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Genome-wide eQTL mapping explores the relationship between gene expressionvalues and DNA variants to understand genetic causes of human disease. Due tothe large number of genes and DNA variants that need to be assessedsimultaneously, current methods for eQTL mapping often suffer from lowdetection power, especially for identifying trans-eQTLs. In this paper, wepropose a new method that utilizes advanced techniques in large-scale signaldetection to pursue the structure of eQTL data and improve the power for eQTLmapping. The new method greatly reduces the burden of joint modeling bydeveloping a new ranking and screening strategy based on the higher criticismstatistic. Numerical results in simulation studies demonstrate the superiorperformance of our method in detecting true eQTLs with reduced computationalexpense. The proposed method is also evaluated in HapMap eQTL data analysis andthe results are compared to a database of known eQTLs.

eQTL Mapping via Effective SNP Ranking and Screening