Yu Haipeng, Morota Gota
Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, 24061, VA, USA.
BMC Genomics. 2021 Feb 15;22(1):119. doi: 10.1186/s12864-021-07414-7.
Genetic connectedness is a critical component of genetic evaluation as it assesses the comparability of predicted genetic values across 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.
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.
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.
遗传关联性是遗传评估的关键组成部分，因为它评估了各单位预测遗传值的可比性。遗传关联性在全基因组预测中量化参考集与验证集之间的联系方面也起着至关重要的作用。尽管其很重要，但目前尚无用于计算关联性统计量的用户友好型软件工具。
我们开发了GCA R包，用于对系谱和基因组数据进行遗传关联性分析。该软件实现了大量各种关联性统计量，这些统计量是预测误差方差或单位效应估计方差的函数。GCA R包可在GitHub上获取，其源代码作为开源代码提供。
GCA R包允许用户轻松评估其数据的关联性。它对于确定跨单位比较个体预测遗传值的潜在风险或测量基因组预测中训练集与测试集之间的关联程度也很有用。
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