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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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An Efficient Stepwise Statistical Test to Identify Multiple Linked Human Genetic Variants Associated with Specific

Iksoo Huh1, Min-Seok Kwon2, Taesung Park3

  • 1Department of Statistics, Seoul National University, Gwanak-gu, Seoul, Korea.

Plos One
|September 26, 2015
PubMed
Summary

This study introduces a new stepwise method using the Cochran-Mantel-Haenszel test to identify multiple genetic variants associated with complex traits in genome-wide association studies (GWAS). The method improves understanding of genetic architecture for diseases like bipolar disorder and obesity.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify genetic variants linked to complex traits.
  • Understanding the joint effects of multiple genetic variants is crucial for complex trait genetics.
  • Current methods often focus on single variants, limiting insights into genetic architecture.

Purpose of the Study:

  • To propose an efficient stepwise method for identifying joint multiple genetic variants in GWAS.
  • To enhance the understanding of the genetic architecture underlying complex phenotypic traits.
  • To provide a feasible method for large-scale GWAS data analysis.

Main Methods:

  • Developed a stepwise method based on the Cochran-Mantel-Haenszel (CMH) test for stratified categorical data.
  • Utilized a novel stratification scheme based on minor allele count criteria for improved feasibility.
  • Evaluated method performance through simulation studies and comparison with existing approaches.

Main Results:

  • The proposed stepwise CMH test demonstrated superior performance compared to logistic regression and Markov blanket methods.
  • The method effectively identified multiple linked genetic variants associated with complex traits.
  • Successful application to real-world genomic datasets for bipolar disorder and obesity.

Conclusions:

  • The stepwise CMH test is an efficient and effective approach for detecting joint genetic variants in GWAS.
  • This method advances the ability to unravel the genetic basis of complex diseases.
  • The findings have implications for understanding the genetic architecture of traits like bipolar disorder and obesity.