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Sansak Nakavisut Principle supervisor: Dr Ron Crump Co-supervisor: Dr Hans Graser

Estimation of genetic parameters between single -record and multiple -record traits using ASREML. Sansak Nakavisut Principle supervisor: Dr Ron Crump Co-supervisor: Dr Hans Graser. Outline. Introduction Univariate analysis of single-record traits ( eg ADG )

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Sansak Nakavisut Principle supervisor: Dr Ron Crump Co-supervisor: Dr Hans Graser

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  1. Estimation of genetic parameters between single-record and multiple-record traits using ASREML Sansak Nakavisut Principle supervisor: Dr Ron Crump Co-supervisor: Dr Hans Graser

  2. Outline • Introduction • Univariate analysis of single-record traits (egADG) • Bivariate analysis of 2 (single-record) traits (eg ADG & FI) • Univariate analysis of multi-record traits (eg NBA) • Bivariate analysis of single & multi-record traits • Problems / solutions • Demonstration

  3. Introduction • Aim: to demonstrate how to estimate genetic parameters from a more complex model using ASREML • Data set up to match the model • Concept of multi-record traits (repeated measurements of the same traits) • Problems you may encounter in bivariate analysis • and solutions

  4. Univariate analysis of single-record trait • One record per animal during the life time • eg BW, TN, BF, FCR, ADG, FI, etc., • Genetic parameters; ha2, hm2 , c2, ram , estimable and reliable or not, depending on data structure • Std. error will reflect data structure (Qnt Qly)

  5. Example: univar. Of ADG “ADG.as” (COMMAND FILE) Analysis of production traits Anim !P Sire !P Dam !P Br 2 !A Sex 2 !A HYS 2 !A ADG FI test.ped !ALPHA demo.dat !MVINCLUDE !DOPART $1 !PART 1 ADG ~ mu Br Sex !r Anim !f HYS “demo.dat” (DATA FILE) TT2H0453 TT2E4326 TT2E2627 LR M TK942 497.75 1.63 TT2H0456 TT2E4326 TT2E2627 LR F TK942 515.17 1.88 TT2H0483 TT2C1953 TT2E4315 LR F TK942 490.48 1.85 TT2H0484 TT2C1953 TT2E4315 LR F TK942 417.43 1.77 TK1H0246 IR1E0003 IR1E0137 LW M TK942 561.45 1.76 TK1H0239 IR1E0030 IR1E0139 LW M TK942 538.46 1.71 TK1H0228 TK1G0056 TK1G0042 LW M TK942 476.68 1.56 TT2H0495 TT2E4326 TT2F4908 LR F TK942 456.94 1.82 TT1H7156 TT1F4957 TT1F4990 LW M TK942 528.25 1.93 TT1H7157 TT1F4957 TT1F4990 LW F TK942 494.59 1.69 TK1H0226 TK1G0056 TK1G0042 LW M TK943 505.18 1.79 TK1H0224 TK1G0056 TK1G0042 LW M TK943 466.32 1.61 … “test.ped” (PEDIGREE FILE) RC3K2178 . . RC3L1647 . . RC3L2652 . . RC3L2731 RC3-0017 RC3-278-9 RC3L3109 RC3-1244 RC3-1274 RC3M3292 RC3K2210 RC3-1798 RC3M3424 RC3L2765 RC3-0567 ……

  6. Results: univariate analysis (ADG) “ADG1.asr” Source Model terms Gamma Component Comp/SE % C Anim 9621 9621 1.10486 1129.25 16.25 0 P Variance 7665 7530 1.00000 1022.08 23.70 0 P “ADG1.pin” F Vp 1 + 2 H h2 1 3 “ADG1.pvc” 3 Vp 1 2151. 44.39 h2 = Anim 1/Vp 1 3= 0.5249 0.0245

  7. Bivariate analysis of single-record traits • Joint analysis of 2 traits (eg ADG and FI) • Genetic parameter estimates; • h2 of trait 1(ADG) (accounted for trait 2(FI)) • h2 of trait 2(FI) (accounted for trait 1(ADG)) • PLUS • rg12, re12, rp12

  8. Bivariate analysis of ADG and FI “ADG.as” (COMMAND FILE) Analysis of production traits Anim !P Sire !P Dam !P Br 2 !A Sex 2 !A HYS 2 !A ADG FI test.ped !ALPHA demo.dat !MVINCLUDE !DOPART $1 !PART 2 ADG FI ~ Trait Tr.Br Tr.Sex !r Tr.Anim !f Tr.HYS 1 2 1 0 Tr 0 US 1 0.1 1 !GP Tr.Anim 2 Tr 0 US 1 0.1 1 !GP Anim asreml –rs4 ADG 2

