Liver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits

Abstract

Understanding the molecular mechanisms of complex traits is essential for developing targeted interventions. We analyzed liver expression quantitative-trait locus (eQTL) meta-analysis data on 1,183 participants to identify conditionally distinct signals. We found 9,013 eQTL signals for 6,564 genes; 23% of eGenes had two signals, and 6% had three or more signals. We then integrated the eQTL results with data from 29 cardiometabolic genome-wide association study (GWAS) traits and identified 1,582 GWAS-eQTL colocalizations for 747 eGenes. Non-primary eQTL signals accounted for 17% of all colocalizations. Isolating signals by conditional analysis prior to coloc resulted in 37% more colocalizations than using marginal eQTL and GWAS data, highlighting the importance of signal isolation. Isolating signals also led to stronger evidence of colocalization: among 343 eQTL-GWAS signal pairs in multi-signal regions, analyses that isolated the signals of interest resulted in higher posterior probability of colocalization for 41% of tests. Leveraging allelic heterogeneity, we predicted causal effects of gene expression on liver traits for four genes. To predict functional variants and regulatory elements, we colocalized eQTL with liver chromatin accessibility QTL (caQTL) and found 391 colocalizations, including 73 with non-primary eQTL signals and 60 eQTL signals that colocalized with both a caQTL and a GWAS signal. Finally, we used publicly available massively parallel reporter assays in HepG2 to highlight 14 eQTL signals that include at least one expression-modulating variant. This multi-faceted approach to unraveling the genetic underpinnings of liver-related traits could lead to therapeutic development.

Keywords: GWAS; allelic heterogeneity; colocalization; complex traits; eQTL meta-analysis; liver; signal identification.

Figure 1

Primary, secondary, and tertiary+ signals detected in liver eQTL (A) Study design. (B) Of 6,564 eGenes, 1,514 (23%) had two signals, and 417 (6%) had three or more signals. A total of 9,013 conditionally distinct signals were identified. (C) Median absolute beta (effect size) by distance from TSS. Uncommon variants (MAF < 10%) have the largest effect sizes; common variants have more modest effect sizes. For all MAF groups, lead variants that are farther from the TSS have smaller absolute betas. (D–F) Violin plots of absolute beta, MAF, and distance from TSS. Among genes with three or more signals, the signal order is as follows: (D) Negatively correlated with absolute beta; (E) inversely correlated with MAF; and (F) positively correlated with distance from the TSS. The y axis on panels (D) and (F) is restricted for increased legibility of figures; for the full y axis, see Figure S3.The box plots within the violin plots show the median value (center line), upper and lower quartiles (box) and 1.5× interquartile range (whiskers).

Figure 2

eQTL-GWAS colocalization is common in non-primary eQTL signals (A) LocusZoom for eQTL eGene HSPA4. APEX identified two signals in low LD with each other (r² = 0.25): the primary signal, rs62375251, is shown in red, and the secondary signal, lead variant rs72801474, is shown in blue. (B) The primary signal of HSPA4 (red highlight) colocalized with WHR and WHRadjBMI, and the secondary signal of HSPA4 (blue highlight) colocalized with ALP, DBP, HDL, and logTG. All GWAS signals are primary signals with no other signal within 500 kb. (C) LocusZoom plots for WHRadjBMI and the isolated APEX summary results for the primary eQTL signal, colored by the eQTL lead variant rs62375251. (D) LocusZoom plots for ALP and the isolated APEX eQTL summary results for the secondary signal, colored by the eQTL lead variant rs72801474.

Figure 3

Role of signal isolation in colocalization (A) Posterior probability of colocalization (coloc PP.H4) as determined with marginal summary data vs. conditional summary data isolating the signal of interest for 343 eQTL-GWAS signal pairs with LD r² ≥ 0.9 between lead variants that colocalized in at least one analysis (PP.H4 ≥ 0.7) and for which isolated conditional files were generated for the GWAS data. Stronger colors indicate greater relative accuracy with which the reported GWAS lead variant was captured in conditional data (red) vs. marginal data (blue). (B) LocusZoom plots showing marginal data for CRP (top) and PELO eQTL (bottom), colored by the GWAS lead variant. Arrows denote the location of ancillary signals. Although both GWAS and eQTL signals are primary signals with strong LD between their lead variants (r² = 0.97), coloc using the marginal data failed to detect a colocalization (PP.H4 = 0.05). (C) In contrast, using conditional data, coloc detected strong evidence of colocalization (PP.H4 = 0.99).

Figure 4

Evidence of causal relationships between gene expression and traits Each point represents a colocalized eQTL signal, and error bars indicate standard error. Solid blue line: overall slope of gene-on-trait effect. Dotted lines: 80% credible interval of slope. Shaded blue: the dispersion of allelic effects around the main gene-on-trait effect. (A) TTC39B eQTL signals colocalized with TC signals; the TTC39B-increasing alleles were associated with lower TC. (B) LITAF signals colocalized with LDL signals; the LITAF-increasing alleles were associated with higher LDL. (C) PXK eQTL signal colocalized with nonHDL signals; the PXK-increasing alleles were associated with lower nonHDL. (D) LIPC eQTL signals colocalized with logTG. The LIPC-lowering alleles were associated with lower logTG.

Figure 5

Liver eQTL signals colocalize with liver caQTL signals (A) Among 391 eQTL-caQTL colocalizations, 61 were with secondary eQTL signals, and 12 were with tertiary+ signals. (B) LocusZoom plot for the STOX2 eQTL signals, plotting marginal data, colored by the eQTL lead variants. (C) LocusZoom plots of summary results isolating the primary colocalized eQTL signal and the colocalized caQTL for caPeak 161507. (D) LocusZoom plots of summary results isolating the secondary signal and the colocalized caQTL for caPeak 161509. (E) Left: The C allele of caPeak 161507 lead variant rs4862298 is associated with higher chromatin accessibility and higher STOX2 expression. Right: Selected ATAC-seq signal tracks are shown for each caQTL genotype of rs4862298. The eQTL lead variant (blue) is located in the caPeak and a region of vertebrate conservation; no LD proxies (r² ≥ 0.8, gold) are located in the caPeak. (F) Left: caQTL lead variant rs17679120 is an LD proxy for the eQTL secondary signal lead variant (rs12501526, r² = 0.92); the rs17679120-G allele is associated with higher chromatin accessibility and higher STOX2 expression. Right: Selected ATAC-seq signal tracks are shown for each caQTL genotype of rs17679120. caQTL lead variant rs17679120 and eQTL lead variant rs12501526 are shown in blue. LD proxies rs12499260 and rs12499263 are located in the caPeak (gold, r² = 0.98 with eQTL lead rs12501526).