Title | Rapid and robust resampling-based multiple-testing correction with application in a genome-wide expression quantitative trait loci study. |
Publication Type | Journal Article |
Year of Publication | 2012 |
Authors | Zhang, Xiang, Shunping Huang, Wei Sun, and Wei Wang |
Journal | Genetics |
Volume | 190 |
Issue | 4 |
Pagination | 1511-20 |
Date Published | 2012 Apr |
ISSN | 1943-2631 |
Keywords | Algorithms, Animals, Computational Biology, Gene Expression Profiling, Genetic Markers, Genome, Human, Genotype, Humans, Hypothalamus, Mice, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Software, Yeasts |
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. |
DOI | 10.1534/genetics.111.137737 |
Alternate Journal | Genetics |
Original Publication | Rapid and robust resampling-based multiple-testing correction with application in a genome-wide expression quantitative trait loci study. |
PubMed ID | 22298711 |
PubMed Central ID | PMC3316660 |
Grant List | MH090338 / MH / NIMH NIH HHS / United States U01 CA134240 / CA / NCI NIH HHS / United States U01CA105417 / CA / NCI NIH HHS / United States U01CA134240 / CA / NCI NIH HHS / United States R01 GM074175 / GM / NIGMS NIH HHS / United States P50 MH090338 / MH / NIMH NIH HHS / United States U01 CA105417 / CA / NCI NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States |
Rapid and robust resampling-based multiple-testing correction with application in a genome-wide expression quantitative trait loci study.
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