Multi-allele species reconstruction using ASTRAL

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.