ArticleGenetics2012
A novel approach for choosing summary statistics in approximate Bayesian computation.
Article in Genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- Evolutionary Dynamics in Structured Populations Under Strong Population Genetic Forces.G3 (Bethesda, Md.) · 2019Article
- Low genetic diversity in a critically endangered primate: shallow evolutionary history or recent population bottleneck?BMC evolutionary biology · 2019Article
- Speciation and hybridization in invasive fire ants.BMC evolutionary biology · 2019Article
- The impact of experimental design choices on parameter inference for models of growing cell colonies.Royal Society open science · 2018Article
- Supervised Machine Learning for Population Genetics: A New Paradigm.Trends in genetics : TIG · 2018Review
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- Likelihood-free inference via classification.Statistics and computing · 2018Article
- Inferring rooted species trees from unrooted gene trees using approximate Bayesian computation.Molecular phylogenetics and evolution · 2017Article
- Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study.PLoS computational biology · 2017Article
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- Fundamentals and Recent Developments in Approximate Bayesian Computation.Systematic biology · 2017Article
- Likelihood-Free Inference in High-Dimensional Models.Genetics · 2016Article
- Deep Learning for Population Genetic Inference.PLoS computational biology · 2016Article
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Authors and funding
3 authors.
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Abstract
The choice of summary statistics is a crucial step in approximate Bayesian computation (ABC). Since statistics are often not sufficient, this choice involves a trade-off between loss of information and reduction of dimensionality. The latter may increase the efficiency of ABC. Here, we propose an approach for choosing summary statistics based on boosting, a technique from the machine-learning literature. We consider different types of boosting and compare them to partial least-squares regression as an alternative. To mitigate the lack of sufficiency, we also propose an approach for choosing summary statistics locally, in the putative neighborhood of the true parameter value. We study a demographic model motivated by the reintroduction of Alpine ibex (Capra ibex) into the Swiss Alps. The parameters of interest are the mean and standard deviation across microsatellites of the scaled ancestral mutation rate (θ(anc) = 4N(e)u) and the proportion of males obtaining access to matings per breeding season (ω). By simulation, we assess the properties of the posterior distribution obtained with the various methods. According to our criteria, ABC with summary statistics chosen locally via boosting with the L(2)-loss performs best. Applying that method to the ibex data, we estimate θ(anc)≈ 1.288 and find that most of the variation across loci of the ancestral mutation rate u is between 7.7 × 10(-4) and 3.5 × 10(-3) per locus per generation. The proportion of males with access to matings is estimated as ω≈ 0.21, which is in good agreement with recent independent estimates.
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