Transcript Slide 1

GxE in commercial pig breeding
reaction norms
selection for the response environment
Pieter Knap
Genus-PIC
Selection of genotypes for a
particular production environment
Between lines
relatively straightforward
Within-line
much more interesting
Selection of genotypes for a particular production environment
Selection between lines
relatively straightforward: usually few lines to choose from
Selection of genotypes for a particular production environment
Selection between lines
relatively straightforward: usually few lines to choose from
Selection of genotypes for a particular production environment
Selection between lines
relatively straightforward: usually few lines to choose from
Selection of genotypes for a particular production environment
Selection between lines
relatively straightforward: usually few lines to choose from
Selection of genotypes for a particular production environment
Selection between lines
relatively straightforward: usually few lines to choose from
Selection of genotypes for a particular production environment
Selection between lines
relatively straightforward: usually few lines to choose from
Selection of genotypes for a particular production environment
Selection between lines
relatively straightforward: usually few lines to choose from
Selection of genotypes for a particular production environment
Within-line selection
much more interesting: continuous variation to choose from
Rischkowsky & Pilling (2007)
Anderson (2004) after
Haldane (1946)
average daily gain (kg / d)
0.70
0.68
0.66
Poster: Antti Kause
0.64
0.62
0.60
very high
Schinckel et al. (1999)
high
low
infectiousness
very low
Within-line selection
much more interesting:
continuous variation to choose from
Anderson (2004) after
Haldane (1946)
Within-line selection
much more interesting:
continuous variation to choose from
Anderson (2004) after
Haldane (1946)
0.70
0.68
0.68
y = 0.30 + 0.57 x
0.66
0.66
0.64
0.64
0.62
0.62
y = –0.30 + 1.43 x
0.60
0.60
very high
Schinckel et al. (1999)
high
low
infectiousness
very low
0.62
0.64
0.66
0.68
0.70
treatment mean: average daily gain (kg / d)
average daily gain (kg / d)
average daily gain (kg / d)
0.70
Within-line selection
much more interesting:
continuous variation to choose from
Anderson (2004) after
Haldane (1946)
E > I : incentive to improve
the environment
I > E : incentive to match
genotype to environment
• Select in the response envrmnt
• Select on data from the
response environment
Knap & Su (2008)
Knap & Su (2008)
Individual reaction norms
intercept :
the conventional EBV
for productivity
(when they differ, the trait is heritable)
slope :
the EBV for environmental
sensitivity of productivity
(when they differ, the trait shows GxE)
two breeding goal traits
phenotype
PN
environment
EN
PN
PC = PN – b × ( EN – EC )
PH
selection
environment
response
environment
PC
PL
b
EL
EC
EH
EN
PN
PC = PN – b × ( EN – EC )
average performance in
commercial conditions:
= the breeding goal trait
genetic
potential
PH
how far away is the
nucleus from the
commercial level ?
PC
PL
b
environmental
sensitivity
EL
EC
EH
EN
Set up the profit equation to derive economic values
P = WT × KO × [Vcarcass+ LEAN × Vlean]
– DAYS120 × [Cday + ADF × Cfeed ]
P = WT × KO × [Vcarcass+ LEAN × Vlean]
– [ PN, DAYS – bDAYS × (DAYSN – DAYSC) ] × [Cday + ADF × Cfeed ]
Two breeding goal traits
Differentiate to derive marginal economic values
P = WT × KO × [Vcarcass+ LEAN × Vlean]
– [ PN, DAYS – bDAYS × (DAYSN – DAYSC) ] × [Cday + ADF × Cfeed ]
MEV(PN, DAYS) = dP / dPN, DAYS = – [Cday + ADF × Cfeed ]
MEV(bDAYS) = dP / dbDAYS = (DAYSN – DAYSC) × [Cday + ADF × Cfeed ]
= – (DAYSN – DAYSC) × MEV(PN, DAYS)
Differentiate to derive marginal economic values
MEV(bDAYS) = dP / dbDAYS = (DAYSN – DAYSC) × [Cday + ADF × Cfeed ] =
= – (DAYSN – DAYSC) × MEV(PN, DAYS)
The MEV of the environmental sensitivity depends on
• the MEV of the trait as such
• the distance selection environment  response environment
Differentiate to derive marginal economic values
Negative MEV : a reduction of
DAYS120 means faster growth
MEV(PN, DAYS) = – [Cday + ADF × Cfeed ] =
= – [0.24 + 2.3 × 0.29 ] = –0.16 € per d
MEV(bDAYS) = – (DAYSN – DAYSC) × MEV(PN, DAYS) =
= –(163 – 179) × –0.16 = –2.56 € per d/d
Negative MEV : a reduction of the slope
brings commercial performance closer
to the potential
An elegant option to
deal with G×E on the
individual level:
Calculate sensitivity
EBVs, and include them
in the index, weighted
by the MEV as usual.
 is that feasible?
