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A unifying framework for robust association testing, estimation, and genetic model selection using the generalized

Christina Loley1, Inke R König, Ludwig Hothorn

  • 11] Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany [2] Medizinische Klinik II, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany.

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PubMed
Summary
This summary is machine-generated.

The MAX test offers a powerful alternative for analyzing genome-wide genetic association studies, outperforming standard methods by robustly handling various inheritance models. This statistical approach enhances genetic discovery across diverse study designs and trait types.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Published on: July 27, 2021

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide genetic association studies (GWAS) typically employ univariate tests for single-nucleotide polymorphisms (SNPs).
  • The standard Cochran-Armitage trend test can lack power for non-additive genetic models.
  • The MAX test provides a robust alternative for detecting associations across multiple inheritance models.

Purpose of the Study:

  • To derive the asymptotic distribution of the MAX test using generalized linear models and the Delta method.
  • To establish the applicability of the MAX test for various trait types and study designs.
  • To provide R code for implementing the MAX test and its model selection capabilities.

Main Methods:

  • Derivation of asymptotic distribution using generalized linear models and multiple contrasts.
  • Application to binary, quantitative, and survival traits in unrelated individuals, family-based studies, and matched pairs.
  • Monte-Carlo simulation to evaluate performance and compare with Pearson's χ(2)-test.

Main Results:

  • The asymptotic MAX test framework demonstrates good type I error control and power for minor allele frequencies (MAFs) ≥0.3.
  • Pearson's χ(2)-test is superior for lower MAFs with rare homozygous genotypes.
  • The MAX test framework provides reliable effect estimates and effective genetic model selection.

Conclusions:

  • The MAX test is a versatile and powerful tool for GWAS, applicable across diverse genetic models and study designs.
  • The asymptotic MAX test performs well for common variants, while Pearson's χ(2)-test is preferred for rare variants.
  • The provided R code facilitates the application of the MAX test in genetic association studies.