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Simulating variance heterogeneity in quantitative genome wide association studies.

Ahmad Al Kawam1,2,3, Mustafa Alshawaqfeh4, James J Cai5

  • 1Electrical & Computer Engineering Dept., Texas A&M University, College Station, TX, USA. ahmad.alkawam@tamu.edu.

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|March 29, 2018
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Summary

Variance heterogeneity analysis in genome-wide association studies (vGWAS) identifies genetic interactions. This study introduces the first quantitative vGWAS simulation procedure to evaluate existing methods and improve future vGWAS development.

Keywords:
GWAS simulationGenome wide association studiesVariance heterogeneity

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) typically analyze mean phenotype values.
  • Variance heterogeneity analysis in GWAS (vGWAS) is an emerging method to detect genetic loci influencing gene-gene and gene-environment interactions by analyzing phenotype variability across genotypes.

Purpose of the Study:

  • To present the first quantitative simulation procedure for vGWAS analysis.
  • To enable the development and evaluation of improved vGWAS methods.

Main Methods:

  • Developed a mathematical framework and algorithm for generating quantitative vGWAS phenotype data from genotype profiles.
  • The simulation model accommodates haploid and diploid genotypes, various dominance modes, and multiple genetic loci affecting mean and variance.

Main Results:

  • Demonstrated the simulation procedure's utility by generating diverse genetic loci types.
  • Evaluated common GWAS and vGWAS analysis methods using simulated data.

Conclusions:

  • The simulation procedure effectively highlights challenges faced by current GWAS and vGWAS tools.
  • This work provides a foundation for advancing vGWAS methodology and analysis.