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GxE in commercial pig breeding reaction norms selection for the response environment Pieter Knap Genus-PIC.

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Presentation on theme: "GxE in commercial pig breeding reaction norms selection for the response environment Pieter Knap Genus-PIC."— Presentation transcript:

1 GxE in commercial pig breeding reaction norms selection for the response environment Pieter Knap Genus-PIC

2 Selection of genotypes for a particular production environment Between lines relatively straightforward Within-line much more interesting

3 Selection of genotypes for a particular production environment Selection between lines relatively straightforward: usually few lines to choose from

4 Selection of genotypes for a particular production environment Selection between lines relatively straightforward: usually few lines to choose from

5 Selection of genotypes for a particular production environment Selection between lines relatively straightforward: usually few lines to choose from

6 Selection of genotypes for a particular production environment Selection between lines relatively straightforward: usually few lines to choose from

7 Selection of genotypes for a particular production environment Selection between lines relatively straightforward: usually few lines to choose from

8 Selection of genotypes for a particular production environment Selection between lines relatively straightforward: usually few lines to choose from

9 Selection of genotypes for a particular production environment Selection between lines relatively straightforward: usually few lines to choose from

10 Selection of genotypes for a particular production environment Within-line selection much more interesting: continuous variation to choose from

11 Rischkowsky & Pilling (2007)

12 Anderson (2004) after Haldane (1946)

13 average daily gain (kg / d) very high infectiousness very low high low Schinckel et al. (1999) Poster: Antti Kause

14 Anderson (2004) after Haldane (1946) Within-line selection much more interesting: continuous variation to choose from

15 Within-line selection much more interesting: continuous variation to choose from Anderson (2004) after Haldane (1946)

16 Schinckel et al. (1999)

17 Within-line selection much more interesting: continuous variation to choose from Anderson (2004) after Haldane (1946)

18 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

19 Knap & Su (2008)

20

21 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

22 environment phenotype E N P N

23 E H P H b P C E C E L P L E N P N P C = P N – b × ( E N – E C ) selection environment response environment

24 E H P H b P C E C E L P L E N P N P C = P N – b × ( E N – E C ) average performance in commercial conditions: = the breeding goal trait genetic potential environmental sensitivity how far away is the nucleus from the commercial level ?

25 P = WT × KO × [V carcass + LEAN × V lean ] – DAYS 120 × [C day + ADF × C feed ] P = WT × KO × [V carcass + LEAN × V lean ] – [ P N, DAYS – b DAYS × (DAYS N – DAYS C ) ] × [C day + ADF × C feed ] Set up the profit equation to derive economic values Two breeding goal traits

26 Differentiate to derive marginal economic values MEV(P N, DAYS ) = dP / dP N, DAYS = – [C day + ADF × C feed ] P = WT × KO × [V carcass + LEAN × V lean ] – [ P N, DAYS – b DAYS × (DAYS N – DAYS C ) ] × [C day + ADF × C feed ] MEV(b DAYS ) = dP / db DAYS = (DAYS N – DAYS C ) × [C day + ADF × C feed ] = – (DAYS N – DAYS C ) × MEV(P N, DAYS )

27 Differentiate to derive marginal economic values MEV(b DAYS ) = dP / db DAYS = (DAYS N – DAYS C ) × [C day + ADF × C feed ] = = – (DAYS N – DAYS C ) × MEV(P N, DAYS ) The MEV of the environmental sensitivity depends on the MEV of the trait as such the distance selection environment  response environment

28 Differentiate to derive marginal economic values MEV(P N, DAYS ) = – [C day + ADF × C feed ] = = – [ × 0.29 ] = –0.16 € per d MEV(b DAYS ) = – (DAYS N – DAYS C ) × MEV(P N, DAYS ) = = –(163 – 179) × –0.16 = –2.56 € per d/d Negative MEV : a reduction of DAYS 120 means faster growth Negative MEV : a reduction of the slope brings commercial performance closer to the potential

29 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 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?

