title: Interrogating multiple aspects of variation in a full resequencing data set to infer human population size changes.
abstract: We present an expanded data set of 50 unlinked autosomal noncoding regions, resequenced in samples of Hausa from Cameroon, Italians, and Chinese. We use these data to make inferences about human demographic history by using a technique that combines multiple aspects of genetic data, including levels of polymorphism, the allele frequency spectrum, and linkage disequilibrium. We explore an extensive range of demographic parameters and demonstrate that our method of combining multiple aspects of the data results in a significant reduction of the compatible parameter space. In agreement with previous reports, we find that the Hausa data are compatible with demographic equilibrium as well as a set of recent population expansion models. In contrast to the Hausa, when multiple aspects of the data are considered jointly, the non-Africans depart from an equilibrium model of constant population size and are compatible with a range of simple bottleneck models, including a 50-90% reduction in effective population size occurring some time after the appearance of modern humans in Africa 160,000-120,000 years ago.
authors.affiliation: Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
authors.forename: Benjamin F
authors.initials: BF
authors.lastname: Voight
authors.forename: Alison M
authors.initials: AM
authors.lastname: Adams
authors.forename: Linda A
authors.initials: LA
authors.lastname: Frisse
authors.forename: Yudong
authors.initials: Y
authors.lastname: Qian
authors.forename: Richard R
authors.initials: RR
authors.lastname: Hudson
authors.forename: Anna
authors.initials: A
authors.lastname: Di Rienzo
mesh_terms: D002163:Cameroon / Q000208:ethnology; D002681:China / Q000208:ethnology; D005787:Gene Frequency; D014644:Genetic Variation / Q000235:genetics*; D006801:Humans; D007558:Italy / Q000208:ethnology; D008957:Models, Genetic; D011156:Population Density; D013997:Time Factors