comparison of strategies to detect epistasis from eqtl data比较的策略从eqtl数据检测上位.pdfVIP

Comparison of Strategies to Detect Epistasis from eQTL Data 1,2 ¨ 2,3 2,3 ´ 1,2. 1,2. Karen Kapur *, Thierry Schupbach , Ioannis Xenarios , Zoltan Kutalik , Sven Bergmann 1 Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland, 2 Swiss Institute of Bioinformatics, Lausanne, Switzerland, 3 Vital IT Group, Swiss Institute of Bioinformatics, Lausanne, Switzerland Abstract Genome-wide association studies have been instrumental in identifying genetic variants associated with complex traits such as human disease or gene expression phenotypes. It has been proposed that extending existing analysis methods by considering interactions between pairs of loci may uncover additional genetic effects. However, the large number of possible two-marker tests presents significant computational and statistical challenges. Although several strategies to detect epistasis effects have been proposed and tested for specific phenotypes, so far there has been no systematic attempt to compare their performance using real data. We made use of thousands of gene expression traits from linkage and eQTL studies, to compare the performance of different strategies. We found that using information from marginal associations between markers and phenotypes to detect epistatic effects yielded a lower false discovery rate (FDR) than a strategy solely using biological annotation in yeast, whereas results from human data were inconclusive. For future studies whose aim is to discover epistatic effects, we recommend incorporating information about marginal associations between SNPs and phenotypes instead of relying