Article: Exploratory analysis for detecting population structures by iterative pruning based on independent component analysis Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2019 Vol.22 No.1 pp.61 - 74 Abstract: As population stratification can produce spurious associations in genome-wide association studies (GWAS), it is necessary to find hidden structures and assign individuals to subpopulations in advance. We suggest an exploratory approach for population structure analysis based on independent component analysis (ICA). ICA is unsupervised approach to identifying and separating mixed sources from observed signals with little prior information. We first reduce the dimensionality of samples by projecting the data to a lower-dimensional subspace built by ICA. The samples are then bisected using fuzzy clustering. Repeating this procedure until some predetermined stopping criterion such as negative entropy, we can detect the population structure and assign individuals to subpopulations. Information about the number of optimal subpopulations can also be obtained. We analyse simulated genotypic data with different degrees of structure. We compare the proposed method to other methods. Real data from the HapMap project are also analysed for illustration. Inderscience Publishers - linking academia, business and industry through research

Title: Exploratory analysis for detecting population structures by iterative pruning based on independent component analysis

Authors: Mira Park; Eunbin Choi; Heonsu Lee; Yongkang Kim; Taesung Park

Addresses: Department of Preventive Medicine, Eulji University, Daejeon, South Korea ' Data Analytics Team of Financial Crisis, Deloitte Anjin LLC, Seoul, South Korea ' Department of Statistics, Korea University, Seoul, South Korea ' Department of Statistics, Seoul National University, Seoul, South Korea ' Department of Statistics, Seoul National University, Seoul, South Korea

Abstract: As population stratification can produce spurious associations in genome-wide association studies (GWAS), it is necessary to find hidden structures and assign individuals to subpopulations in advance. We suggest an exploratory approach for population structure analysis based on independent component analysis (ICA). ICA is unsupervised approach to identifying and separating mixed sources from observed signals with little prior information. We first reduce the dimensionality of samples by projecting the data to a lower-dimensional subspace built by ICA. The samples are then bisected using fuzzy clustering. Repeating this procedure until some predetermined stopping criterion such as negative entropy, we can detect the population structure and assign individuals to subpopulations. Information about the number of optimal subpopulations can also be obtained. We analyse simulated genotypic data with different degrees of structure. We compare the proposed method to other methods. Real data from the HapMap project are also analysed for illustration.

Keywords: independent component analysis; pruning; genome-wide association study; negative entropy; sub-population.

DOI: 10.1504/IJDMB.2019.099288

International Journal of Data Mining and Bioinformatics, 2019 Vol.22 No.1, pp.61 - 74

Received: 18 Jan 2019
Accepted: 28 Jan 2019
Published online: 24 Apr 2019 *

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Title: Exploratory analysis for detecting population structures by iterative pruning based on independent component analysis

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