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Estimation of the covariance structure from SNP allele frequencies

  • Jan van Waaij , Zilong Li and Carsten Wiuf EMAIL logo
Published/Copyright: May 26, 2022
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Statistical Applications in Genetics and Molecular Biology
From the journal Statistical Applications in Genetics and Molecular Biology Volume 21 Issue 1

Abstract

We propose two new statistics, V ̂ and S ̂ , to disentangle the population history of related populations from SNP frequency data. If the populations are related by a tree, we show by theoretical means as well as by simulation that the new statistics are able to identify the root of a tree correctly, in contrast to standard statistics, such as the observed matrix of F 2-statistics (distances between pairs of populations). The statistic V ̂ is obtained by averaging over all SNPs (similar to standard statistics). Its expectation is the true covariance matrix of the observed population SNP frequencies, offset by a matrix with identical entries. In contrast, the statistic S ̂ is put in a Bayesian context and is obtained by averaging over pairs of SNPs, such that each SNP is only used once. It thus makes use of the joint distribution of pairs of SNPs. In addition, we provide a number of novel mathematical results about old and new statistics, and their mutual relationship.

Keywords: admixture model; F 2-statistics; SNP evolution
Corresponding author: Carsten Wiuf, Department of Mathematical Science, University of Copenhagen, Copenhagen 2100, Denmark, E-mail:

Acknowledgments

CW and JvW are supported by the Independent Research Fund Denmark (grant number: 8021-00360B) and the University of Copenhagen through the Data+ initiative. ZI is supported by the Novo Nordisk Foundation, Denmark (grant number: NNF20OC0061343).

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Received: 2022-01-21
Accepted: 2022-05-02
Published Online: 2022-05-26

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/sagmb-2022-0005
Keywords for this article
admixture model; F 2-statistics; SNP evolution

Articles in the same Issue

  1. Review Article
  2. Challenges for machine learning in RNA-protein interaction prediction
  3. Research Articles
  4. Distinct characteristics of correlation analysis at the single-cell and the population level
  5. pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples
  6. Use of SVM-based ensemble feature selection method for gene expression data analysis
  7. A robust association test with multiple genetic variants and covariates
  8. Estimation of the covariance structure from SNP allele frequencies
  9. GMEPS: a fast and efficient likelihood approach for genome-wide mediation analysis under extreme phenotype sequencing
  10. Sparse latent factor regression models for genome-wide and epigenome-wide association studies
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