GxE in commercial pig breedingreaction norms

selection for the response environment

Pieter KnapGenus-PIC

Selection of genotypes for a particular production environment

Between linesrelatively straightforward

Within-linemuch more interesting

Selection of genotypes for a particular production environment

Selection between linesrelatively straightforward: usually few lines to choose from

Selection of genotypes for a particular production environment

Selection between linesrelatively straightforward: usually few lines to choose from

Selection of genotypes for a particular production environment

Selection between linesrelatively straightforward: usually few lines to choose from

Selection of genotypes for a particular production environment

Selection between linesrelatively straightforward: usually few lines to choose from

Selection of genotypes for a particular production environment

Selection between linesrelatively straightforward: usually few lines to choose from

Selection of genotypes for a particular production environment

Selection between linesrelatively straightforward: usually few lines to choose from

Selection of genotypes for a particular production environment

Selection between linesrelatively straightforward: usually few lines to choose from

Selection of genotypes for a particular production environment

Within-line selectionmuch more interesting: continuous variation to choose from

Rischkowsky & Pilling (2007)

Anderson (2004) after Haldane (1946)

0.60

0.62

0.64

0.66

0.68

0.70

aver

age

daily

gai

n (k

g /

d)

very highinfectiousness

very lowhigh low

Schinckel et al. (1999)

Poster: Antti Kause

Anderson (2004) after Haldane (1946)

Within-line selectionmuch more interesting:

continuous variation to choose from

Within-line selectionmuch more interesting:

continuous variation to choose from

Anderson (2004) after Haldane (1946)

0.60

0.62

0.64

0.66

0.68

0.70

aver

age

daily

gai

n (k

g /

d)

very highinfectiousness

very lowhigh low 0.62 0.64 0.66 0.68 0.70

treatment mean: average daily gain (kg / d)

0.60

0.62

0.64

0.66

0.68

0.70

average daily gain (kg / d)

y = 0.30 + 0.57 x

y = –0.30 + 1.43 x

Schinckel et al. (1999)

Within-line selectionmuch 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 theresponse environment

Knap & Su (2008)

Knap & Su (2008)

Individual reaction norms

intercept : the conventional EBVfor 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

environment

phen

otyp

e

EN

PN

EH

PH

b

PC

EC

EL

PL

EN

PN

PC = PN – b × ( EN – EC )

selection environment

response environment

EH

PH

b

PC

EC

EL

PL

EN

PN

PC = PN – b × ( EN – EC )

average performance in commercial conditions:= the breeding goal trait

genetic potential

environmental sensitivity

how far away is the nucleus from the

commercial level ?

P = WT × KO × [Vcarcass+ LEAN × Vlean]

– DAYS120 × [Cday + ADF × Cfeed ]

P = WT × KO × [Vcarcass+ LEAN × Vlean]

– [ PN, DAYS – bDAYS × (DAYSN – DAYSC) ] × [Cday + ADF × Cfeed ]

Set up the profit equation to derive economic values

Two breeding goal traits

Differentiate to derive marginal economic values

MEV(PN, DAYS) = dP / dPN, DAYS = – [Cday + ADF × Cfeed ]

P = WT × KO × [Vcarcass+ LEAN × Vlean]

– [ PN, DAYS – bDAYS × (DAYSN – DAYSC) ] × [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

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 DAYS120 means faster growth

Negative MEV : a reduction of the slope brings commercial performance closer to the potential

Individual reaction norms

intercept : the conventional EBVfor 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

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?

Line B; parity 1 only

66 farms with 33.641 records of33.641 daughters of 792 sires

Line B; all parities

93 farms with 73.352 records of52.120 daughters of 1091 sires

Lines A, B and AB; all parities

144 farms with 346.030 records of121.104 daughters of 2040 sires

Litter size: daughter group reaction norms

sires sires sires

Line B; parity 1 only

66 farms with 33.641 records of33.641 daughters of 792 sires

Line B; all parities

93 farms with 73.352 records of52.120 daughters of 1091 sires

Lines A, B and AB; all parities

144 farms with 346.030 records of121104 daughters of 2040 sires

Litter size reaction norms of sires: standard error of slope vs. HYS environmental range

Line B; parity 1 only

66 farms with 33.641 records of33.641 daughters of 792 sires

Line B; all parities

93 farms with 73.352 records of52.120 daughters of 1091 sires

Lines A, B and AB; all parities

144 farms with 346.030 records of121104 daughters of 2040 sires

sires sires sires

Litter size reaction norms of sires: standard error of slope vs. number of daughters

sires siressiressiressires siressiressiressires

Line B; parity 1 only

66 farms with 33.641 records of33.641 daughters of 792 sires

Line B; all parities

93 farms with 73.352 records of52.120 daughters of 1091 sires

Lines A, B and AB; all parities

144 farms with 346.030 records of121104 daughters of 2040 sires

Litter size reaction norms of sires: standard error of slope vs. slope

h2 rG

intcpt 10 26±7

slope 8±3

h2 rG

intcpt 9 69±5

slope 2±0.4

h2 rG

intcpt 10 –9±15

slope 15±8

Knap & Su (2008)

