solGS

solGS predicts genomic estimated breeding values (GEBVs) from high-throughput genome-wide marker data to support genomic selection for quantitative traits in breeding programs.

Key Features:

• Genomic Estimated Breeding Values (GEBVs): Implements prediction of GEBVs using genomic marker data.
• RR-BLUP Modeling: Applies Ridge-Regression Best Linear Unbiased Predictor (RR-BLUP) for genomic prediction.
• High-throughput Genome-wide Markers: Operates on high-density genome-wide marker datasets for selection analyses.
• Data Management: Uses the Chado Natural Diversity database schema for storage and management of genomic and diversity data.
• Statistical Analyses: Estimates phenotypic correlation and heritability and computes selection indices.
• Data Visualization and Export: Generates graphical visualizations of analysis outputs and provides exports in text format.
• Adaptability: Employs a modular design to accommodate diverse datasets and breeding program requirements.

Scientific Applications:

• Genomic selection in breeding programs: Enables selection based on predicted GEBVs to improve quantitative trait outcomes and genetic gain.
• Implementation in cassava research: Applied within the NEXTGEN Cassava breeding database for cassava breeding and research.

Methodology:

Applies Ridge-Regression Best Linear Unbiased Predictor (RR-BLUP) to genome-wide marker data to predict GEBVs; estimates phenotypic correlation and heritability; calculates selection indices; stores data using the Chado Natural Diversity schema; and produces graphical outputs with text-format export.