Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits - PubMed (original) (raw)

. 2019 Oct 3;105(4):773-787.

doi: 10.1016/j.ajhg.2019.09.001. Epub 2019 Sep 26.

Arthur Ko 2, John C Kidd 3, Kevin W Currin 1, Sarah M Brotman 1, Maren E Cannon 1, Ying Wu 1, Cassandra N Spracklen 1, Anne U Jackson 4, Heather M Stringham 4, Ryan P Welch 4, Christian Fuchsberger 5, Adam E Locke 6, Narisu Narisu 7, Aldons J Lusis 8, Mete Civelek 9, Terrence S Furey 10, Johanna Kuusisto 11, Francis S Collins 7, Michael Boehnke 4, Laura J Scott 4, Dan-Yu Lin 3, Michael I Love 12, Markku Laakso 11, Päivi Pajukanta 13, Karen L Mohlke 14

Affiliations

Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits

Chelsea K Raulerson et al. Am J Hum Genet. 2019.

Abstract

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic traits including type 2 diabetes (T2D), lipid levels, body fat distribution, and adiposity, although most causal genes remain unknown. We used subcutaneous adipose tissue RNA-seq data from 434 Finnish men from the METSIM study to identify 9,687 primary and 2,785 secondary cis-expression quantitative trait loci (eQTL; <1 Mb from TSS, FDR < 1%). Compared to primary eQTL signals, secondary eQTL signals were located further from transcription start sites, had smaller effect sizes, and were less enriched in adipose tissue regulatory elements compared to primary signals. Among 2,843 cardiometabolic GWAS signals, 262 colocalized by LD and conditional analysis with 318 transcripts as primary and conditionally distinct secondary cis-eQTLs, including some across ancestries. Of cardiometabolic traits examined for adipose tissue eQTL colocalizations, waist-hip ratio (WHR) and circulating lipid traits had the highest percentage of colocalized eQTLs (15% and 14%, respectively). Among alleles associated with increased cardiometabolic GWAS risk, approximately half (53%) were associated with decreased gene expression level. Mediation analyses of colocalized genes and cardiometabolic traits within the 434 individuals provided further evidence that gene expression influences variant-trait associations. These results identify hundreds of candidate genes that may act in adipose tissue to influence cardiometabolic traits.

Keywords: GWAS; adipocyte; colocalization; diabetes; eQTL; lipid; obesity; secondary signal; trans-ancestry; waist-hip ratio.

Copyright © 2019 American Society of Human Genetics. All rights reserved.

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Conflict of interest statement

M.E.C. is a current employee of Invitae, Corp. and Y.W. is a current employee of Pfizer, Inc.

Figures

Figure 1

Figure 1

Comparison of Primary and Secondary eQTL Association Signals (A) For each type of transcript (x-axis), the number of transcripts associated with at least one nearby variant that met the genome-wide FDR <1% is shown (y-axis). (B) Density plot showing the distance from the lead eQTL variant to the TSS. Dashed lines represent the median distance to the TSS for primary (purple) and secondary (green) eQTLs. (C) Enrichment of primary and secondary eQTLs in chromatin marks from Roadmap adipose nuclei chromatin marks and open chromatin ATAC-seq peaks from adipose tissue. The horizontal lines represent the logistic regression coefficient for enrichment with vertical lines representing the 95% confidence interval around the coefficient. (D) Absolute value of the effect sizes of eQTL variants that overlap Roadmap adipose nuclei promoters and enhancers. Black horizontal lines represent the median effect size for primary and secondary eQTL variants.

Figure 2

Figure 2

Cardiometabolic GWAS and eQTL Colocalizations, Stratified by Trait Each point represents the lead GWAS variant of a signal colocalized by LD and conditional analysis with an eQTL for the named gene. Plots show eQTLs at GWAS loci for (A) T2D, (B) WHRadjBMI, and (C) lipids. The x-axis shows chromosomal positions of colocalized signals and the y-axis shows the −log10 p values of the GWAS variant’s association with gene expression level in adipose tissue. After matching the effect allele from the eQTL study with the risk allele from the GWAS, risk alleles that are associated with increased gene expression level are shown in red and risk alleles that are associated with decreased gene expression level are shown in blue. Triangles indicate that more than one gene is colocalized with the GWAS variant (other genes would appear at the same x-axis position). Variants colocalized as secondary eQTLs are designated by a star after the gene name. Only the strongest associations for the named traits are shown; full results can be found in Table S9.

Figure 3

Figure 3

A WHRadjBMI Signal Colocalizes with the Secondary eQTL for DGKQ Locus plots for the DGKQ locus, colored by two distinct signals present in the METSIM eQTL data. The lead GWAS variant associated with WHRadjBMI at this locus, rs11724804 (top), is associated with DGKQ expression but is not in LD with the variant most strongly associated with DGKQ expression, rs11731377 (eSNP; middle). After conditional analysis, a second eQTL signal for DGKQ is apparent and is colocalized with the lead GWAS variant (bottom). Variants are colored by the strength of their LD with the lead variant for each signal (diamond), with darker colors indicating stronger LD.

Figure 4

Figure 4

Cross-ancestry Comparison of Colocalized GWAS-eQTL Signals Locus plots showing the regional associations for BMI in Japanese individuals, colored by East Asian LD (EAS) (top) and gene expression in Finns (bottom), colored by Finnish LD. (A) A DAP3 eQTL signal is colocalized with a BMI signal near GON4L. The lead variant is shared between the GWAS and eQTL studies and the patterns of association are similar. (B) A SULT1A2 eQTL signal is colocalized with a BMI signal near IL27/NUPR1. The LD between the lead eQTL and GWAS variants is low in East Asians (r2 = 0.22) but high in Finns (r2 = 0.93).

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