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Performance and limitations of linkage-disequilibrium-based methods for inferring the genomic landscape of recombination and detecting hotspots: a simulation studyuse asterix (*) to get italics
Marie Raynaud, Pierre-Alexandre Gagnaire, Nicolas GaltierPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
<p style="text-align: justify;">Knowledge of recombination rate variation along the genome provides important insights into genome and phenotypic evolution. Population genomic approaches offer an attractive way to infer the population-scaled recombination rate ⍴=4Ner using the linkage disequilibrium information contained in DNA sequence polymorphism data. Such methods have been used in a broad range of plant and animal species to build genome-wide recombination maps. However, the reliability of these inferences has only been assessed under a restrictive set of conditions. Here, we evaluate the ability of one of the most widely used coalescent-based programs, LDhelmet, to infer a genomic landscape of recombination with the biological characteristics of a human-like landscape including hotspots. Using simulations, we specifically assessed the impact of methodological (sample size, phasing errors, block penalty) and evolutionary parameters (effective population size (Ne), demographic history, mutation to recombination rate ratio) on inferred map quality. We report reasonably good correlations between simulated and inferred landscapes, but point to limitations when it comes to detecting recombination hotspots. False positive and false negative hotspots considerably confound fine-scale patterns of inferred recombination under a wide range of conditions, particularly when Ne is small and the mutation/recombination rate ratio is low, to the extent that maps inferred from populations sharing the same recombination landscape appear uncorrelated. We thus address a message of caution for the users of these approaches, at least for genomes with complex recombination landscapes such as in humans.</p>, should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://, should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://, should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://
Population-scaled recombination rate, LDhelmet, simulations, linkage disequilibrium, recombination landscapes, recombination hotspots
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Bioinformatics, Evolutionary genomics, Population genomics
Sebastian Ramos-Onsins, [], Alexander Suh, [], Amanda Larracuente, [], Fanny Pouyet, [], Peter Ralph, [] No need for them to be recommenders of PCI Genomics. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe []
2022-04-05 14:59:14
Sebastian E. Ramos-Onsins