title: Study of large and highly stratified population datasets by combining iterative pruning principal component analysis and structure.
abstract: BACKGROUND
  The ever increasing sizes of population genetic datasets pose great challenges for population structure analysis. The Tracy-Widom (TW) statistical test is widely used for detecting structure. However, it has not been adequately investigated whether the TW statistic is susceptible to type I error, especially in large, complex datasets. Non-parametric, Principal Component Analysis (PCA) based methods for resolving structure have been developed which rely on the TW test. Although PCA-based methods can resolve structure, they cannot infer ancestry. Model-based methods are still needed for ancestry analysis, but they are not suitable for large datasets. We propose a new structure analysis framework for large datasets. This includes a new heuristic for detecting structure and incorporation of the structure patterns inferred by a PCA method to complement STRUCTURE analysis.
  
  
  RESULTS
  A new heuristic called EigenDev for detecting population structure is presented. When tested on simulated data, this heuristic is robust to sample size. In contrast, the TW statistic was found to be susceptible to type I error, especially for large population samples. EigenDev is thus better-suited for analysis of large datasets containing many individuals, in which spurious patterns are likely to exist and could be incorrectly interpreted as population stratification. EigenDev was applied to the iterative pruning PCA (ipPCA) method, which resolves the underlying subpopulations. This subpopulation information was used to supervise STRUCTURE analysis to infer patterns of ancestry at an unprecedented level of resolution. To validate the new approach, a bovine and a large human genetic dataset (3945 individuals) were analyzed. We found new ancestry patterns consistent with the subpopulations resolved by ipPCA.
  
  
  CONCLUSIONS
  The EigenDev heuristic is robust to sampling and is thus superior for detecting structure in large datasets. The application of EigenDev to the ipPCA algorithm improves the estimation of the number of subpopulations and the individual assignment accuracy, especially for very large and complex datasets. Furthermore, we have demonstrated that the structure resolved by this approach complements parametric analysis, allowing a much more comprehensive account of population structure. The new version of the ipPCA software with EigenDev incorporated can be downloaded from http://www4a.biotec.or.th/GI/tools/ippca.
authors.affiliation: Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand.
authors.forename: Tulaya
authors.initials: T
authors.lastname: Limpiti
authors.forename: Apichart
authors.initials: A
authors.lastname: Intarapanich
authors.forename: Anunchai
authors.initials: A
authors.lastname: Assawamakin
authors.forename: Philip J
authors.initials: PJ
authors.lastname: Shaw
authors.forename: Pongsakorn
authors.initials: P
authors.lastname: Wangkumhang
authors.forename: Jittima
authors.initials: J
authors.lastname: Piriyapongsa
authors.forename: Chumpol
authors.initials: C
authors.lastname: Ngamphiw
authors.forename: Sissades
authors.initials: S
authors.lastname: Tongsima
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002417:Cattle / Q000235:genetics*; D005828:Genetics, Population; D015894:Genome, Human; D006239:Haplotypes; D006801:Humans; D044382:Population Groups / Q000235:genetics*; D025341:Principal Component Analysis