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 linkagedisequilibrium 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.

Internações Por Condições Sensíveis À Atenção Primária À Saúde Decorrentes Das Doenças Cardiovasculares

“…[24][25] The registration and follow-up of hypertensive patients in the ESF, in different teams and municipalities can vary from 50%-97%. 14 In Mato Grosso do Sul, a greater homogeneity in the findings was found, with follow-ups of 30 to 49.8% of the total number of patients. Differently from the situation of the set of 66 municipalities in Santa Catarina, among them, more than 60% do not have a register for patients with hypertension and diabetes, and in up to 83% of the actions do not have an impact on the disease.…”

Applying meta-analysis to Genotype-Tissue Expression data from multiple tissues to identify eQTLs and increase the number of eGenes

“…The eGene p-value depends on two important factors: the number of variants |V |, and the LD of the variants. In practice, α pgt is computed by doing a permutation test (Sul et al, 2015;Duong et al, 2016). In brief, in the k th permutation, one would permute the gene expressions among the individuals, and compute a new p…”

“…The first layer of multiple testing correction is applied within a tissue and corrects for LD of the variants tested against the gene. This correction can be done by using the permutation test to compute the eGene p-value for the gene in the tissue (Sul et al, 2013(Sul et al, , 2015The GTEx Consortium, 2015;Duong et al, 2016).…”

“…To determine if p g is significant, one needs to compute its eGene pvalue denoted as α pg (The GTEx Consortium, 2015). To control for multiple testing when LD of the variants exists, one can compute α pg using a permutation test (The GTEx Consortium, 2015;Sul et al, 2015;Duong et al, 2016). The permutation test creates a distribution of the observed p g under the null hypothesis.…”

Power, false discovery rate and Winner’s Curse in eQTL studies

“…Thousands of permutations are required to achieve accurate results, thus there is a high computational cost. Approximations have been investigated for calculating permutation pvalues, such as those in FastQTL 20 and MVN 21 . For example, FastQTL provides an option to approximate the tail of the empirical null distributions of p-values using a beta distribution thereby reducing the number of permutations required 20 .…”