The use of DNA testing to predict the genetic merit of dairy and beef cattle has become  commonplace since the introduction of the 50,000 single nucleotide polymorphism chip in 2009. The dairy industry rapidly adopted the technology, and as of 2015, the dairy industry is on track to have run genomic tests on over 1 million animals. To date, the accurate prediction of genetic merit using genomic information has been limited to within-breed predictions. This means that every breed needs to develop its own reference population of at least 1,000 phenotyped, genotyped animals to develop accurate genomic prediction equations.  Additionally, equations need to be continually updated or refreshed each generation to remain accurate. As a result, the promise of genomic technology has been realized to a much more modest extent in the beef industry, in part due to the presence of a number of different beef breeds, necessitating the development of separate reference populations for each breed.

The traits that can be predicted using genomics are restricted to those for which phenotypes are available in the reference population. As such, all of the traits that breed associations typically collect data on were available to enable the development of genomic predictions for those traits. However, these are the “easy” traits — in that they are often relatively easy to measure, and genetic merit estimates were already available on these traits prior to the development of genomics.  There are many valuable and economically relevant traits for which there are no records and hence no selection criteria, and therefore, they are not currently included in genetic improvement programs. Typically these are hard or expensive-to-measure traits, or those that are collected late in an animal’s life.

In the dairy industry, researchers have variously investigated selection for a number of novel phenotypes including milk fatty acid composition, persistency of lactation, rectal temperature, residual feed intake and feed efficiency, methane production, claw health, immune response and dairy-cattle health. The beef industry is also examining the potential to select for feed efficiency, improved fertility and cattle health. All of these novel phenotypes are not routinely recorded, and for some of them (e.g., individual feed intake), the cost of routine recording is prohibitive.               

Bovine respiratory disease (BRD) complex is a trait that falls into this category. Complex diseases, such as BRD, involve the influence of many genes, and as a result, disease outcomes are difficult to predict. However, incorporating genomic information into the calculation of genetic merit estimates for mastitis in dairy cattle has demonstrated the ability to predict and select for disease risk. Preliminary data show BRD susceptibility has moderate heritability (0.21), supporting the development of genetic predictions of BRD to enable selection for this important trait complex.

Despite the fact that BRD is the leading cause of mortality in both the beef and dairy industries nationally, routine recording of disease incidence is not currently being fed back into the national genetic evaluation systems. However, it is clearly a very valuable trait. In an economic simulation of the relative economic value of selection, it was determined that selection to avoid BRD should be weighted approximately seven times more heavily in a terminal sire selection index than weaning weight, post-weaning average daily gain and feed intake, and that these traits should receive two to three times more emphasis than marbling score and yield grade. 

It was for this reason that a group of geneticists at several large U.S. universities got together and obtained USDA funding for a project called the Bovine Respiratory Disease Coordinated Agricultural Project to attempt to address this problem using the tools of genomics.

The premise behind the project was to take DNA from large (> 1,000) cohorts of both Holstein dairy calves and Bos taurus feedlot beef cattle that were diagnosed with BRD, using the McGuirk standardized scoring system, and their immediate neighbor or pen mate that remained healthy. The DNA profiles were then compared between these “cases” and “controls” using something called a “Genome Wide Association Study.” The goal was to find genetic markers in these “reference population” that could be used to predict susceptibility to BRD in selection or test candidates.

The results of the dairy study in pre-weaned Holstein calves showed over 100 genomic regions that were significantly associated with BRD, many of which were associated with biologically meaningful genes. The team is now trying to close in on the actual causative mutations that are resulting in these associations. It is possible that this hunt will be facilitated by imputation of the genotypes of animals in the reference population using data from the 1000 Bulls project. As suggested by its name, this large project is sequencing the entire 3 billion base-pair sequence of over 1000 bulls of many different breeds. This information can be used to impute the full genome sequence of animals that have been genotyped using a lower density genotyping panel (Figure 1). An added advantage of this is that causative mutations are likely to be more persistent across generations.

Ultimately the markers identified will need to be tested to assess how much of the genetic variation in BRD susceptibility they are associated with in an independent validation population.  The purpose of this validation step is to use phenotypes available on an independent set of genotyped individuals to those used in the reference population to produce an estimate of the reliability of the genomic prediction for BRD susceptibility. The accuracy of genomic selection is known to rapidly decrease with increasing genetic distance between the reference population and the animal being tested, although this is likely to be less of a problem when using causative mutations. The individuals sampled to form the validation population should be representative of the likely selection candidates. Ultimately, it is envisioned that selection candidates will be genotyped, and in addition to other traits, the genomic information will provide a genetic prediction of BRD susceptibility. Given there is currently no selection criterion for this valuable trait, even if only a handful of mutations are reliably associated with genetic variation in BRD susceptibility, this would be extremely valuable information. 

