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Adjusting for population stratification and relatedness with sequencing data.

Yiwei Zhang1, Wei Pan1

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN 55455, USA.

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|December 19, 2014
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Summary

This study compares methods for genetic association studies, evaluating principal component and linear mixed models using common or rare variants for population stratification adjustment.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Population stratification and relatedness can inflate Type I errors and reduce power in genetic association studies.
  • Accurate adjustment is crucial for reliable genetic findings.
  • Genome-wide sequencing data offers opportunities to refine these adjustments.

Purpose of the Study:

  • To compare the performance of principal component-based and linear mixed models for adjusting genetic association studies.
  • To investigate the utility of common versus rare variants in these adjustment methods.
  • To evaluate these approaches for both quantitative and binary traits.

Main Methods:

  • Utilized Genetic Analysis Workshop 18 data.
  • Employed principal component analysis for population stratification adjustment.
  • Applied linear mixed models for relatedness adjustment.
  • Compared the use of common variants versus rare variants.

Main Results:

  • Performance differences between principal component and linear mixed models were observed.
  • The choice between common and rare variants impacted adjustment effectiveness.
  • Results varied between the quantitative and binary traits analyzed.

Conclusions:

  • Both principal component and linear mixed models are viable for addressing population structure in genetic studies.
  • The selection of genetic variants (common vs. rare) is an important consideration for optimal adjustment.
  • Further research is warranted to refine these methods for diverse genetic data and trait types.