Abstract
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.
Highlights
Human population-genetic studies generally assume that the proportions of reproducing females and males are equal
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
Summary
Human population-genetic studies generally assume that the proportions of reproducing females and males are equal. Some examples of sex-bias include matrilocality (the practice of females remaining in their place of birth after marriage), and patrilocality [1, 2]; patrilineal inheritance in herder groups [3]; polygamy, the practice of a male having multiple female sexual partners, and polyandry, which is the opposite; female- and male-biased migration; and sexual selection These factors, along with a variance in reproductive success that is greater in males than females [4, 5], cause male and female effective sizes to differ [6, 7]. Labuda et al initially found evidence for male bias based on recombination rates [14], their conclusion changed to one of a female bias after an error in their analysis was corrected [15, 16] These studies used standard sex-bias estimators of Q, the ratio of X-chromosomal to autosomal effective population sizes. Other recent sex-bias studies analyzed admixture fraction on the X-chromosome and autosomes and found evidence for sexbiased admixture in human populations
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