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GCA: An R package for genetic connectedness analysis using pedigree and genomic data

View ORCID ProfileHaipeng Yu, View ORCID ProfileGota Morota
doi: https://doi.org/10.1101/696419
Haipeng Yu
Virginia Polytechnic Institute and State University
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  • For correspondence: haipengyu@vt.edu
Gota Morota
Virginia Polytechnic Institute and State University
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  • For correspondence: morota@vt.edu
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Abstract

Background: Genetic connectedness is a critical component of genetic evaluation as it assesses the comparability of predicted genetic values across management units. Genetic connectedness also plays an essential role in quantifying the linkage between reference and validation sets in whole-genome prediction. Despite its importance, there is no user-friendly software tool available to calculate connectedness statistics. Results: We developed the GCA R package to perform genetic connectedness analysis for pedigree and genomic data. The software implements a large collection of various connectedness statistics as a function of prediction error variance or variance of unit effect estimates. The GCA R package is available at GitHub and the source code is provided as open source. Conclusion: The GCA R package allows users to easily assess the connectedness of their data. It is also useful to determine the potential risk of comparing predicted genetic values of individuals across units or measure the connectedness level between training and testing sets in genomic prediction.

Footnotes

  • The code boxes 4, 5 and 6 have been updated and typos have been fixed.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted July 09, 2019.
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GCA: An R package for genetic connectedness analysis using pedigree and genomic data
Haipeng Yu, Gota Morota
bioRxiv 696419; doi: https://doi.org/10.1101/696419
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GCA: An R package for genetic connectedness analysis using pedigree and genomic data
Haipeng Yu, Gota Morota
bioRxiv 696419; doi: https://doi.org/10.1101/696419

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