title: FastPop: a rapid principal component derived method to infer intercontinental ancestry using genetic data.
abstract: BACKGROUND
  Identifying subpopulations within a study and inferring intercontinental ancestry of the samples are important steps in genome wide association studies. Two software packages are widely used in analysis of substructure: Structure and Eigenstrat. Structure assigns each individual to a population by using a Bayesian method with multiple tuning parameters. It requires considerable computational time when dealing with thousands of samples and lacks the ability to create scores that could be used as covariates. Eigenstrat uses a principal component analysis method to model all sources of sampling variation. However, it does not readily provide information directly relevant to ancestral origin; the eigenvectors generated by Eigenstrat are sample specific and thus cannot be generalized to other individuals.
  
  
  RESULTS
  We developed FastPop, an efficient R package that fills the gap between Structure and Eigenstrat. It can: 1, generate PCA scores that identify ancestral origins and can be used for multiple studies; 2, infer ancestry information for data arising from two or more intercontinental origins. We demonstrate the use of FastPop using 2318 SNP markers selected from the genome based on high variability among European, Asian and West African (African) populations. We conducted an analysis of 505 Hapmap samples with European, African or Asian ancestry along with 19661 additional samples of unknown ancestry. The results from FastPop are highly consistent with those obtained by Structure across the 19661 samples we studied. The correlations of the results between FastPop and Structure are 0.99, 0.97 and 0.99 for European, African and Asian ancestry scores, respectively. Compared with Structure, FastPop is more efficient as it finished ancestry inference for 19661 samples in 16 min compared with 21-24 h required by Structure. FastPop also provided scores based on SNP weights so the scores of reference population can be applied to other studies provided the same set of markers are used. We also present application of the method for studying four continental populations (European, Asian, African, and Native American).
  
  
  CONCLUSIONS
  We developed an algorithm that can infer ancestries on data involving two or more intercontinental origins. It is efficient for analyzing large datasets. Additionally the PCA derived scores can be applied to multiple data sets to ensure the same ancestry analysis is applied to all studies.
authors.affiliation: Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. yafang.li@dartmouth.edu.
authors.forename: Yafang
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. jinyoung.byun@dartmouth.edu.
authors.forename: Jinyoung
authors.initials: J
authors.lastname: Byun
authors.affiliation: Department of Genetics, Dartmouth College, Hanover, NH, USA. guoshuai.cai@dartmouth.edu.
authors.forename: Guoshuai
authors.initials: G
authors.lastname: Cai
authors.affiliation: Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. xiangjun.xiao@dartmouth.edu.
authors.forename: Xiangjun
authors.initials: X
authors.lastname: Xiao
authors.affiliation: Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. Younghun.Han@dartmouth.edu.
authors.forename: Younghun
authors.initials: Y
authors.lastname: Han
authors.affiliation: Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. silenroc.olivier@gmail.com.
authors.forename: Olivier
authors.initials: O
authors.lastname: Cornelis
authors.affiliation: Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. jedinulos@gmail.com.
authors.forename: James E
authors.initials: JE
authors.lastname: Dinulos
authors.affiliation: Centre for Cancer Genetic Epidemiology, Cambridge University, Cambridge, UK. jgd29@cam.ac.uk.
authors.forename: Joe
authors.initials: J
authors.lastname: Dennis
authors.affiliation: Centre for Cancer Genetic Epidemiology, Cambridge University, Cambridge, UK. dfe20@medscho.cam.ac.uk.
authors.forename: Douglas
authors.initials: D
authors.lastname: Easton
authors.affiliation: Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. ivan.p.gorlov@dartmouth.edu.
authors.forename: Ivan
authors.initials: I
authors.lastname: Gorlov
authors.affiliation: Rowe Program in Genetics, U.C. Davis, Davis, CA, USA. mfseldin@ucdavis.edu.
authors.forename: Michael F
authors.initials: MF
authors.lastname: Seldin
authors.affiliation: Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. christopher.i.amos@dartmouth.edu.
authors.forename: Christopher I
authors.initials: CI
authors.lastname: Amos
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D005006:Ethnicity / Q000235:genetics*; D005828:Genetics, Population; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D060148:HapMap Project; D006801:Humans; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D025341:Principal Component Analysis; D044469:Racial Groups / Q000235:genetics*; D012984:Software