Modeling Human Population Separation History Using Physically Phased Genomes.
Genetics
; 205(1): 385-395, 2017 01.
Article
en En
| MEDLINE
| ID: mdl-28049708
ABSTRACT
Phased haplotype sequences are a key component in many population genetic analyses since variation in haplotypes reflects the action of recombination, selection, and changes in population size. In humans, haplotypes are typically estimated from unphased sequence or genotyping data using statistical models applied to large reference panels. To assess the importance of correct haplotype phase on population history inference, we performed fosmid pool sequencing and resolved phased haplotypes of five individuals from diverse African populations (including Yoruba, Esan, Gambia, Maasai, and Mende). We physically phased 98% of heterozygous SNPs into haplotype-resolved blocks, obtaining a block N50 of 1 Mbp. We combined these data with additional phased genomes from San, Mbuti, Gujarati, and Centre de'Etude du Polymorphism Humain European populations and analyzed population size and separation history using the pairwise sequentially Markovian coalescent and multiple sequentially Markovian coalescent models. We find that statistically phased haplotypes yield a more recent split-time estimation compared with experimentally phased haplotypes. To better interpret patterns of cross-population coalescence, we implemented an approximate Bayesian computation approach to estimate population split times and migration rates by fitting the distribution of coalescent times inferred between two haplotypes, one from each population, to a standard isolation-with-migration model. We inferred that the separation between hunter-gatherer populations and other populations happened â¼120-140 KYA, with gene flow continuing until 30-40 KYA; separation between west-African and out-of-African populations happened â¼70-80 KYA; while the separation between Maasai and out-of-African populations happened â¼50 KYA.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Genoma Humano
/
Genética de Población
/
Modelos Genéticos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
/
Male
Idioma:
En
Revista:
Genetics
Año:
2017
Tipo del documento:
Article