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Related Experiment Video

Updated: Oct 7, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Rare variant association tests for ancestry-matched case-control data based on conditional logistic regression.

Shanshan Cheng1, Jingjing Lyu1, Xian Shi1

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, P.R. China.

Briefings in Bioinformatics
|January 12, 2022
PubMed
Summary

Conditional logistic regression (CLR) improves disease association studies by controlling population stratification. CLR-based tests offer robust error control and increased power for discovering disease-associated genes using diverse case-control ratios.

Keywords:
common controlsconditional logistic regressionmatched analysispopulation stratificationrare variant association tests

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Computational Biology

Background:

  • Human sequencing data is rapidly expanding, necessitating cost-effective methods like external controls for disease association studies.
  • Controlling for population stratification via ancestry matching is crucial to prevent spurious genetic associations.
  • Standard logistic regression models for rare variant association tests can be unreliable with varying case-control ratios across strata.

Purpose of the Study:

  • To develop novel statistical tests for disease association studies that effectively control for population stratification.
  • To enhance the power and reliability of rare variant association tests, particularly when using external controls with matched ancestry.
  • To address the limitations of standard logistic regression models in handling diverse case-control ratios across ancestry strata.

Main Methods:

  • Proposed conditional logistic regression (CLR) based tests: CLR-Burden, CLR-SKAT, and CLR-MiST.
  • Utilized ancestry matching within the CLR framework to control for population stratification.
  • Conducted extensive simulation studies to evaluate type 1 error rates and statistical power.

Main Results:

  • CLR-based tests demonstrated robust control of type 1 errors across various matching schemes.
  • The proposed CLR tests were more powerful than standard Burden, SKAT, and MiST tests.
  • CLR tests accommodate varying case-control ratios, enabling full-matching schemes for efficient data utilization.

Conclusions:

  • The CLR model combined with ancestry matching provides a general and effective strategy for controlling population stratification in disease association studies.
  • CLR-based tests offer superior statistical power and reliable error control compared to existing methods.
  • These methods facilitate accelerated discovery of disease-associated genes by maximizing the use of available case and control data.