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QRank: A novel quantile regression tool for eQTL discovery

Banff International Research Station for Mathematical Innovation and Discovery

Over the past decade, there has been a remarkable improvement in our understanding of the role of genetic variation in complex human diseases, especially via genome-wide association studies. However, the underlying molecular mechanisms are still poorly characterized, impending the development of therapeutic interventions. Identifying genetic variants that influence the expression level of a gene, i.e. expression quantitative trait loci (eQTLs), can help us understand how genetic variants influence traits at the molecular level. While most eQTL studies focus on identifying mean effects on gene expression using linear regression, evidence suggests that genetic variation can impact the entire distribution of the expression level. Motivated by the potential in higher order associations, several studies investigated variance eQTLs. In this paper, we develop a Quantile Rank-score based test (QRank), which provides an easy way to identify eQTLs that are associated with the conditional quantile functions of gene expression. We have applied the proposed QRank to the Genotype-Tissue Expression (GTEx) project, and found that the proposed method complements the existing methods, and identifies new eQTLs with heterogeneous effects across different quantile levels. Notably, we show that the eQTLs identified by QRank but missed by linear regression are associated with greater enrichment in genome-wide significant SNPs from the GWAS catalog, and are also more likely to be tissue specific than those identified by linear regression.

Mathematics; Statistics; Biology and other natural sciences

video/mp4

Author affiliation: Columbia University

2017-09-24

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/63094

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/