The identification of cis-acting regulatory variation in primary tissues has the potential to elucidate the genetic basis of complex traits and further an understanding of transcriptomic diversity across cell types. Expression quantitative trait locus (eQTL) association analysis using RNA sequencing (RNA-seq) data can improve upon the detection of cis-acting regulatory variation by leveraging allele-specific expression (ASE) patterns in association analysis.
Mayo Clinic researchers, first author Nicholas Larson, M.S., Ph.D., conducted a comprehensive evaluation of cis-acting eQTLs by analyzing RNA-seq gene-expression data and genome-wide high-density genotypes. Published in the American Journal of Human Genetics, the study examines the properties of detected cis-eQTLs within the context of genes preferentially expressed in prostate tissue, as well as their relation to prostate-specific regulatory elements.
The study examined 471 samples of normal primary prostate tissue. Using statistical models that integrate ASE information, Mayo Clinic researchers identified extensive cis-eQTLs across the prostate transcriptome and found that approximately 70 percent of expressed genes corresponded to a significant eQTL at a gene-level false-discovery rate of 0.05.
Overall, cis-eQTLs were heavily concentrated near the transcription start and stop sites of affected genes, and effects were negatively correlated with distance. Multiple instances of cis-acting co-regulation were identified by using phased genotype data and 233 SNPs were discovered as the most strongly associated eQTLs for more than one gene. Researchers also noted significant enrichment of previously reported prostate cancer risk SNPs in prostate eQTLs.
These results indicate the benefit of assessing ASE data in cis-eQTL analyses by showing better reproducibility of prior eQTL findings than of eQTL mapping based on total expression alone. Further, these results will be valuable in guiding studies examining disease of the human prostate.