genetic correlations between the performance of purebred and crossbred pigs sansak nakavisut dr.ron...
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Genetic correlations between the performance of
purebred and crossbred pigs
Sansak Nakavisut
Dr.Ron Crump
Matias Suarez
Dr.Hans Graser
Introduction•Nucleus herds test & select purebreds
•But the end products are crossbreds (multipliers and commercial herds)
•This assumes rg between purebreds and crossbreds of “1”
• If not “1”, testing and genetic evaluation procedure may need some changes
In general•Estimation of rg between purebreds and crossbreds NOT possible or NOT reliable
• Performance test records on crossbreds not available
• Crossbred records may be available BUT from different environments (nucleus VS multiplier herds)
Thai government pig breeding farms
•Crossbreds were performance tested
•Purebreds and crossbreds were produced in the same conditions (environments, herds, management, feeding..)
•Litter records available from both crossbred and purebred sows
•Allows reliable estimation of rg between pure- and crossbreds
Objectives
•To estimate genetic correlations (rg)
between the performance of
purebreds and crossbreds
•To validate the conventional genetic
evaluation procedure (whether rg = 1)
Breeding diagram
DU LRLW
LRxLWLWxLR
DUx(LRxLW)DUx(LWxLR)
GGP
GP
PS
Performance test records by breeds
Breed No. records
DU 1431
LW 2712 7666 (84%) purebreds
LR 3523
LRXLW 464 962 (11%) 2-way
LWXLR 498
DUX(LRLW) 197 447 ( 5%) 3-way
DUX(LWLR) 250
Total 9075
Litter records by breedsBreed
of sows
Litter
records
No. of sows
DU 1133 422
LW 4573 10558 (89%) 1376
LR 4852 1601
LRXLW 534 1265 (11%)
238
LWXLR 731 271
Total 11823 3908
Traits to be analysed• PRODUCTION
1. TDG
2. ADG
3. FCR
4. BF
5. Body Length
• REPRODUCTION
1. NPB
2. NBA
3. LS3W
4. LWB
5. LW3W
6. GEST
Statistical model for test records
1 1 1 1 1 1 1 1 1 1
2 2 2 2 2 2 2 2 2 2
0 0 0 0
0 0 0 0
y X b ZQ g Z a W c e
y X b ZQ g Z a W c e
21 11 12
22 21 22
21 11
22 22
21 11
22 22
0 0 0 0
0 0 0 0
0 0 0 0 0var
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
a a
a a
c
c
e
e
a A A
a A A
c I
c I
e I
e I
assumption
Statistical model for litter records
assumption
1 1 1 1 1 1 1 1 1 1
2 2 2 2 2 2 2 2 2 2
0 0 0 0
0 0 0 0
y X b ZQ g Z a W pe e
y X b ZQ g Z a W pe e
21 11 12
22 21 22
21 11
22 22
21 11
22 22
0 0 0 0
0 0 0 0
0 0 0 0 0var
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
a a
a a
pe
pe
e
e
a A A
a A A
pe I
pe I
e I
e I
Fixed effect model for test records
Breed Sex tHYS AgeIn FiWt
TDG
ADG
FCR
BF
BL
Fixed effect model for litter records
ijklm i j k l m i ijklmY = Br + fHYS + AgeC + Par + LitBr (Br ) + e
Bri breed
fHYSj farrowing herd-year-season
AgeCk age class of the farrowing sow
Parl parity of litter
LitBrm(Bri) nested litter-breed within breed of sow
The same fixed effects were fitted for all litter traits
Results: rg (pure&cross)
Trait rg h2 (pure) c2 (pure) h2 (cross) c2 (cross)
TDG 0.78 ± 0.49 0.25 ± 0.03 0.24 ± 0.02 0.12 ± 0.09 0.60 ± 0.05
ADG 0.84 ± 0.22 0.29 ± 0.03 0.32 ± 0.02 0.44 ± 0.10 0.37 ± 0.05
FCR 0.66 ± 0.30 0.15 ± 0.03 0.23 ± 0.02 0.33 ± 0.09 0.05 ± 0.04
BF 0.96 ± 0.35 0.33 ± 0.04 0.10 ± 0.02 0.18 ± 0.08 0.15 ± 0.04
BL 0.74 ± 0.23 0.38 ± 0.03 0.12 ± 0.01 0.26 ± 0.08 0.10 ± 0.04
Trait rg h2 (pure) r (pure) h2 (cross) r (cross)
NPB 0.21 ± 0.41 0.09 ± 0.02 0.14 ± 0.01 0.10 ± 0.07 0.12 ± 0.04
NBA 0.37 ± 0.53 0.10 ± 0.02 0.15 ± 0.01 0.05 ± 0.06 0.11 ± 0.04
LS3W 0.21 ± 0.44 0.09 ± 0.02 0.15 ± 0.01 0.08 ± 0.06 0.11 ± 0.04
LWB 0.32 ± 0.32 0.11 ± 0.02 0.16 ± 0.01 0.16 ± 0.07 0.17 ± 0.04
LW3W 0.33 ± 0.39 0.10 ± 0.02 0.19 ± 0.01 0.13 ± 0.04 0.13 ± 0.04
GEST 0.52 ± 0.35 0.12 ± 0.02 0.19 ± 0.01 0.18 ± 0.09 0.32 ± 0.04
Discussion (production traits)
• rg (pure&cross) high enough => testing and
selecting of purebreds in the nucleus can
genetically improve the production traits of
crossbreds in multiplier and commercial herds
• To reduce cost of unnecessary test of
crossbreds
• Conventional testing of purebreds is validated
Discussion (reproduction traits)
• rg (pure&cross) NOT “1” (although positive) =>
selecting on purebred litter records can genetically improve the reproduction of crossbred sows BUT not as efficient as incorporating crossbred records in the genetic evaluation procedure.
• Crossbred records readily available in most multipliers with no extra cost => combine them with purebred records to produce EBVs for litter traits
Conclusion
•Genetic correlations between pure- and crossbreds for production traits are high
•Therefore, conventional testing and selecting of purebreds is validated by this study
Conclusion
•Genetic correlations between pure- and crossbreds for reproduction traits are low to moderate
•Therefore, we must include crossbred information into the genetic evaluation procedure to improve reproduction traits of crossbred sows
ThanksDept. Livestock Development (DLD)
Thailand
UNERS / IPRSAGBU UNE
Genetic links b/w cross & purebreds
51 common grandparents(21 grandsires & 30 grandams)
544Purebred litters
(5%)(Total = 10558)
481 Crossbred litters
(38%)(Total = 1265)
Litter records
Methods and Models
• Treat pure- and crossbred records as different
traits eg. ADGp and ADGc
• Bivariate analysis of the two separate traits using
ASReml; animal model
• Estimate additive genetic covariances and genetic
correlations between the two traits
Material and methods
• DU, LW, LR & their crosses
• 11-year (1993-2003)
• 9075 performance test records
•11823 litter records (from 3908 sows)
Number of breeds per contemporary group (HYS)
0
5
10
15
20
25
30
35
40
45
1 2 3 4 5
0
5
10
15
20
25
30
35
40
1 2 3 4 5 6
Test records Litter records
Breeds/CG (tHYS) Breeds/CG (fHYS)
Frequency (%)Frequency (%)
Genetic links b/w cross & purebreds
80 common parents(39 sires & 41 dams)
415 Purebred records
(5%)(Total = 7666)
429 Crossbred records
(30%)(Total = 1409)
Performance test records