Abstract
Heritable variation in gene expression forms a crucial bridge between genomic variation and the biology of many traits. However, most expression quantitative trait loci (eQTLs) remain unidentified. We mapped eQTLs by transcriptome sequencing in 1012 yeast segregants. The resulting eQTLs accounted for over 70% of the heritability of mRNA levels, allowing comprehensive dissection of regulatory variation. Most genes had multiple eQTLs. Most expression variation arose from trans-acting eQTLs distant from their target genes. Nearly all trans-eQTLs clustered at 102 hotspot locations, some of which influenced the expression of thousands of genes. Fine-mapped hotspot regions were enriched for transcription factor genes. While most genes had a local eQTL, most of these had no detectable effects on the expression of other genes in trans. Hundreds of non-additive genetic interactions accounted for small fractions of expression variation. These results reveal the complexity of genetic influences on transcriptome variation in unprecedented depth and detail.
Highlights
Differences in gene expression among individuals arise in part from DNA sequence differences in regulatory elements and in regulatory genes
In order to avoid downward bias in the overlap between eQTLs and protein QTL (pQTL) caused by false negatives, we focused on strong QTLs in each dataset and asked if they overlapped a significant QTL in the other dataset (Materials and methods; see Supplementary Note 5 for results based on all distant QTLs)
The high power of our study allowed us to identify genome-wide significant eQTLs that jointly explain over 70% of gene expression heritability
Summary
Differences in gene expression among individuals arise in part from DNA sequence differences in regulatory elements and in regulatory genes. Regions of the genome that contain regulatory variants can be identified by tests of genetic linkage or association between mRNA levels and DNA polymorphisms in large collections of individuals. Regions for which such tests show statistical significance are known as eQTLs (Albert and Kruglyak, 2015). Regulatory variation is widespread in the species for which it has been studied; in humans, the expression of nearly every gene appears to be influenced by one or more eQTL (Aguet et al, 2017; Battle et al, 2014). Human GWAS must test a very large number of variants, resulting in a high multiple-testing burden and low statistical power.
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