Abstract
BackgroundGenomic 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.ResultsWe 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. It has an intuitive web-interface for selecting a training population for modeling and estimating genomic estimated breeding values of selection candidates. It estimates phenotypic correlation and heritability of traits and selection indices of individuals. Raw data is stored in a generic database schema, Chado Natural Diversity, co-developed by multiple database groups. Analysis output is graphically visualized and can be interactively explored online or downloaded in text format. An instance of its implementation can be accessed at the NEXTGEN Cassava breeding database, http://cassavabase.org/solgs.ConclusionssolGS enables breeders to store raw data and estimate GEBVs of individuals online, in an intuitive and interactive workflow. It can be adapted to any breeding program.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0398-7) contains supplementary material, which is available to authorized users.
Highlights
Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods
All individuals in a trial with phenotype and genotype data can be used to create the prediction model for the trait. They can choose a trial or combination of trials, relevant to their target environment, and include all individuals in fitting the model. They will get the prediction model, its accuracy value, heritability of the trait, the genomeic estimated breeding values (GEBVs) of all the individuals used in the model, additive genetic effects of each marker, and a list of relevant selection populations to which the model can be applied to predict their GEBVs for the trait
Once relevant data is in the database, data analysis, visualization and sharing is a matter of point-and-click on an intuitively designed workflow
Summary
Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Individuals are genotyped, with the same set of markers as the training set, and the prediction model is used to predict their GEBVs for the trait of interest. They will get a list of training populations and trials containing individuals with genotype data and that are phenotyped for the trait of their interest (Additional file 2).
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