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United States Department of Agriculture

Agricultural Research Service

Research Project: Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information

Location: Animal Genomics and Improvement Laboratory

Title: Analysis of health trait data from on-farm computer systems in the U.S. I: Pedigree and genomic variance components estimation

Authors
item Parker Gaddis, K
item Cole, John
item Clay, J
item Maltecca, C

Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: February 23, 2013
Publication Date: July 8, 2013
Citation: Parker Gaddis, K.L., Cole, J.B., Clay, J.H., Maltecca, C. 2013. Analysis of health trait data from on-farm computer systems in the U.S. I: Pedigree and genomic variance components estimation. Journal of Dairy Science. 96(E-Suppl. 1):443–444 (abstr. 446).

Technical Abstract: With an emphasis on increasing profit through increased dairy cow production, a negative relationship with fitness traits such as fertility and health traits has become apparent. Decreased cow health can impact herd profitability through increased rates of involuntary culling and decreased or lost milk sales. Improvement of health traits through genetic selection is an appealing tool; however, there is no mandated or consistent recording system for health data in the US. Producer-recorded health information may provide a wealth of information for improvement of dairy cow health, thus improving the profitability of a farm. The principal objective of this study was to use health data collected from on-farm computer systems to estimate variance components and heritability for health traits commonly experienced by dairy cows. The single-step method was then used to incorporate genomic data in a multiple trait analysis. Single-trait binomial analyses were performed for nine health traits using a sire model. Health traits included cystic ovaries, digestive disorders, displaced abomasum, ketosis, lameness, mastitis, metritis, reproductive disorders, and retained placenta. Parity and year-season were included in the models as fixed effects and herd-year and sire were included in the models as random effects. Heritability estimates ranged from 0.027 (SE = 0.06) for cystic ovaries to 0.20 (SE = 0.02) for displaced abomasum. Variance component estimates were then used in a multiple trait analysis including the aforementioned health traits with the exception of reproductive disorders. Heritability estimates calculated from the multiple trait model ranged from 0.019 (95% HPD = 0.01, 0.03) for lameness to 0.13 (95% HPD = 0.11, 0.16) for displaced abomasum. A strong genetic correlation was found between displaced abomasum and ketosis (0.81) as well as between metritis and ketosis (0.45). The single-step genomic analysis calculated heritability estimates that ranged from 0.01 (95% HPD = 0.004, 0.014) for lameness to 0.09 (95% HPD = 0.073, 0.107) for mastitis. Comparable correlations were found using genomic information. From the results of these analyses, it was concluded that genetic selection for health traits using producer-recorded data is feasible.

Last Modified: 12/22/2014