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

BMC Bioinformatics

Methodology Article

Tulaya Limpiti¹  Chumpol Ngamphiw²  Pongsakorn Wangkumhang²  Jittima Piriyapongsa²  Philip J Shaw²  Anunchai Assawamakin²  Sissades Tongsima²  Apichart Intarapanich³ 

[1] Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, 10520, Bangkok, Thailand;National Center for Genetic Engineering and Biotechnology, Thailand Science Park, 12120, Pathumthani, Thailand;National Electronics and Computer Technology Center, Thailand Science Park, 12120, Pathumthani, Thailand;

关键词: Complex Dataset;    Population Dataset;    African Individual;    Separate Subpopulation;    Ancestral Cluster;   

DOI  :  10.1186/1471-2105-12-255

 received in 2010-10-08, accepted in 2011-06-23,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsA 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.ConclusionsThe 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.

【 授权许可】

Unknown   
© Limpiti et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.