Article (Scientific journals)
Iterative pruning PCA improves resolution of highly structured populations.
Intarapanich, Apichart; Shaw, Philip J.; Assawamakin, Anunchai et al.
2009In BMC Bioinformatics, 10, p. 382
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Keywords :
Algorithms; Animals; Computational Biology/methods; Genetic Variation; Genetics, Population; Humans; Models, Genetic; Population/genetics; Principal Component Analysis/methods
Abstract :
[en] BACKGROUND: Non-random patterns of genetic variation exist among individuals in a population owing to a variety of evolutionary factors. Therefore, populations are structured into genetically distinct subpopulations. As genotypic datasets become ever larger, it is increasingly difficult to correctly estimate the number of subpopulations and assign individuals to them. The computationally efficient non-parametric, chiefly Principal Components Analysis (PCA)-based methods are thus becoming increasingly relied upon for population structure analysis. Current PCA-based methods can accurately detect structure; however, the accuracy in resolving subpopulations and assigning individuals to them is wanting. When subpopulations are closely related to one another, they overlap in PCA space and appear as a conglomerate. This problem is exacerbated when some subpopulations in the dataset are genetically far removed from others. We propose a novel PCA-based framework which addresses this shortcoming. RESULTS: A novel population structure analysis algorithm called iterative pruning PCA (ipPCA) was developed which assigns individuals to subpopulations and infers the total number of subpopulations present. Genotypic data from simulated and real population datasets with different degrees of structure were analyzed. For datasets with simple structures, the subpopulation assignments of individuals made by ipPCA were largely consistent with the STRUCTURE, BAPS and AWclust algorithms. On the other hand, highly structured populations containing many closely related subpopulations could be accurately resolved only by ipPCA, and not by other methods. CONCLUSION: The algorithm is computationally efficient and not constrained by the dataset complexity. This systematic subpopulation assignment approach removes the need for prior population labels, which could be advantageous when cryptic stratification is encountered in datasets containing individuals otherwise assumed to belong to a homogenous population.
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
Intarapanich, Apichart
Shaw, Philip J.
Assawamakin, Anunchai
Wangkumhang, Pongsakorn
Ngamphiw, Chumpol
Chaichoompu, Kridsadakorn ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique
Piriyapongsa, Jittima
Tongsima, Sissades
Language :
English
Title :
Iterative pruning PCA improves resolution of highly structured populations.
Publication date :
2009
Journal title :
BMC Bioinformatics
eISSN :
1471-2105
Publisher :
BioMed Central, United Kingdom
Volume :
10
Pages :
382
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 31 January 2014

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