Sire model without A
DATAFILE test.dat NUMBER_OF_TRAITS 1 NUMBER_OF_EFFECTS 2 OBSERVATION(S) 3 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 2 cross 2 3 cross RANDOM_RESIDUAL VALUES 10 RANDOM_GROUP 2 RANDOM_TYPE diagonal FILE (CO)VARIANCES 1
Sire model with A
DATAFILE test.dat NUMBER_OF_TRAITS 1 NUMBER_OF_EFFECTS 2 OBSERVATION(S) 3 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 2 cross 2 3 cross RANDOM_RESIDUAL VALUES 10 RANDOM_GROUP 2 RANDOM_TYPE add_sire FILE sire.ped (CO)VARIANCES 1
Multiple (2) trait sire model
DATAFILE test.dat NUMBER_OF_TRAITS 2 NUMBER_OF_EFFECTS 2 OBSERVATION(S) 3 4 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 1 2 cross 2 2 3 cross RANDOM_RESIDUAL VALUES 10 1 1 5 RANDOM_GROUP 2 RANDOM_TYPE add_sire FILE sire.ped (CO)VARIANCES 1 0.1 0.1 1
Animal model
DATAFILE test.dat NUMBER_OF_TRAITS 1 NUMBER_OF_EFFECTS 2 OBSERVATION(S) 3 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 2 cross 5 10 cross RANDOM_RESIDUAL VALUES 10 RANDOM_GROUP 2 RANDOM_TYPE add_animal FILE animal.ped (CO)VARIANCES 1
Multiple trait animal model
# Example 1: 2 trait animal model DATAFILE test.dat NUMBER_OF_TRAITS 2 NUMBER_OF_EFFECTS 2 OBSERVATION(S) 3 4 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 1 2 cross 5 5 10 cross RANDOM_RESIDUAL VALUES 10 1 1 5 RANDOM_GROUP 2 RANDOM_TYPE add_animal FILE animal.ped (CO)VARIANCES 1 0.1 0.1 1
# Example 2: different model for each trait DATAFILE test.dat NUMBER_OF_TRAITS 2 NUMBER_OF_EFFECTS 3 OBSERVATION(S) 3 4 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 2 2 cross 5 5 10 cross 6 7 30 cross RANDOM_RESIDUAL VALUES 10 1 1 5 RANDOM_GROUP 2 RANDOM_TYPE add_animal FILE animal.ped (CO)VARIANCES 1 0.1 0.1 1 RANDOM_GROUP 3 RANDOM_TYPE diagonal FILE (CO)VARIANCES 1 0 0 1
# This one works with Gibbs sampling programs DATAFILE test.dat NUMBER_OF_TRAITS 2 NUMBER_OF_EFFECTS 5 OBSERVATION(S) 3 4 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 0 2 cross 0 2 2 cross 5 5 10 cross 6 0 30 cross 0 7 20 cross RANDOM_RESIDUAL VALUES 10 1 1 5 RANDOM_GROUP 3 RANDOM_TYPE add_animal FILE animal.ped (CO)VARIANCES 1 0.1 0.1 1 RANDOM_GROUP 4 RANDOM_TYPE diagonal FILE (CO)VARIANCES 1 0 0 0 RANDOM_GROUP 5 RANDOM_TYPE diagonal FILE (CO)VARIANCES 0 0 0 1
OR
DATAFILE test.dat NUMBER_OF_TRAITS 2 NUMBER_OF_EFFECTS 5 OBSERVATION(S) 3 4 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 0 2 cross 0 2 2 cross 5 5 10 cross 6 0 30 cross 0 7 30 cross RANDOM_RESIDUAL VALUES 10 1 1 5 RANDOM_GROUP 3 RANDOM_TYPE add_animal FILE animal.ped (CO)VARIANCES 1 0.1 0.1 1 RANDOM_GROUP 4 5 RANDOM_TYPE diagonal FILE (CO)VARIANCES 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 # Can you see the difference between these 3 setups in Example 2?
