ArticleG3 (Bethesda, Md.)2022
Gene flow biases population genetic inference of recombination rate.
Article in G3 (Bethesda, Md.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Inferring fine-scale rates of mutation and recombination in the coppery titi monkey (Plecturocebus cupreus).Heredity · 2026Article
- Natural selection and recombination interact to structure genome-wide variation in pines.The Plant journal : for cell and molecular biology · 2026Article
- Recent Insights Into the Evolutionary Genomics of the Critically Endangered Aye-Aye (Daubentonia madagascariensis).American journal of primatology · 2025Review
- Mixed Outcomes in Recombination Rates After Domestication: Revisiting Theory and Data.Molecular ecology · 2025Review
- Inferring Fine-Scale Mutation and Recombination Rate Maps in Aye-Ayes (Ecology and evolution · 2025Article
- Accounting for Chimerism in Demographic Inference: Reconstructing the History of Common Marmosets (Callithrix jacchus) from High-Quality, Whole-Genome, Population-Level Data.Molecular biology and evolution · 2025Article
- Diversity in Recombination Hotspot Characteristics and Gene Structure Shape Fine-Scale Recombination Patterns in Plant Genomes.Molecular biology and evolution · 2024Article
- On the estimation of genome-average recombination rates.Genetics · 2024Article
- High prevalence of PRDM9-independent recombination hotspots in placental mammals.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- The Effects of Mutation and Recombination Rate Heterogeneity on the Inference of Demography and the Distribution of Fitness Effects.Genome biology and evolution · 2024Article
- The role of recombination dynamics in shaping signatures of direct and indirect selection across theProceedings. Biological sciences · 2024Article
- Fine-Scale Map Reveals Highly Variable Recombination Rates Associated with Genomic Features in the Eurasian Blackcap.Genome biology and evolution · 2024Article
- Deciphering dengue: novel RNA barcoding segments for enhanced serotype-specific identification and global surveillance of dengue viruses.Frontiers in microbiology · 2024Article
- The effects of mutation and recombination rate heterogeneity on the inference of demography and the distribution of fitness effects.bioRxiv : the preprint server for biology · 2023Article
- Rapid Evolution of the Fine-scale Recombination Landscape in Wild House Mouse (Mus musculus) Populations.Molecular biology and evolution · 2023Article
- Article
- The recombination landscape of the Khoe-San likely represents the upper limits of recombination divergence in humans.Genome biology · 2022Article
- Recommendations for improving statistical inference in population genomics.PLoS biology · 2022Article
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2 authors.
Funding
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Abstract
Accurate estimates of the rate of recombination are key to understanding a host of evolutionary processes as well as the evolution of the recombination rate itself. Model-based population genetic methods that infer recombination rates from patterns of linkage disequilibrium in the genome have become a popular method to estimate rates of recombination. However, these linkage disequilibrium-based methods make a variety of simplifying assumptions about the populations of interest that are often not met in natural populations. One such assumption is the absence of gene flow from other populations. Here, we use forward-time population genetic simulations of isolation-with-migration scenarios to explore how gene flow affects the accuracy of linkage disequilibrium-based estimators of recombination rate. We find that moderate levels of gene flow can result in either the overestimation or underestimation of recombination rates by up to 20-50% depending on the timing of divergence. We also find that these biases can affect the detection of interpopulation differences in recombination rate, causing both false positives and false negatives depending on the scenario. We discuss future possibilities for mitigating these biases and recommend that investigators exercise caution and confirm that their study populations meet assumptions before deploying these methods.
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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 Socratic graph.