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On the testing of Hardy-Weinberg proportions and equality of allele frequencies in males and females at biallelic genetic markers

NCJ Number
255693
Date Published
2018
Length
15 pages
Author(s)
Jan. Graffelman; Bruce. S. Weir
Agencies
NIJ-Sponsored
Publication Type
Research (Applied/Empirical), Report (Study/Research), Report (Grant Sponsored), Program/Project Description
Grant Number(s)
2017-DN-BX-0136
Annotation
Since the usual chi-square or exact test for Hardy-Weinberg equilibrium assumes equality of allele frequencies in the sexes, the current article proposes ways to break this interdependence in assumptions of the two tests by proposing an omnibus exact test that can test both hypotheses jointly, as well as a likelihood ratio approach that permits these phenomena to be tested both jointly and separately.
Abstract
Quality control filtering of genetic data is a crucial procedure in modern genetic studies. Extensive procedures and protocols are used to filter genetic data prior to their use in association tests. Such procedures include, but are not limited to, gender checks, assessment of relatedness between individuals, population substructure investigation, tests for Hardy-Weinberg equilibrium, and missing data analysis. The current project focused on two closely related aspects of the quality control of biallelic genetic markers, the equality of allele frequencies (EAF) in the sexes and Hardy-Weinberg proportions (HWP). Under normal conditions, it is expected that an autosomal genetic marker will have equal allele frequencies in males and females, and that genotype frequencies will agree with the Hardy-Weinberg law. This article indicates that performing the Hardy-Weinberg quality control part in an automated numerical way is not without problems. In this context, the ternary diagram, stratified for males and females, is an excellent graphical tool that contributes to a better understanding of a genetic marker. It is not feasible to inspect all ternary diagrams in a genome-wide association study (GWAS), but it may be feasible to calculate all AICs in order to filter out and inspect those SNPs not corresponding to the (most) expected scenario(s). The authors do not recommend automated elimination of SNPs with unlikely scenarios from GWASs, but rather encourage inspection of significant GWAS findings with the tools described in this article. 7 figures and 2 tables (publisher abstract modified)
Date Created: July 20, 2021