ArticleG3 (Bethesda, Md.)2016
Detecting Recombination Hotspots from Patterns of Linkage Disequilibrium.
Article in G3 (Bethesda, Md.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Inferring fine-scale rates of mutation and recombination in the coppery titi monkey (Plecturocebus cupreus).Heredity · 2026Article
- Why recombination hotspots?PLoS genetics · 2026Review
- Novel crossover and recombination hotspots massively spread across primate genomes.Biology direct · 2024Article
- Rapid Evolution of the Fine-scale Recombination Landscape in Wild House Mouse (Mus musculus) Populations.Molecular biology and evolution · 2023Article
- Variation in fine-scale recombination rate in temperature-evolved Drosophila melanogaster populations in response to selection.G3 (Bethesda, Md.) · 2022Article
- Article
- Negative linkage disequilibrium between amino acid changing variants reveals interference among deleterious mutations in the human genome.PLoS genetics · 2021Article
- Stationary distribution of the linkage disequilibrium coefficient rTheoretical population biology · 2019Article
- Divergent Fine-Scale Recombination Landscapes between a Freshwater and Marine Population of Threespine Stickleback Fish.Genome biology and evolution · 2019Article
- Mapping Recombination Rate on the Autosomal Chromosomes Based on the Persistency of Linkage Disequilibrium Phase Among Autochthonous Beef Cattle Populations in Spain.Frontiers in genetics · 2019Article
- 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
- Association between ABO Blood Group and Risk of Congenital Heart Disease: A 6-year large cohort study.Scientific reports · 2017Article
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Authors and funding
2 authors.
Funding
Abstract
With recent advances in DNA sequencing technologies, it has become increasingly easy to use whole-genome sequencing of unrelated individuals to assay patterns of linkage disequilibrium (LD) across the genome. One type of analysis that is commonly performed is to estimate local recombination rates and identify recombination hotspots from patterns of LD. One method for detecting recombination hotspots, LDhot, has been used in a handful of species to further our understanding of the basic biology of recombination. For the most part, the effectiveness of this method (e.g., power and false positive rate) is unknown. In this study, we run extensive simulations to compare the effectiveness of three different implementations of LDhot. We find large differences in the power and false positive rates of these different approaches, as well as a strong sensitivity to the window size used (with smaller window sizes leading to more accurate estimation of hotspot locations). We also compared our LDhot simulation results with comparable simulation results obtained from a Bayesian maximum-likelihood approach for identifying hotspots. Surprisingly, we found that the latter computationally intensive approach had substantially lower power over the parameter values considered in our simulations.
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