Accurate and fast multiple-testing correction in eQTL studies
Sul, Jae Hoon; Raj, Towfique; de Jong, Simone; de Bakker, Paul I W; Raychaudhuri, Soumya; Ophoff, Roel A; Stranger, Barbara E; Eskin, Eleazar; Han, Buhm
(2015) American Journal of Human Genetics, volume 96, issue 6, pp. 857 - 68
(Article)
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
... read more
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.
show less
Download/Full Text
The full text of this publication is not available.
Keywords: Data Interpretation, Statistical, Gene Expression Regulation, Genes, Genetic Variation, Humans, Multivariate Analysis, Normal Distribution, Polymorphism, Single Nucleotide, Probability, Quantitative Trait Loci, Sample Size, Statistics, Nonparametric, Evaluation Studies, Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S.
ISSN: 0002-9297
Publisher: Cell Press
Note: Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
(Peer reviewed)