INFERRING ROOTED POPULATION TREES USING ASYMMETRIC NEIGHBOR JOINING

• 成果类型: Article
• 署名作者: Zhai, Yongliang; Bouchard-Cote, Alexandre
• 署名单位: University of British Columbia
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/16-AOAS964
• 发表日期: 2016
• 页码: 2047-2074
• 关键词: bayesian phylogenetic inference; maximum-likelihood; diffusion-processes; evolutionary trees; dna-sequences; differentiation; histories; algorithm; ape

摘要

We introduce a new inference method to estimate evolutionary distances for any two populations to their most recent common ancestral population using single-nucleotide polymorphism allele frequencies. Our model takes fixation into consideration, making it nonreversible, and guarantees that the distribution of reconstructed ancestral frequencies is contained on the interval [0, 1]. To scale this method to large numbers of populations, we introduce the asymmetric neighbor joining algorithm, an efficient method for reconstructing rooted bifurcating nonclock trees. Asymmetric neighbor joining provides a scalable rooting method applicable to any nonreversible evolutionary modeling setups. We explore the statistical properties of asymmetric neighbor joining, and demonstrate its accuracy on synthetic data. We validate our method by reconstructing rooted phylogenetic trees from the Human Genome Diversity Panel data. Our results are obtained without using an outgroup, and are consistent with the prevalent recent single-origin model.