes in each quartile (Q1-Q4) with a pLI score are 1,951, 3,230, 4,979, and 5,476 respectively. (B) Proportion of genes in TwinsUK with a given number of signals, by pLI score <0.9 or ≥0.9 and gene expression quartile. N genes with pLI <0.9 and pLI≥0.9 for Q1 (1,761 and 190), Q2 (2,906 and 324), Q3 (4,188 and 791), and Q4 (3,868 and 1,608). The darkest blue are the genes without an eQTL signal and the lightest blue are genes with five or more eQTL signals. For each expression level quartile, the proportion of genes with multiple signals was substantially lower for genes with pLI ≥0.9 than for genes with pLI <0.9: this trend was particularly pronounced in the highest expression category. (C) Proportion of genes in TwinsUK for each eQTL signal number with a pLI score ≥0.9. Each set of bar graphs is for a different gene expression quartile.
Extended Data Fig. 7 Proportion of eQTL signals colocalized with GWAS signals.
Per signal number proportion of eQTL signals colocalized with ≥ 1 GWAS signal. (A) all eQTL signals; (B) eQTL signals by quartiles of meta-analysis eQTL significance. Significance threshold for inclusion of eQTL in plots is P ≤ 1e-6. (A, B) Points are the proportion of eQTL signals colocalized with ≥1 GWAS signal; error bars are the standard errors of the proportion of signals colocalized. The total number of signals included in each plot and p-values for the one sample test of proportions (one-sided) are listed in Supplementary Table 23.
Extended Data Fig. 8 Mendelian randomization using MRLocus for select allelic series.
Plots of the effect size of the GWAS signals (y-axis) versus the effect size of the eQTL signals (x-axis) from MRLocus. These six eQTL gene-trait pairs have 2 or 3 pairs of GWAS and eQTL signals colocalized with each other. Each point represents the effect sizes of the colocalized GWAS-eQTL signals; x and y bars represent the standard errors of the eQTL and GWAS effect sizes, respectively. The solid blue lines represent the slope of the effect of the gene on the trait, and dotted blue lines represent an 80% credible interval on the slope.
Extended Data Fig. 9 WHRadjBMI GWAS and ZNRF3 eQTL conditional signal plots.
LocusZoom plots for WHRadjBMI signal 1 conditioned on signal 2 (top left) and WHRadjBMI signal 2 conditioned on signal 1 (bottom left). Plots for ZNRF3 signal 1 conditioned on signal 2 (top right) and ZNRF3 signal 2 conditioned on signal 1 (bottom right). The dots are colored by LD with the lead variant. Both plots are colored by the GWAS lead variant represented by a purple diamond. ‘AB1’ indicates all-but-one. Significance threshold for eQTLs is P ≤ 1e-6 and for GWAS is P ≤ 5e-8.
Extended Data Fig. 10 HDL-C GWAS and PDE3A eQTL conditional signal plots.
Left column, LocusZoom plot for HDL-C signal 1 conditioned on all the other signals, followed by plots for each signal conditioned on all other signals. Right column, plot for PDE3A signal 1 conditioned on all the other signals, followed by plots for signal 4 conditioned on all other signals, then signal 2 and signal 3. y-axes show the -log10 P-value after conditioning. The dots are colored by LD with the lead variant. Both plots are colored by the GWAS lead variant represented by a purple diamond. ‘AB1’ indicates all-but-one. Significance threshold for eQTLs is P ≤ 1e-6 and for GWAS is P ≤ 5e-8.
Supplementary information
Supplementary Information
Supplementary Methods, Supplementary Results, Supplementary Figs. 1–8 and Supplementary References.
Supplementary Tables
Supplementary Tables 1–34.
Supplementary Data 1
Table of read depth, number of genes tested and samples sizes of each eQTL study.
Supplementary Data 2
ADIPOQ counts per million for each individual in each study.
Supplementary Data 3
Table of number of eQTL genes, eQTL signals and sample size per study.
Supplementary Data 4
The eQTL variants and P values for each of the five studies and the meta-analysis (EUR) near the ADIPOQ locus.
Supplementary Data 5
Median gene expression levels in METSIM by meta-analysis signal number and including pLI score.
Supplementary Data 6
GARFIELD enrichment results by eQTL signal number.
Supplementary Data 7
Luciferase assay raw data for hWAT and SGBS cells.
Supplementary Data 8
Luciferase assay raw data for LHCN-M2 cells.
Source data
Source Data Fig. 1
Conditionally distinct eQTL signals for each of the five studies and the two meta-analyses.
Source Data Fig. 2
The eQTL variants and P values for each of the five studies and the meta-analysis (EUR) near the GLYCTK locus.
Source Data Fig. 3
The eQTL signals with TSS and MAF information. Heritability estimates and pLI scores for eQTL genes.
Source Data Fig. 4
The eQTL variants and P values for the male and female eQTL meta-analysis near the ADORA1 locus.
Source Data Fig. 5
ZNRF3 and PDE3A MRLocus results and eQTL variants and P values for the eQTL meta-analysis near the PDE3A and ZNRF3 loci.
Source Data Fig. 6
All-but-one eQTL variants and P values near the SEMA3C locus, promoter and enhancer enrichment, and luciferase assay raw data.
Source Data Extended Data Fig. 1
Male and female eQTL results.
Source Data Extended Data Fig. 2
Counts and percentages of adipose eQTL signals overlapping with STARNET eQTL signals.
Source Data Extended Data Fig. 3
Counts and percentages of adipose eQTL signals overlapping with eQTLGen eQTL signals.
Source Data Extended Data Fig. 4
The eQTL signals with TSS and MAF information.
Source Data Extended Data Fig. 5
Heritability estimates from TwinsUK by meta-analysis eQTL signal number.
Source Data Extended Data Fig. 6
Heritability estimates from TwinsUK by meta-analysis eQTL signal number for genes with pLI scores.
Source Data Extended Data Fig. 7
Proportion of eQTL signals colocalized with GWAS signals separated by signal number and strength of eQTL.
Source Data Extended Data Fig. 8
MRLocus results for six eQTL-GWAS allelic series.
Source Data Extended Data Fig. 9
All-but-one eQTL variants and P values near the ZNRF3 locus
Source Data Extended Data Fig. 10
All-but-one eQTL variants and P values near the PDE3A locus
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Brotman, S.M., El-Sayed Moustafa, J.S., Guan, L. et al. Adipose tissue eQTL meta-analysis highlights the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits. Nat Genet 57, 180–192 (2025). https://doi.org/10.1038/s41588-024-01982-6
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DOI: https://doi.org/10.1038/s41588-024-01982-6
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