Methods and models for unravelling human evolutionary history

被引:88
|
作者
Schraiber, Joshua G. [1 ]
Akey, Joshua M. [1 ]
机构
[1] Univ Washington, Dept Genome Sci, Seattle, WA 98195 USA
关键词
APPROXIMATE BAYESIAN COMPUTATION; CONDITIONAL SAMPLING DISTRIBUTION; WHOLE-GENOME ASSOCIATION; HUMAN-POPULATION HISTORY; STRUCTURED POPULATIONS; LACTASE-PERSISTENCE; MAXIMUM-LIKELIHOOD; NATURAL-SELECTION; MONTE-CARLO; SNP DATA;
D O I
10.1038/nrg4005
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
The genomes of contemporary humans contain considerable information about the history of our species. Although the general contours of human evolutionary history have been defined with increasing resolution throughout the past several decades, the continuing deluge of massively large sequencing data sets presents new opportunities and challenges for understanding human evolutionary history. Here, we review the signatures that demographic history imparts on patterns of DNA sequence variation, statistical methods that have been developed to leverage information contained in genome-scale data sets and insights gleaned from these studies. We also discuss the importance of using exploratory analyses to assess data quality, the strengths and limitations of commonly used population genomics methods, and factors that confound population genomics inferences.
引用
收藏
页码:727 / 740
页数:14
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