  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.
