Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, analysis, and sharing. A bioinformatics infrastructure for data storage and access, and user-friendly web-based tool for analysis and sharing output is needed to make GS more practical for breeders.
The authors have developed a web-based tool, called solGS, for predicting genomic estimated breeding values (GEBVs) of individuals, using a Ridge-Regression Best Linear Unbiased Predictor (RR-BLUP) model.
This work is part of the “Next Generation Cassava Breeding Project” which is supported by the UK Department for International Development, in partnership with the Bill & Melinda Gates Foundation.
Isaak Y Tecle, Jeremy D Edwards, Naama Menda, Chiedozie Egesi, Ismail Y Rabbi, Peter Kulakow, Robert Kawuki, Jean-Luc Jannink and Lukas A Mueller. solGS: a web-based tool for genomic selection. BMC Bioinformatics 2014 15:398 https://doi.org/10.1186/s12859-014-0398-7
solGS: a web-based tool for genomic selection
Published 14 December 2014