 Identification, Replication, and Functional Fine-Mapping of Expression Quantitative Trait Loci in Primary Human Liver TissueFederico Innocenti, Gregory M Cooper, Ian B Stanaway, Eric R Gamazon, Joshua D Smith, Snezana Mirkov, Jacqueline Ramirez, Wanqing Liu, Yvonne S Lin, Cliona Moloney, Shelly Force Aldred, Nathan D Trinklein, Erin Schuetz, Deborah A Nickerson, Ken E Thummel, Mark J Rieder, Allan E Rettie, Mark J Ratain, Nancy J Cox and Christopher D Brown PLOS Genetics, 2011, vol. 7, issue 5, 1-16 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. Author Summary: Many disease-associated genetic variants do not alter protein sequences and are difficult to precisely identify. Discovery of expression quantitative trait loci (eQTL), or correlations between genetic variants and gene expression levels, offers one means of addressing this challenge. However, eQTL studies in primary cells have several shortcomings. In particular, their reproducibility is largely unknown, the variables that generate unreliable associations are uncharacterized, and the resolution of their findings is constrained by linkage disequilibrium. We performed a three-way replication study of eQTLs in primary human livers. We demonstrated that ∼67% of cis-eQTL associations are replicated in an independent study and that known polymorphisms overlapping expression probes, SNP-to-gene distance, and unmeasured confounding variables all influence the replication rate. We fine-mapped 14 eQTLs and identified causative polymorphisms in the promoter or 3′UTR for 3 genes, suggesting that a considerable fraction of eQTLs are driven by proximal variants that are amenable to functional isolation. Finally, we found hundreds of overlaps between SNPs associated with complex traits and replicated eQTL SNPs. Our data provide both cautionary (i.e. non-reproducibility of many strong eQTLs) and optimistic (i.e. precise identification of functional non-coding variants) forecasts for future eQTL analyses and the complex traits that they influence. Date: 2011 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pgen00:1002078 DOI: 10.1371/journal.pgen.1002078 Access Statistics for this article More articles in PLOS Genetics from Public Library of Science |
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