body weight deviations as resilience indicator in chickens · 2018. 10. 2. · method 1...
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Body weight deviations as resilience
indicator in chickens
August 30, 2018 Tom Berghof
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Wageningen University & Research▪ Han Mulder▪ Henk Bovenhuis▪ Henk Parmentier▪ Jan van der Poel▪ Joop Arts▪ Francois Karangali
ERA-NET SusAn/NWO-ALWNWO-TTWHendrix Genetics
Co-authors/Acknowledgement
▪ Michael Aldridge▪ Marieke Poppe
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Resilient livestock
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Resilience
“the capacity of an animal
to be minimally affected by disturbances, or
to rapidly return to the state pertained before exposure to a disturbance”
(adjusted from Colditz and Hine, 2016, Anim Prod Sci)
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Growth curve layer chicken
Based on: Dekalb White cs product guide cage
https://www.dekalb-poultry.com/en/product/dekalb-white/
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Disturbances cause deviations
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Disturbances cause deviations
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Disturbances cause deviations
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Within-family variance of body weight
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Aim
1. Estimate genetic variance of body weight deviations
2. Investigate the potential of these deviations as resilience indicators
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Proposed resilience indicators
▪ Proposed resilience indicators (Berghof et al., submitted)
● Variance of deviations (var)(f.e. Elgersma et al., 2018, J Diary Sci; Putz et al., 2018, WCGALP; submitted)
● Skewness of deviations (skew) (based on Scheffer et al., 2015, Annu Rev Ecol Evol Syst)
● (lag-one) Autocorrelation of deviations (rauto)(based on Scheffer et al., 2015, Annu Rev Ecol Evol Syst)
▪ Resilient animals have few(er) or small(er) deviations
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Natural antibody-selection lines
▪ High line and Low line selected on natural antibody levels (Berghof, 2018, PhD thesis)
● Base population and 5 generations: 8,007 individuals
● h2 = 0.12
● Difference in E. coli resistance
Hypothesis: Selection lines differ in resilience
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Material
▪ Body weight at 8 moments:
● 4, 8, 12, 16, 20, 28, 32 and 36 weeks of age
▪ ≥5 observations per individual
→ 1,235 females with 6.7 observations (8,237 observations)
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Methods
▪ Method 1 (‘simple’ method)
1. Expected production based on average of line*generation*age
2. Deviations = Observed − Expected
3. Standardize deviations
4. Calculate ln(var), skew, and rauto
→ Univariate
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Methods
▪ Method 1 (‘simple’ method)
→ Univariate
▪ Method 2 (‘complex’ method)
1. Standardize body weights based on line*generation*age
→ DHGLM (Rönnegård et al., 2010, Genet Sel Evol; Felleki et al., 2012, Genet Res)
→ ln(var)
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Methods
▪ Resilience indicators
● ln(var)1
● skew1
● rauto1
● ln(var)2
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Additive genetic variation
ln(var)1 skew1 rauto1 ln(var)2
𝝈𝒂𝟐 0.09 (0.04) 0.06 (0.03) 0.02 (0.01) 0.07 (0.03)
ℎ2 0.11 (0.05) 0.09 (0.04) 0.08 (0.04) -
(NS)
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Genetic correlations
ln(var)1 skew1
skew1 −0.22 (0.30) -
rauto1 0.45 (0.26) 0.27 (0.33)
ln(var)1
ln(var)2 0.998 (0.003)
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Genetic correlations natural antibodies
Natural antibodies
ln(var)1 −0.33 (0.18)
skew1 −0.04 (0.20)
rauto1 0.02 (0.20)
ln(var)2 −0.33 (0.16)
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-0,08
-0,04
0,00
0,04
0,08
0 1 2 3 4 5
EB
V
Generation
rauto1
-0,08
-0,04
0,00
0,04
0,08
0 1 2 3 4 5
EB
V
Generation
ln(var)2
-0,08
-0,04
0,00
0,04
0,08
0 1 2 3 4 5EB
V
Generation
skew1
-0,08
-0,04
0,00
0,04
0,08
0 1 2 3 4 5
EB
V
Generation
ln(var)1
Line differences
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Take-home messages
Body weight deviations have a heritable component
Body weight deviations are genetically correlated to a physiological characteristics of the immune system
Body weight deviations might be used as resilience indicators
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Repeated measurements and heritability
Berghof et al., submitted
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Additive genetic variation
ln(var)1 skew1 rauto1 ln(var)2
𝝈𝒂𝟐 0.09 (0.04) 0.06 (0.03) 0.02 (0.01) 0.07 (0.03)
𝜎𝑝𝑒2 - - - 0.46 (0.04)
𝜎𝑒2 0.72 (0.04) 0.69 (0.04) 0.20 (0.01) 0.61 (0.01)
ℎ2 0.11 (0.05) 0.09 (0.04) 0.08 (0.04) -
(NS)
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Perspective
▪ Different resilience indicators → different information
▪ Resilience indicators (i.e. deviations) depend on frequency and variety of disturbances
▪ Number of observations is low and influences estimations
▪ ‘Simple’ method gives similar results to ‘complex’ method
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Natural antibodies!!!
Star et al., 2007, Poult Sci
Sun et al., 2011, Poult Sci
Wondmeneh et al., 2015, Poult Sciv
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Natural antibodies binding KLH
Antigen binding antibodies present in individuals without exposure to this antigen
Keyhole Limpet Hemocyanin (KLH)
▪ Plasma titers
● IgTotal → Total levels
● IgM (IgTotal)
● IgA
● IgGIgM
IgG
IgA
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Natural antibody-selection lines
Each generation:
▪ ~500 individuals per line
▪ Selection on own observation
▪ Selection of 25 ‘best’ males per line and 50 ‘best’ females per line
▪ No exchange between lines
▪ Housed together, randomized and mixed
More information: Berghof et al., 2018, Vaccine
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Selection progress
∆ = 2.25 titer points
High vs Low: ~4.8x more
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Genetic correlations natural antibodies
Total Ig IgM IgG
ln(var)1 −0.33 (0.18) −0.33 (0.17) −0.34 (0.21)
skew1 −0.04 (0.20) −0.07 (0.18) −0.03 (0.23)
rauto1 0.02 (0.20) 0.07 (0.18) 0.04 (0.23)
ln(var)2 −0.33 (0.16) −0.20 (0.16) −0.33 (0.18)
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Variance of deviations
▪ Low variance in milk production deviations → less production-related diseases and higher longevity in dairy cows (Elgersma et al., 2018, J Diary Sci)
▪ High variance in daily feed intake and duration at feeder→ higher mortality and more health treatments in pigs in a ‘natural challenge environment’ (Putz et al., 2018, WCGALP; submitted)
▪ Resilience might be measured on any trait with frequent observations
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Natural antibodies?
†
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Remaining questions
▪ Functional protection or biomarker?
▪ Life-long effect?
▪ All diseases, including viral diseases?
▪ Physiological limits or optimum?
▪ Other species?
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Mortality Experiment I