Breeding and Genetics Symposium: networks and pathways to guide genomic selection

Area of Science:

• Animal Genetics and Breeding
• Genomics
• Quantitative Genetics

Background:

• Polygenic traits significantly impact livestock profitability and sustainability.
• Traditional breeding methods like phenotypic selection have achieved substantial genetic gains.
• Genomic selection (GS) utilizes dense single nucleotide polymorphisms (SNPs) to enhance prediction accuracy.

Purpose of the Study:

• To explore methods for identifying genes influencing polygenic traits in livestock.
• To investigate how incorporating functional genomic information can improve genomic predictions.
• To facilitate functional genomic selection for economically important traits.

Main Methods:

• Leveraging associations between dense SNP genotypes and phenotypes.
• Analyzing multi-trait associations to define gene networks.
• Classifying SNPs within network genes based on their expected effects on gene products.
• Integrating livestock genotypes, phenotypes, gene expression, and genomic variants with existing biological knowledge.

Main Results:

• Genomic predictions incorporating functional SNP information showed higher accuracy than naive whole-genome predictions (e.g., for beef tenderness).
• Gene networks derived from multi-trait associations, when corroborated with functional annotation, provide compelling insights.
• Functional genomic selection offers a promising approach to increase selection accuracy and accelerate genetic improvement.

Conclusions:

• Identifying specific variants for polygenic traits remains challenging but is aided by various tools.
• Coupling diverse data sources (genomics, phenomics, transcriptomics, functional knowledge) enhances understanding of polygenic trait mechanisms.
• Functional genomic selection can significantly improve breeding strategies for economically important livestock traits.