A new approach to estimate parameters of speciation models with application to apes

Abstract

How populations diverge and give rise to distinct species remains a fundamental question in evolutionary biology, with important implications for a wide range of fields, from conservation genetics to human evolution. A promising approach is to estimate parameters of simple speciation models using polymorphism data from multiple loci. Existing methods, however, make a number of assumptions that severely limit their applicability, notably, no gene flow after the populations split and no intralocus recombination. To overcome these limitations, we developed a new Markov chain Monte Carlo method to estimate parameters of an isolation-migration model. The approach uses summaries of polymorphism data at multiple loci surveyed in a pair of diverging populations or closely related species and, importantly, allows for intralocus recombination. To illustrate its potential, we applied it to extensive polymorphism data from populations and species of apes, whose demographic histories are largely unknown. The isolation-migration model appears to provide a reasonable fit to the data. It suggests that the two chimpanzee species became reproductively isolated in allopatry ∼850 Kya, while Western and Central chimpanzee populations split ∼440 Kya but continued to exchange migrants. Similarly, Eastern and Western gorillas and Sumatran and Bornean orangutans appear to have experienced gene flow since their splits ∼90 and over 250 Kya, respectively.

New insights on the speciation history and nucleotide diversity of three boreal spruce species and a Tertiary relict

“…Speciation parameters were estimated under an IM model using the software MIMAR (Becquet and Przeworski, 2007). MIMAR implements a Markov Chain Monte Carlo method to estimate speciation parameters from a slightly different version of Wakeley and Hey (1997) summary statistics.…”

“…Pinus taeda was the only outgroup in analyses involving P. breweriana, and both Pinus taeda and P. breweriana were used as outgroups (Ran et al, 2006) to minimize the error in inferring the ancestral states in pairwise analyses of the remaining three spruce species. S 1 , S 2 , S s and S f were then calculated according to the MIMAR manual supplied by Becquet and Przeworski (2007). Specifically, for polymorphic sites, if 0o f i p1 in each species, the allele is shared; if f i ¼ 0, f j ¼ 1, iaj, the allele is fixed in the sample j; and if f i ¼ 0, and 0o f j o1, iaj, the allele is specific to sample j.…”

“…We generated simulated datasets for parameters sampled from the posterior distribution estimated by MIMAR, and compared the observed values of a number of statistics: S 1 , S 2 , S s , S f , p, F st and Tajima's D to the distribution of the summary statistics of the simulated data in accordance with Becquet and Przeworski (2007). The P-value for each statistics was calculated for both standard IM model and growth model.…”

Limits of Spatial Vision in Sumatran Orangutans (Pongo abelii)

“…Evaluations of human phylogenetic relationships typically cite a common ancestor with chimpanzees (Pan troglodytes) 6 million years ago (MYA), and a common ancestor with orangutans 14 MYA (Goodman et al, 1998). Genetic comparisons estimate the split between two extant species of orangutan, Sumatran (Pongo abelii) and Bornean (Pongo pygmaeus), to be 330 thousand years ago (Mailund, Dutheil, Hobolth, Lunter, & Schierup, 2011) or even earlier (Becquet & Przeworski, 2007).…”

Estimating time to the most recent common ancestor (TMRCA): comparison and application of eight methods

“…10 Many of the existing population genetics inference and methodologies have been built on the foundation of the coalescent theory, [11][12][13] although these can be generally classified according to the type of genetic data used as input and the assumptions about population demography (Table 1). For example, one class of methods for estimating the time to the most recent common ancestor (TMRCA) considers multiple neutral loci each of ∼ 1000 bases only in multiple populations, such as MIMAR 14,15 and GPho-CS. 16 Another class of methods infers the TMRCA from full chromosomal information, such as CoalHMM, 17 PSMC, 18 and MSMC.…”