title: Computationally Efficient Composite Likelihood Statistics for Demographic Inference.
abstract: Many population genetics tools employ composite likelihoods, because fully modeling genomic linkage is challenging. But traditional approaches to estimating parameter uncertainties and performing model selection require full likelihoods, so these tools have relied on computationally expensive maximum-likelihood estimation (MLE) on bootstrapped data. Here, we demonstrate that statistical theory can be applied to adjust composite likelihoods and perform robust computationally efficient statistical inference in two demographic inference tools: ∂a∂i and TRACTS. On both simulated and real data, the adjustments perform comparably to MLE bootstrapping while using orders of magnitude less computational time.
authors.affiliation: Department of Molecular and Cellular Biology, University of Arizona.
authors.forename: Alec J
authors.initials: AJ
authors.lastname: Coffman
authors.affiliation: Department of Ecology and Evolutionary Biology, University of Arizona.
authors.forename: Ping Hsun
authors.initials: PH
authors.lastname: Hsieh
authors.affiliation: Department of Human Genetics and Genome Quebec Innovation Centre, McGill University, Montreal, QC, Canada.
authors.forename: Simon
authors.initials: S
authors.lastname: Gravel
authors.affiliation: Department of Molecular and Cellular Biology, University of Arizona rgutenk@email.arizona.edu.
authors.forename: Ryan N
authors.initials: RN
authors.lastname: Gutenkunst
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003710:Demography; D005828:Genetics, Population / Q000379:methods*; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic
keywords: composite likelihood; demographic inference; likelihood ratio test; parameter uncertainties