A Unifying Regression-based Framework for X-chromosome Inclusive Hardy–Weinberg Equilibrium Test
Lin Zhang, PhD
Assistant Professor of the Department of Statistics and Actuarial Science| Simon Fraser University
WHEN: Wednesday, April 1, 2026, from 3:30 to 4:30 p.m.
WHERE: Hybrid | 2001 Â鶹´«Ã½ÍøÕ¾ College Avenue, Rm 1140;
NOTE:Â Lin Zhang will be presenting in-person at SPGHÂ
Abstract
How to best perform Hardy-Weinberg equilibrium (HWE) testing for an X chromosomal SNP is not clear, even using a sample of unrelated individuals. One simple strategy is to use female data only and apply the Pearson’s chi-square test. Alternatively, earlier work has proposed a 2 d.f. test that includes the deviation of observed male genotype counts from the expected, based on the pooled allele frequency estimated using both sexes. Here we propose a new regression-based method that (a) analyzes both autosomal and X chromosomal SNPs, (b) adjusts for covariate effects if needed, (c) analyzes related individuals, (d) analyzes samples from multiple populations, (e) includes the existing tests as special cases, and (f) leads to the development of new tests. The proposed method builds from our recent robust allele-based (RA) regression method developed for conducting allelic association analysis. The RA-based framework for X chromosomal SNPs is flexible and versatile to develop new tests that are robust to sex differences in allele frequency (sdMAF), and is suitable for analyzing variants subject to sex-specific selection. We illustrate the proposed method by application to high coverage whole genome sequence data from the 1000 Genomes Project.
Speaker Bio
Dr. Lin Zhang is an Assistant Professor of the Department of Statistics and Actuarial Science at Simon Fraser University. Her research interest lies in solving genomic problems using statistics and machine learning algorithms, with a special focus on heterogeneous genetic data and single-cell multi-omics data.