30 Line B; parity 1 only 66 farms with records of daughters of 792 sires Line B; all parities 93 farms with records of daughters of 1091 sires Lines A, B and AB; all parities 144 farms with records of daughters of 2040 sires Litter size: daughter group reaction norms

31 sires Line B; parity 1 only 66 farms with records of daughters of 792 sires Line B; all parities 93 farms with records of daughters of 1091 sires Lines A, B and AB; all parities 144 farms with records of daughters of 2040 sires Litter size reaction norms of sires: standard error of slope vs. HYS environmental range

32 Line B; parity 1 only 66 farms with records of daughters of 792 sires Line B; all parities 93 farms with records of daughters of 1091 sires Lines A, B and AB; all parities 144 farms with records of daughters of 2040 sires sires Litter size reaction norms of sires: standard error of slope vs. number of daughters

33 Line B; parity 1 only 66 farms with records of daughters of 792 sires Line B; all parities 93 farms with records of daughters of 1091 sires Lines A, B and AB; all parities 144 farms with records of daughters of 2040 sires Litter size reaction norms of sires: standard error of slope vs. slope h 2 r G intcpt1026±7 slope 8±3 h 2 r G intcpt969±5 slope2±0.4 h 2 r G intcpt10–9±15 slope15±8 Knap & Su (2008)

34 Line B; parity 1 only 66 farms with records of daughters of 792 sires Line B; all parities 93 farms with records of daughters of 1091 sires Lines A, B and AB; all parities 144 farms with records of 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

35 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 ?

36 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 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

37 The individual reaction norm approach is not feasible 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.

38 Van Sambeek (2010) Reciprocal Recurrent Selection Commercial Sibling Test Combined Crossbred & Purebred Selection

39 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:

40 … 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

41 An example: PIC's GN-Xbred program multiplication commercial breeding stock GN commercial crossbred slaughter pigs PICTraq Database selection decisions CBVs GN progeny performance data Commercial progeny performance data Commercial sow performance data crossbred halfsib performance  CBVs of GN selection candidates  crossbred halfsibs of purebred GN selection candidates Xbred sow performance  CBVs of GN selection candidates

42 GN-Xbred logistics sire lines dam lines

43 Reciprocal Recurrent Selection Commercial Sibling Test Combined Crossbred & Purebred Selection Is this useful? Depends on the coheritability ΔG C|N ~ h C × r G (C,N) × h N ΔG C|C ~ h C × h C is h C > r G (C,N) × h N ?  is r G (C,N) low enough ?  what about h N vs h C ? !! effective heritabilities !! The crucial aspects : Can the trait be recorded at all in nucleus conditions ? And on how many animals ?

44 Cecchinato et al. (2010): stillbirth rate r G = 0.25 ± 0.34 Bosch et al. (2000): litter size 0.40 < r G < 0.59 Zumbach et al. (2007): ADG 0.53 < r G < 0.80; BFT and LMD 0.78 < r G < 0.89 Ibáñez-Escriche et al. (2011): lean percentage 0.81 < r EBV < 0.96 Brandt & Täubert (1998): ADG and BFT 0.87 < r G < 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:

45 ADG BFD DFI RFI crossbred commercial performance r EBV = 0.55 r EBV = 0.54 r EBV = –0.06 r EBV = 0.06 r EBV = 0.85 r EBV = 0.78 r EBV = 0.85 r EBV = 0.80 crossbred commercial performance purebred nucleus performance Knap & Wang (2012) Poster: Helene Gilbert et al.

46 crossbred commercial performance purebred nucleus performance crossbred commercial performance r EBV = 0.33 r EBV = 0.24 grower-finisher mortality rate Poster: Geir Steinheim et al.

47 low r G (C,N) many more data from C than from N much more variation in C : σ 2 = p × (1 – p) and p is much higher

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

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

50 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

51 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

52 E > I : incentive to improve the environment Genetic Services: live consultancy at the customer level

53 Genetic Services: manuals & documentation

54 Genetic Services: manuals & documentation

55 Genetic Services: manuals & documentation

56 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

57 GxE in commercial pig breeding reaction norms selection for the response environment Pieter Knap Genus-PIC


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