Visualization of results from genomic evaluations.

Area of Science:

• Animal Genetics
• Bioinformatics
• Data Visualization

Background:

• Genomic predictions of estimated breeding values (EBV) in dairy cattle involve vast datasets with tens of thousands of markers across 30 chromosomes.
• The sheer volume of genomic data makes direct comparison, detailed analysis, and tabulation challenging.

Purpose of the Study:

• To explore the utility of graphical representations for visualizing complex genomic prediction data in dairy cattle.
• To demonstrate how well-designed graphics can enhance data interpretation and reveal insights obscured by large datasets.

Main Methods:

• Developing and applying various graphical methods to visualize genomic data, including marker effect distributions across the genome and chromosomal genetic correlations.
• Utilizing techniques such as plotting marker effects, differentiating chromosomes with colors/textures, and creating stacked graphs for trait comparisons.
• Generating high-resolution graphics for chromosomal EBV and using small multiples for genetic correlation matrices.

Main Results:

• Graphics present more information in a smaller area, facilitating easier detection of subtle differences compared to data grids.
• Visualizations reveal the distribution of marker effects across the genome and relationships among markers.
• Specific graphical approaches identified patterns of association among traits (e.g., on chromosome 18) and potential locations of causative mutations for recessive traits.

Conclusions:

• Graphical visualization significantly improves the interpretability of complex genomic prediction data for dairy cattle.
• These methods allow for denser information presentation and easier identification of genetic patterns and regions of interest.
• Automated generation of these graphics can be integrated into online systems, offering novel insights at minimal cost.