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Related Concept Videos

Genetic Screens02:46

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Updated: Nov 7, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Score tests for scale effects, with application to genomic analysis.

David Soave1,2, Jerald F Lawless3, Philip Awadalla2,4

  • 1Department of Mathematics, Wilfrid Laurier University, Waterloo, Ontario, Canada.

Statistics in Medicine
|April 28, 2021
PubMed
Summary
This summary is machine-generated.

Score tests for scale effects in genetic association studies often fail to control errors. Permutation approximations offer robust type 1 error control across various distributions and covariate imbalances.

Keywords:
gene expressionheteroscedasticitylocation-scale modelsscale testvariance heterogeneity

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Score tests for variance or scale effects are crucial in genomic and genetic association studies.
  • Incorporating adjustment covariates in location-scale models is essential for accurate analysis.

Purpose of the Study:

  • To evaluate the performance of score tests for scale effects with arbitrary error distributions.
  • To investigate the type 1 error control of model-based asymptotic versus permutation distribution approximations.

Main Methods:

  • Score tests based on Gaussian and Laplacian double generalized linear models were examined.
  • Numerical properties were assessed under various error distributions and covariate imbalances.
  • Permutation distribution approximations were proposed and compared to asymptotic methods.

Main Results:

  • Model-based asymptotic distributions showed poor type 1 error control in relevant settings.
  • Permutation distribution approximations demonstrated good type 1 error control.
  • The methods were validated using differential gene expression analysis in breast cancer samples.

Conclusions:

  • Permutation approximations are recommended for robust type 1 error control in scale effect testing.
  • Score tests for scale effects require careful consideration of distribution assumptions.
  • The findings have implications for accurate genomic and genetic association studies.