  9. Results: bivariate analysis “ADG2.asr” Source Model terms Gamma Component Comp/SE % C 1 Residual UnStruct 1 1022.23 1022.23 23.71 0 P 2 Residual UnStruct 1 2.25786 2.25786 14.78 0 P 3 Residual UnStruct 2 0.259174E-01 0.259174E-01 29.36 0 P 4 Tr.Anim UnStruct 1 1128.76 1128.76 16.25 0 P 5 Tr.Anim UnStruct 1 2.28471 2.28471 9.78 0 P 6 Tr.Anim UnStruct 2 0.165168E-01 0.165168E-01 13.14 0 P Covariance/Variance/Correlation Matrix UnStructured 1022. 0.4387 2.258 0.2592E-01 Covariance/Variance/Correlation Matrix UnStructured 1129. 0.5291 2.285 0.1652E-01 “ADG2.pvc” 7 VpADG 1 2151. 44.38 8 VpFI 3 0.4243E-01 0.8272E-03 9 Covp12 2 4.543 0.1506 h2_ADG = Tr.Anim 4/VpADG 1 7= 0.5248 0.0245 h2_FI = Tr.Anim 6/VpFI 3 8= 0.3892 0.0248 rg = Tr.Anim /SQR[Tr.Anim *Tr.Anim ]= 0.5291 0.0361 rp = Covp12 /SQR[VpADG 1*VpFI 3 ]= 0.4755 0.0109 “ADG2.pin” F VpADG 1+4 F VpFI 3+6 F Covp12 2+5 H h2_ADG 4 7 H h2_FI 6 8 R rg 4 5 6 R rp 7 9 8

  10. Univariate analysis of multi-record trait • When rg between 2 measurements is 1 or close to unity, “repeated measurements of the same trait”, within-individual variance is caused by temporary differences of environment • Otherwise, we should treat them as “2 different traits” they are not under the same genetic control • Genetic parameter estimates; • ha2, hm2 , c2, ram if data structure allows • PLUS repeatability (r)

  11. data set-up for multi-record trait (NBA) For repeatability model Treat NBA1-4 as different traits AnSDFixedParNBA A 1 2 . 1 10 A 1 2 . 2 11 A 1 2 . 3 10 A 1 2 . 4 13 B 3 4 . 1 9 B 3 4 . 2 12 C 3 5 . 1 10 C 3 5 . 2 11 C 3 5 . 3 13 . . . AnSDFixedNBA1NBA2NBA3NBA4 A 1 2 . 10 11 10 13 B 3 4 . 9 12 . . C 3 5 . 10 11 13 . . . .

  12. Example: repeatability model (NBA) “NBA.as” Analysis of NBA (repeatibility model) Anim !P Sire !P Dam !P Br 2 !A Sex 2 !A HYS 2 !A NBA repro.ped !ALPHA NBA.dat !REPEAT !MAXIT 50 !MVINCLUDE NBA ~ mu Br !r Anim ide(Anim)!f HYS “NBA.dat” CA1G0037 CA1F33910 CA1F0040 LW F TK951 11 CA1G0037 CA1F33910 CA1F0040 LW F TK952 12 CA1G0037 CA1F33910 CA1F0040 LW F TK953 5 CA1G0038 CA1F33910 CA1F0040 LW F TK943 9 CA1G0038 CA1F33910 CA1F0040 LW F TK953 5 CA1G0038 CA1F33910 CA1F0040 LW F TK961 6 CA1G0038 CA1F33910 CA1F0040 LW F TK963 3 CA1G0038 CA1F33910 CA1F0040 LW F TK972 2 CA1G0038 CA1F33910 CA1F0040 LW F TK973 12 CA1G0050 CA1E0226 CA1E0040 LW F TK952 10 . . .

