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

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 .

SUBMITTER: Zhang M

PROVIDER: S-EPMC8236193 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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

Zhang Miao M Liu Yiwen Y Zhou Hua H Watkins Joseph J Zhou Jin J

BMC bioinformatics 20210626 1

<h4>Background</h4>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.<h4>Results</h4>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 ...[more]

PMID: 34174829

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