eQTL Mapping via Effective SNP Ranking and Screening

TL;DR

This paper introduces a novel eQTL mapping method that enhances detection power for gene-variant associations by employing advanced ranking and screening techniques, reducing computational costs and improving detection of trans-eQTLs.

Contribution

It proposes a new SNP ranking and screening strategy based on higher criticism to improve eQTL detection power and computational efficiency.

Findings

Superior detection of true eQTLs in simulations

Reduced computational expense compared to existing methods

Effective identification of trans-eQTLs in real data

Abstract

Genome-wide eQTL mapping explores the relationship between gene expression values and DNA variants to understand genetic causes of human disease. Due to the large number of genes and DNA variants that need to be assessed simultaneously, current methods for eQTL mapping often suffer from low detection power, especially for identifying trans-eQTLs. In this paper, we propose a new method that utilizes advanced techniques in large-scale signal detection to pursue the structure of eQTL data and improve the power for eQTL mapping. The new method greatly reduces the burden of joint modeling by developing a new ranking and screening strategy based on the higher criticism statistic. Numerical results in simulation studies demonstrate the superior performance of our method in detecting true eQTLs with reduced computational expense. The proposed method is also evaluated in HapMap eQTL data analysis and the results are compared to a database of known eQTLs.