PCAdmix: principal components-based assignment of ancestry along each chromosome in individuals with admixed ancestry from two or more populations. Academic Article Article uri icon

Overview

MeSH

  • Algorithms
  • Computer Simulation
  • Genomics
  • Humans
  • Phylogeography
  • United States

MeSH Major

  • Chromosomes, Human
  • Continental Population Groups
  • Genotype
  • Models, Genetic
  • Polymorphism, Single Nucleotide
  • Population Dynamics
  • Principal Component Analysis

abstract

  • Identifying ancestry along each chromosome in admixed individuals provides a wealth of information for understanding the population genetic history of admixture events and is valuable for admixture mapping and identifying recent targets of selection. We present PCAdmix (available at https://sites.google.com/site/pcadmix/home ), a Principal Components-based algorithm for determining ancestry along each chromosome from a high-density, genome-wide set of phased single-nucleotide polymorphism (SNP) genotypes of admixed individuals. We compare our method to HAPMIX on simulated data from two ancestral populations, and we find high concordance between the methods. Our method also has better accuracy than LAMP when applied to three-population admixture, a situation as yet unaddressed by HAPMIX. Finally, we apply our method to a data set of four Latino populations with European, African, and Native American ancestry. We find evidence of assortative mating in each of the four populations, and we identify regions of shared ancestry that may be recent targets of selection and could serve as candidate regions for admixture-based association mapping.

publication date

  • August 2012

has subject area

  • Algorithms
  • Chromosomes, Human
  • Computer Simulation
  • Continental Population Groups
  • Genomics
  • Genotype
  • Humans
  • Models, Genetic
  • Phylogeography
  • Polymorphism, Single Nucleotide
  • Population Dynamics
  • Principal Component Analysis
  • United States

Research

keywords

  • Comparative Study
  • Evaluation Studies
  • Journal Article

Identity

Language

  • eng

PubMed Central ID

  • PMC3740525

Digital Object Identifier (DOI)

  • 10.3378/027.084.0401

PubMed ID

  • 23249312

Additional Document Info

start page

  • 343

end page

  • 364

volume

  • 84

number

  • 4