Identification, Replication, and Functional Fine-Mapping of Expression Quantitative Trait Loci in Primary Human Liver Tissue

Abstract

The discovery of expression quantitative trait loci (“eQTLs”) can help to unravel genetic contributions to complex traits. We identified genetic determinants of human liver gene expression variation using two independent collections of primary tissue profiled with Agilent (n = 206) and Illumina (n = 60) expression arrays and Illumina SNP genotyping (550K), and we also incorporated data from a published study (n = 266). We found that ∼30% of SNP-expression correlations in one study failed to replicate in either of the others, even at thresholds yielding high reproducibility in simulations, and we quantified numerous factors affecting reproducibility. Our data suggest that drug exposure, clinical descriptors, and unknown factors associated with tissue ascertainment and analysis have substantial effects on gene expression and that controlling for hidden confounding variables significantly increases replication rate. Furthermore, we found that reproducible eQTL SNPs were heavily enriched near gene starts and ends, and subsequently resequenced the promoters and 3′UTRs for 14 genes and tested the identified haplotypes using luciferase assays. For three genes, significant haplotype-specific in vitro functional differences correlated directly with expression levels, suggesting that many bona fide eQTLs result from functional variants that can be mechanistically isolated in a high-throughput fashion. Finally, given our study design, we were able to discover and validate hundreds of liver eQTLs. Many of these relate directly to complex traits for which liver-specific analyses are likely to be relevant, and we identified dozens of potential connections with disease-associated loci. These included previously characterized eQTL contributors to diabetes, drug response, and lipid levels, and they suggest novel candidates such as a role for NOD2 expression in leprosy risk and C2orf43 in prostate cancer. In general, the work presented here will be valuable for future efforts to precisely identify and functionally characterize genetic contributions to a variety of complex traits.

Genetic effects on gene expression across human tissues

“…13a; 84% in whole blood, 87% in subcutaneous adipose, 94% in lymphoblastoid cell lines (LCLs), and 93% in sun-protected skin). trans -eQTLs have not replicated consistently in human studies, compared to cis -eQTLs 21,35–37 , owing in part to insufficient statistical power and a limited number of studies with comparable tissues and cohorts, but also reflecting potential false positive associations. We tested trans -eQTLs discovered at 10% FDR in GTEx for replication in the TwinsUK data.…”

Mice lacking lipid droplet-associated hydrolase, a gene linked to human prostate cancer, have normal cholesterol ester metabolism

“…A SNP in the LDAH gene associated with reduced mRNA expression is also associated with an increased risk for prostate cancer (9,10,13,14). Prostate cancer is one of the cancer types known to upregulate lipid metabolism, and CE accumulation in LDs In summary, our data suggest that SNPs in LDAH affect prostate cancer risk through a mechanism other than CE hydrolysis activity and that LDAH has an alternative LDassociated metabolic function.…”

“…The SNP rs13385191 in intron 6 of LDAH is associated with increased prostate cancer risk (9)(10)(11)(12). A rare A>G variant is associated with a difference in LDAH mRNA abundance, and prostate cancer risk is inversely correlated with its expression (13,14). In addition, rs13385191 is associated with nonfatal outcome of prostate cancer (9,12).…”

66 Clinical validity of new genetic biomarkers of irinotecan neutropenia: An independent replication study

“…UGT1A1*93 is a − 3156G>A change discovered during a resequencing study of the region 5′ to the UGT1A exon 1. 31 According to an analysis of more than 150 human livers where genome-wide genotyping data were available, UGT1A1*93 is a major determinant of decreased levels of the UGT1A1 protein (Pearson's r = − 0.46, P = 3.5 × 10 −9 ), 30,32 and additional preliminary data corroborate these findings. 33 Because UGT1A1*93 is in partial linkage disequilibrium with UGT1A1*28 among White patients (r 2 = 0.68), 34 our results suggest that UGT1A1*93, based on its greater estimate of effect for ANC nadir, may be a more robust marker for neutropenia than UGT1A1*28 ( Table 2).…”