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Accurate and fast multiple-testing correction in eQTL studies.

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.

journal_name

Am J Hum Genet

journal_title

American journal of human genetics

authors

Sul JH,Raj T,de Jong S,de Bakker PI,Raychaudhuri S,Ophoff RA,Stranger BE,Eskin E,Han B

doi

10.1016/j.ajhg.2015.04.012

subject

Has Abstract

pub_date

2015-06-04 00:00:00

pages

857-68

issue

6

eissn

0002-9297

issn

1537-6605

pii

S0002-9297(15)00149-4

journal_volume

96

pub_type

杂志文章

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