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

Area of Science:

• Animal Breeding and Genetics
• Quantitative Genetics
• Bioinformatics

Background:

• Genetic connectedness is crucial for comparing genetic values across populations and linking reference/validation sets in genomic prediction.
• Existing methods lack user-friendly software for calculating essential connectedness statistics.
• This gap hinders efficient genetic evaluation and genomic prediction analyses.

Purpose of the Study:

• To develop a user-friendly software tool for comprehensive genetic connectedness analysis.
• To provide a flexible package for both pedigree and genomic data.
• To facilitate the assessment of genetic evaluation comparability and genomic prediction linkage.

Main Methods:

• Development of the GCA R package.
• Implementation of diverse connectedness statistics based on prediction error variance and unit effect variance.
• Open-source availability on GitHub for broad accessibility.

Main Results:

• The GCA R package enables straightforward calculation of genetic connectedness statistics.
• The software supports analysis for both pedigree and genomic datasets.
• It quantifies prediction error variance and unit effect variance.

Conclusions:

• The GCA R package offers an accessible solution for genetic connectedness analysis.
• It aids in evaluating the reliability of comparing genetic values across different units.
• The package is valuable for assessing training and testing set linkage in genomic prediction.