Background: 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. 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. Conclusions: 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.

中文翻译：

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GCA：使用谱系和基因组数据进行遗传连通性分析的R包

背景：遗传连通性是遗传评估的重要组成部分，因为它可以评估跨单位预测遗传值的可比性。遗传联系在全基因组预测中量化参考和验证集之间的联系方面也起着至关重要的作用。尽管它很重要，但没有可用于计算连接性统计信息的用户友好软件工具。结果：我们开发了GCA R软件包，以进行谱系和基因组数据的遗传联系分析。该软件根据预测误差方差或单位效应估计值的方差实现各种连通性统计信息的大量收集。GCA R软件包可在GitHub上获得，源代码作为开源提供。结论：GCA R软件包使用户可以轻松评估其数据的连通性。确定比较跨单位个体的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度也是有用的。