Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/3/2010
Publication Date: 6/1/2010
Publication URL: http://hdl.handle.net/10113/43294
Citation: Cole, J.B., Van Raden, P.M. 2010. Visualization of Results from Genomic Evaluations. Journal of Dairy Science. 93(6):2727-2740. Interpretive Summary: Genomic predictions of estimated breeding values (EBV) include effects of tens-of-thousands of markers distributed over thirty chromosomes for many traits. With so many numbers, comparisons are difficult and tables quickly grow very large. Guidelines are provided for presenting genomic data graphically. Genetic variances can be plotted to compare chromosomes. Potential mates may be compared using chromosomal EBV. Interesting associations among traits can be identified using correlation matrices. Marker effects can be used to locate causative loci for new recessives. Graphics can be produced automatically and added to online query systems. High-quality graphics can improve our understanding of the data.
Technical Abstract: Genomic predictions of estimated breeding values (EBV) include effects of tens-of-thousands of markers distributed over thirty chromosomes for many traits. There are so many numbers that data are difficult to compare, levels of detail are obscured, and data cannot easily be tabulated. Well-designed graphics can present more information in a smaller area than text or tables and provide insight into the data. Humans can detect subtle differences among graphics more easily than among data grids, allowing information to be presented with greater density. Genomic data can be visualized at several levels, such as the distribution of marker effects across the genome and relationships among markers on the same chromosome. All markers affecting a trait can be plotted on the same ordinate to visualize the distribution of marker effects across the genome, colors or textures can be used to differentiate between chromosomes, and stacked graphs can be constructed to compare interesting groups of traits. Chromosomal EBV can be presented as sparklines to provide an overview of individual animals for comparison to potential mates. Small multiples of chromosomal genetic correlation matrices can be used in conjunction with edge exclusion graphs to identify interesting patterns of association among traits, such as that on chromosome 18 associated with calving traits, conformation, and economic merit. Line plots of marker effects for autosomal recessives can be used to quickly locate chromosomal regions in which causative mutations are probably located, identifying areas of interest for further study.