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Quantile-specific confounding: correction for subtle population stratification via quantile regression.

Chen Wang1, Marco Masala2, Edoardo Fiorillo2

  • 1Department of Biostatistics, Columbia University, New York, NY 10027, United States.

Genetics
|July 21, 2025
PubMed
Summary
This summary is machine-generated.

Quantile regression offers improved correction for subtle population structure in genome-wide association studies. This method better adjusts for principal components, enhancing genetic analyses using human height data.

Keywords:
GWASpopulation stratificationquantile regressionquantile specific confounding

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Subtle population structure is a persistent challenge in genome-wide association studies (GWAS).
  • Accurate control for population stratification is crucial for reliable genetic association findings.
  • Existing methods may not fully capture complex population effects.

Purpose of the Study:

  • To demonstrate the utility of quantile regression for correcting population structure in GWAS.
  • To evaluate the performance of quantile regression compared to traditional methods using human height as a model trait.
  • To improve the accuracy of genetic association analyses by accounting for quantile-specific covariate effects.

Main Methods:

  • Application of quantile regression, an extension of linear regression, to GWAS data.
  • Utilizing principal components as covariates to adjust for population structure.
  • Analysis of human height data from large-scale biobanks (UK Biobank, SardiNIA/ProgeNIA).

Main Results:

  • Quantile regression effectively corrects for subtle population structure.
  • The method's ability to adjust for quantile-specific effects enhances population structure correction.
  • Demonstrated improved performance in analyzing human height GWAS data.

Conclusions:

  • Quantile regression provides a robust approach for addressing population structure in GWAS.
  • This statistical method offers a valuable tool for enhancing the precision of genetic association studies.
  • The findings support the broader application of quantile regression in human genetics research.