Accurate and Fast Multiple-Testing Correction in eQTL Studies |
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| Authors: | Jae Hoon Sul Towfique Raj Simone de Jong Paul IW de Bakker Soumya Raychaudhuri Roel A Ophoff Barbara E Stranger Eleazar Eskin Buhm Han |
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| Institution: | 1 Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Harvard University, Boston, MA 02115, USA;2 Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA;3 Harvard Medical School, Harvard University, Boston, MA 02115, USA;4 Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Department of Neurology, Brigham and Women’s Hospital, Boston, MA 02115, USA;5 Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA;6 Departments of Epidemiology and Medical Genetics, University Medical Center Utrecht, Utrecht 3584 CG, the Netherlands;7 Arthritis Research UK Epidemiology Unit, Musculoskeletal Research Group, University of Manchester, Manchester Academic Health Sciences Centre, Manchester M13 9PT, UK;8 Division of Rheumatology, Brigham and Women’s Hospital, Harvard Medical School, Harvard University, Boston, MA 02115, USA;9 Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht 3584 CG, the Netherlands;10 Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA;11 Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA;12 Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA;13 Computer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, USA;14 Asan Institute for Life Sciences, Asan Medical Center, Seoul 138-736, Republic of Korea;15 Department of Medicine, University of Ulsan College of Medicine, Seoul 138-736, Republic of Korea |
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| Abstract: | In studies of expression quantitative trait loci (eQTLs), it is of increasing interest to identify eGenes, the genes whose expression levels are associated with variation at a particular genetic variant. Detecting eGenes is important for follow-up analyses and prioritization because genes are the main entities in biological processes. To detect eGenes, one typically focuses on the genetic variant with the minimum p value among all variants in cis with a gene and corrects for multiple testing to obtain a gene-level p value. For performing multiple-testing correction, a permutation test is widely used. Because of growing sample sizes of eQTL studies, however, the permutation test has become a computational bottleneck in eQTL studies. In this paper, we propose an efficient approach for correcting for multiple testing and assess eGene p values by utilizing a multivariate normal distribution. Our approach properly takes into account the linkage-disequilibrium structure among variants, and its time complexity is independent of sample size. By applying our small-sample correction techniques, our method achieves high accuracy in both small and large studies. We have shown that our method consistently produces extremely accurate p values (accuracy > 98%) for three human eQTL datasets with different sample sizes and SNP densities: the Genotype-Tissue Expression pilot dataset, the multi-region brain dataset, and the HapMap 3 dataset. |
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