  13. Results: repeatability model “NBA.asr” Source Model terms Gamma Component Comp/SE % C Anim 5421 5421 0.112836 0.644805 6.04 0 P ide(Anim) 5421 5421 0.652040E-01 0.372611 3.69 0 P Variance 10948 10690 1.00000 5.71454 62.25 0 P “NBA.pin” F Vp 1+2+3 F repeat 1+2 H h2 1 4 H r 5 4 “NBA.pvc” h2 = Anim 1/Vp 1 4= 0.0958 0.0155 r = repeat 5/Vp 1 4= 0.1511 0.0108

  14. Bivariate analysis of single & multi-record traits • Data Set-up • Command file( .as file) & Model • Which terms to estimate what • Problems / Solutions • Demonstration

  15. Data Set-up: single-multi records Anim Br Sex HYS NBA_ADG NBA_FI Tr . . . YL2O3774 LR F YL022 9 9 1 YL2O3774 LR F YL031 12 12 1 YL2O3774 LR F YL032 11 11 1 YL2O3779 LR F YL022 10 10 1 YL2O3779 LR F YL031 8 8 1 YL2O3798 LR F YL023 11 11 1 YL2O3798 LR F YL031 10 10 1 YL2O3800 LR F YL023 7 7 1 TT2H0453 LR M TK942 497.75 1.63 2 TT2H0456 LR F TK942 515.17 1.88 2 TT2H0483 LR F TK942 490.48 1.85 2 TT2H0484 LR F TK942 417.43 1.77 2 TK1H0246 LW M TK942 561.45 1.76 2 TK1H0239 LW M TK942 538.46 1.71 2 TK1H0228 LW M TK942 476.68 1.56 2 TT2H0495 LR F TK942 456.94 1.82 2 TT1H7156 LW M TK942 528.25 1.93 2 TT1H7157 LW F TK942 494.59 1.69 2 . . . Multi-records of NBA Single-record of ADG & FI

  16. Ve2 - Ve1 - Cov e12 Cov e12 G structure Command file: single-multi records Analysis of NBA and ADG traits Anim !P Br 2 !A Sex 2 !A HYS 2 !A NBA_ADG NBA_FI Tr 2 demo.ped !ALPHA demo1.dat !REPEAT !MAXIT 50 !MVINCLUDE NBA_ADG ~ Tr Tr.Br at(Tr,2).Sex !r Tr.Anim ide(Anim) !GU, at(Tr,1).ide(Anim) uni(Tr,2) !GU !f Tr.HYS 0 0 1 Tr.Anim 2 Tr 0 US 1 0.1 1 !GP Anim Ve1 = residual V. left from the model

  17. Concept: single-multi records

  18. Results: single-multi records (NBA-ADG) Source Model terms Gamma Component Comp/SE % C 1 ide(Anim) 11414 11414 0.764115 4.36474 2.25 0 U 2 at(Tr,1).ide(Anim) 11414 11414 -0.701052 -4.00452 -2.06 0 U 3 uni(Tr,2) 18613 18613 176.829 1010.08 23.44 0 U 4 Variance 18613 18220 1.00000 5.71215 62.25 0 P 5 Tr.Anim UnStruct 1 0.116258 0.664080 6.21 0 P 6 Tr.Anim UnStruct 1 -0.856536 -4.89266 -2.27 0 P 7 Tr.Anim UnStruct 2 198.460 1133.63 16.29 0 P “demo.pin” F (8)Ve2 1+3+4 F (9)Vp1 5+1+2+4 F (10)Vp2 7+8 F (11)Covp12 1+6 F (12)repeat1 1+2+5 H h12 5 9 H r1 12 9 H h22 7 10 R rg 5 6 7 R rp 9 11 10 “demo.pvc” h12 = Tr.Anim 5/(9)Vp1 9= 0.0986 0.0154 r1 = (12)repe 12/(9)Vp1 9= 0.1521 0.0108 h22 = Tr.Anim 7/(10)Vp2 10= 0.5263 0.0245 rg = Tr.Anim /SQR[Tr.Anim*Tr.Anim]= -0.1783 0.0777 rp = (11)Covp/SQR[(9)Vp1 *(10)Vp2]= -0.0044 0.0173

