Bayesian inference of ancient human demography from individual genome sequences

Gronau, I., Hubisz, M. J., Gulko, B., Danko, C. G., Siepel, A. (October 2011) Bayesian inference of ancient human demography from individual genome sequences. Nat Genet, 43 (10). pp. 1031-4. ISSN 1061-4036

DOI: 10.1038/ng.937


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 approximately 9,000.

Item Type: Paper
Uncontrolled Keywords: 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
Subjects: bioinformatics
bioinformatics > genomics and proteomics > alignment > sequence alignment
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > genomes
Investigative techniques and equipment > assays > whole genome sequencing
CSHL Authors:
Communities: CSHL labs > Siepel lab
Depositing User: Matt Covey
Date: October 2011
Date Deposited: 15 Jan 2015 17:18
Last Modified: 15 Jan 2015 17:18
PMCID: PMC3245873
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