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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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A powerful statistical framework for generalization testing in GWAS, with application to the HCHS/SOL.

Tamar Sofer1, Ruth Heller2, Marina Bogomolov3

  • 1Department of Biostatistics, University of Washington, Seattle, WA, USA.

Genetic Epidemiology
|January 17, 2017
PubMed
Summary

New methods improve genome-wide association studies (GWAS) by formally testing genotype-phenotype association generalization across ancestries. This approach enhances power and controls statistical error rates for more reliable genetic discoveries.

Keywords:
multiple testingone-sided P-valuesshared genetics

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify genotype-phenotype associations.
  • Generalization in GWAS refers to replicating associations in populations of different ancestry.
  • Current generalization methods lack robust statistical error control and optimal power.

Purpose of the Study:

  • To develop a formal statistical framework for quantifying evidence of genotype-phenotype association generalization across diverse ancestries.
  • To introduce methods that control generalization-specific error rates (FWERg and FDRg) and leverage two-stage GWAS designs.
  • To improve the power and reliability of detecting generalized genetic associations.

Main Methods:

  • Developed directional generalization FWER (FWERg) and FDR (FDRg) controlling r-values.
  • Proposed a statistical framework accounting for association direction consistency between discovery and follow-up studies.
  • Applied methods to published Single Nucleotide Polymorphism-(SNP)-trait associations and tested SNP selection thresholds.

Main Results:

  • The new framework controls FWERg or FDRg under various SNP selection rules.
  • Using a more lenient P-value threshold in the discovery GWAS often increases power for generalization.
  • In a total cholesterol GWAS, testing SNPs with P<6.6x10-5 generalized more associations (27 regions) than using the genome-wide significance threshold (15 regions).

Conclusions:

  • The proposed statistical framework provides robust control over generalization error rates in GWAS.
  • Leveraging two-stage designs with optimized P-value thresholds enhances the power to detect generalized genetic associations.
  • These methods offer a more rigorous and powerful approach for validating genetic findings across diverse populations.