Bovine respiratory disease (BRD)
Despite the fact that bovine respiratory disease (BRD) complex is the leading cause of mortality in the beef industry nationally, disease incidence data that may be routinely recorded at the feedlot level are not currently being fed back into the national genetic evaluation systems. However, BRD susceptibility is clearly a very valuable trait . In an economic simulation of the relative economic value of selection, it was determined that BRD incidence should be weighted approximately 7 times more heavily in a terminal sire selection index than weaning weight, postweaning average daily gain, or feed intake, and that these traits should receive 2-3 times more emphasis than marbling score and yield grade .
Commercially available records on BRD in beef cattle
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 of recording 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 in dairy cattle (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.
Many feedlots record data on BRD incidence and treatment. Currently these data are not being used for genetic improvement, and often are not shared beyond the feedyard. Yet this information, if appropriately collected, recorded, and submitted could result in valuable selection tools, especially if used in concert with DNA marker tests that may result from ongoing research.
The following is a summary of data recorded relative to BRD that are available from US feedlots using commercially available feedlot management software packages. A survey, conducted by the BIF BRD Guidelines Committee in cooperation with Production Animal Consultation, LLC, summarized the data currently being collected in two feedlot management software packages with the results shown below where the numbers in parentheses indicate the percent of records containing non-null entries for the data fields listed. Note that this data summary does not imply all data are correctly recorded and the committee acknowledges that the numbers may be skewed by situations where the data in question are not necessary (i.e. no severity score present when it represents a lung score for a non-BRD pull). However, as this summary indicates there is considerable potential to leverage information already routinely collected in the feedlot sector.
- In date (100%)
- Out date (100% if closed)
- Sex (100%)
- Owner (74%)
- Buyer (41%)
- Origin (71%)
- Starting average weight (100%)
- Ending average weight (100% if closed)
- Starting head (100%)
- Ending head (100% if closed)
- Risk (1%)
- Breed (0%)
- Date given (100%)
- Products applied (100%)
- Head applied to (92%)
- Charges for products applied (95%)
- Date (100%)
- Weight (99%)
- Rectal Temperature (74%)
- Severity score (41%; Note: Current data appears to be relatively vague in nature)
- Products applied (100%)
- Cost of products applied (69%)
- Pen rider (6%)
- Doctor (4%)
- Diagnosis (100% - doesn't mean it isn't unknown or other occasionally)
- Date died (100%)
- Date railed (100%)
- Pen rider for rail/death (1%)
- Doctor for rail/death (1%)
- Diagnosis for death/rail (100%)
- Death/rail weight (1%)
Feed info (when available)
- Daily ration formulation (25%)
- Daily pounds fed per lot (25%)
- Ration fed to lot per day (25%)
Feedlot Data Collection Recommendations:
Based on the survey results above, BIF suggests collection and reporting of a basic set of phenotype and management information that will ultimately be useful for genetic evaluation:
- Animal IDs for entire lot of cattle.
- Lot information: In and out dates, sex, owner/origin.
- Treatment information (tied to animal, only available on treated animals): Date, Rectal Temperature, Diagnosis (if available)
- Animal info: date died/railed for reason of clinical case of BRD, breed
The above information should then be applied to produce the following phenotype for BRD—a binary outcome indicating a “yes/no” for clinical signs of BRD. This will likely be the most widely available BRD-related phenotype.
In some scenarios, additional information may be available given a individual feedlot’s health protocol and/or diagnoses process. Of particular interest as additional data, given standard data recorded above, would be results from either Thoracic Ultrasound or scores from computer-aided auscultation systems (e.g. Whisper ®). In those cases where more detailed information is available, animals exhibiting clinical signs of BRD could be further classified into 2 categories, Presumed BRD and Confirmed BRD as follows:
- Presence of a presumed case of BRD (pBRD) is associated with an Increased Respiratory Rate and/or Effort, Evidence of Depression and Evidence of Reduced Feed Intake (lack of gut fill) although recording of these secondary symptoms beyond rectal temperature may not occur.
- Confirmed BRD (oBRD) will be defined as a case where there is objective evidence of lung pathology consistent with pneumonia. This evidence would either be Thoracic Ultrasound results obtained by a trained technician or Results of >1 on the “Whisper automated auscultation system”.
Depending on feedlot-specific protocols and recording, oBRD animals as defined above may be further classified as Active BRD or Chronic BRD as follows:
- Active BRD (aBRD) cases will be defined as an oBRD case plus a rectal temperature over 104F (40C) demonstrating evidence of an active inflammatory response.
- Chronic BRD (cBRD) cases will be defined as an oBRD case plus a rectal temperature below 104F (40C) or a record of previous treatment for BRD demonstrating evidence of the lack of an active inflammatory response
Genetic Evaluation Recommendations
Data collected above are best utilized for genetic improvement through genetic evaluation. As such, appropriate statistical models much be used for the calculation of those EPD. At a minimum this includes appropriate accounting for relevant fixed effects.
