The inference of sex-biased human demography from whole-genome data

Sex-biased demographic events (“sex-bias”) involve unequal numbers of females and males. These events are typically inferred from the relative amount of X-chromosomal to autosomal genetic variation and have led to conflicting conclusions about human demographic history. Though population size changes alter the relative amount of X-chromosomal to autosomal genetic diversity even in the absence of sex-bias, this has generally not been accounted for in sex-bias estimators to date. Here, we present a novel method to identify sex-bias from genetic sequence data that models population size changes and estimates the female fraction of the effective population size during each time epoch. Compared to recent sex-bias inference methods, our approach can detect sex-bias that changes on a single population branch without requiring data from an outgroup or knowledge of divergence events. When applied to simulated data, conventional sex-bias estimators are biased by population size changes, especially recent growth or bottlenecks, while our estimator is unbiased. We next apply our method to high-coverage exome data from the 1000 Genomes Project and estimate a male bias in Yorubans (47% female) and Europeans (44%), possibly due to stronger background selection on the X chromosome than on the autosomes. Finally, we apply our method to the 1000 Genomes Project Phase 3 high-coverage Complete Genomics whole-genome data and estimate a female bias in Yorubans (63% female), Europeans (84%), Punjabis (82%), as well as Peruvians (56%), and a male bias in the Southern Han Chinese (45%). Our method additionally identifies a male-biased migration out of Africa based on data from Europeans (20% female). Our results demonstrate that modeling population size change is necessary to estimate sex-bias parameters accurately. Our approach gives insight into signatures of sex-bias in sexual species, and the demographic models it produces can serve as more accurate null models for tests of selection.Author summary: Sex-biased demographic events involve unequal numbers of females and males, and is referred to as “sex-bias”. In humans, short-range migrations (e.g., due to marriage practices) are known to be sex-biased, and some long-range migrations, such as the one out of Africa, are hypothesized to be sex-biased. The recent availability of large-scale genomic sequencing data provides a unique opportunity to study sex-bias in human populations. However, existing sex-bias methods do not account for population size changes, like expansions and bottlenecks, or can only estimate a single sex-bias parameter on a population branch, which can lead to incorrect conclusions. We developed a sex-bias method which explicitly models population size changes, and we show that it outperforms competing methods on simulated data. When applied to human genetic data, our method identifies an overall female sex-bias in globally-distributed populations and a male-biased bottleneck in Europeans. Our method can also be used to assess sex-bias in other sexual species.