Genetic data to be mined to identify mastitis resistance

Project to generate knowledge about what genes are related to mastitis and provide a prediction model for disease resistance.

September 9, 2020

4 Min Read
Genetic data to be mined to identify mastitis resistance

The University of Maryland (UMD) was recently awarded a grant from the U.S. Department of Agriculture's National Institute of Food & Agriculture (NIFA) to analyze millions of records maintained by the Council on Dairy Cattle Breeding (CDCB) in order to identify genes and underlying mechanisms for disease resistance in dairy cattle.

Under the new partnership among UMD, the dairy industry and NIFA, researchers will be able to capitalize on the wealth of industry data available to make strides in the genetic understanding and prevention of cattle diseases that haven't been feasible with smaller data sets, the university said.

“With this project, we are generating some knowledge about what genes are related to mastitis and other dairy cattle diseases as well as providing a model that predicts which animals are more resistant to the disease,” explained Li Ma, associate professor in animal and avian sciences at UMD and lead investigator on this grant. “If we can use this large data set to extract health traits and identify genes related to disease resistance, when a producer selects a sire or a cow for breeding, they can use information about health and not just focus on milk production and yield. This can create animals that are not only high producers but also disease resistant.”

According to Ma and CDCB, during the last 50 years, milk production from dairy animals in the U.S. has more than doubled by following traditional breeding paradigms and has skyrocketed even more recently with the introduction of genomic selection in 2009. Based on the DNA of dairy cattle, producers have been able to select for animals that will produce the most milk, naturally enhancing breeding practices and greatly improving production in the industry.

“Disease risk has also increased in the same time, however,” Ma said. “That’s why we try now to put more focus on disease resistance with a similar approach, using selection combined with DNA or genetics data to try to improve disease resistance with data already collected from the dairy industry.”

Collaborating with CDCB is a “win-win” for industry, academia and future research, Ma said. Huge volumes of data have been generated for production purposes but can be used for much more, he added.

“This big database maintained by CDCB is an industry-supported database where they upload data on the cow’s performance, reproduction, disease records -- anything that is important to the value of that cow. They send in a DNA sample to get a sense of the value of the cow, and all of those records — over 2 million cows and tens of millions of performance records — are maintained in this industry database. Data is already generated by industry and sitting there waiting for analysis. Now that the data is available, we can do more research and application for the industry and for animal welfare.”

This work will most directly benefit the dairy industry and the welfare of their cattle, Ma explained, because it will help producers select animals that are the most disease resistant. From a research perspective, however, there are many additional opportunities.

“People may also be interested in knowing which genes are related to the disease, which mutations or which versions of the gene are causing more disease and less disease,” Ma said. “So, people may also do some molecular biology functional studies, and this information will help to drive future research. For example, if there is a big genetic target identified from this project, researchers can use genome editing to help cure or correct the disease in question and inform future research methods or breeding practices. A better understanding of the disease itself and its underlying mechanisms is always important in fighting disease in the long run.”

With this grant, Ma will be looking at dairy cattle diseases as a whole, but with a particular emphasis on mastitis, which can only be studied from a genetic perspective with a very large data set, since it is mostly caused by an environmental pathogen.

“Mastitis is caused by a bacterial infection, so it is a very complicated disease to study with genomics," he said. "Because it is a complicated disease genetically, you need a large data set to have the power to see the true signal. Previous genetic studies haven’t had the power because of limited sample sizes, but with this database from industry and millions of records, we are now in a good position to study the genetic component and see how we can help prevent this disease and avoid the use of antibiotics whenever possible.”

Ma added, “Using a genomics approach, we are trying to enhance the ability of the animal to protect itself from the disease. This will not only reduce the incidence of mastitis but also the use of antibiotics, so long term, the benefit is higher for the animal’s welfare, from an environmental perspective and on the production side. This is a win for everybody involved.”

The research team includes Ma (principal investigator) and Zhengguo Xiao from animal and avian sciences at UMD, Christian Maltecca from North Carolina State University, Kristen Parker Gaddis from CDCB, Pamela Adkins from the University of Missouri and John Cole from USDA's Animal Genomics & Improvement Laboratory.

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