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Genome-Wide Inference of Ancestral Recombination Graphs
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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.
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“ArgWeaver (4), which is widely considered the gold standard in ARG inference, requires prior knowledge of demographic model parameters.”
From An ancestral recombination graph of human, Neanderthal, and Denisovan genomes (2021) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“BEAGLE (3), ArgWeaver (4), and Rent+ (5) were designed for small datasets and require substantial time and/or memory to be used with large sequencing panels.”
From An ancestral recombination graph of human, Neanderthal, and Denisovan genomes (2021) · cites this paper as [4]
Experimental — accuracy not yet measuredIn the citing paper
“Based on these estimates, TCC → T rate acceleration seems to have occurred between 25,000 and 60,000 y ago, not long after Europeans diverged from Asians (SI Appendix, section 4).”
From Evidence for recent, population-specific evolution of the human mutation rate (2015) · cites this paper as [24]
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“In the last few years, a new class of methods has emerged, which base the population genetic inference on reconstructed coalescent information 23,33,34 .These methods manifested promising test power and resolution for inferring selection, as the genome-wide coalescent trees provide a complete record of genealogies, mutations, and recombination 33 .”
From A Chronological Atlas of Natural Selection in the Human Genome during the Past Half-million Years (2015) · cites this paper as [33]
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Besenbacher, S., Mailund, T., & Schierup, M. H. (2008) · Genetics · doi:10.1534/genetics.108.092643 · Cited by 21
Wu, Y. (2008) · Journal of Computational Biology · doi:10.1089/cmb.2007.0116 · Cited by 19
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EFFICIENT COMPUTATION OF MINIMUM RECOMBINATION WITH GENOTYPES (NOT HAPLOTYPES)
Wu, Y., & Gusfield, D. (2007) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720007002631 · Cited by 2
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Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes
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Ancient gene flow from early modern humans into Eastern Neanderthals
Kuhlwilm, M., Gronau, I., Hubisz, M. J., de Filippo, C., Prado-Martinez, J., Kircher, M., Fu, Q., Burbano, H. A., Lalueza-Fox, C., de la Rasilla, M., et al. (2016) · Nature · doi:10.1038/nature16544 · Cited by 577
Self-citationGazal, S., Finucane, H. K., Furlotte, N. A., Loh, P.-R., Palamara, P. F., Liu, X., Schoech, A., Bulik-Sullivan, B., Neale, B. M., Gusev, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3954 · Cited by 525
A method for genome-wide genealogy estimation for thousands of samples
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Efficient ancestry and mutation simulation with msprime 1.0
Baumdicker, F., Bisschop, G., Goldstein, D., Gower, G., Ragsdale, A. P., Tsambos, G., Zhu, S. J., Eldon, B., Ellerman, E. C., Galloway, J., et al. (2021) · Genetics · doi:10.1093/genetics/iyab229 · Cited by 488
Metadata
- Authors
- Rasmussen, M. D., Hubisz, M. J., Gronau, I., & Siepel, A.
- Affiliations
- Cornell University (US)
- European Bioinformatics Institute (GB)
- Corresponding author
- Rasmussen, Matthew D.
- Siepel, Adam
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-05-15
- Volume
- 10
- Issue
- 5
- Pages
- e1004342
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC4022496
- OpenAlex
- W2137706757
- Semantic Scholar
- ba7a79e9daa8da0f51d5014c68d24f0962dd36ec
- Cited by
- 371
- References
- 115
- Citations by year
- 2026: 24 · 2025: 36 · 2024: 38 · 2023: 26 · 2022: 31 · 2021: 29 · 2020: 30 · 2019: 28 · 2018: 54 · 2017: 13 · 2016: 29 · 2015: 21 · 2014: 10
- FWCI
- 9.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Science Foundation (1053575)
- University of Texas at Austin
- European Bioinformatics Institute
- National Institutes of Health (OCI-1053575)
- National Institute of General Medical Sciences (R01GM102192, GM102192)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Keywords
- ancestral recombination graph, threading, hidden Markov models, orthologous sequences, human genome sequence, natural selection signatures, balancing selection, directional selection, background selection, hitchhiking, allele age, coalescence
- MeSH terms
- Algorithms, Computer Simulation, Humans, Markov Chains, Models, Genetic, Monte Carlo Method, Recombination, Genetic, Selection, Genetic, Genome, Human, Evolution, Molecular
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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