Abstract </head> <body text="#000000" bgcolor="#FFFFFF" link="#0000EF" vlink="#CF30CF" alink="#FF0000" nosave> <br> <center><h2> Cp Considering Dependency among Genes and Markers for False discovery Control in eQTL Mapping </h2> <br> <h2> Liang Chen </h2> <h3> (Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA) </h3> </center> <br> "Genetical genomics" searches for genetic loci associated with gene expression variations in a study population. Such genetic loci are called expression Quantitative Trait Loci (eQTL). Although genetical genomics is a promising approach in many fields, the analysis of eQTL data involving thousands of genes and thousands of markers presents many statistical and computational challenges. Here, we focus on the multiple testing problem. Besides the dependence among markers, the dependence among genes can also have significant impact on data analysis and interpretation. However, the dependency among genes is largely ignored in previous studies. We propose to consider both the mean as well as the variance of false discovery number for multiple comparison adjustment to handle dependence among hypotheses. This is achieved by developing a variance estimator for false discovery number, and using the upper bound of false discovery proportion (uFDP) for false discovery control. More importantly, we introduce a weighted version of uFDP (wuFDP) to improve the statistical power of eQTL identification. The relative performance of uFDP control and wuFDP control is illustrated through simulation studies and real data analysis. <br><br><br> <a href="index.html">Back to main page</a><br><br> <hr> <p><br><br> </body> </html>