4.6 Article

Parsimonious Inference of Hybridization in the Presence of Incomplete Lineage Sorting

期刊

SYSTEMATIC BIOLOGY
卷 62, 期 5, 页码 738-751

出版社

OXFORD UNIV PRESS
DOI: 10.1093/sysbio/syt037

关键词

Phylogenetic networks; hybridization; incomplete lineage sorting; coalescent; multi-labeled trees

资金

  1. National Science Foundation [DBI-1062463, CCF-1302179]
  2. National Library of Medicine [R01LM009494]
  3. Alfred P. Sloan Research Fellowship
  4. Guggenheim Fellowship
  5. Direct For Computer & Info Scie & Enginr
  6. Division of Computing and Communication Foundations [1302179] Funding Source: National Science Foundation
  7. Div Of Biological Infrastructure
  8. Direct For Biological Sciences [1062463] Funding Source: National Science Foundation

向作者/读者索取更多资源

Hybridization plays an important evolutionary role in several groups of organisms. A phylogenetic approach to detect hybridization entails sequencing multiple loci across the genomes of a group of species of interest, reconstructing their gene trees, and taking their differences as indicators of hybridization. However, methods that follow this approach mostly ignore population effects, such as incomplete lineage sorting (ILS). Given that hybridization occurs between closely related organisms, ILS may very well be at play and, hence, must be accounted for in the analysis framework. To address this issue, we present a parsimony criterion for reconciling gene trees within the branches of a phylogenetic network, and a local search heuristic for inferring phylogenetic networks from collections of gene-tree topologies under this criterion. This framework enables phylogenetic analyses while accounting for both hybridization and ILS. Further, we propose two techniques for incorporating information about uncertainty in gene-tree estimates. Our simulation studies demonstrate the good performance of our framework in terms of identifying the location of hybridization events, as well as estimating the proportions of genes that underwent hybridization. Also, our framework shows good performance in terms of efficiency on handling large data sets in our experiments. Further, in analysing a yeast data set, we demonstrate issues that arise when analysing real data sets. Although a probabilistic approach was recently introduced for this problem, and although parsimonious reconciliations have accuracy issues under certain settings, our parsimony framework provides a much more computationally efficient technique for this type of analysis. Our framework now allows for genome-wide scans for hybridization, while also accounting for ILS.

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