DOI: 10.6076/d17w2t
title: Multi-allele species reconstruction using ASTRAL

description: Genome-wide phylogeny reconstruction is becoming increasingly common, and
 one driving factor behind these phylogenomic studies is the promise that
 the potential discordance between gene trees and the species tree can be
 modeled. Incomplete lineage sorting is one cause of discordance that
 bridges population genetic and phylogenetic processes. ASTRAL is a species
 tree reconstruction method that seeks to find the tree with minimum
 quartet distance to an input set of inferred gene trees. However, the
 published ASTRAL algorithm only works with one sample per species. To
 account for polymorphisms in present-day species, one can sample multiple
 individuals per species to create multi-allele datasets. Here, we
 introduce how ASTRAL can handle multi-allele datasets. We show that the
 quartet-based optimization problem extends naturally, and we introduce
 heuristic methods for building the search space specifically for the case
 of multi-individual datasets. We study the accuracy and scalability of the
 multi-individual version of ASTRAL-III using extensive simulation studies
 and compare it to NJst, the only other scalable method that can handle
 these datasets. We do not find strong evidence that using multiple
 individuals dramatically improves accuracy. When we study the trade-off
 between sampling more genes versus more individuals, we find that sampling
 more genes is more effective than sampling more individuals, even under
 conditions that we study where trees are shallow (median length: ≈ 1Ne)
 and ILS is extremely high.
description: We simulate two new datasets
 (see Table below): a heterogeneous dataset
 (<em>D1</em>) where many parameters are simultaneously changed
 and ILS levels are extremely high. a more homogeneous
 dataset (<em>D2</em>) where parameters are less varied and the
 amount of ILS is controlled to create three model conditions.
 We use SimPhy to generate species trees and gene
 trees according to the MSC model. All replicates have 5 individuals per
 species. <em>D1.</em> This dataset includes 330
 replicates. The number of genes was uniformly sampled between 50 and 1000
 per replicate. The number of species was uniformly sampled between 20 and
 200. The species tree birth rate parameter is randomly sampled from a log
 uniform distribution in [10<sup>-7</sup>,10<sup>-6</sup>], and the death rate is sampled from a log uniform distribution, bounded from below by 10<sup>-7</sup> and bounded by the birth rate parameter from above. The population size is sampled from a uniform distribution in [10<sup>5</sup>,10<sup>6</sup>]. We sampled a maximum species tree height for each replicate from a log-normal distribution with an expected value of 500000 generations (ranging between 0.19M and 1M in 90% of replicates).  <em>D2.</em> This dataset has three model conditions, varying number of generations: 0.5M, 1M, or 2M. Each has 50 replicates with 200 species (and one outgroup) and 1000 genes with species birth rate set to 10<sup>-6</sup> under a birth-only model. The population size is 200,000. We also create two versions of it where only one or two individuals per species are randomly sub-sampled.