Simulating variance heterogeneity in quantitative genome wide association studies

Ahmad Al Kawam, Mustafa Alshawaqfeh, James J. Cai, Erchin Serpedin, Aniruddha Datta

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Background: Analyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects variability in phenotype values across genotypes, as opposed to typical GWAS analysis, which detects variations in the mean phenotype value. Results: A handful of vGWAS analysis methods have been recently introduced in the literature. However, very little work has been done for evaluating these methods. To enable the development of better vGWAS analysis methods, this work presents the first quantitative vGWAS simulation procedure. To that end, we describe the mathematical framework and algorithm for generating quantitative vGWAS phenotype data from genotype profiles. Our simulation model accounts for both haploid and diploid genotypes under different modes of dominance. Our model is also able to simulate any number of genetic loci causing mean and variance heterogeneity. Conclusions: We demonstrate the utility of our simulation procedure through generating a variety of genetic loci types to evaluate common GWAS and vGWAS analysis methods. The results of this evaluation highlight the challenges current tools face in detecting GWAS and vGWAS loci.

Original languageEnglish
Article number72
JournalBMC Bioinformatics
Volume19
DOIs
Publication statusPublished - 21 Mar 2018

Fingerprint

Variance Heterogeneity
Genome-Wide Association Study
Genome
Genes
Locus
Genotype
Phenotype
Genetic Loci
Gene-environment Interaction
Gene
Simulation Model
Gene-Environment Interaction
Simulation Study
Haploidy

Keywords

  • Genome wide association studies
  • GWAS simulation
  • Variance heterogeneity

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Simulating variance heterogeneity in quantitative genome wide association studies. / Al Kawam, Ahmad; Alshawaqfeh, Mustafa; Cai, James J.; Serpedin, Erchin; Datta, Aniruddha.

In: BMC Bioinformatics, Vol. 19, 72, 21.03.2018.

Research output: Contribution to journalArticle

Al Kawam, Ahmad ; Alshawaqfeh, Mustafa ; Cai, James J. ; Serpedin, Erchin ; Datta, Aniruddha. / Simulating variance heterogeneity in quantitative genome wide association studies. In: BMC Bioinformatics. 2018 ; Vol. 19.
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