Translation in industry

The incorporation of a diagnostic marker set for BRD susceptibility is perhaps easiest to envision in the dairy industry. This is partly due to the fact that there is a single predominant Holstein breed that was used in the reference population and the use of a single Lifetime Net Merit ($NM) selection index by the U.S. dairy industry. Selection indices provide a way to appropriately weight different traits into an economic index that provides a single value that ranks animals based on expected profit. The $NM has evolved over the years, starting off originally focused only on milk and fat production in 1971, and maturing in 2015 to a balanced index that considers 12 traits associated with production, reproduction, type traits and health traits.

The marker set that is shown to be associated with BRD susceptibility could be directly included on the genotyping chip that is currently being used for dairy genomic predictions. This development will require the incorporation of BRD susceptibility into the $NM index at an appropriate emphasis. Such calculations will depend upon how accurate the markers are at predicting BRD susceptibility in the selection population (i.e., reliability) and the economic value of the trait. Even if the markers predict only 20 percent of the genetic variation for this trait, this is likely to be valuable information given the significant economic costs associated with BRD. 

Translation to the beef industry is likely to be a little more problematic due both to the larger number of breeds and breed associations involved in beef-cattle genetic evaluation and the structure of the beef industry. We do not yet know whether the trait of BRD susceptibility in beef cattle will be associated with several large-effect single mutations or have a more polygenic inheritance pattern (i.e., be associated with many small-effect loci). In the former case we can likely genotype all breeds for these large-effect causative mutations and get accurate predictions that will be robust over generations. This assumes that the same causative mutations are segregating across breeds.  If BRD susceptibility is more polygenic in nature, this will make it more difficult to identify these small-effect causative mutations. Each breed association will need to assess how accurate the markers are at predicting BRD susceptibility in their breed.

Perhaps as difficult as these technical challenges is the industry challenge of incorporating the trait of disease susceptibility into beef-cattle selection indices and selection decisions. Not all beef breed associations have economic selection indices, which would leave the value determination of BRD susceptibility up to the individual breeder. Selection against BRD susceptibility would obviously have great value to the feedlot sector, but breeders will need some incentive to include it in their selection indices, especially given most producers do not retain ownership of their cattle through the feedyard.

There needs to be some value transfer of the benefits derived from procuring cattle that remain free from disease in the feedlot back to the producers who are providing those cattle. Such value transfer might be analogous to a backgrounding premium, but in this case the premium would be associated with including a cattle health trait in their breeding program.

The importance of recording health traits

Genomics has the potential to accelerate the rate of genetic improvement in low heritability, hard-to-measure traits such as disease status. Several studies show that the use of direct health observations is an effective way to incorporate heath traits into breeding programs. Such observations require a standardized system to record diagnoses to ensure phenotypes are comparable between farms. Consistent recording of health data is more difficult than for other traits due to subjectivity of diagnosis and reporting. Several studies have shown that for use in genetic evaluations, common health disorders recorded by farmers are of a similar quality as those documented by veterinarians. A recent study showed that genetic selection for health traits (including cystic ovaries, displaced abomasum, ketosis, lameness, mastitis, metritis and retained placenta) using producer-recorded health data collected from on-farm computer systems is feasible in the United States.

To be successful, there needs to be a balance between the effort required to collect these health data and subsequent benefits. Electronic systems that make such data capture easy and automated are likely key to the long-term success. The authors conclude that “The development of genomic selection methodologies, with accompanying substantial gains in reliability for low-heritability traits, may dramatically improve the feasibility of genetic improvement of dairy cow health.”

Conclusion

The USDA, through the Agricultural and Food Research Initiative competitive grants program, is investing in several other similar grants focused on using DNA-based technologies to make genetic progress in other traits that have proven difficult to improve using traditional selection schemes. These include projects focused on the development of genomic approaches to improve feed efficiency and fertility. None of these traits are the “low hanging fruit” of genetic improvement. They are typically traits that are measured late in life, are expensive to measure or are not routinely measured at all and frequently have low heritability, making it difficult to differentiate the genetic component of phenotype from the environmental influences. However, a successful adoption resulting in a 1 percent improvement in feed efficiency or fertility, or reduced BRD disease incidence would translate into a huge cost savings for the U.S. cattle industry.

Acknowledgements

The Bovine Respiratory Disease Consortium Coordinated Agricultural Project (BRD CAP) was funded by the USDA Agriculture and Food Research Initiative (AFRI Grant no. 2011-68004-30367; J. E. Womack, Texas A&M, PD). More information about the BRD CAP can be found at brdcomplex.org/.

This article is an abbreviated version of a paper that appears in the 2015 Proceedings of the 48th Annual Conference of the American Association of Bovine Practitioners, reproduced with permission.