生物
次等位基因频率
单核苷酸多态性
遗传建筑学
遗传力
遗传学
SNP公司
自然选择
选择(遗传算法)
等位基因频率
等位基因
核苷酸多型性
数量性状位点
基因
基因型
人工智能
计算机科学
作者
Jian Zeng,Ronald de Vlaming,Yang Wu,Matthew R. Robinson,Luke R. Lloyd‐Jones,Loïc Yengo,Chloe X. Yap,Angli Xue,Julia Sidorenko,Allan F. McRae,Joseph E. Powell,Grant W. Montgomery,Andres Metspalu,Tõnu Esko,Greg Gibson,Naomi R. Wray,Peter M. Visscher,Jian Yang
出处
期刊:Nature Genetics
[Nature Portfolio]
日期:2018-04-13
卷期号:50 (5): 746-753
被引量:379
标识
DOI:10.1038/s41588-018-0101-4
摘要
We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits. BayesS estimates SNP-based heritability, polygenicity, and the relationship between effect size and minor allele frequency using genome-wide SNP data. Applying BayesS to UK Biobank data identifies signatures of natural selection for 23 complex traits.
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