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Am J Hum Genet. 2015 Jun 4;96(6):857-68. doi: 10.1016/j.ajhg.2015.04.012. Epub 2015 May 28.

Accurate and fast multiple-testing correction in eQTL studies.

Author information

1
Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Harvard University, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
2
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Harvard University, Boston, MA 02115, USA; Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA.
3
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.
4
Departments of Epidemiology and Medical Genetics, University Medical Center Utrecht, Utrecht 3584 CG, the Netherlands.
5
Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Harvard University, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Arthritis Research UK Epidemiology Unit, Musculoskeletal Research Group, University of Manchester, Manchester Academic Health Sciences Centre, Manchester M13 9PT, UK; Division of Rheumatology, Brigham and Women's Hospital, Harvard Medical School, Harvard University, Boston, MA 02115, USA.
6
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; Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht 3584 CG, the Netherlands; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
7
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA; Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA.
8
Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Computer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: eeskin@cs.ucla.edu.
9
Asan Institute for Life Sciences, Asan Medical Center, Seoul 138-736, Republic of Korea; Department of Medicine, University of Ulsan College of Medicine, Seoul 138-736, Republic of Korea. Electronic address: buhm.han@amc.seoul.kr.

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.

PMID:
26027500
PMCID:
PMC4457958
DOI:
10.1016/j.ajhg.2015.04.012
[Indexed for MEDLINE]
Free PMC Article

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