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PMID: 21926973 Published · epublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Bayesian inference of ancient human demography from individual genome sequences.

Nature genetics ·Vol. 43 ·No. 10 ·2011-09-18 ·Pages 1031-4

Gronau I, Hubisz MJ, Gulko B, Danko CG, Siepel A

Abstract

Whole-genome sequences provide a rich source of information about human evolution. Here we describe an effort to estimate key evolutionary parameters based on the whole-genome sequences of six individuals from diverse human populations. We used a Bayesian, coalescent-based approach to obtain information about ancestral population sizes, divergence times and migration rates from inferred genealogies at many neutrally evolving loci across the genome. We introduce new methods for accommodating gene flow between populations and integrating over possible phasings of diploid genotypes. We also describe a custom pipeline for genotype inference to mitigate biases from heterogeneous sequencing technologies and coverage levels. Our analysis indicates that the San population of southern Africa diverged from other human populations approximately 108-157 thousand years ago, that Eurasians diverged from an ancestral African population 38-64 thousand years ago, and that the effective population size of the ancestors of all modern humans was ∼9,000.

MeSH Terms
Bayes Theorem Chromosome Mapping Evolution, Molecular Gene Flow Genetic Drift Genetic Variation Genetics, Population Genome, Human Humans Models, Genetic Population Density Population Dynamics Sequence Alignment Validation Studies as Topic
Authors & Affiliations
5 authors, click to expand affiliations / ORCID
Gronau Ilan
Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.
Hubisz Melissa J
Gulko Brad
Danko Charles G
Siepel Adam
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Article Info
Journal
Nature genetics
Abbr.
Nat Genet
ISSN
1546-1718
Published
2011-09-18
Epub
2011-00-18
Pages
1031-4
Language
English
Region
United States
NLM ID
9216904
PMCID
PMC3245873
Subset
IM
Grants
NICHD NIH HHS · T32 HD052471 · United States
NICHD NIH HHS · T32 HD052471-01A1 · United States
NICHD NIH HHS · T32HD052471 · United States
Corrections
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