ArticleBMC genomics2021
GCA: an R package for genetic connectedness analysis using pedigree and genomic data.
Article in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- On the ability of the LR method to detect bias when there is pedigree misspecification and lack of connectedness.Genetics, selection, evolution : GSE · 2024Article
- SNP- and haplotype-based single-step genomic predictions for body weight, wool, and reproductive traits in North American Rambouillet sheep.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2023Article
- Estimation of Population-Specific Genetic Parameters Important for Long-Term Optimum Contribution Selection-Case Study on a Dairy Istrian Sheep Breed.Animals : an open access journal from MDPI · 2021Article
- Spatial modelling improves genetic evaluation in smallholder breeding programs.Genetics, selection, evolution : GSE · 2020Article
- An assessment of genomic connectedness measures in Nellore cattle.Journal of animal science · 2020Article
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2 authors.
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Abstract
backgroundGenetic 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.
resultsWe 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.
conclusionsThe 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.
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