A novel nonlinear dimension reduction approach to infer population structure for low-coverage sequencing data

BMC Bioinformatics

Hua Zhou¹  Jin Zhou²  Yiwen Liu³  Joseph Watkins³  Miao Zhang⁴ 

[1] Department of Biostatistics, University of California, Los Angeles;Department of Epidemiology and Biostatistics, University of Arizona;Department of Mathematics, University of Arizona;Interdisciplinary Program in Statistics and Data Science, University of Arizona;

关键词: Dimension reduction;    Non-linear kernel;    Low-coverage;    Population structure;    Data-adaptive;   

DOI  :  10.1186/s12859-021-04265-7

来源: DOAJ

【 摘 要 】

Abstract Background Low-depth sequencing allows researchers to increase sample size at the expense of lower accuracy. To incorporate uncertainties while maintaining statistical power, we introduce MCPCA_PopGen to analyze population structure of low-depth sequencing data. Results The method optimizes the choice of nonlinear transformations of dosages to maximize the Ky Fan norm of the covariance matrix. The transformation incorporates the uncertainty in calling between heterozygotes and the common homozygotes for loci having a rare allele and is more linear when both variants are common. Conclusions We apply MCPCA_PopGen to samples from two indigenous Siberian populations and reveal hidden population structure accurately using only a single chromosome. The MCPCA_PopGen package is available on https://github.com/yiwenstat/MCPCA_PopGen .

【 授权许可】

Unknown