Vasse 150910 wayne

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Vasse 150910 wayne

  1. 1. EBVs and the breeding herd – What’s happening out there? Wayne Pitchford Kath Donoghue
  2. 2. Genetic change in growth traits 50kg
  3. 3. Genetic change in body composition
  4. 4. Genetic change in profit per cow
  5. 5. Changing Genetic Potential 962 progeny analysed across 77 herds 623 progeny analysed across 20 herds Sire 600d Wt (kg) MCWt (kg) Milk (kg) DC (days) Rib Fat (mm) Rump Fat (mm) RBY (%) IMF % Long Fed Index ($) 1 +122 +119 +20 -8.7 -3.2 -3.2 +1.6 +0.6 +103 2 +57 +50 0 -4.7 +4.5 +3.6 -2.0 +3.7 +98
  6. 6. Data collection  Traits: – Weight and Height – P8 and Rib fat and IMF% – Eye muscle area and Condition Score  Time of collection: – 1st parity: Pre Calving (PC1) and Weaning (W1) – 2nd parity: Pre Calving (PC2) and Weaning (W2)
  7. 7. Industry herds: Bald Blair Angus Barwidgee Angus Booroomooka Angus Chis Angus Eastern Plains Angus Kenny’s Creek Angus Rennylea Angus South Boorook Herefords Te Mania Angus Tuwhareto Angus Twynam Angus Willalooka Angus Wirruna Herefords Yalgoo Herefords Yavenvale Herefords Ultrasound technicians: Jim Green Liam Cardile Matt Wolcott Acknowledgements
  8. 8. Data structure Angus (n=5,949) Hereford (n=1,452) PC1 4,867 1,124 W1 3,735 645 PC2 2,772 918 W2 2,109 485
  9. 9. Average (SD) σ2 p (SE) h2 (SE) A H A H A H n=3,735 n=645 WT (kg) 515 (64) 503 (43) 2,003 (58) 1,868 (114) 0.39 (0.05) 0.42 (0.12) P8 (mm) 5.8 (2.9) 6.8 (3.0) 5.4 (0.17) 17 (13) 0.51 (0.06) 0.97 (0.06) Rib (mm) 5.0 (2.2) 5.0 (1.8) 3.2 (0.10) 3.3 (0.21) 0.49 (0.05) 0.54 (0.14) EMA (cm2 ) 59 (9) 58 (6) 42 (1.2) 40 (2.4) 0.32 (0.05) 0.47 (0.11) IMF (%) 5.5 (1.9) 2.9 (0.08) 0.39 (0.05) Descriptive statistics – W1
  10. 10. Phenotypic relationships for same trait over time Trait PC1-W1 W1-PC2 PC2-W2 WT 0.66 (0.01) 0.79 (0.009) 0.72 (0.01) P8 0.47 (0.02) 0.74 (0.01) 0.57 (0.02) Rib 0.49 (0.02) 0.72 (0.01) 0.57 (0.02) EMA 0.45 (0.02) 0.63 (0.02) 0.50 (0.02) IMF 0.51 (0.01) 0.69 (0.01) 0.54 (0.02)
  11. 11. Trait PC1-W1 W1-PC2 PC2-W2 WT 0.88 (0.03) 0.94 (0.03) 0.95 (0.02) P8 0.70 (0.05) 0.89 (0.04) 0.92 (0.04) Rib 0.65 (0.05) 0.96 (0.04) 0.96 (0.03) EMA 0.68 (0.07) 0.84 (0.06) 0.85 (0.07) IMF 0.76 (0.06) 0.92 (0.04) 0.86 (0.06) Genetic relationships for same trait over time
  12. 12. Change in weight: 1st lactation (n=3,615) 0 100 200 300 400 500 600 -330 -290 -250 -210 -170 -130 -90 -50 -10 30 70 110 150 190 230 270 Change in WT, kg Numberoffemales
  13. 13. 0 100 200 300 400 500 600 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 Change in P8 (mm) Numberoffemales Change in P8: 1st lactation (n=3,616)
  14. 14. 0 100 200 300 400 500 600 -34 -28 -22 -16 -10 -4 2 8 14 20 26 32 Change inEMA,sq.cm Numberoffemales Change in EMA: 1st lactation (n=3,623)
  15. 15. Change in Weight over time Trait 1st lactation Weaning – Calving 2nd lactation 1st lactation 0.16 (0.04) -0.53 (0.02) 0.10 (0.03) Weaning – Calving -0.51 (0.23) 0.09 (0.04) -0.43 (0.03) 2nd lactation 0.62 (0.20) -0.30 (0.27) 0.14 (0.05) rp above; rg below
  16. 16. Change in traits: 1st lactation Trait WT P8 EMA IMF WT 0.48 (0.01) 0.43 (0.01) 0.36 (0.02) P8 0.70 (0.10) 0.39 (0.02) 0.40 (0.02) EMA 0.70 (0.12) 0.68 (0.11) 0.35 (0.02) IMF 0.71 (0.12) 0.76 (0.10) 0.74 (0.11) rp above; rg below
  17. 17. Messages  Currently rapid change in cattle growth, carcass composition and profit. Will accelerate!  Cow weight and body composition is heritable and repeatable over time  Cows change in weight and composition substantially throughout year  Change in weight is lowly heritable
  18. 18. Thank you for the enormous contribution from the Vasse team and for listening!