Dominance model
DATAFILE dom.dat NUMBER_OF_TRAITS 1 NUMBER_OF_EFFECTS 4 OBSERVATION(S) 3 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 1 cross 4 1 cov 2 30001 cross 5 10412 cross RANDOM_RESIDUAL VALUES 100 RANDOM_GROUP 3 RANDOM_TYPE add_an_upginb FILE add.ped (CO)VARIANCES 10 RANDOM_GROUP 4 RANDOM_TYPE par_dom FILE dom.ped (CO)VARIANCES 2
Animal model with UPG
DATAFILE test.dat NUMBER_OF_TRAITS 2 NUMBER_OF_EFFECTS 2 OBSERVATION(S) 3 4 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 1 2 cross 5 5 13 cross RANDOM_RESIDUAL VALUES 10 1 1 5 RANDOM_GROUP 2 RANDOM_TYPE add_an_upg FILE animal.ped (CO)VARIANCES 1 0.1 0.1 1
Animal model with inbreeding
DATAFILE test.dat NUMBER_OF_TRAITS 2 NUMBER_OF_EFFECTS 2 OBSERVATION(S) 3 4 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 1 2 cross 5 5 13 cross RANDOM_RESIDUAL VALUES 10 1 1 5 RANDOM_GROUP 2 RANDOM_TYPE add_an_upginb FILE animal.ped (CO)VARIANCES 1 0.1 0.1 1
Repeatability model 1
DATAFILE test.dat NUMBER_OF_TRAITS 1 NUMBER_OF_EFFECTS 3 OBSERVATION(S) 3 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 2 cross 5 5 cross 5 10 cross RANDOM_RESIDUAL VALUES 10 RANDOM_GROUP 2 RANDOM_TYPE add_animal FILE animal.ped (CO)VARIANCES 1 RANDOM_GROUP 3 RANDOM_TYPE diagonal FILE (CO)VARIANCES 1
Repeatability model 2
DATAFILE test.dat NUMBER_OF_TRAITS 2 NUMBER_OF_EFFECTS 3 OBSERVATION(S) 3 4 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 1 2 cross 5 5 5 cross 5 5 10 cross RANDOM_RESIDUAL VALUES 10 1 1 5 RANDOM_GROUP 2 RANDOM_TYPE add_animal FILE animal.ped (CO)VARIANCES 1 0.1 0.1 1 RANDOM_GROUP 3 RANDOM_TYPE diagonal FILE (CO)VARIANCES 1 0.1 0.1 1
Maternal effect model
DATAFILE maternal.dat NUMBER_OF_TRAITS 1 NUMBER_OF_EFFECTS 4 OBSERVATION(S) 4 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 3 946 cross 1 22473 cross 2 22473 cross 2 22473 cross RANDOM_RESIDUAL VALUES 1050 RANDOM_GROUP 2 3 RANDOM_TYPE add_animal FILE maternal.ped (CO)VARIANCES 450 -100 -100 340 RANDOM_GROUP 4 RANDOM_TYPE diagonal FILE (CO)VARIANCES 370
Random regression model 1
DATAFILE test.dat1 NUMBER_OF_TRAITS 2 NUMBER_OF_EFFECTS 9 OBSERVATION(S) 3 4 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 1 2 cross 6 6 1 cov 7 7 1 cov 2 2 5 cross 6 6 5 cov 2 2 7 7 5 cov 2 2 2 2 10 cross 6 6 10 cov 2 2 7 7 10 cov 2 2 RANDOM_RESIDUAL VALUES 10 1 1 5 RANDOM_GROUP 4 5 6 RANDOM_TYPE diagonal FILE (CO)VARIANCES 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 RANDOM_GROUP 7 8 9 RANDOM_TYPE add_animal FILE animal.ped (CO)VARIANCES 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1
Random regression model 2
DATAFILE test.dat2 NUMBER_OF_TRAITS 2 NUMBER_OF_EFFECTS 10 OBSERVATION(S) 3 4 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 1 2 cross 6 6 1 cov 7 7 1 cov 8 8 1 cov 6 6 5 cov 2 2 7 7 5 cov 2 2 8 8 5 cov 2 2 6 6 10 cov 2 2 7 7 10 cov 2 2 8 8 10 cov 2 2 RANDOM_RESIDUAL VALUES 10 1 1 5 RANDOM_GROUP 5 6 7 RANDOM_TYPE diagonal FILE (CO)VARIANCES 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 RANDOM_GROUP 8 9 10 RANDOM_TYPE add_animal FILE animal.ped (CO)VARIANCES 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1
test.dat1: example data set using Legendre polynomial 1 1 21.10000 12.10000 11 -1.73205 2.23607 0 2 2 22.20000 12.20000 12 -1.23718 .59324 1 1 3 21.30000 12.30000 13 -.74231 -.50197 1 2 1 22.10000 12.40000 14 -.24744 -1.04958 0 1 2 21.20000 12.10000 15 .24744 -1.04958 1 2 3 22.30000 12.20000 16 .74231 -.50197 1 1 4 21.40000 12.30000 17 1.23718 .59324 2 2 5 22.50000 12.40000 18 1.73205 2.23607 3
