  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.
