ArticleEcology and evolution2013
Why to account for finite sites in population genetic studies and how to do this with Jaatha 2.0.
Article in Ecology and evolution, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Reconciling Gene Tree Discordance and Biogeography in European Crows.Molecular ecology · 2025Article
- Article
- Genome-wide SNP profiling of worldwide goat populations reveals strong partitioning of diversity and highlights post-domestication migration routes.Genetics, selection, evolution : GSE · 2018Article
- New Insights on Water Buffalo Genomic Diversity and Post-Domestication Migration Routes From Medium Density SNP Chip Data.Frontiers in genetics · 2018Article
- Triallelic Population Genomics for Inferring Correlated Fitness Effects of Same Site Nonsynonymous Mutations.Genetics · 2016Article
- Computationally Efficient Composite Likelihood Statistics for Demographic Inference.Molecular biology and evolution · 2016Article
- Evaluating the ability of the pairwise joint site frequency spectrum to co-estimate selection and demography.Frontiers in genetics · 2015Article
- Keeping it local: evidence for positive selection in Swedish Arabidopsis thaliana.Molecular biology and evolution · 2014Article
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4 authors.
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Abstract
With the advent of next-generation sequencing technologies, large data sets of several thousand loci from multiple conspecific individuals are available. Such data sets should make it possible to obtain accurate estimates of population genetic parameters, even for complex models of population history. In the analyses of large data sets, it is difficult to consider finite-sites mutation models (FSMs). Here, we use extensive simulations to demonstrate that the inclusion of FSMs is necessary to avoid severe biases in the estimation of the population mutation rate θ, population divergence times, and migration rates. We present a new version of Jaatha, an efficient composite-likelihood method for estimating demographic parameters from population genetic data and evaluate the usefulness of Jaatha in two biological examples. For the first application, we infer the speciation process of two wild tomato species, Solanum chilense and Solanum peruvianum. In our second application example, we demonstrate that Jaatha is readily applicable to NGS data by analyzing genome-wide data from two southern European populations of Arabidopsis thaliana. Jaatha is now freely available as an R package from the Comprehensive R Archive Network (CRAN).
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