TECHNICAL BULLETIN August 1, 2017 ASSOCIATIONS BETWEEN WELLNESS TRAIT PREDICTIONS FROM CLARIFIDE PLUS AND OBSERVED HEALTH OUTCOMES IN HOLSTEIN CATTLE Dairy producers can use CLARIFIDE Plus as a tool to evaluate young calves and heifers to better manage disease prevalence and improve herd profitability. Anthony McNeel, 1 Brenda Reiter, 1 Daniel Weigel 2 and Fernando Di Croce 1 1 Global Genetic Technical Services & 2 Outcomes Research Zoetis Genetics 333 Portage Street Kalamazoo, MI 407-4931 KEY POINTS This external field study of six wellness traits in U.S. Holstein cows demonstrates the association between direct genetic predictions of the wellness traits and disease outcomes. Cows lowest genetic risk group () for mastitis (MAST-) had 7.4 percentage points lower frequency of disease which represents a 47% difference in incidence of mastitis when compared to the highest risk group (). This translates into decreased losses per cow of $15.60. Cows genetically for metritis (MET-) and lameness (LAME-) had 45 and 33 lower incidence as a percentage for metritis and lameness, respectively, translating to $32.30 and $6. fewer losses per cow per lactation as compared to cows group. Genomically enhanced genetic evaluations of young calves and heifers can be used to effectively predict future health performance. This presents a compelling opportunity for dairy producers to help manage disease and improve profitability when combined with sound management practices.
INTRODUCTION The desire to continuously improve the health and wellness of dairy animals has led to a growing interest use of genetic improvement as part of a comprehensive health management strategy for dairy cattle. 1 3 Genetic improvement programs that incorporate direct genetic assessment of disease risk into selection and breeding strategies have the potential to improve animal well-being and minimize the impact of culling, increased expenses, increased labor and reduced milk sales on profitability. 4 Producer-recorded health events have been used to describe genetic among dairy sires susceptibility of their daughters to common health disorders such as mastitis, lameness and metritis. 4 7 Reducing the frequency of these health events (commonly referred to as wellness traits) through genetic selection presents dairy producers with a unique opportunity to help manage disease incidence and improve profitability when combined with sound management practices. In response to industry needs for genetic improvement of dairy wellness traits, a genetic evaluation was developed to generate six wellness trait predictions for Holstein cattle. 8 Retained Placenta (RP-) Metritis (MET-) Ketosis (KET-) Displaced Abomasum (DA-) Mastitis (MAST-) Lameness (LAME-) A multi-year external field herd study was undertaken to evaluate wellness trait predictions in an independent population under field conditions representative of contemporary dairy production in the U.S. The objective of this study was to evaluate the association between genomic wellness trait predictions derived from CLARIFIDE Plus and the observed incidence of health events in commercial dairy cattle with first 305 days of a given lactation. 9 HERDS AND ANIMALS The study enrolled 11 Holstein herds sizes averaging 4,180 lactating cows distributed across major U.S. dairy producing areas based on four criteria: 1. The herds did not contribute phenotypic information to the Zoetis genetic evaluation for wellness traits. 2. of recorded herd events was similar to national incidence wellness traits genetic evaluation for at least five of the six health events. 3. The herds were not currently applying selection pressure (such as heifer culling) based on genomic predictions. 4. The herds were projected to have a minimum of 200 first parity and second parity calving events between September 1 and December 31, 2015. From these 11 herds, Holstein females with a projected calving date between September 1, 2015, and December 31, 2015, and an upcoming first or second parity were identified. A random subset of these animals was selected within age group and a DNA tissue sample was collected for genetic testing. All genotypes were simultaneously evaluated by the Council of Dairy Cattle Breeding to ensure compatibility with animal identification, parentage and breed. These quality control measures reduced the enrolled population of animals, from 3,462 to 2,875 Holstein cows. 2
