Update on Disease Susceptibility

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Update on Disease Susceptibility

• Department of Animal Sciences
• Colorado State University

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The economics industry concern for cattle health
Bovine Respiratory Disease

• 1997 estimates put prevention and treatment of
  disease in the feedlot at gt3 billion (Griffin,
  1997)
• 1.1 million cattle with an estimated value of
  over 692 million were lost to respiratory causes
  in 2005 (USDA, 2006).
• 16 pounds reduction in hot carcass weight for
  animals treated in 1st 40 days (Snowder et al.,
  2007)
• Lung damage (yes/no) 34 pounds of carcass
  weight (Engler, 2007)

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Feedlot morbidity/mortality rates
McAllister, 2010
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BRD incidence rate

• Over 14 of all feedlot placements develop BRD
  (USDA, 2001)
• 5 times the prevalence of the next highest
  reported disease

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Genetics of Feedlot Health Project

• Funded by Pfizer, Inc. (Pfizer Animal Genetics)
• Hypothesis
• Susceptibility/resistance to disease is, in part,
  genetically controlled and that genetic control
  can be characterized by DNA markers. This
  genetic control is likely manifest through two
  mechanisms
• The animals ability to cope with stress and
  therefore reduce the risk of becoming sick.
• The variation among animals relative to their
  immunological ability to counteract disease
  challenges.

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Study Background

• Steers from a single ranch source were shipped to
  cooperating commercial feedlot in Lamar, CO
• Split over 2 years
• Calves vaccinated on ranch 2x each year
• No arrival vaccination first year
• Arrival vaccination in second year

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Relationships
Ability to cope with stress
Immunological response
Disease Challenge
Phenotypes Collected
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Animal Data

• Phenotypes characterizing morbidity/mortality
• Sick (yes/no)
• Time to recovery/mortality
• Treatment records
• Treatment protocol, body temperatures, weight
  change
• Visual scores for nasal and eye discharge, cough,
  and depression and respiration rate
• Lung lesion scores
• Mortality information
• Necropsy results, bacteriology, etc

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Animal Data

• Phenotypes characterizing exposure, stress and
  immune response
• BVD III, PI3, IBR, BRSV exposure
• Circulating cortisol and IL levels
• IgG levels

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Animal Data

• Phenotypes characterizing performance
• Weightsarrival and re-implant
• Carcass performance
• HCW, MS, QG, REA, BF
• Ultrasound through the feeding period
• Arrival with 2 additional observations at
  re-implant
• Temperament measures
• Flight speed
• Chute score

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BRD Treatment Rates

• Year 1 45 treated
• Year 27.1 treated

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Treatment rates over time
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Characterization of disease
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Treatment versus Lung Damage
Overall BRD    
  LUNG DAMAGE NO LUNG DAMAGE
TREATED 82.66 17.34
TREATED 20.51 4.30
NOT TREATED 76.79 23.21
NOT TREATED 57.74 17.45
  78.25 21.75
In comparison to literature reports (Loneragan,
Whittum, Thompson)
Lung Lesions No Lung Lesions
Treated/Pulled 70 30
Not Treated/Pulled 56 44
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Treatment Effects on Carcass Performance
Treated Hot Carcass Weight LM Area Backfat Thickness (mm) Marbling Score Calculated Yield Grade
No 357.0 82.7 13.1 406 3.09
Yes 352.6 82.6 12.4 394 2.99
Significance lt.05 NS lt.05 lt.05 lt.05
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Treatment Effects on Carcass Performance by
Number of Treatments
Treatment Number Hot Carcass Weight LM Area Backfat Thickness (mm) Marbling Score Calculated Yield Grade
0 357.0 82.7 13.1 406 3.09
1 354.1 82.8 12.6 394 2.99
2 352.6 83.7 12.3 391 2.91
3 334.0 77.2 12.3 405 3.08
Significance lt.05 lt.05 lt.05 lt.05 lt.05
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As expected Treatment influences performance
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From the perspective of a feedlot trial

• What is predictive?
• Does ranch treatment influence feedlot
  performance?

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(No Transcript)
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Source treatment effects on probability of
feedlot treatment

• Highly significant effects of ranch treatment on
  probability of feedlot treatment (Plt.001)
• Receiving weight had no effect on probability of
  treatment in the feedlot.

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Remember.

• Hypothesis
• Susceptibility/resistance to disease is, in part,
  genetically controlled and that genetic control
  can be characterized by DNA markers.
• Is there genetic variation for traits in this
  study?

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Heritabilities
Hot Carcass Weight Marbling Score REA Arrival Exit Velocity Arrival Chute Score
.30 .62 .40 .17 .19
What about the health traits?
Treated (Yes/No) Lung Score (binary lt1 vs gt1) Number of Treatments
.23 .04 .04
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Compared to other studies

• Treatment (yes/no)
• On the higher end of literature estimates.
• Snowder et al., 2006 was .18 on the underlying
  scale

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Is susceptibility related to other performance
characteristics?
Estimates of genetic (rg ) and environmental (re ) correlations of bovine respiratory disease treatment classifications with carcass traits Estimates of genetic (rg ) and environmental (re ) correlations of bovine respiratory disease treatment classifications with carcass traits Estimates of genetic (rg ) and environmental (re ) correlations of bovine respiratory disease treatment classifications with carcass traits Estimates of genetic (rg ) and environmental (re ) correlations of bovine respiratory disease treatment classifications with carcass traits Estimates of genetic (rg ) and environmental (re ) correlations of bovine respiratory disease treatment classifications with carcass traits Estimates of genetic (rg ) and environmental (re ) correlations of bovine respiratory disease treatment classifications with carcass traits
Trt Trt Number of Treatments Number of Treatments
rg re   rg re
HCW 0.19 0.30 -0.05 0.02 0.23 0.42 -0.07 0.02
LM area 0.03 0.25 -0.01 0.02 -0.05 0.35 -0.02 0.02
MS -0.30 0.21 -0.06 0.02 -0.29 0.29 -0.03 0.02
Fat -0.004 .26 -0.05 0.02   -0.06 0.35 -0.05 0.02
McAllister, 2010
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Summary

• Treatment for BRD influences animal performance
  in the feedlot.
• Genetic variation exists for susceptibility to
  BRD.

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Ongoing Investigation

• Can we better distinguish between sick versus
  healthy individuals in our statistical
  analyses?
• What is the true trait of interest?
• What proportion of genetic variance can we
  explain with SNP data?

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Acknowledgements

• Graduate Students!
• Brian Brigham
• Chase McAllister
• Scott Speidel
• Amanda Pepper
• Gabriela Marquez
• Cory Pendley
• Brandon Meiwes
• Leanne Matthews
• Megan Rolf
• Ed Creason
• Many others

• Pfizer Animal Genetics
• Guy Loneragan, West Texas AM University
• Hana Van Campen, CSU
• Kraig Peel, CSU
• Bob Weaber, University of Missouri
• Christopher Chase, South Dakota State University
• Janeen Salak-Johnson, University of Illinois
• John Pollak, Cornell University (MARC)
• John Wagner, CSU-Southeast Colorado Research
  Center
• Tony Bryant, Five Rivers Ranch Cattle Feeding

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Questions?