Individual reaction norms
intercept :
the conventional EBV
for productivity
(when they differ, the trait is heritable)
slope :
the EBV for environmental
sensitivity of productivity
(when they differ, the trait shows G×E)
two breeding goal traits
Litter size: daughter group reaction norms
Line B; parity 1 only
Line B; all parities
Lines A, B and AB; all parities
66 farms with 33.641 records of
33.641 daughters of 792 sires
93 farms with 73.352 records of
52.120 daughters of 1091 sires
144 farms with 346.030 records of
121.104 daughters of 2040 sires
Litter size reaction norms of sires: standard error of slope vs. HYS environmental range
sires
Line B; parity 1 only
Line B; all parities
Lines A, B and AB; all parities
66 farms with 33.641 records of
33.641 daughters of 792 sires
93 farms with 73.352 records of
52.120 daughters of 1091 sires
144 farms with 346.030 records of
121104 daughters of 2040 sires
sires
sires
Litter size reaction norms of sires: standard error of slope vs. number of daughters
sires
Line B; parity 1 only
Line B; all parities
Lines A, B and AB; all parities
66 farms with 33.641 records of
33.641 daughters of 792 sires
93 farms with 73.352 records of
52.120 daughters of 1091 sires
144 farms with 346.030 records of
121104 daughters of 2040 sires
sires
sires
Litter size reaction norms of sires: standard error of slope vs. slope
Line B; parity 1 only
Line B; all parities
Lines A, B and AB; all parities
66 farms with 33.641 records of
33.641 daughters of 792 sires
93 farms with 73.352 records of
52.120 daughters of 1091 sires
144 farms with 346.030 records of
121104 daughters of 2040 sires
sires
sires
intcpt
slope
h2
intcpt
10
slope
15±8
Knap & Su (2008)
rG
–9±15
h2
10
8±3
rG
26±7
sires
h2
rG
intcpt
9
69±5
slope
2±0.4
Litter size: daughter group reaction norms
Line B; parity 1 only
66 farms with 33.641 records of
33.641 daughters of 792 sires
Same data (Line B; all parities) 
analyzed with SAS
I>E>G
?
Line B; all parities
Lines A, B and AB; all parities
93 farms with 73.352 records of
52.120 daughters of 1091 sires
144 farms with 346.030 records of
121.104 daughters of 2040 sires
E>I>G
E > I : incentive to improve
the environment
I > E : incentive to match
genotype to environment
• Select in the response envrmnt
• Select on data from the
response environment
An elegant option to
deal with G×E on the
individual level:
Calculate sensitivity
EBVs, and include them
in the index, weighted
by the MEV as usual.
 is that feasible?
Individual reaction norms
intercept :
the conventional EBV
for productivity
(when they differ, the trait is heritable)
slope :
the EBV for environmental
sensitivity of productivity
Not for pigs,
two breeding
goal traits
today
(when they differ, the trait shows G×E)
The individual reaction norm approach is not
feasible for commercial pig breeding, today
Simplify
Most extreme:
E as a continuous
variable
(=
reaction
norms)
Poster: Ann McLaren et al.
Poster: Anna-Maria
 Tyrisevä et al.
