Bayesian inference of ancient human demography from individual genome sequences

Abstract

Besides their value for biomedicine, individual genome sequences are a rich source of information about human evolution. Here we describe an effort to estimate key evolutionary parameters from sequences for six individuals from diverse human populations. We use a Bayesian, coalescent-based approach to extract information about ancestral population sizes, divergence times, and migration rates from inferred genealogies at many neutrally evolving loci from across the genome. We introduce new methods for accommodating gene flow between populations and integrating over possible phasings of diploid genotypes. We also describe a custom pipeline for genotype inference to mitigate biases from heterogeneous sequencing technologies and coverage levels. Our analysis indicates that the San of Southern Africa diverged from other human populations 108–157 thousand years ago (kya), that Eurasians diverged from an ancestral African population 38–64 kya, and that the effective population size of the ancestors of all modern humans was ~9,000.

Diversification in wild populations of the model organism Anolis carolinensis: A genome‐wide phylogeographic investigation

“…To obtain a secondary estimate of effective population sizes, as well as identify relative divergence timing, we used G‐PhoCS (Gronau et al., 2011). We used the recommended settings for the program that had been utilized in recent phylogeographic datasets (Campagna et al., 2015).…”

Tracing ancestor rice of Suriname Maroons back to its African origin

“…Here we establish the strong similarity, and hence likely origin, of the first extant New World landrace of O. glaberrima to landraces from the Upper Guinean forests in West Africa. We collected African rice from a Maroon market in Paramaribo, Suriname, propagated it, sequenced its genome 6 and compared it with genomes of 109 accessions representing O. glaberrima diversity across West Africa. By analysing 1,649,769 single nucleotide polymorphisms (SNPs) in clustering analyses, the Suriname sample appears sister to an Ivory Coast landrace, and shows no evidence of introgression from Asian rice.…”

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

“…GPho-CS produced a considerably lower TMRCA estimate for the Malay and Indian whole-genome sequencing data, and this may be because of two reasons: (1) GPho-CS has previously reported lower accuracy to infer recent events 16 and (2) GPho-CS relied on a different mutation rate. Presently, the method calibrates the mutation rate from the number of mutation events from an outgroup species to which the divergence time has to be assumed.…”

“…Presently, the method calibrates the mutation rate from the number of mutation events from an outgroup species to which the divergence time has to be assumed. 16 By including a chimpanzee sequence in the model and assuming the divergence time from chimpanzee to be 6.5 Mya, this produced an average mutation rate of 6.96 × 10 − 10 that is only 70% of the default mutation rate of 10 − 9 Figure 4 Malay-Indian TMRCA estimation by PSMC and MSMC. Illustrate the estimation of TMRCA by (a) PSMC and (b) MSMC on whole-genome sequencing data for the 22 autosomal chromosomes from Southeast Asian Malays and South Asian Indians.…”

“…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. 19 The third class of methods essentially infers the TMRCA on the extent of linkage disequilibrium (LD), population diversity measured by the F ST parameter and population allele frequency, such as the approaches by Hayes and colleagues (abbreviated subsequently as T-LD), 20,21 by McEvoy and colleagues (abbreviated subsequently as T-FST), 22 and DADI.…”