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Related Experiment Video

Updated: May 25, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Rapid and robust resampling-based multiple-testing correction with application in a genome-wide expression

Xiang Zhang1, Shunping Huang, Wei Sun

  • 1Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio 44106, USA.

Genetics
|February 3, 2012
PubMed
Summary

We developed REM, a novel algorithm for accurate and efficient multiple testing correction in genome-wide expression quantitative trait loci (eQTL) studies. REM significantly reduces computational cost without compromising P-value accuracy.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide expression quantitative trait loci (eQTL) studies are crucial for understanding the genetic basis of complex traits.
  • Multiple testing correction is a significant computational challenge in large-scale eQTL studies due to numerous genetic markers and expression traits.
  • Existing computationally efficient methods for P-value approximation rely on potentially invalid assumptions about genetic marker correlations.

Purpose of the Study:

  • To address the computational challenges of multiple testing correction in eQTL studies.
  • To develop a novel algorithm for calculating exact resampling-based P-values efficiently.
  • To provide a method that does not depend on assumptions about genetic marker correlation structures.

Main Methods:

  • Proposed a novel algorithm named Rapid and Exact Multiple testing correction by Resampling (REM).
  • REM prunes the search space by skipping genetic markers with small upper bounds on test statistics.
  • The algorithm is applicable to various resampling-based methods, including permutation and bootstrap procedures.

Main Results:

  • REM calculates exact resampling-based P-values with significantly reduced computational cost compared to existing methods.
  • The algorithm was validated on yeast, inbred mouse, and human rare variant eQTL datasets.
  • REM demonstrated accurate P-value estimation across diverse datasets.

Conclusions:

  • REM offers a computationally efficient and accurate solution for multiple testing correction in eQTL studies.
  • The method's independence from correlation structure assumptions makes it broadly applicable.
  • The REM software is publicly available for use in genetic research.