  19. 19. Contemporary groups Number Avg size Min size Max size PC1 188 57 2 182 PC2 86 87 2 171 Pre-Calving: • Preg status • Parity 1 wean status (PC2 only) • Season (Autumn, Spring) • Herd • Breeder management group
  20. 20. Number Avg size Min size Max size W1 117 110 2 368 W2 87 70 2 161 Weaning: • Preg status of current parity • Preg status of future parity • Wean status of current parity • Season (Autumn, Spring) • Herd • Breeder management group Contemporary groups
  21. 21. Model of analysis Animal model fitted using ASReml:  Contemporary group  Age of animal  Direct genetic effect Bivariate analyses:  Same trait across 4 time points (PC1, W1, PC2, W2)  Different traits within same time point
  22. 22. Contemporary groups Number Avg size Min size Max size PC1 188 57 2 182 PC2 86 87 2 171 Pre-Calving: • Preg status • Parity 1 wean status (PC2 only) • Season (Autumn, Spring) • Herd • Breeder management group
  23. 23. Number Avg size Min size Max size W1 117 110 2 368 W2 87 70 2 161 Weaning: • Preg status of current parity • Preg status of future parity • Wean status of current parity • Season (Autumn, Spring) • Herd • Breeder management group Contemporary groups
  24. 24. Model of analysis Animal model fitted using ASReml:  Contemporary group  Age of animal  Direct genetic effect Bivariate analyses:  Same trait across 4 time points (PC1, W1, PC2, W2)  Different traits within same time point
  25. 25. Descriptive statistics – PC1 Average (SD) σ2 p (SE) h2 (SE) A H A H A H n=4,867 n=1,124 WT (kg) 487 (70) 456 (33) 1,209 (33) 1,083 (50) 0.55 (0.05) 0.46 (0.08) P8 (mm) 5.8 (3.1) 6.4 (2.4) 4.2 (0.11) 6.0 (0.30) 0.52 (0.05) 0.56 (0.09) Rib (mm) 4.6 (2.2) 4.4 (1.4) 2.1 (0.06) 2.1 (0.10) 0.50 (0.05) 0.65 (0.08) EMA (cm2 ) 57 (10) 50 (6) 36 (0.86) 39 (1.7) 0.30 (0.04) 0.15 (0.06) IMF (%) 5.2 (2.0) 2.3 (0.06) 0.34 (0.04)
  26. 26. Average (SD) σ2 p (SE) h2 (SE) A H A H A H n=2,772 n=918 WT (kg) 552 (73) 533 (41) 1,933 (63) 1,671 (80) 0.36 (0.06) 0.07 (0.07) P8 (mm) 6.1 (3.2) 7.6 (3.0) 5.5 (0.18) 9.1 (0.45) 0.33 (0.06) 0.20 (0.08) Rib (mm) 4.9 (2.4) 4.9 (1.7) 3.0 (0.10) 3.0 (0.14) 0.28 (0.06) 0.18 (0.08) EMA (cm2 ) 61 (9.8) 55 (7) 45 (1.4) 47 (2.3) 0.25 (0.05) 0.25 (0.09) IMF (%) 5.5 (2.2) 2.8 (0.09) 0.28 (0.06) Descriptive statistics – PC2
  27. 27. Average (SD) σ2 p (SE) h2 (SE) A H A H A H n=2,109 n=485 WT (kg) 585 (74) 588 (49) 2,981 (119) 2,539 (173) 0.54 (0.07) 0.30 (0.14) P8 (mm) 7.9 (4.1) 11 (4.8) 9.8 (0.42) 23 (1.6) 0.64 (0.08) 0.37 (0.17) Rib (mm) 6.6 (3.1) 6.7 (2.6) 5.7 (0.24) 6.7 (0.44) 0.57 (0.08) 0.05 (0.11) EMA (cm2 ) 63 (9.4) 62 (7) 42 (1.5) 48 (3.2) 0.23 (0.06) 0.14 (0.12) IMF (%) 6.1 (1.8) 2.5 (0.09) 0.37 (0.07) Descriptive statistics – W2
