ArticleGenetics2013
A fast estimate for the population recombination rate based on regression.
Article in Genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 45 citations in OpenAlex.
- Genomic evidence for shared common ancestry of East African hunting-gathering populations and insights into local adaptation.Proceedings of the National Academy of Sciences of the United States of America · 2019Trial
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- Population genomics reveals population structure, glacial divergence, and intertidal adaptation in the Chinese chiton (Liolophura sinensis).Communications biology · 2026Article
- Neural posterior estimation for population genetics.bioRxiv : the preprint server for biology · 2026Article
- Sero-Molecular Markers and Genetic Diversity of Hepatitis B Virus Isolated From Hemodialysis Patients From Jenin District, West Bank, Palestine.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2025Article
- IntroUNET: Identifying introgressed alleles via semantic segmentation.PLoS genetics · 2024Article
- IntroUNET: identifying introgressed alleles via semantic segmentation.bioRxiv : the preprint server for biology · 2024Article
- Fine human genetic map based on UK10K data set.Human genetics · 2022Article
- Differential ability of network and natural language information on social media to predict interpersonal and mental health traits.Journal of personality · 2021Article
- High-resolution population-specific recombination rates and their effect on phasing and genotype imputation.European journal of human genetics : EJHG · 2021Article
- Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning.Molecular biology and evolution · 2021Article
- Extensive haplotypes are associated with population differentiation and environmental adaptability in Upland cotton (Gossypium hirsutum).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2020Article
- Population-Specific Recombination Maps from Segments of Identity by Descent.American journal of human genetics · 2020Article
- Article
- Predicting the Landscape of Recombination Using Deep Learning.Molecular biology and evolution · 2020Article
- Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations.Science advances · 2019Article
- LDJump: Estimating variable recombination rates from population genetic data.Molecular ecology resources · 2019Article
- The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference.Molecular biology and evolution · 2019Article
- Supervised Machine Learning for Population Genetics: A New Paradigm.Trends in genetics : TIG · 2018Review
- Population Genomics Reveals Speciation and Introgression between Brown Norway Rats and Their Sibling Species.Molecular biology and evolution · 2017Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recombination is a fundamental evolutionary force. Therefore the population recombination rate ρ plays an important role in the analysis of population genetic data; however, it is notoriously difficult to estimate. This difficulty applies both to the accuracy of commonly used estimates and to the computational efforts required to obtain them. Some particularly popular methods are based on approximations to the likelihood. They require considerably less computational efforts than the full-likelihood method with not much less accuracy. Nevertheless, the computation of these approximate estimates can still be very time consuming, in particular when the sample size is large. Although auxiliary quantities for composite likelihood estimates can be computed in advance and stored in tables, these tables need to be recomputed if either the sample size or the mutation rate θ changes. Here we introduce a new method based on regression combined with boosting as a model selection technique. For large samples, it requires much less computational effort than other approximate methods, while providing similar levels of accuracy. Notably, for a sample of hundreds or thousands of individuals, the estimate of ρ using regression can be obtained on a single personal computer within a couple of minutes while other methods may need a couple of days or months (or even years). When the sample size is smaller (n ≤ 50), our new method remains computational efficient but produces biased estimates. We expect the new estimates to be helpful when analyzing large samples and/or many loci with possibly different mutation rates.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.