solGS: a web-based tool for genomic selection

Abstract

Background: 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.

Results: We 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.

Conclusions: solGS 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.

Figure 1

Example of single prediction model output. A single trait model output includes model summary (A), a graphical representation of the phenotype data (collapsed; Figure 2), model accuracy (B), the GEBVs of individuals in the training population (C), and marker effects (collapsed). From the same model page, breeders can apply the model to predict GEBVs of selection populations (D, E). GEBVs can be viewed in the browser using interactive graphs and be downloaded in text format.

Figure 2

Graphical representation of phenotype data used in a model. Panel A shows an example interactive scatter plot of the phenotype data used in the model, where as panel B displays the frequency distribution of the same phenotype data.

Figure 3

Relationship plots. Panel A shows an example phenotypic correlation among traits in a training population. Panel B shows the relationship between the GEBVs and phenotype values (as deviations from the mean) for a trait in a training population. Mousing over a data point in both plots shows the data for the corresponding coordinates.

Figure 4

Example of multiple prediction models output. Panel A shows a list of models simultaneously fitted for multiple traits from a single training population (Additional file 2), with their correspoxnding accuracy and heritability of the traits. Detailed results of each model can be viewed by clicking the trait names (Figure 1). In panels B and C are lists of selection populations that the models can simultaneously be applied to estimate the GEBVs for the respective traits. Display of a trait name indicates the prediction for the trait is done. In panel D, the selection index calculator is shown for individuals, from training and selection populations, with GEBVs.