genetic correlations between the performance of purebred and crossbred pigs

Genetic correlations between the performance of

purebred and crossbred pigs

Sansak NakavisutDr.Ron CrumpMatias 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 diagramDU LRLW

LRxLWLWxLR

DUx(LRxLW)DUx(LWxLR)

GGP

GP

PS

Performance test records by breeds

Breed No. records

DU 1431LW 2712 7666 (84%) purebredsLR 3523LRXLW 464

962 (11%) 2-wayLWXLR 498DUX(LRLW) 197

447 ( 5%) 3-wayDUX(LWLR) 250Total 9075

Litter records by breedsBreedof sows

Litter records

No. of sows

DU 1133 422LW 4573 10558 (89%) 1376LR 4852 1601LRXLW 534

1265 (11%)238

LWXLR 731 271Total 11823 3908

Traits to be analysed• PRODUCTION1. TDG2. ADG3. FCR4. BF5. Body Length

• REPRODUCTION1. NPB2. NBA3. LS3W4. LWB5. LW3W6. GEST

Statistical model for test records1 1 1 1 1 1 1 1 1 1

2 2 2 2 2 2 2 2 2 2

0 0 0 00 0 0 0

y X b ZQ g Z a W c ey 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 00 0 0 0

0 0 0 0 0var

0 0 0 0 00 0 0 0 00 0 0 0 0

a a

a a

c

c

e

e

a A Aa A Ac Ic Ie Ie 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 00 0 0 0

y X b ZQ g Z a W pe ey 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 00 0 0 0

0 0 0 0 0var

0 0 0 0 00 0 0 0 00 0 0 0 0

a a

a a

pe

pe

e

e

a A Aa A Ape Ipe Ie Ie 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 ± 0.05ADG 0.84 ± 0.22 0.29 ± 0.03 0.32 ± 0.02 0.44 ± 0.10 0.37 ± 0.05FCR 0.66 ± 0.30 0.15 ± 0.03 0.23 ± 0.02 0.33 ± 0.09 0.05 ± 0.04BF 0.96 ± 0.35 0.33 ± 0.04 0.10 ± 0.02 0.18 ± 0.08 0.15 ± 0.04BL 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 ± 0.04NBA 0.37 ± 0.53 0.10 ± 0.02 0.15 ± 0.01 0.05 ± 0.06 0.11 ± 0.04LS3W 0.21 ± 0.44 0.09 ± 0.02 0.15 ± 0.01 0.08 ± 0.06 0.11 ± 0.04LWB 0.32 ± 0.32 0.11 ± 0.02 0.16 ± 0.01 0.16 ± 0.07 0.17 ± 0.04LW3W 0.33 ± 0.39 0.10 ± 0.02 0.19 ± 0.01 0.13 ± 0.04 0.13 ± 0.04GEST 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 4 505

10152025303540

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