ArticleJournal of computational biology : a journal of computational molecular cell biology2016
Improved Versions of Common Estimators of the Recombination Rate.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Fine human genetic map based on UK10K data set.Human genetics · 2022Article
- LDJump: Estimating variable recombination rates from population genetic data.Molecular ecology resources · 2019Article
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2 authors.
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Abstract
The scaled recombination parameter [Formula: see text] is one of the key parameters, turning up frequently in population genetic models. Accurate estimates of [Formula: see text] are difficult to obtain, as recombination events do not always leave traces in the data. One of the most widely used approaches is composite likelihood. Here, we show that popular implementations of composite likelihood estimators can often be uniformly improved by optimizing the trade-off between bias and variance. The amount of possible improvement depends on parameters such as the sequence length, the sample size, and the mutation rate, and it can be considerable in some cases. It turns out that approximate Bayesian computation, with composite likelihood as a summary statistic, also leads to improved estimates, but now in terms of the posterior risk. Finally, we demonstrate a practical application on real data from Drosophila.
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