The number of recombination events in a sample history: conflict graph and lower bounds
Abstract
We consider the following problem: Given a set of binary sequences, determine lower bounds on the minimum number of recombinations required to explain the history of the sample, under the infinite-sites model of mutation. The problem has implications for finding recombination hotspots and for the Ancestral Recombination Graph reconstruction problem. Hudson and Kaplan gave a lower bound based on the four-gamete test. In practice, their bound Rm often greatly underestimates the minimum number of recombinations. The problem was recently revisited by Myers and Griffiths, who introduced two new lower bounds Rh and Rs which are provably better, and also yield good bounds in practice. However, the worst-case complexities of their procedures for computing Rh and Rs are exponential and super-exponential, respectively. In this paper, we show that the number of nontrivial connected components, Rc, in the conflict graph for a given set of sequences, computable in time O(nm2), is also a lower bound on the minimum number of recombination events. We show that in many cases, Rc is a better bound than Rh. The conflict graph was used by Gusfield et al. to obtain a polynomial time algorithm for the galled tree problem, which is a special case of the Ancestral Recombination Graph (ARG) reconstruction problem. Our results also offer some insight into the structural properties of this graph and are of interest for the general Ancestral Recombination Graph reconstruction problem.
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Because the four-gamete test is known to underestimate the true number of ancestral recombination events, SARGE will systematically underestimate the true number of ancestral recombination events in a dataset by design.
“Because the four-gamete test is known to underestimate the true number of ancestral recombination events (12, 13), SARGE will systematically underestimate the true number of ancestral recombination events in a dataset by design.”
From An ancestral recombination graph of human, Neanderthal, and Denisovan genomes (2021) · cites this paper as [13]
Cites
Clark, A. G., Weiss, K. M., Nickerson, D. A., Taylor, S. L., Buchanan, A., Stengård, J., Salomaa, V., Vartiainen, E., Perola, M., Boerwinkle, E., et al. (1998) · The American Journal of Human Genetics · doi:10.1086/301977 · Cited by 435 · see citing works
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An ancestral recombination graph of human, Neanderthal, and Denisovan genomes
Schaefer, N. K., Shapiro, B., & Green, R. E. (2021) · Science Advances · doi:10.1126/sciadv.abc0776 · Cited by 106 · see citing works
“Because the four-gamete test is known to underestimate the true number of ancestral recombination events (12, 13), SARGE will systematically underestimate the true number of ancestral recombination events in a dataset by design.”
Background
- Authors
- Bafna, V., & Bansal, V.
- Affiliations
- University of California San Diego (US)
- ORCID
- Bafna, V.
- Venue
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Published
- 2004-04
- Volume
- 1
- Issue
- 2
- Pages
- 78-90
- ISSN
- 1545-5963 (ISSN-L)
- OpenAlex
- W2124253802
- Cited by
- 39
- References
- 1
- Citations by year
- 2021: 1 · 2015: 1 · 2013: 2
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Computational Biology and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genome Rearrangement Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Algorithms and Data Compression
- Keywords
- recombination events, conflict graph, lower bounds, infinite sites model, recombination hotspots, binary sequences, connected components, population genetics
- MeSH terms
- Alcohol Dehydrogenase, Algorithms, Animals, Drosophila melanogaster, Haplotypes, Models, Genetic, Mutation, Phylogeny, Recombination, Genetic, Evolution, Molecular, Computational Biology
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