QRank: a novel quantile regression tool for eQTL discovery.
BACKGROUND
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 higher order associations, several studies investigated variance eQTLs.


RESULTS
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 project, an international tissue bank for studying the relationship between genetic variation and gene expression in human tissues, and found that the proposed QRank 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 eQTLs identified by linear regression.


METHODS
An R package is available on R CRAN at https://cran.r-project.org/web/packages/QRank .


BACKGROUND
xs2148@cumc.columbia.edu.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Heilbrunn Department of Population & Family Health, Columbia University, New York, NY, USA.
Xiaoyu
X
Song
Department of Biostatistics, Columbia University, New York, NY, USA.
Gen
G
Li
Department of Biostatistics, Columbia University, New York, NY, USA.
Zhenwei
Z
Zhou
Department of Biostatistics, Columbia University, New York, NY, USA.
Xianling
X
Wang
Department of Biostatistics, Columbia University, New York, NY, USA.
Iuliana
I
Ionita-Laza
Department of Biostatistics, Columbia University, New York, NY, USA.
Ying
Y
Wei
D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D014644:Genetic Variation; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D040641:Quantitative Trait Loci; D012984:Software