  19. Comparison: univariate – (semi)bivariate “univariate analysis of NBA” Source Model terms Gamma Component Comp/SE % C Anim 5421 5421 0.112836 0.644805 6.04 0 P ide(Anim) 5421 5421 0.652040E-01 0.372611 3.69 0 P Variance 10948 10690 1.00000 5.71454 62.25 0 P “(semi)bivariate analysis of NBA-ADG” Source Model terms Gamma Component Comp/SE % C ide(Anim) 11414 11414 0.764115 4.36474 2.25 0 U at(Tr,1).ide(Anim) 11414 11414 -0.701052 -4.00452 -2.06 0 U uni(Tr,2) 18613 18613 176.829 1010.08 23.44 0 U Variance 18613 18220 1.00000 5.71215 62.25 0 P Tr.Anim UnStruct 1 0.116258 0.664080 6.21 0 P Tr.Anim UnStruct 1 -0.856536 -4.89266 -2.27 0 P Tr.Anim UnStruct 2 198.460 1133.63 16.29 0 P “(semi)bivariate analysis of NBA-ADG” Source Model terms Gamma Component Comp/SE % C ide(Anim) 11414 11414 0.764115 4.36474 2.25 0 U at(Tr,1).ide(Anim) 11414 11414 -0.701052 -4.00452 -2.06 0 U uni(Tr,2) 18613 18613 176.829 1010.08 23.44 0 U Variance 18613 18220 1.00000 5.71215 62.25 0 P Tr.Anim UnStruct 1 0.116258 0.664080 6.21 0 P Tr.Anim UnStruct 1 -0.856536 -4.89266 -2.27 0 P Tr.Anim UnStruct 2 198.460 1133.63 16.29 0 P “univariate analysis of ADG” Source Model terms Gamma Component Comp/SE % C Anim 9621 9621 1.10486 1129.25 16.25 0 P Variance 7665 7530 1.00000 1022.08 23.70 0 P

  20. Estimates: univariate – (semi)bivariate “NBA.pvc” h2 = Anim 1/Vp 1 4= 0.0958 0.0155 r = repeat 5/Vp 1 4= 0.1511 0.0108 “demo.pvc” h12 = Tr.Anim 5/(9)Vp1 9= 0.0986 0.0154 r1 = (12)repe 12/(9)Vp1 9= 0.1521 0.0108 h22 = Tr.Anim 7/(10)Vp2 10= 0.5263 0.0245 rg = Tr.Anim /SQR[Tr.Anim*Tr.Anim]= -0.1783 0.0777 rp = (11)Covp/SQR[(9)Vp1 *(10)Vp2]= -0.0044 0.0173 “demo.pvc” h12 = Tr.Anim 5/(9)Vp1 9= 0.0986 0.0154 r1 = (12)repe 12/(9)Vp1 9= 0.1521 0.0108 h22 = Tr.Anim 7/(10)Vp2 10= 0.5263 0.0245 rg = Tr.Anim /SQR[Tr.Anim*Tr.Anim]= -0.1783 0.0777 rp = (11)Covp/SQR[(9)Vp1 *(10)Vp2]= -0.0044 0.0173 “ADG1.pvc” h2 = Anim 1/Vp 1 3= 0.5249 0.0245

  21. Problems / solutions • Convergence failed • Rescaling variable(s) to have similar Vp for both traits • eg FI = 1.1 kg/d => 11 (/10)kg/d to match NBA of 10 pigs/litter • Re-run 99% solved • Rescaling may change missing values to zero • eg “.” x10 = 0 • Be careful, keep missing value as it is • Bizarre Outputs from similar analyses • Check the order of terms in .asr carefully • Even with the same set-up of files, ASREML may report terms in different orders than some previous runs • Re-order components in .pin file to match .asr file

  22. Example: problem of order Source Component at(Tr,2).Litter 276.084 ide(Anim) 3.34576 at(Tr,1).ide(Anim) -3.11344 uni(Tr,2) 417.221 Variance 6.16018 Tr.Anim 0.785246 Tr.Anim -4.33941 Tr.Anim 174.395 Source Component at(Tr,2).Litter 35.6195 uni(Tr,2) 55.0355 ide(Anim) 0.912272 at(Tr,1).ide(Anim) -0.669691 Variance 6.16330 Tr.Anim 0.772876 Tr.Anim -1.41296 Tr.Anim 28.7313 F E11 5 F E22 2+4+5 F E12 2 F P11 2+3+5+6 F P22 8+1+10 F P12 2+7 F S11 2+3+6 H c2 1 13 H r11 15 12 H h21 6 12 H h22 8 13 R rg 6 7 8 R rp 12 14 13 R re 9 11 10 F E11 5 F E22 2+3+5 F E12 3 F P11 3+4+5+6 F P22 8+1+10 F P12 3+7 F S11 3+4+6 H c2 1 13 H r11 15 12 H h21 6 12 H h22 8 13 R rg 6 7 8 R rp 12 14 13 R re 9 11 10

  23. Demonstration

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