  28. 28. Trait WT-P8 WT-EMA WT-IMF P8-EMA P8-IMF EMA-IMF PC1 0.18 (0.02) 0.41 (0.02) 0.20 (0.02) 0.30 (0.02) 0.42 (0.02) 0.32 (0.02) W1 0.39 (0.02) 0.53 (0.01) 0.35 (0.02) 0.45 (0.02) 0.57 (0.01) 0.45 (0.02) PC2 0.30 (0.02) 0.46 (0.02) 0.24 (0.02) 0.36 (0.02) 0.51 (0.02) 0.35 (0.02) W2 0.39 (0.02) 0.49 (0.02) 0.29 (0.03) 0.47 (0.02) 0.49 (0.02) 0.39 (0.02) Phenotypic relationships between traits
  29. 29. Trait WT-P8 WT-EMA WT-IMF P8-EMA P8-IMF EMA-IMF PC1 0.09 (0.08) 0.48 (0.07) 0.21 (0.08) 0.28 (0.08) 0.42 (0.07) 0.31 (0.09) W1 0.27 (0.09) 0.51 (0.08) 0.29 (0.10) 0.44 (0.08) 0.70 (0.06) 0.42 (0.09) PC2 0.12 (0.13) 0.53 (0.10) -0.04 (0.15) 0.13 (0.15) 0.47 (0.11) 0.21 (0.15) W2 0.45 (0.09) 0.66 (0.09) 0.15 (0.13) 0.68 (0.09) 0.57 (0.09) 0.37 (0.15) Genetic relationships between traits
  30. 30.  Moderate, consistent phenotypic relationships  Mostly consistent genetic relationships  WT higher rg with EMA than fat  Fat traits (P8, Rib, IMF) appear highly correlated  Reanalyse when data collection complete Messages
  31. 31. 0 50 100 150 200 250 300 350 -330 -290 -250 -210 -170 -130 -90 -50 -10 30 70 110 150 190 230 270 Change in WT, kg Numberoffemales Change in weight: Between parities (n=1,614)
  32. 32. 0 50 100 150 200 250 300 350 -330 -290 -250 -210 -170 -130 -90 -50 -10 30 70 110 150 190 230 270 Change in WT, kg Numberoffemales Change in weight: Parity 2 (n=2,062)
  33. 33. Trait Parity 1 Between parities Parity 2 Parity 1 0.30 (0.05) -0.44 (0.02) 0.11 (0.03) Between parities -0.72 (0.13) 0.16 (0.05) -0.46 (0.02) Parity 2 0.97 (0.10) -0.67 (0.18) 0.26 (0.06) rp above; rg below Change in P8 over time
  34. 34. Trait Parity 1 Between parities Parity 2 Parity 1 0.16 (0.04) -0.46 (0.02) 0.04 (0.03) Between parities -0.85 (0.13) 0.11 (0.04) -0.52 (0.02) Parity 2 0.34 (0.30) -0.72 (0.23) 0.06 (0.03) rp above; rg below Change in EMA over time
  35. 35. Trait Parity 1 Between parities Parity 2 Parity 1 0.17 (0.04) -0.39 (0.02) 0.09 (0.03) Between parities -0.51 (0.23) 0.10 (0.05) -0.52 (0.02) Parity 2 0.63 (0.23) -0.73 (0.28) 0.10 (0.04) rp above; rg below Change in IMF over time
  36. 36. Messages  Other composition traits have similar trends to WT  Do we need to scan cows?  Seedstock sector – selection for adaptation  Breeder sector – monitoring condition/adaptation  Reanalyse when data collection complete
  37. 37. Trait WT P8 EMA IMF WT 0.34 (0.02) 0.31 (0.02) 0.26 (0.02) P8 0.57 (0.20) 0.32 (0.02) 0.28 (0.02) EMA 0.84 (0.17) 0.79 (0.24) 0.29 (0.02) IMF 0.79 (0.18) 0.51 (0.24) 0.62 (0.29) rp above; rg below Change in traits: Parity 2
