| Literature DB >> 32642765 |
Jun Huang1,2, Tomáš Flouri1, Ziheng Yang1.
Abstract
We use computer simulation to examine the information content in multilocus data sets for inference under the multispecies coalescent model. Inference problems considered include estimation of evolutionary parameters (such as species divergence times, population sizes, and cross-species introgression probabilities), species tree estimation, and species delimitation based on Bayesian comparison of delimitation models. We found that the number of loci is the most influential factor for almost all inference problems examined. Although the number of sequences per species does not appear to be important to species tree estimation, it is very influential to species delimitation. Increasing the number of sites and the per-site mutation rate both increase the mutation rate for the whole locus and these have the same effect on estimation of parameters, but the sequence length has a greater effect than the per-site mutation rate for species tree estimation. We discuss the computational costs when the data size increases and provide guidelines concerning the subsampling of genomic data to enable the application of full-likelihood methods of inference.Keywords: BPP; Bayesian inference; MSC; MSC with introgression; MSci; information content; multispecies coalescent
Mesh:
Year: 2020 PMID: 32642765 DOI: 10.1093/molbev/msaa166
Source DB: PubMed Journal: Mol Biol Evol ISSN: 0737-4038 Impact factor: 16.240