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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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A unified set-based test with adaptive filtering for gene-environment interaction analyses.

Qianying Liu1, Lin S Chen2, Dan L Nicolae3

  • 1Sanofi, Cambridge, Massachusetts 02139, U.S.A.

Biometrics
|October 27, 2015
PubMed
Summary

This study introduces a unified set-based test for gene-environment interaction (GxE) analysis, optimizing filtering thresholds for improved statistical power in genome-wide studies. The method enhances the discovery of complex genetic interactions by adaptively selecting significant single nucleotide polymorphisms (SNPs).

Keywords:
A unified testAdaptive filteringGene-environment interactionsGenome-wide interaction studiesSet-based (or gene-based) test

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Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide gene-environment interaction (GxE) studies commonly use filtering to enhance statistical power.
  • Existing methods often filter single nucleotide polymorphisms (SNPs) before analyzing GxE.
  • There is a need for methods that jointly consider filtering and set-based testing for GxE.

Purpose of the Study:

  • To propose a unified set-based test for GxE analysis that incorporates filtering information.
  • To develop an adaptive method for determining optimal filtering thresholds.
  • To improve the power of detecting GxE in genome-wide studies.

Main Methods:

  • Developed a unified set-based test statistic that accounts for filtering on individual parameters.
  • Derived the exact distribution and approximated the power function to determine optimal filtering thresholds.
  • Proposed a resampling algorithm for calculating P-values based on estimated optimal thresholds.

Main Results:

  • The optimal filtering threshold in gene-based GxE analysis is primarily dependent on gene size.
  • The unified test effectively integrates filtering and set-based testing.
  • Simulation studies demonstrated the method's performance and power.

Conclusions:

  • The proposed unified set-based test provides an adaptive and powerful approach for GxE analysis.
  • The method is effective in identifying significant GxE signals, particularly in gene-based analyses.
  • Applied to pancreatic cancer data, the method facilitated a genome-wide gene-gender interaction analysis.