two E classes (e.g. nucleus & commercial)
…or anything in between
Reciprocal Recurrent
Selection
Commercial Sibling Test
Combined Crossbred &
Purebred Selection
Van Sambeek (2010)
Theory:
• Standal (1968)
• McNew & Bell (1971)
• Biswas et al. (1971)
• Wei Ming & Van der Werf (1994)
•Baumung et al. (1997)
• Bijma & Van Arendonk (1998)
• Spilke et al. (1998)
• Misztal et al. (1998)
• Dekkers & Chakraborty (2004)
An example: PIC's GN-Xbred program
• semen of GN boars is first used
on crossbred sows
 crossbred progeny
multiplication
… grown on commercial farms
• after that, semen is
used for GN matings
 purebred progeny
GN
commercial crossbred sows
commercial crossbred slaughter pigs
An example: PIC's GN-Xbred program
selection
decisions
 crossbred halfsibs of purebred
GN selection candidates
CBVs
GN
• crossbred halfsib performance multiplication
 CBVs of GN selection
commercial breeding stock
candidates
• Xbred sow performance
 CBVs of GN selection
candidates
GN progeny performance data
PICTraq
Database
Commercial sow
performance data
commercial crossbred slaughter pigs
Commercial progeny performance data
GN-Xbred logistics
sire lines
dam lines
Is this useful?
Depends on the coheritability
The crucial aspects : • ΔGC|N ~ hC × rG (C,N) ×
Reciprocal Recurrent
Can the trait be recorded• at
ΔGC|C ~ hC × hC
Selection
all in nucleus conditions ?
• is
And on how
manyTest
animals
? hC > rG (C,N) × hN ?
Commercial
Sibling
Combined Crossbred &
Purebred Selection
hN
 is rG (C,N) low enough ?
 what about hN vs hC ?
• !! effective heritabilities !!
Theory:
•Baumung et al. (1997)
• Standal (1968)
• Bijma & Van Arendonk (1998)
• McNew & Bell (1971)
• Spilke et al. (1998)
• Biswas et al. (1971)
• Misztal et al. (1998)
• Wei Ming & Van der Werf (1994)
• Dekkers & Chakraborty (2004)
• Cecchinato et al. (2010): stillbirth rate
• Bosch et al. (2000): litter size
rG = 0.25 ± 0.34
0.40 < rG < 0.59
• Zumbach et al. (2007): ADG 0.53 < rG < 0.80;
• Ibáñez-Escriche et al. (2011): lean percentage
• Brandt & Täubert (1998): ADG and BFT
BFT and LMD 0.78 < rG < 0.89
0.81 < rEBV < 0.96
0.87 < rG < 1.0
rEBV = –0.06
DFI
RFI
Poster: Helene Gilbert etr al.= 0.54
rEBV = 0.06
ADG
rEBV = 0.80
ADG
Knap & Wang (2012)
rEBV = 0.55
rEBV = 0.85
BFD
rEBV = 0.78
EBV
BFD
purebred nucleus performance
DFI
RFI
crossbred commercial performance
crossbred commercial performance
rEBV = 0.85
grower-finisher mortality rate
Poster: Geir Steinheim et al.
purebred nucleus performance
rEBV = 0.24
crossbred commercial performance
crossbred commercial performance
rEBV = 0.33
• low rG (C,N)
• many more data from C than from N
• much more variation in C :
1.0
With xbred data
0.9
σ2 = p × (1 – p) and p is much higher
Without xbred data
EBV Accuracy
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Carcass
growth
rate
Crossbred
G/F
Feed
Mortality
Intake
Scrotal
Hernia
Liability
Total Born Stillbirths
E > I : incentive to improve
This is the actual worldwide situation
the environment
in technified pig production,
according to the evidence that I have
I > E : incentive to match
genotype to environment
• Select in the response envrmnt
• Select on data from the
response environment
E > I : incentive to improve
the environment
I > E : incentive to match
This is what we are targeting,
in terms of genetic evaluation:
~ "better safe than sorry"
genotype to environment
• Select in the response envrmnt
• Select on data from the
response environment
E > I : incentive to improve
the environment
I > E : incentive to match
genotype to environment
• Select in the response envrmnt
• Select on data from the
response environment
In better conditions,
the better animals
are more better
Genetic variation can be
• detected more easily
• exploited and valuated
more easily
Incentive for the breeder:
more diversity in better
conditions  improve them
E > I : incentive to improve the environment
Genetic Services: live consultancy at the customer level
Genetic
Services:
manuals &
documentation
Genetic
Services:
manuals &
documentation
Genetic
Services:
manuals &
documentation
Conclusions
• in technified pig production, G×E is probably not dramatic
• individual reaction norms are the perfect way to deal with it
• but statistically very demanding and too data-hungry
• CCPS is a feasible compromise, and it works very well
• improving production conditions (i) improves performance
and (ii) makes the better animals more better
GxE in commercial pig breeding
reaction norms
selection for the response environment
Pieter Knap
Genus-PIC