Study of large and highly stratified population datasets by combining iterative pruning principal component analysis and STRUCTU

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 si...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Tulaya Limpiti, Apichart Intarapanich, Anunchai Assawamakin, Philip J. Shaw, Pongsakorn Wangkumhang, Jittima Piriyapongsa, Chumpol Ngamphiw, Sissades Tongsima
• Keywords: Biometrics, BMCBI 2011, Population Samples, Population Stratification, Principal Component Analysis