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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Exploiting gene-environment independence in haplotype-based inferences for population-based case-control studies with

Lingxiao Wang1, Daoying Lin2, Yan Li1

  • 1The Joint Program in Survey Methodology, University of Maryland, College Park, Maryland.

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|November 21, 2019
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Summary

New statistical methods improve haplotype association studies using complex sampling. These methods accurately account for design complexities, enhancing genetic association analysis in population studies.

Keywords:
case-control studiesgene-environment interactionshaplotypemultistage clustered samplingretrospective maximum likelihood

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Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Complex sampling designs are increasingly used in population-based case-control studies.
  • Haplotype-based genetic association studies often overlook complex sampling, potentially biasing results.
  • Existing methods may not adequately address design complications in haplotype frequency and regression coefficient estimation.

Purpose of the Study:

  • To develop novel statistical methods for haplotype-based association studies that incorporate complex sampling.
  • To evaluate the performance of these new methods under various complex sampling scenarios.
  • To assess the impact of Hardy-Weinberg equilibrium and gene-environment independence assumptions.

Main Methods:

  • Developed innovative one-step and two-step statistical approaches for haplotype association analysis.
  • Employed simulation studies to compare proposed methods against existing ones.
  • Investigated the influence of retrospective sampling assumptions.

Main Results:

  • The proposed methods demonstrated superior performance compared to existing approaches across diverse complex sampling designs.
  • Simulation results confirmed the effectiveness of the new statistical techniques.
  • The methods were successfully applied to a real-world kidney cancer case-control study.

Conclusions:

  • The developed statistical methods effectively account for complex sampling in haplotype-based association studies.
  • These methods offer improved accuracy and reliability for genetic association analyses in complex population samples.
  • The findings have implications for future genetic epidemiological research utilizing complex sampling strategies.