STUDY PARAMETERS This study analyzed all lactation events (i.e., first and second lactation records) occurring before August 24, 2016, and up to 305 days in milk (DIM). Health events including retained placenta, metritis, ketosis, displaced abomasum, mastitis and lameness were collected from onfarm herd management software. Health event recording was standardized during processing of the lactation records (i.e., RETP=RP). Health events occurring before calving (negative DIM), along with retained placenta and metritis events occurring after 50 DIM, were excluded from the analysis in order to remove poor records and non-transition related events. In this study, individual lactation records were assigned to one of three possible phenotypes by trait: Healthy (0) no record of the health event before 306 DIM Diseased (1) one or more recorded events before 306 DIM Excluded (.) lactation record with insufficient DIM to be considered healthy Once tissue samples were genotyped, low density genotypes were imputed up to 45,425 markers and genomically enhanced PTAs (gptas) were then estimated using the single step evaluation method 10 per the standard procedure in the genetic evaluation for wellness traits as described by Vukasinovic, et al., 2017 (CLARIFIDE Plus, Zoetis). 8 Wellness trait gptas were then converted into a standardized transmitting ability () where a value of represents the average expected disease risk and values greater than reflect animals with lower expected average disease risk. Higher values are more desirable for all traits, thus selecting for a larger will apply selection pressure for reduced disease risk. Wellness traits s were used to rank and assign cows to percentile groups within herd and age group: ; 26 5; 51 7;. As shown in Table 1, the genetic merit of the study population is similar in genetic makeup to the population in the genetic evaluation. Table 1: Descriptive statistics of genomic standardized transmitting abilities () and reliabilities (REL) for wellness traits based on 2,875 Holstein heifers and cows with predictions. 9 Trait Median SD Minimum Maximum RP- MET- KET- DA- MAST- LAME- 101 5.3 79 114 REL (%) 48 5 25 59 101 5.1 79 114 REL (%) 47 5 24 59 101 5.3 69 113 REL (%) 48 5 25 58 101 5.0 75 REL (%) 47 5 24 58 5.2 81 113 REL (%) 48 5 25 59 5.4 78 115 REL (%) 47 5 25 58 3
TISTICALLY SIGNIFICANT RESULTS A general linear mixed model with a logit transformation was used to analyze binary data (healthy/diseased) with the appropriate adjustments for animal, herd, age group and lactation. Differences in disease incidence (marginal means) were statistically significant between the genetic groups (Table 2) for retained placenta (P=0.0003), metritis (P<0.0001), ketosis (P=0.0017), displaced abomasum (P=0.0014), mastitis (P<0.0001) and lameness (P=0.0336). These results indicate that genomic data of young calves and heifers can be used to effectively predict future health performance. When one compares the genetic groups of the to the for each wellness trait (Table 2), the percent between these groups ranged between 33% and 94% for lameness and displaced abomasum, respectively. Table 2: Disease incidence (marginal means) of the genetic groups when animals are ranked by (quartiles) and estimated disease cost per cow. 9 RP- MET- KET- DA- MAST- LAME- Percentile Group 4 LS-Means Disease Prevalence (Marginal Mean) Estimated Disease Cost Per Cow 11-16 93.6 4. $9.30 26 5 99.2 3.3% $6.88 51 7 102.6 2. $5.10 106.7 1.6% $3.26 93.7 23.6% $70.92 26 5 98.8 18. $55.47 51 7 102.0 19.1% $57.42 106.1 12.9% $38.58 94.0 3.2% $3.75 26 5 99.2 2. $2.87 51 7 102.5 1.7% $1.97 106.3 1. $1.73 93.9 1.13% $5.58 26 5 99.5 0.47% $2.32 51 7 102.5 0.13% $0.64.7 0.07% $0.35 92.3 15.9% $33.63 26 5 97.7 11.2% $23.65 51 7 101.0 11.1% $23.31.1 8. $18.00 92.4 11.4% $20.23 26 5 97.8 8.7% $15.40 51 7 101.3 8.6% $15.28.9 7.6% $13.37 P-value 0.0003 <0.0001 0.0017 0.0014 <0.0001 0.0336
