A Population-Aware Retrospective Regression to Detect Genome-Wide Variants with Sex Difference in Allele Frequency

TL;DR

This paper introduces a population-aware retrospective regression method to detect sex differences in allele frequency across the genome, accounting for ancestral diversity and applied to 1000 Genomes data.

Contribution

It presents a novel regression framework that improves detection of sex differences in allele frequency across populations, addressing limitations of previous methods.

Findings

Identified 76 novel sex-difference variants after modeling ancestral differences.

Demonstrated the method's robustness across diverse populations.

Provided insights into sex-specific genetic variation across ancestries.

Abstract

Sex difference in allele frequency is an emerging topic that is critical to our understanding of ascertainment bias, as well as data quality particularly of the largely overlooked X chromosome. To detect sex difference in allele frequency for both X chromosomal and autosomal variants, existing methods are conservative when applied to samples from multiple ancestral populations, such as African and European populations. Additionally, it remains unexplored whether the sex difference in allele frequency differs between populations, which is important to trans-ancestral genetic studies. We thus developed a novel retrospective regression-based testing framework to provide interpretable and easy-to-implement solutions to answer these questions. We then applied the proposed methods to the high-coverage whole genome sequence data of the 1000 Genomes Project, robustly analyzing all samples available from the five super-populations. We had 76 novel findings by recognizing and modeling ancestral differences.