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Recoverability of ancestral recombination graph topologies.
Hayman, Elizabeth; Ignatieva, Anastasia; Hein, Jotun.
Affiliation
  • Hayman E; Department of Mathematics, University of Oxford, Andrew Wiles Building, Oxford OX2 6GG, UK. Electronic address: elizabeth.hayman@keble.ox.ac.uk.
  • Ignatieva A; Department of Statistics, University of Warwick, Coventry CV4 7AL, UK; Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, UK.
  • Hein J; Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, UK; The Alan Turing Institute, British Library, London NW1 2DB, UK.
Theor Popul Biol ; 154: 27-39, 2023 12.
Article in En | MEDLINE | ID: mdl-37544486
ABSTRACT
Recombination is a powerful evolutionary process that shapes the genetic diversity observed in the populations of many species. Reconstructing genealogies in the presence of recombination from sequencing data is a very challenging problem, as this relies on mutations having occurred on the correct lineages in order to detect the recombination and resolve the ordering of coalescence events in the local trees. We investigate the probability of reconstructing the true topology of ancestral recombination graphs (ARGs) under the coalescent with recombination and gene conversion. We explore how sample size and mutation rate affect the inherent uncertainty in reconstructed ARGs, which sheds light on the theoretical limitations of ARG reconstruction methods. We illustrate our results using estimates of evolutionary rates for several organisms; in particular, we find that for parameter values that are realistic for SARS-CoV-2, the probability of reconstructing genealogies that are close to the truth is low.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Recombination, Genetic / Algorithms Language: En Journal: Theor Popul Biol Year: 2023 Type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Recombination, Genetic / Algorithms Language: En Journal: Theor Popul Biol Year: 2023 Type: Article