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


Autoři: Shaila Musharoff aff001;  Suyash Shringarpure aff001;  Carlos D. Bustmante aff001;  Sohini Ramachandran aff002
Působiště autorů: Department of Genetics, Stanford University, Stanford, CA, USA aff001;  Center for Computational Molecular Biology, Brown University, Providence, RI, USA aff002;  Ecology and Evolutionary Biology, Brown University, Providence, RI, USA aff003
Vyšlo v časopise: The inference of sex-biased human demography from whole-genome data. PLoS Genet 15(9): e32767. doi:10.1371/journal.pgen.1008293
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008293

Souhrn

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 (43%), 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.

Klíčová slova:

Biology and life sciences – Cell biology – Chromosome biology – Chromosomes – Sex chromosomes – X chromosomes – Autosomes – Population biology – Population metrics – Population size – Evolutionary biology – Genetics – Population genetics – Genetic loci – People and places – Geographical locations – Europe – Research and analysis methods – Simulation and modeling – Mathematical and statistical techniques – Statistical methods – Test statistics – Physical sciences – Mathematics – Statistics


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Štítky
Genetika Reprodukční medicína

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