Genomic predictions of estimated breeding values (EBV) for dairy cattle include effects of tens of thousands of markers distributed over 30 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. Subtle differences can be detected more easily among graphics 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 high-resolution graphics embedded in text to provide an overview of individual animals for comparison to potential mates. Small multiples of chromosomal genetic correlation matrices from which nonsignificant values have been excluded can be used 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 recessive traits can be used to quickly locate chromosomal regions in which causative mutations are probably located, identifying areas of interest for further study. These graphics are easily produced automatically and added to online query systems, providing users with novel information at little cost.
2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.