Accurate Bayesian inference of sex chromosome karyotypes and sex-linked scaffolds from low-depth sequencing data.

The identification of sex-linked scaffolds and the genetic sex of individuals, i.e. their sex karyotype, is a fundamental step in population genomic studies. If sex-linked scaffolds are known, single individuals may be sexed based on read counts of next-generation sequencing data. If both sex-linked scaffolds as well as sex karyotypes are unknown, as is often the case for non-model organisms, they have to be jointly inferred. For both cases, current methods rely on arbitrary thresholds, which limits their power for low-depth data. In addition, most current methods are limited to euploid sex karyotypes (XX and XY). Here we develop BeXY, a fully Bayesian method to jointly infer the posterior probabilities for each scaffold to be autosomal, X- or Y-linked and for each individual to be any of the sex karyotypes XX, XY, X0, XXX, XXY, XYY and XXYY. If the sex-linked scaffolds are known, it also identifies autosomal trisomies and estimates the sex karyotype posterior probabilities for single individuals. As we show with downsampling experiments, BeXY has higher power than all existing methods. It accurately infers the sex karyotype of ancient human samples with as few as 20,000 reads and accurately infers sex-linked scaffolds from data sets of just a handful of samples or with highly imbalanced sex ratios, also in the case of low-quality reference assemblies. We illustrate the power of BeXY by applying it to both whole-genome shotgun and target enrichment sequencing data of ancient and modern humans, as well as several non-model organisms.