Estimation of genetic parameters between

single-record and multiple-record traits

using ASREMLSansak Nakavisut

Principle supervisor: Dr Ron CrumpCo-supervisor: Dr Hans Graser

Outline• Introduction

• Univariate analysis of single-record traits (eg ADG)

• 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

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

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, hm

2 , c2, ram ,

estimable and reliable or not, depending on

data structure

•Std. error will reflect data structure (Qnt Qly)

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 !ALPHAdemo.dat !MVINCLUDE !DOPART $1

!PART 1ADG ~ mu Br Sex !r Anim !f HYS

“demo.dat” (DATA FILE)

TT2H0453 TT2E4326 TT2E2627 LR M TK942 497.75 1.63TT2H0456 TT2E4326 TT2E2627 LR F TK942 515.17 1.88TT2H0483 TT2C1953 TT2E4315 LR F TK942 490.48 1.85TT2H0484 TT2C1953 TT2E4315 LR F TK942 417.43 1.77TK1H0246 IR1E0003 IR1E0137 LW M TK942 561.45 1.76TK1H0239 IR1E0030 IR1E0139 LW M TK942 538.46 1.71TK1H0228 TK1G0056 TK1G0042 LW M TK942 476.68 1.56TT2H0495 TT2E4326 TT2F4908 LR F TK942 456.94 1.82TT1H7156 TT1F4957 TT1F4990 LW M TK942 528.25 1.93TT1H7157 TT1F4957 TT1F4990 LW F TK942 494.59 1.69TK1H0226 TK1G0056 TK1G0042 LW M TK943 505.18 1.79TK1H0224 TK1G0056 TK1G0042 LW M TK943 466.32 1.61…

“test.ped” (PEDIGREE FILE)

RC3K2178 . .RC3L1647 . .RC3L2652 . .RC3L2731 RC3-0017 RC3-278-9RC3L3109 RC3-1244 RC3-1274RC3M3292 RC3K2210 RC3-1798RC3M3424 RC3L2765 RC3-0567……

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 + 2H h2 1 3

“ADG1.pvc”

3 Vp 1 2151. 44.39 h2 = Anim 1/Vp 1 3= 0.5249 0.0245

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

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 FItest.ped !ALPHAdemo.dat !MVINCLUDE !DOPART $1!PART 2ADG FI ~ Trait Tr.Br Tr.Sex !r Tr.Anim !f Tr.HYS1 2 10Tr 0 US 1 0.1 1 !GPTr.Anim 2Tr 0 US 1 0.1 1 !GPAnim

R ADG FI

ADG 1

FI 0.1 1

G ADG FI

ADG 1

FI 0.1 1

asreml –rs4 ADG 2

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.pin”

F VpADG 1+4F VpFI 3+6F Covp12 2+5H h2_ADG 4 7H h2_FI 6 8R rg 4 5 6R rp 7 9 8

“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

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, hm

2 , c2, ram if data structure allows

• PLUS repeatability (r)

data set-up for multi-record trait (NBA)

An S D Fixed Par NBAA 1 2 . 1 10A 1 2 . 2 11A 1 2 . 3 10A 1 2 . 4 13B 3 4 . 1 9B 3 4 . 2 12C 3 5 . 1 10C 3 5 . 2 11C 3 5 . 3 13...

An S D Fixed NBA1 NBA2 NBA3 NBA4A 1 2 . 10 11 10 13B 3 4 . 9 12 . .C 3 5 . 10 11 13 ....

For repeatability model Treat NBA1-4 as different traits

Example: repeatability model (NBA)“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

.

.

.

“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

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+3F repeat 1+2H h2 1 4H 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

Bivariate analysis of single & multi-record traits

•Data Set-up

•Command file( .as file) & Model

•Which terms to estimate what

•Problems / Solutions

•Demonstration

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

Multi-records of NBA

Single-record of ADG & FI

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 !ALPHAdemo1.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.HYS0 0 1Tr.Anim 2Tr 0 US 1 0.1 1 !GPAnim

Command file: single-multi records

G structure

Cov e12

Ve2 - Ve1 - Cov e12

Ve1 = residual V. left from the model

Concept: single-multi records

R NBA1 NBA2 NBA3 NBAn ADG

NBA1 Ve1

NBA2 Cov11 Ve1

NBA3 Cov11 Cov11 Ve1

NBAn Cov11 Cov11 Cov11 Ve1

ADG Cov12 Cov12 Cov12 Cov12 Ve2

R NBA ADG

NBA Ve1

ADG Cov12 Ve2

Results: single-multi records (NBA-ADG) Source Model terms Gamma Component Comp/SE % C1 ide(Anim) 11414 11414 0.764115 4.36474 2.25 0 U2 at(Tr,1).ide(Anim) 11414 11414 -0.701052 -4.00452 -2.06 0 U3 uni(Tr,2) 18613 18613 176.829 1010.08 23.44 0 U4 Variance 18613 18220 1.00000 5.71215 62.25 0 P5 Tr.Anim UnStruct 1 0.116258 0.664080 6.21 0 P6 Tr.Anim UnStruct 1 -0.856536 -4.89266 -2.27 0 P7 Tr.Anim UnStruct 2 198.460 1133.63 16.29 0 P

“demo.pin”

F (8)Ve2 1+3+4F (9)Vp1 5+1+2+4F (10)Vp2 7+8F (11)Covp12 1+6F (12)repeat1 1+2+5H h1

2 5 9H r1 12 9H h2

2 7 10R rg 5 6 7R 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

“(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

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

“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

“(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

Estimates: univariate – (semi)bivariate

“ADG1.pvc”

h2 = Anim 1/Vp 1 3= 0.5249 0.0245

“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

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

Example: problem of order

F E11 5F E22 2+4+5F E12 2F P11 2+3+5+6F P22 8+1+10F P12 2+7F S11 2+3+6H c2 1 13H r11 15 12H h21 6 12H h22 8 13R rg 6 7 8R rp 12 14 13R re 9 11 10

F E11 5F E22 2+3+5F E12 3F P11 3+4+5+6F P22 8+1+10F P12 3+7F S11 3+4+6H c2 1 13H r11 15 12H h21 6 12H h22 8 13R rg 6 7 8R rp 12 14 13R re 9 11 10

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

Demonstration