ASSOCIATION BETWEEN THE PHENOTYPIC INCIDENCE AND GENETIC PERCENTILE GROUP FOR IMPORTANT WELLNESS TRAITS 9 Figure 1: Association between the phenotypic incidence of retained placenta (bars) and genetic percentile group (quartiles, orange line and dots) of retained placenta. 4% 3% 2% 1% 4. A 93.6 W Retained Placenta P=0.0003 3.3% A,B 99.2 X 102.6 Y 2. B,C 106.7 Z 1.6% C 26 5 51 76% RP Figure 3: Association between the phenotypic incidence of ketosis (bars) and genetic percentile group (quartiles, orange line and dots) of ketosis. 4% 3% 2% 1% 3.2% A 94.0 W 2. A,B 99.2 X Ketosis P=0.0017 102.5 Y 106.3 Z 1.7% B,C 1. C 26 5 51 76% KET Figure 5: Association between the phenotypic incidence of mastitis (bars) and genetic percentile group (quartiles, orange line and dots) of mastitis. Figure 2: Association between the phenotypic incidence of metritis (bars) and genetic percentile group (quartiles, orange line and dots) of metritis. 2 2 1 1 23.6% A 93.7 W 18. B 19.1% B 98.8 X Metritis P<0.0001 102.0 Y 106.1 Z 12.9% C 26 5 51 76% MET Figure 4: Association between the phenotypic incidence of displaced abomasum (bars) and genetic percentile group (quartiles, orange line and dots) of displaced abomasum. Displaced Abomasum P=0.0014 1.2% 1.12% A.7 D 1. 102.5 C 0.8% 99.5 B 0.6% 0.47% 93.9 A A,B 0.4% 0.2% 0.13% B 0.07% C 26 5 51 76% DA Figure 6: Association between the phenotypic incidence of lameness (bars) and genetic percentile group (quartiles, orange line and dots) of lameness. 2 Mastitis P<0.0001 1 Lameness P=0.0336 1 1 15.9% A 92.3 W 11.2% B 11.1% B 97.7 X 101.0 Y.1 Z 8. B 1 11.4% A 92.4 W 8.7% A,B 8.6% B 7.6% B 97.8% X 101.3 Y.9 Z 26 5 51 76% 26 5 51 76% MAST LAME 5
In addition, cows lowest genetic risk group () for mastitis had 7% less disease frequency compared to the higher risk group (), representing a 47% difference in incidence of mastitis (Figure 5) and $15.63 fewer losses per cow per lactation. The average economical difference per animal was calculated by multiplying the cost per disease case times the frequency of disease for the genetic quartile; then calculating the difference between these two groups results. Similar results were observed for metritis and lameness with a 10.7% and 3.8% less disease frequency when comparing cows lower genetic risk group () to the highest risk group, demonstrating a difference in incidence of 4 and 33%, respectively. Cows have an average of approximately $32.34 and $6.86 less expenses compared to the for metritis and lameness, respectively. Odds ratio estimates (Table 3) indicate that cows were at least 1.6 times more likely to experience the health event for lameness compared to the through 305 DIM. This ratio was as high as 17.1 times more likely for a cow to experience a DA if they were vs group for DA- and the remaining traits were approximately 2 to 3 times higher ir respective traits and events. Table 3: Odds ratios between the and genetic groups when animals are ranked by quartiles. 9 Trait Odds Ratio ( vs ) RP- 2.9 MET- 2.1 KET- 2.2 DA- 17.1 MAST- 2.0 LAME- 1.6 6
