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Title: 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 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 to samples from two indigenous Siberian populations and reveal hidden population structure accurately using only a single chromosome. The package is available onhttps://github.com/yiwenstat/MCPCA_PopGen.

 
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Award ID(s):
2054253
NSF-PAR ID:
10252957
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Springer Science + Business Media
Date Published:
Journal Name:
BMC Bioinformatics
Volume:
22
Issue:
1
ISSN:
1471-2105
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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