Line B; parity 1 only

66 farms with 33.641 records of33.641 daughters of 792 sires

Line B; all parities

93 farms with 73.352 records of52.120 daughters of 1091 sires

Lines A, B and AB; all parities

144 farms with 346.030 records of121.104 daughters of 2040 sires

Litter size: daughter group reaction norms

E > I > G

I > E > G

?Same data (Line B; all parities) analyzed with SAS

E > I : incentive to improve the environment

I > E : incentive to match genotype to environment

• Select in the response envrmnt

• Select on data from theresponse environment

?

Individual reaction norms

intercept : the conventional EBVfor 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

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?

Not for pigs, today

The individual reaction norm approach is notfeasible for commercial pig breeding, today

Simplify

Most extreme:

E as a continuous variable (= reaction norms)

two E classes (e.g. nucleus & commercial)

…or anything in between

Poster: Ann McLaren et al.Poster: Anna-Maria Tyrisevä et al.

Van Sambeek (2010)

Reciprocal Recurrent Selection

Commercial Sibling Test

Combined Crossbred & Purebred Selection

• 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)

Theory:

… grown on commercial farms

An example: PIC's GN-Xbred program

• after that, semen is

used for GN

matings

• semen of GN boars is first used

on crossbred sows

multiplication

commercial crossbred sows

GN

commercial crossbred slaughter pigs

crossbred progeny

purebred progeny

An example: PIC's GN-Xbred program

multiplication

commercial breeding stock

GN

commercial crossbred slaughter pigs

PICTraqDatabase

selection decisions

CBVs

GN progeny performance data

Commercial progeny performance data

Commercial sowperformance data

• crossbred halfsib performance

CBVs of GN selection

candidates

crossbred halfsibs of purebred

GN selection candidates

• Xbred sow performance

CBVs of GN selection

candidates

GN-Xbred logistics

sire lines

dam lines

Reciprocal Recurrent Selection

Commercial Sibling Test

Combined Crossbred & Purebred Selection

Is this useful?

Depends on the coheritability

• ΔGC|N ~ hC × rG (C,N) × hN

• ΔGC|C ~ hC × hC

• is hC > rG (C,N) × hN ?

is rG (C,N) low enough ?

what about hN vs hC ?

• !! effective heritabilities !!

The crucial aspects :Can the trait be recorded at all in nucleus conditions ?

And on how many animals ?

• Cecchinato et al. (2010): stillbirth rate rG = 0.25 ± 0.34

• Bosch et al. (2000): litter size 0.40 < rG < 0.59

• Zumbach et al. (2007): ADG 0.53 < rG < 0.80; BFT and LMD 0.78 < rG < 0.89

• Ibáñez-Escriche et al. (2011): lean percentage 0.81 < rEBV < 0.96

• Brandt & Täubert (1998): ADG and BFT 0.87 < rG < 1.0

• 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)

Theory:

ADG

ADG

BFD

BFD

DFI

DFI

RFI

RFI

cros

sbre

d co

mm

erci

al p

erfo

rman

ce

rEBV = 0.55

rEBV = 0.54

rEBV = –0.06

rEBV = 0.06

rEBV = 0.85

rEBV = 0.78

rEBV = 0.85

rEBV = 0.80

crossbred comm

ercial performance

purebred nucleus performanceKnap & Wang (2012)

Poster: Helene Gilbert et al.

cros

sbre

d co

mm

erci

al p

erfo

rman

ce

purebred nucleus performance

crossbred comm

ercial performance

rEBV = 0.33 rEBV = 0.24

grower-finisher mortality rate

Poster: Geir Steinheim et al.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

With xbred data

EB

V A

ccu

racy

• low rG (C,N)

• many more data from C than from N

• much more variation in C :

σ2 = p × (1 – p) and p is much higher

E > I : incentive to improve the environment

I > E : incentive to match genotype to environment

• Select in the response envrmnt

• Select on data from theresponse environment

This is the actual worldwide situation in technified pig production,according to the evidence that I have

E > I : incentive to improve the environment

I > E : incentive to match genotype to environment

• Select in the response envrmnt

• Select on data from theresponse environment

This is what we are targeting,in terms of genetic evaluation:~ "better safe than sorry"

E > I : incentive to improve the environment

I > E : incentive to match genotype to environment

• Select in the response envrmnt

• Select on data from theresponse environment

In better conditions,the better animalsare 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 breedingreaction norms

selection for the response environment

Pieter KnapGenus-PIC