Genome-wide inference of ancestral recombination graphs

Rasmussen, M. D., Hubisz, M. J., Gronau, I., Siepel, A. (2014) Genome-wide inference of ancestral recombination graphs. PLoS Genetics, 10 (5). e1004342. ISSN 15537390 (ISSN)

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DOI: 10.1371/journal.pgen.1004342


The complex correlation structure of a collection of orthologous DNA sequences is uniquely captured by the "ancestral recombination graph" (ARG), a complete record of coalescence and recombination events in the history of the sample. However, existing methods for ARG inference are computationally intensive, highly approximate, or limited to small numbers of sequences, and, as a consequence, explicit ARG inference is rarely used in applied population genomics. Here, we introduce a new algorithm for ARG inference that is efficient enough to apply to dozens of complete mammalian genomes. The key idea of our approach is to sample an ARG of [Formula: see text] chromosomes conditional on an ARG of [Formula: see text] chromosomes, an operation we call "threading." Using techniques based on hidden Markov models, we can perform this threading operation exactly, up to the assumptions of the sequentially Markov coalescent and a discretization of time. An extension allows for threading of subtrees instead of individual sequences. Repeated application of these threading operations results in highly efficient Markov chain Monte Carlo samplers for ARGs. We have implemented these methods in a computer program called ARGweaver. Experiments with simulated data indicate that ARGweaver converges rapidly to the posterior distribution over ARGs and is effective in recovering various features of the ARG for dozens of sequences generated under realistic parameters for human populations. In applications of ARGweaver to 54 human genome sequences from Complete Genomics, we find clear signatures of natural selection, including regions of unusually ancient ancestry associated with balancing selection and reductions in allele age in sites under directional selection. The patterns we observe near protein-coding genes are consistent with a primary influence from background selection rather than hitchhiking, although we cannot rule out a contribution from recurrent selective sweeps.

Item Type: Paper
Uncontrolled Keywords: Algorithms Computer Simulation *Evolution, Molecular *Genome, Human Humans Markov Chains Models, Genetic Monte Carlo Method *Recombination, Genetic Selection, Genetic/*genetics
Subjects: bioinformatics
bioinformatics > genomics and proteomics
bioinformatics > genomics and proteomics > alignment > sequence alignment
bioinformatics > genomics and proteomics > computers > computer software
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > genomes
CSHL Authors:
Communities: CSHL labs > Siepel lab
Depositing User: Matt Covey
Date Deposited: 13 Jan 2015 20:19
Last Modified: 13 Jan 2015 20:19
PMCID: PMC4022496
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