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Impact of including the cause of missing records on genetic evaluations for growth in Impact of including the cause of missing records on genetic evaluations for growth in

Impact of including the cause of missing records on genetic evaluations for growth in - PowerPoint Presentation

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Impact of including the cause of missing records on genetic evaluations for growth in - PPT Presentation

Mary Kate Hollifield 1 D Lourenco 1 S Tsuruta 1 M Bermann 1 J Howard 2 I Misztal 1 ASAS Annual Meeting July 16 th 2021 1 Department of Animal and Dairy Science University of Georgia Athens GA ID: 932264

model hcw missing trait hcw model trait missing liv tag genetic weight bias evaluations 000 ear retained smithfield 650

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Slide1

Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs

Mary Kate Hollifield1, D. Lourenco1, S. Tsuruta1, M. Bermann1,J. Howard2, I. Misztal1

ASAS Annual Meeting

July 16

th, 2021

1

Department of Animal and Dairy Science, University of Georgia, Athens, GA

2

Smithfield Premium Genetics, Rose Hill, NC

Slide2

Mortality and culling detrimental to financial gain

Lowly heritable traits = lengthy genetic progressIndividual identification essential for breeding programsLivability and HCW affected by many complicated traitsIntroduction

Slide3

Observe the impact of missing records on growth trait evaluations

Analyze retained ear tag and livability traitsCompare genetic parameters of hot carcass weight and birth weight with and without including cause of missing recordsObjectives

Slide4

Data

Smithfield Premium GeneticsPedigree: 1,965,077 Three-way crossbred terminal animals2014 - 2019Materials and Methods

Four traits:

Birth weight

Hot carcass weightRetained ear tagLivability

Slide5

Trait

Code

Level

N

%

BW

 

 

471360

100.0

HCW

 

 

221311

47.0

RT

1

Missing Tag

134523

28.5

2

Retained Tag

336837

71.5

LIV

0

Missing Tag

134523

28.51Died/Culled10451322.22Harvested23232449.3

Slide6

Model and Analyses

(M1) Four-trait threshold-linear model

(BW-HCW-RT-LIV)

(M2) Two-trait linear model(BW-HCW)

 

Materials and Methods

THRGIBBS1f90

50,000 rounds

LR Method

Accuracy, bias, dispersion, Pearson correlations

Slide7

Results

BW

HCW

RT

LIV

Model 1

BW

0.11 ± 0.00

0.31 ± 0.03

0.34 ± 0.03

0.56 ± 0.03

HCW

0.07 ± 0.00

0.26 ± 0.04

0.09 ± 0.05

RT

0.02 ± 0.00

0.00 ± 0.06

LIV

0.04 ± 0.00

Model 2

BW

0.11 ± 0.00

0.32 ± 0.03

HCW

0.07 ± 0.00

Slide8

Results

Trait

Model

Bias

BW

M1

0.33

0.74

0.00

0.59

M2

0.33

0.75

0.01

0.59

HCW

M1

0.37

0.93

-0.01

0.74

M2

0.37

0.91

0.06

0.74

RT

M1

0.190.650.00

0.56

LIV

M1

0.23

0.78

0.00

0.56

Trait

Model

Bias

BW

M1

0.33

0.74

0.00

0.59

M2

0.33

0.75

0.010.59HCWM10.370.93-0.010.74M20.370.910.060.74RTM10.190.650.000.56LIVM10.230.780.000.56

Slide9

No performance differences observed with missing trait information included in the model

Positive genetic correlations between BW and RT and LIVAlternative options:Social interaction modelsLow density genotyping for identificationRT and LIV are not necessary to include in HCW evaluations

Conclusions

Slide10

marykate.hollifield@uga.edu