We fully expect pen to be an important environmental factor when conducting either traditional (i.e. quantitative) or genomic analyses of BRD phenotypes. In terms of shedding and transmission, the most likely vectors will be pen mates. Historically, we would have suggested adding pen to the contemporary group definition which would likely be defined similarly to currently used weaning contemporary group definitions (herd or origin, weight/arrival date, etc.). However, in the case of BRDC phenotypes, we are concerned such an approach may not result in useful data due to over specifying and subdividing contemporary group structure to the point that little variability exists. We suggest two approaches to alleviate this situation:
- Fit pen(lot) as a separate main effect outside of the traditional contemporary group structure. Thus, there will be fewer class effects to account for overall. This pen definition will likely require a season component to accommodate the reuse of the pen.
- Consider at least pen(lot) and possibly the traditional contemporary group effect as random rather than fixed. As a result, pen effects will be regressed (shrunk) relative to the information content of the pen. Given the undefined nature of the epidemiology involved and the fact the pens will often be confounded with genetics of the animal (due to animals from the same source being grouped), we feel this would be the best compromise in model fitting. This approach would also allow correlation structures amongst pens based on proximity to account for disease transmission across pens (should those data become available).
Contemporary groups without variability for incidence of BRD should be excluded from the analysis. Sex should be included in the statistical model.
If data are available with multiple levels of BRD categories (pBRD, aBRD, cBRD, oBRD), we suggest fitting a multiple-trait model to account for the differences in specificity of the traits being recorded. For example, the multiple-trait model would include BRD with multiple categories as a trait, and also utilize incidence data (yes/no for BRD) as a second trait; thereby maximizing use of data from both recording systems. We also suggest running other indicator traits such as entry weight, weaning weight, or carcass weight as correlated traits that may indicate fitness going into the lot or animals that are suffering from a lack of fitness upon harvest. Eventual use of these indicator traits will depend on their perceived relevance after estimating genetic correlations.
Future Guidelines Considerations
Defining BRD phenotypes is considerably more complex than other phenotypes typically recorded and used in genetic evaluation such as birth weight, weaning weight, etc. Given current ongoing research on BRD disease etiology, diagnoses, prevention, and treatment, these recommendations will likely need to be revisited on a more frequent basis than is typical for other performance traits.
One of the challenges the committee struggled with was whether the best approach would be guidelines for simple, easy to collect and record observations (e.g. treated for BRD; yes/no) as opposed to guidelines for more detailed, costly phenotypes requiring laboratory samples and analysis and/or analysis of post-mortem samples. There will likely be more observations recorded for simpler, less-detailed phenotypes but with a correspondingly lower heritability. Conversely more detailed, sparse phenotypes are likely more heritable with a greater degree of accuracy. Ultimately the committee chose to develop guidelines that allow for both approaches and could then be used in a multiple-trait setting for genetic evaluation of susceptibility to BRD.
The BIF greatly appreciates the time and effort of BRD Guidelines Committee:
Dr. Larry Kuehn, USDA Meat Animal Research Center, Nebraska
Dr. Dee Griffin, DVM, Great Plain Veterinary Educational Center, University of Nebraska
Dr. James Lowe, DVM, College of Veterinary Medicine, University of Illinois
Dr. Holly Neibergs, Department of Animal Sciences, Washington State University
Dr. Chris Seabury, Veterinary Medicine & Biomedical Sciences, Texas A&M University
Dr. Alison Van Eenennaam, University of California Cooperative Extension, University of California-Davis.
Dr. R. Mark Enns, Department of Animal Sciences, Colorado State University, BIF Western Region Secretary
- Neibergs, H. L., J. S. Neibergs, A. J. Wojtowicz, J. F. Taylor, C. M. Seabury and J. E. Womack (2014). Economic Benefits of using Genetic Selection to Reduce the Prevalence of Bovine Respiratory Disease Complex in Beef Feedlot Cattle. 46th Annual Research Symposium and Annual Meeting. Lincoln, Nebraska.
- Van Eenennaam, A. L. and M. D. MacNeil (2011). What weighting should be given bovine respiratory disease (BRD) resistance in selection decisions? 43rd Annual Research Symposium and Annual Meeting Bozeman, Montana. 43: 61-68.
- Egger-Danner, C., J. B. Cole, J. E. Pryce, N. Gengler, B. Heringstad, A. Bradley and K. F. Stock (2015). "Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits." Animal 9(2): 191-207.
- Parker Gaddis, K. L., J. B. Cole, J. S. Clay and C. Maltecca (2014). "Genomic selection for producer-recorded health event data in US dairy cattle." J Dairy Sci 97(5): 3190-3199.