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Comparison of association methods for dense marker data.

Silviu-Alin Bacanu1, Matthew R Nelson, Margaret G Ehm

  • 1GlaxoSmithKline, Research Triangle Park, North Carolina 27709, USA. Silviu-alin.a.bacanu@gsk.com

Genetic Epidemiology
|June 14, 2008
PubMed
Summary
This summary is machine-generated.

Simulating high linkage disequilibrium (LD) markers and rare variants is crucial for genome scans. This study evaluates statistical test performance, recommending false discovery rate (FDR) adjustments for optimal power in genetic association studies.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Computational biology

Background:

  • Genome-wide association studies (GWAS) increasingly utilize dense marker data.
  • Theoretical challenges persist in simulating high linkage disequilibrium (LD) and rare variants.
  • Efficient analysis methods for correlated markers and adjustments for multiple testing are needed.

Purpose of the Study:

  • To address theoretical questions in dense genome scan analysis.
  • To investigate the performance of single-point, multipoint, and hybrid statistical tests.
  • To evaluate methods for simulating markers in high LD and rare disease variants.

Main Methods:

  • Utilized two simulation designs: one with theoretically derived LD patterns and another with real data-based LD patterns.
  • Employed polychoric correlation for simulating markers in LD and rare variants in theoretical simulations.
  • Compared the power of various statistical tests, including single-point, multipoint, and hybrid approaches, with different adjustment methods.

Main Results:

  • Statistical tests assuming only additive genotype effects showed suboptimal power in certain scenarios.
  • A false discovery rate (FDR)-adjusted combination of tests for additive, dominant, and recessive effects demonstrated near-optimal power.
  • The standard genotypic chi(2) test performed adequately, and a proposed "exact" multiple testing adjustment method offered higher power than Bonferroni correction.

Conclusions:

  • Additive-only tests should be used cautiously in dense genome scans.
  • FDR-adjusted tests or the genotypic chi(2) test are recommended for robust association analysis.
  • Hybrid methods can increase power but are computationally demanding; an "exact" adjustment method is proposed for improved power over Bonferroni.