  38. 38. Trait WT P8 EMA IMF WT 0.20 (0.03) 0.23 (0.02) 0.18 (0.03) P8 0.29 (0.28) 0.15 (0.03) 0.26 (0.03) EMA 0.79 (0.19) -0.13 (0.30) 0.24 (0.02) IMF 0.49 (0.36) 0.42 (0.31) 0.70 (0.20) rp above; rg below Change in traits: Between parities
  39. 39. Messages  Phenotypic correlations still moderate  Most genetic correlations lower than Parity 1 & 2  May need to scan cows?  High SE on all rg  Reanalyse when data collection complete
  40. 40.  All traits are moderately heritable  EMA & IMF similar to published estimates in yearling heifers  P8 & Rib higher than published estimates- stage of physiology??  WT similar to Angus MCW (0.41)  Reanalyse when data collection complete Messages
  41. 41. Messages  Phenotypic correlations moderate to high  Genetic correlations high to very high  May not need repeated measures on cows  PC1-W1 phenotypic & genetic correlations lowest  Only first 2 parities measured  Need to compare to young measures  Reanalyse when data collection complete
  42. 42.  Low h2 for change in weight  Change in parity 1 reasonable indicator of change in parity 2, genetically (rg = 0.62)  But rp is 0 so environmental correlation must be negative???  Change in parity 1 is in opposite direction to change between parities, (rg = -0.51, rp = -0.53 ) Messages
  43. 43. Messages  Genetically – all traits changing in same direction  Phenotypic is more moderate  Do we need to scan cows?  Seedstock sector – selection for adaptation  Breeder sector – monitoring condition/adaptation
  44. 44. Change in body composition and maternal output Trait change:  Have to be in same CG at both time points  Age at first time point  Direct genetic effect Maternal output (Calf weaning weight):  Unadjusted calf WWT  CG: CG of dam for trait change  PRELIMINARY ANALYSIS!
  45. 45. Trait change & maternal output: Parity 1 Calf WWT (h2 =0.12) Phenotypic correlation Genetic correlation WT -0.13 (0.02) -0.24 (0.20) P8 -0.12 (0.02) -0.31 (0.18) Rib -0.11 (0.02) -0.26 (0.17) EMA -0.12 (0.02) -0.50 (0.19) IMF -0.14 (0.02) -0.51 (0.18)
  46. 46. Calf WWT (h2 =0.17) Trait Phenotypic correlation Genetic correlation WT -0.15 (0.02) -0.54 (0.19) P8 0.04 (0.02) -0.10 (0.25) Rib 0.02 (0.02) -0.10 (0.26) EMA 0.04 (0.02) -0.47 (0.28) IMF -0.05 (0.02) -0.67 (0.23) Trait change & maternal output: Parity 2
  47. 47. Messages  Increased calf WWT moderately associated genetically with loss of condition in cows  Low to zero phenotypic correlations  h2 of calf WWT low, but is maternal genetic component (1/4 Vdir + Vmat)  High SE on all rg  Make further refinements to model  Reanalyse when data collection complete
  48. 48. Future analyses  Yearling measures with later life traits  Fertility analyses  Lifetime maternal productivity

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