GxE in commercial pig breeding reaction norms selection for the response environment Pieter Knap

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. 0.70 0.68 0.66 Poster: AnttiKause average daily gain (kg / d) 0.64 0.62 0.60 very high high very low low infectiousness Schinckel et al. (1999)

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

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

16. 0.70 0.70 y = 0.30 + 0.57 x 0.68 0.68 0.66 0.66 average daily gain (kg / d) average daily gain (kg / d) 0.64 0.64 0.62 0.62 y = –0.30 + 1.43 x 0.60 0.60 0.62 0.64 0.66 0.68 0.70 very high high very low low infectiousness treatment mean: average daily gain (kg / d) 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. Knap & Su (2008)

21. 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

22. P N phenotype environment E N

23. P N PC = PN– b × ( EN– EC ) P H selection environment response environment P C P L b E E E E N L C H

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

25. 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

26. 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)

27. Differentiate to derive marginal economic values • The MEV of the environmental sensitivity depends on • the MEV of the trait as such • the distance selection environment  response environment MEV(bDAYS) = dP / dbDAYS = (DAYSN – DAYSC) × [Cday + ADF × Cfeed ] = = – (DAYSN – DAYSC) × MEV(PN, DAYS)

28. Differentiate to derive marginal economic values MEV(PN, DAYS) = – [Cday + ADF × Cfeed ] = = – [0.24 + 2.3 × 0.29 ] = –0.16 € per d Negative MEV : a reduction of DAYS120means faster growth 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

29. 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?

30. Litter size: daughter group reaction norms Line B; all parities 93 farms with 73.352 records of52.120 daughters of 1091 sires Line B; parity 1 only 66 farms with 33.641 records of33.641 daughters of 792 sires Lines A, B and AB; all parities 144 farms with 346.030 records of121.104 daughters of 2040 sires

31. Litter size reaction norms of sires: standard error of slope vs. HYS environmental range Line B; all parities 93 farms with 73.352 records of52.120 daughters of 1091 sires Line B; parity 1 only 66 farms with 33.641 records of33.641 daughters of 792 sires Lines A, B and AB; all parities 144 farms with 346.030 records of121104 daughters of 2040 sires sires sires sires

32. Litter size reaction norms of sires: standard error of slope vs. number of daughters Line B; all parities 93 farms with 73.352 records of52.120 daughters of 1091 sires Line B; parity 1 only 66 farms with 33.641 records of33.641 daughters of 792 sires Lines A, B and AB; all parities 144 farms with 346.030 records of121104 daughters of 2040 sires sires sires sires

33. Litter size reaction norms of sires: standard error of slope vs. slope Line B; all parities 93 farms with 73.352 records of52.120 daughters of 1091 sires Line B; parity 1 only 66 farms with 33.641 records of33.641 daughters of 792 sires Lines A, B and AB; all parities 144 farms with 346.030 records of121104 daughters of 2040 sires h2rG intcpt 10 26±7 slope 8±3 h2rG intcpt 9 69±5 slope 2±0.4 h2rG intcpt 10 –9±15 slope 15±8 Knap & Su (2008)

34. Litter size: daughter group reaction norms Line B; all parities 93 farms with 73.352 records of52.120 daughters of 1091 sires Line B; parity 1 only 66 farms with 33.641 records of33.641 daughters of 792 sires Lines A, B and AB; all parities 144 farms with 346.030 records of121.104 daughters of 2040 sires ? E > I > G Same data (Line B; all parities)  analyzed with SAS I > E > G

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 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

37. 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.

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

39. Theory: • Baumung et al. (1997) • Bijma & Van Arendonk (1998) • Spilke et al. (1998) • Misztal et al. (1998) • Dekkers & Chakraborty (2004) • Standal (1968) • McNew & Bell (1971) • Biswas et al. (1971) • Wei Ming & Van der Werf (1994)

40. An example: PIC's GN-Xbred program GN multiplication commercial crossbred sows commercial crossbred slaughter pigs • semen of GN boars is first used on crossbred sows  crossbred progeny … grown on commercial farms • after that, semen is used for GN matings  purebred progeny

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

42. GN-Xbred logistics sire lines dam lines

43. 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 hNvshC ? • !! effective heritabilities !! The crucial aspects :Can the trait be recorded at all in nucleus conditions ? And on how many animals ? Reciprocal Recurrent Selection Commercial Sibling Test Combined Crossbred & Purebred Selection

44. Theory: • Baumung et al. (1997) • Bijma & Van Arendonk (1998) • Spilke et al. (1998) • Misztal et al. (1998) • Dekkers & Chakraborty (2004) • Standal (1968) • McNew & Bell (1971) • Biswas et al. (1971) • Wei Ming & Van der Werf (1994) • 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

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

46. grower-finisher mortality rate rEBV = 0.24 rEBV = 0.33 Poster: GeirSteinheim et al. crossbred commercial performance crossbred commercial performance purebred nucleus performance

47. lowrG (C,N) • many more data from C than from N • much more variation in C : σ2 = p × (1 – p) and p is much higher

48. E > I : incentive to improve the environment This is the actual worldwide situation 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

49. E > I : incentive to improve the environment This is what we are targeting,in terms of genetic evaluation: ~ "better safe than sorry" • I > E : incentive to match genotype to environment • Select in the response envrmnt • Select on data from the response environment

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