ArticleHeredity2013
A sequential coalescent algorithm for chromosomal inversions.
Article in Heredity, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Not Just Ne Ne-More: New Applications for SMC from Ecology to Phylogenies.Genome biology and evolution · 2026Review
- Natural selection exerted by historical coronavirus epidemic(s): comparative genetic analysis in China Kadoorie Biobank and UK Biobank.BMC genomics · 2025Article
- The Length of Haplotype Blocks and Signals of Structural Variation in Reconstructed Genealogies.Molecular biology and evolution · 2025Article
- Efficient ancestry and mutation simulation with msprime 1.0.Genetics · 2022Article
- Chromosomal Speciation in the Genomics Era: Disentangling Phylogenetic Evolution of Rock-wallabies.Frontiers in genetics · 2017Article
- The Evolution of Genome Structure by Natural and Sexual Selection.The Journal of heredity · 2017Review
- Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes.PLoS computational biology · 2016Article
- Advances in computer simulation of genome evolution: toward more realistic evolutionary genomics analysis by approximate bayesian computation.Journal of molecular evolution · 2015Article
- Long range linkage disequilibrium across the human genome.PloS one · 2013Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chromosomal inversions are common in natural populations and are believed to be involved in many important evolutionary phenomena, including speciation, the evolution of sex chromosomes and local adaptation. While recent advances in sequencing and genotyping methods are leading to rapidly increasing amounts of genome-wide sequence data that reveal interesting patterns of genetic variation within inverted regions, efficient simulation methods to study these patterns are largely missing. In this work, we extend the sequential Markovian coalescent, an approximation to the coalescent with recombination, to include the effects of polymorphic inversions on patterns of recombination. Results show that our algorithm is fast, memory-efficient and accurate, making it feasible to simulate large inversions in large populations for the first time. The SMC algorithm enables studies of patterns of genetic variation (for example, linkage disequilibria) and tests of hypotheses (using simulation-based approaches) that were previously intractable.
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