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