BayesRCO

BayesRCO implements a Bayesian hierarchical framework to improve genomic prediction of complex traits by modeling genome-wide genetic variation grouped into potentially overlapping annotation categories informed by prior biological knowledge for applications in livestock and plant breeding.

Key Features:

• Integration of Biological Annotations: Incorporates genomic annotations and prior biological information to inform the contribution of genome positions to trait variation.
• Handling Multi-Annotated Markers: Addresses markers associated with multiple annotations via two Bayesian approaches—BayesRC+ (a cumulative approach integrating contributions of multiple annotation categories) and BayesRC[Formula: see text] (a preferential model that interprets contributions selectively).
• Performance Evaluation: Validated using simulations across diverse genetic architectures and annotation types and applied to a backcross population of growing pigs, showing modest but significant improvements when informative annotations are used.
• Marker Prioritization and Interpretation: Prioritizes multi-annotated markers based on posterior variance (BayesRC+) and provides interpretative inference for multi-annotated markers (BayesRC[Formula: see text]).
• Annotation Construction Strategies: Emphasizes constructing relevant annotations from public databases to maximize prediction accuracy and biological relevance.

Scientific Applications:

• Livestock breeding: Improves genomic prediction and selection in livestock populations, demonstrated on a backcross population of growing pigs.
• Plant breeding: Enhances phenotype prediction and marker interpretation for crop improvement using genome-wide annotations.
• Genetic architecture analysis: Enables interpretation and prioritization of markers to elucidate genetic underpinnings of complex traits across diverse genetic architectures.

Methodology:

Bayesian hierarchical framework applied to genome-wide variants grouped into potentially overlapping annotation categories; two Bayesian models (BayesRC+ cumulative model and BayesRC[Formula: see text] preferential model); marker prioritization via posterior variance; evaluation via simulations with diverse genetic architectures and annotation types and application to a backcross population of growing pigs.