test.dat2: example data set using linear spline 1 1 21.100000 12.100000 11 1.000000 .000000 .000000 2 2 22.200000 12.200000 12 .666667 .333333 .000000 1 3 21.300000 12.300000 13 .333333 .666667 .000000 2 1 22.100000 12.400000 14 .000000 1.000000 .000000 1 2 21.200000 12.100000 15 .000000 .750000 .250000 2 3 22.300000 12.200000 16 .000000 .500000 .500000 1 4 21.400000 12.300000 17 .000000 .250000 .750000 2 5 22.500000 12.400000 18 .000000 .000000 1.000000
Random regression model with different order
DATAFILE test.dat1 NUMBER_OF_TRAITS 2 NUMBER_OF_EFFECTS 8 OBSERVATION(S) 3 4 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 1 2 cross 6 6 1 cov 7 7 1 cov 2 2 5 cross 6 6 5 cov 2 2 7 7 5 cov 2 2 2 2 10 cross 6 6 10 cov 2 2 RANDOM_RESIDUAL VALUES 10 1 1 5 RANDOM_GROUP 4 5 6 RANDOM_TYPE diagonal FILE (CO)VARIANCES 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 1 RANDOM_GROUP 7 8 RANDOM_TYPE add_animal FILE animal.ped (CO)VARIANCES 1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 1
Maternal Random regression model with different order
DATAFILE test.dat1 NUMBER_OF_TRAITS 2 NUMBER_OF_EFFECTS 10 OBSERVATION(S) 3 4 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 1 2 cross 6 6 1 cov 7 7 1 cov 2 2 10 cross 6 6 10 cov 2 2 7 7 10 cov 2 2 8 8 10 cross 6 6 10 cov 8 8 8 8 10 cross 6 6 10 cov 8 8 RANDOM_RESIDUAL VALUES 10 1 1 5 RANDOM_GROUP 4 5 6 7 8 RANDOM_TYPE add_animal FILE animal.ped (CO)VARIANCES 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1 RANDOM_GROUP 9 10 RANDOM_TYPE diagonal FILE (CO)VARIANCES 1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 1
Random regression model with heterogeneous residual variances
### using airemlf90 # Example 1: with intercept DATAFILE test.dat NUMBER_OF_TRAITS 1 NUMBER_OF_EFFECTS 9 OBSERVATION(S) 3 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 2 cross 6 1 cov 7 1 cov 5 5 cross 6 5 cov 5 7 5 cov 5 5 10 cross 6 10 cov 5 7 10 cov 5 RANDOM_RESIDUAL VALUES 10 RANDOM_GROUP 4 5 6 RANDOM_TYPE diagonal FILE (CO)VARIANCES 1 0.1 0.1 0.1 1 0.1 0.1 0.1 1 RANDOM_GROUP 7 8 9 RANDOM_TYPE add_animal FILE animal.ped (CO)VARIANCES 1 0.1 0.1 0.1 1 0.1 0.1 0.1 1 OPTION hetres_pos 6 7 OPTION hetres_pol 4.0 1.0 0.1
### using airemlf90 # Example 2: with no intercept DATAFILE test.dat NUMBER_OF_TRAITS 1 NUMBER_OF_EFFECTS 7 OBSERVATION(S) 3 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 2 cross 6 1 cov 7 1 cov 6 5 cov 5 7 5 cov 5 6 10 cov 5 7 10 cov 5 RANDOM_RESIDUAL VALUES 10 RANDOM_GROUP 4 5 RANDOM_TYPE diagonal FILE (CO)VARIANCES 1 0.1 0.1 1 RANDOM_GROUP 6 7 RANDOM_TYPE add_animal FILE animal.ped (CO)VARIANCES 1 0.1 0.1 1 OPTION hetres_pos 6 7 OPTION hetres_pol 1.0 0.1
### using gibbs3f90 DATAFILE test.dat NUMBER_OF_TRAITS 1 NUMBER_OF_EFFECTS 9 OBSERVATION(S) 3 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT [EFFECT NESTED] 1 2 cross 6 1 cov 7 1 cov 5 5 cross 6 5 cov 5 7 5 cov 5 5 10 cross 6 10 cov 5 7 10 cov 5 RANDOM_RESIDUAL VALUES 10 RANDOM_GROUP 4 5 6 RANDOM_TYPE diagonal FILE (CO)VARIANCES 1 0.1 0.1 0.1 1 0.1 0.1 0.1 1 RANDOM_GROUP 7 8 9 RANDOM_TYPE add_animal FILE animal.ped (CO)VARIANCES 1 0.1 0.1 0.1 1 0.1 0.1 0.1 1 OPTION hetres_int 8 5
Competitive model
DATAFILE competition.dat NUMBER_OF_TRAITS 1 NUMBER_OF_EFFECTS 19 OBSERVATION(S) 24 WEIGHT(S) EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED] 2 88 cross 3 362 cross 21 2409 cross 4 8004 cross 22 0 cov 5 22 0 cov 6 22 0 cov 7 22 0 cov 8 22 0 cov 9 22 0 cov 10 22 0 cov 11 22 0 cov 12 22 0 cov 13 22 0 cov 14 22 0 cov 15 22 0 cov 16 22 0 cov 17 22 0 cov 18 22 8004 cov 19 RANDOM_RESIDUAL VALUES 1225.8 RANDOM_GROUP 4 5 RANDOM_TYPE add_animal FILE renadd04.ped (CO)VARIANCES 267.03 25.313 25.313 104.44 RANDOM_GROUP 2 RANDOM_TYPE diagonal FILE (CO)VARIANCES 89.187 RANDOM_GROUP 3 RANDOM_TYPE diagonal FILE (CO)VARIANCES 167.34