Figure 7: Percent phenotypic incidence of all six wellness traits predictions between the and groups through 305 DIM. 9 5. 4. 3. 2. Retained Placenta 4. 64% incidence of Retained Placenta 1.6% 1.2% 1. 0.8% 0.6% 0.4% Displaced Abomasum 1.13% 94% incidence of Displaced Abomasum 1. 0. 0.2% 0. 0.07% 26% 24% 22% 2 18% 16% 14% 12% 1 23.6% Metritis 4 incidence of Metritis 12.9% 17% 1 13% 11% 9% 7% 15.9% Mastitis 47% incidence of Mastitis 8. 3. 3. 2. 2. 1. 1. 3.2% Ketosis 53% incidence of Ketosis 1. 12% 11% 1 9% 8% 7% 11.4% Lameness 33% incidence of Lameness 7.6% 0. 6% 0. SUMMARY Genomic predictions for production, reproduction and type for young calves and heifers are broadly recognized as valuable information in commercial dairies, and are highly correlated with future performance. This field study demonstrates that genomic wellness trait predictions from CLARIFIDE Plus similarly provide accurate estimates of future performance, in this instance, for disease risk. Improving wellness traits through direct genetic selection presents a compelling opportunity for dairy producers to help manage disease and improve profitability when coupled with sound management practices. 7
LITERATURE CITED 1. Weigel KA, Lawlor TJ, Vanraden PM, Wiggans GR. Use of Linear Type and Production Data to Supplement Early Predicted Transmitting Abilities for Productive Life. J Dairy Sci 1998;81(7):2040-2044. 2. Heriazon A, Quinton M, Miglior F, Leslie KE, Sears W, Mallard BA. Phenotypic and genetic parameters of antibody and delayed-type hypersensitivity responses of lactating Holstein cows. Vet Immunol Immunopatho 2013;154(3-4):83-92. 3. Thompson-Crispi KA, Sargolzaei M, Ventura R, Abo-Ismail M, Miglior F, Schenkel F, Mallard BA. A genome-wide association study of immune response traits in Canadian Holstein cattle. BMC Genomics 2014:15(1):1-10. 4. Parker Gaddis KL, Cole JB, Clay JS, Maltecca C. Genomic selection for producer-recorded health event data in U.S. dairy cattle. J Dairy Sci 2014;97(5):31-3199. 5. Zwald NR, Weigel KA, Chang YM, Welper RD, Clay JS. Genetic selection for health traits using producer-recorded data. I. rates, heritability estimates, and sire breeding values. J Dairy Sci 2004;87(12):4287-4294. 6. Neuenschwander TFO, Miglior F, Jamrozik J, Berke O, Kelton DF, Schaeffer LR. Genetic Parameters for Producer-Recorded Health Data in Canadian Holstein Cattle. Animal 2012;6(04):571-578. 7. Zwald NR, Weigel KA, Chang YM, Welper RD, Clay JS. Genetic analysis of clinical mastitis data from on-farm management software using threshold models. J Dairy Sci 2005;89(1):330-336. 8. Vukasinovic N, Bacciu N, Przybyla CA, Boddhireddy P, DeNise SK. Development of genetic and genomic evaluation for wellness traits in U.S. Holstein cows. J Dairy ScI 2017;(1):428-438. 9. McNeel AK, Reiter B, Weigel D, Osterstock J, DiCroce F. (2017) Validation of genomic predictions for wellness traits in US Holstein cows. Journal of Dairy Science. https://doi: 10.3168/jds.2016-12323 10. Misztal I, Legarra A, Aguilar I. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J Dairy Sci 2009;92(9):4648-4655. 11. Guard C. Retained Placenta: Causes and Treatments. Advances in Dairy Technology 1999;11:81-86. 12. Dairy Cattle Reproduction Council. The Value of Uterine Health: the Diseases, the Causes and the Financial Implications. 2001. 13. McArt JAA, Nydam DV, Overton MW. Hyperketonemia in early lactation dairy cattle: A deterministic estimate of component and total cost per case. J Dairy Sci 2015;98:2043-2054. 14. Guard C. The costs of common diseases of dairy cattle. In: Proceedings. Excerpt from 2009 CVC Kansas City Proceedings. 15. Cha E, Bar D, Hertl JA, Tauer LW, Bennett G, González RN, Schukken YH, Welcome FL, Gröhn YT. The cost and management of different types of clinical mastitis in dairy cows estimated by dynamic programming. J Dairy Sci 2011;94(9):4476-4487. 16. Cha E, Hertl JA, Bar D, Gröhn YT. The cost of different types of lameness in dairy cows calculated by dynamic programming. Prev Vet Med 2010;97(1):1-8. All trademarks are the property of Zoetis Services LLC or a related company or licensor unless otherwise noted. 2017 Zoetis Services LLC. All rights reserved. CLR-00248 8