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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: Genomic selection for producer-recorded health event data in US dairy cattle

item Gaddis Parker, Kristen
item Cole, John
item Clay, John
item Maltecca, Christian

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/4/2014
Publication Date: 5/1/2014
Publication URL:
Citation: Gaddis Parker, K., Cole, J.B., Clay, J., Maltecca, C. 2014. Genomic selection for producer-recorded health event data in US dairy cattle. Journal of Dairy Science. 97(5):3190-3199.

Interpretive Summary: Measurements of production and health in the dairy cow are negatively related, and high-yielding cows are often more likely to get sick than average-performing cows. Costs associated with common diseases range from $39 (cystic ovaries) to $340 (left-displaced abomasum) per incident, and represent a substantial economic loss to farmers when all 9 million dairy cows in the US are considered. Genomic selection may be a valuable tool for breeding genetically superior cows for health traits, but such approaches usually depend on a national database containing millions of performance records. In this study, we combined pedigree and genotype data with health observations recorded by dairy farmers and used the information to predict the genetic merit of dairy bulls for problems commonly experienced by their daughters. Methods of grouping health events into categories were also explored. We found that all health traits studied are controlled in part by genetics, and that the use of DNA marker information increases the confidence we can place in our estimates of genetic merit. The use of genomic selection will allow farmers to produce cows butter able to resist common diseases, which will increase the profit to dairy farmers and reduce the need for veterinary treatments, such as antibiotics.

Technical Abstract: Emphasizing increased profit through increased dairy cow production has revealed a negative relationship with fitness and health traits. 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 U.S. Producer-recorded health information may provide a wealth of information for improvement of dairy cow health, thus improving profitability. 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. This was performed using only pedigree information, as well as using genomic data combined with pedigree data using the single-step method. The single-step method was used to incorporate genomic data in a multiple trait analysis. A multiple-trait threshold liability analysis was performed for seven health traits using a sire model. Health traits included cystic ovaries, displaced abomasum, ketosis, lameness, mastitis, metritis, and retained placenta. Parity and year-season were included as fixed effects, and herd-year and sire were included as random effects. Heritability estimates calculated from the multiple trait model ranged from 0.02 (SD = 0.005) for lameness to 0.22 (SD = 0.03) for displaced abomasum and 0.22 (SD = 0.04) for retained placenta. A significant genetic correlation was found between displaced abomasum and ketosis (0.66), as well as between retained placenta and metritis (0.56). The single-step genomic analysis produced heritability estimates that ranged from 0.02 (SD = 0.005) for lameness to 0.36 (SD = 0.08) for retained placenta, as well as comparable genetic correlations. Sire reliabilities increased on average approximately 30% with the incorporation of genomic data. From the results of these analyses, it was concluded that genetic selection for health traits using producer-recorded data is feasible, and that the inclusion of genomic data substantially improves reliabilities for these traits.

Last Modified: 10/16/2017
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