ArticleProceedings of the National Academy of Sciences of the United States of America2009
Population genomic inference of recombination rates and hotspots.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Article
- A general and efficient representation of ancestral recombination graphs.bioRxiv : the preprint server for biology · 2024Article
- Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed).BMC bioinformatics · 2024Article
- Review
- A simulation study to examine the impact of recombination on phylogenomic inferences under the multispecies coalescent model.Molecular ecology · 2022Article
- From molecules to populations: appreciating and estimating recombination rate variation.Nature reviews. Genetics · 2020Review
- A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks.Advances in neural information processing systems · 2018Article
- Effects of Demographic History on the Detection of Recombination Hotspots from Linkage Disequilibrium.Molecular biology and evolution · 2018Article
- Background selection as null hypothesis in population genomics: insights and challenges fromPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Article
- A Consensus Method for Ancestral Recombination Graphs.Journal of molecular evolution · 2017Article
- Detecting Recombination Hotspots from Patterns of Linkage Disequilibrium.G3 (Bethesda, Md.) · 2016Article
- New Software for the Fast Estimation of Population Recombination Rates (FastEPRR) in the Genomic Era.G3 (Bethesda, Md.) · 2016Article
- A Glance at Recombination Hotspots in the Domestic Cat.PloS one · 2016Article
- Article
- Article
- Detecting structure of haplotypes and local ancestry.Genetics · 2014Article
- Extensive linkage disequilibrium and parallel adaptive divergence across threespine stickleback genomes.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2012Article
- Article
- Contrasting methods of quantifying fine structure of human recombination.Annual review of genomics and human genetics · 2010Review
- Inference of microbial recombination rates from metagenomic data.PLoS genetics · 2009Article
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2 authors.
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Abstract
As more human genomic data become available, fine-scale recombination rate variation can be inferred on a genome-wide scale. Current statistical methods to infer recombination rates that can be applied to moderate, or large, genomic regions are limited to approximated likelihoods. Here, we develop a Bayesian full-likelihood method using Markov Chain Monte Carlo (MCMC) to estimate background recombination rates and hotspots. The probability model is inspired by the observed patterns of recombination at several genomic regions analyzed in sperm-typing studies. Posterior probabilities and Bayes factors of recombination hotspots along chromosomes are inferred. For moderate-size genomic regions (e.g., with <100 SNPs), the full-likelihood method is used. Larger regions are split into subintervals (typically each having between 20 and 50 markers). The likelihood is approximated based on the genealogies for each subinterval. The background recombination rates, hotspots, and parameters are evaluated by using a parallel computing approach and assuming shared parameters across the subintervals. Simulation analyses show that our method can accurately estimate the variation in recombination rates across genomic regions. In particular, clusters of hotspots can be distinguished even though weaker hotspots are present. The method is applied to SNP data from the HLA region, the MS32, and chromosome 19.
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