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United States Department of Agriculture

Agricultural Research Service

Research Project: IMPROVING GENETIC PREDICTIONS FOR DAIRY ANIMALS USING PHENOTYPIC AND GENOMIC INFORMATION Title: Visualization of Results from Genomic Predictions

Author
item Cole, John

Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: March 5, 2009
Publication Date: July 12, 2009
Citation: Cole, J.B. 2009. Visualization of Results from Genomic Predictions. Journal of Dairy Science. 92(E-Suppl. 1):314(abstr. 281).

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. Graphics can present data with higher density than text or tables and provide additional insight into the data. Estimates of marker effects are not currently exchanged between countries but plots of results can be shared without disclosing sensitive information. Genomic data can be visualized at several levels, such as the distribution of marker effects across the genome, proportions of additive genetic variance explained by markers on a chromosome, and relationships among markers on the same chromosome. Ratios of actual to expected genetic variance can be plotted as bar graphs, making it easy to identify chromosomes that deviate from expectations; stacked bar graphs allow for simultaneous comparisons of methods of estimating variance ratios. 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, 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 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. These graphics are easily produced automatically and add to online query systems, providing users with novel information at little cost.

Last Modified: 4/16/2014
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