Iterative PCA for population structure analysis

An extension of principal component analysis called ipPCA has been proposed earlier for analyzing structure in genetic data. This non-parametric framework iteratively classifies individuals into subpopulations. However, it is prone to false positives when dealing with large datasets and mixedtype genetic markers. We address these shortcomings by introducing a unified encoding scheme and suggesting a new terminating criterion for ipPCA. To validate the improvements, simulated datasets as well as real bovine and large human genetic datasets are analyzed. It is observed that the estimation of the number of subpopulations and the individual assignment accuracy have been improved. Furthermore, the structure resolved by this approach can be used to identify subset of individuals for further parametric population structure analysis.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Tulaya Limpiti, Apichart Intarapanich, Anunchai Assawamakin, Pongsakorn Wangkumhang, Sissades Tongsima
• Keywords: ICASSP 2011, Mixedtype Genetic Markers, Parametric Population Structure, Principal Component Analysis, Signal Processing