Zhang X, et al. (2012) Rapid and robust resampling-based multiple-testing correction with application in a genome-wide expression quantitative trait loci study. Genetics 190(4):1511-20
Abstract: Genome-wide expression quantitative trait loci (eQTL) studies have emerged as a powerful tool to understand the genetic basis of gene expression and complex traits. In a typical eQTL study, the huge number of genetic markers and expression traits and their complicated correlations present a challenging multiple-testing correction problem. The resampling-based test using permutation or bootstrap procedures is a standard approach to address the multiple-testing problem in eQTL studies. A brute force application of the resampling-based test to large-scale eQTL data sets is often computationally infeasible. Several computationally efficient methods have been proposed to calculate approximate resampling-based P-values. However, these methods rely on certain assumptions about the correlation structure of the genetic markers, which may not be valid for certain studies. We propose a novel algorithm, rapid and exact multiple testing correction by resampling (REM), to address this challenge. REM calculates the exact resampling-based P-values in a computationally efficient manner. The computational advantage of REM lies in its strategy of pruning the search space by skipping genetic markers whose upper bounds on test statistics are small. REM does not rely on any assumption about the correlation structure of the genetic markers. It can be applied to a variety of resampling-based multiple-testing correction methods including permutation and bootstrap methods. We evaluate REM on three eQTL data sets (yeast, inbred mouse, and human rare variants) and show that it achieves accurate resampling-based P-value estimation with much less computational cost than existing methods. The software is available at http://csbio.unc.edu/eQTL.
|Status: Published||Type: Journal Article||PubMed ID: 22298711|
Topics addressed in this paper
- To find other papers on a gene and topic, click on the colored ball in the appropriate box.
- displays other papers with information about that topic for that gene.
- displays other papers in SGD that are associated with that topic.
The topic is addressed in these papers but does not describe a specific gene or chromosomal feature.
- To go to the Locus page for a gene, click on the gene name.