a. giontella 1 , l.buttazzoni 2 , m. silvestrelli 1 , c. baiocco 2 e c. pieramati 1
DESCRIPTION
“ The selection index of the Italian Saddle horse stallions ”. A. Giontella 1 , L.Buttazzoni 2 , M. Silvestrelli 1 , C. Baiocco 2 e C. Pieramati 1 1 Centro di Studio del Cavallo Sportivo, Facoltà di Medicina Veterinaria, Perugia 2 Associazione Nazionale Allevatori Suini, Roma. - PowerPoint PPT PresentationTRANSCRIPT
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A. Giontella1, L.Buttazzoni2, M. Silvestrelli1, C. Baiocco2 e C. Pieramati1
1 Centro di Studio del Cavallo Sportivo, Facoltà di Medicina Veterinaria, Perugia
2 Associazione Nazionale Allevatori Suini, Roma
“The selection index of the Italian Saddle horse stallions”
DRUENTO, 31 Ottobre 2008
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ADMISSION TO FIRST NINE PERFORMANCE TEST EDITIONS
Horses must qualify in 2 steps:
2. Selection before Performance test through free jumping evaluation
1. Preselection (2 years) for "gaits":
Objective evaluation of candidates’ athletic performance
trotting measurement
speed
amplitude
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10th edition:
no test for “gait” for 2 years old horses
For 3 years old horses, 4 test steps
Stance andmorpho-functional measurament andevaluation
Obedience test
free jumping (objective scores based on the results)
free jumping with 3 judges scores
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In all editions
1. Horse daily assigned to a random trainer
2. Twice a week jumping
These same traits are scored at the end of the training period by 2 PROFESSIONAL RIDERS
score
score
character
gaits
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In the first 10 editions of the Performance Test, for licensing stallions in the III section of the Italian Saddle Horse Studbook
completed the testing period
264 candidates
115 candidates
are approved
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MATERIAL AND METHOD
The dataset file:
5226 scores
94 trainers
character
gaits
14371 scores
jumping
+ 500 PROFESSIONAL RIDERS scores For 3 same traits
GENEALOGICAL DATA from 3737 animals
obtained from 4 generation ancestors of these 264 horses
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TRAINING PERIOD INDEX
3 different ways
separately and for each trait without use the genealogical information
with 3 single trait BLUP models
with multiple trait BLUP model
in all 3 models were
FIXED EFFECTS
- date
- trainer
RANDOM EFFECTS
- horse
- horse X trainer interaction
- error
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FINAL SCORE INDEX
3 different ways
separately and for each trait without use the genealogical information
with 3 single trait BLUP models
with multiple trait BLUP model
in all 3 models were
FIXED EFFECTS
- professional rider
RANDOM EFFECTS
- horse
- error
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different WEIGTHING
scores
TRAINING PERIOD
FINAL SCORES
Analyzed SEPARATELY
10% final scores90% training period
5%5%
90%
prof. rider
prof. rider
training
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TOTAL INDEX
calculated by assigning a weight
25%
“character” “gaits”
50%
“jumping”
25%
25%
50%
character
gaits
jumping
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RESULTS AND DISCUSSION
The pedigree files showed that the relationships between the tested horses are still very few:
N° sons / n° parents
1,071
1,571,13
TOTALfirst 7 edition (Silvestrelli et al.)
264 candidate
168 sires
246 mares
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For estimated the genetic values were used two different software:
MTDFREML (Boldman et al., 1993)
www.tzv.fal.de/~eg
VCE (Kovac e Groeneveld, 2003)
www.aipl.arsusda.gov/curtvt/mtdfreml.ht
ml
this to get a comparative analysis
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Variance component ratios in the model with unrelated animals
0,465 ± 0,0160,276 ± 0,0120,528 ± 0,014error
0,034 ± 0,0070,076 ± 0,0040,085 ± 0,004Horse trainer
0,500 ± 0,0160,648 ± 0,0140,387 ± 0,015animal
JumpingGaitsCarattereComponent
Variance component ratios in the single trait BLUP models
0,502 ± 0,0180,305 ± 0,0120,528 ± 0,014error
0,037 ± 0,0070,084 ± 0,0040,085 ± 0,004Horse trainer
0,461 ± 0,0180,611 ± 0,0150,386 ± 0,016animal
JumpingGaitsCharacterComponent
Variance component ratios in the Multiple Trait BLUP models
0,392 ± 0,0080,407 ± 0,0080,490 ± 0,015
0,371 ± 0,0060,305 ± 0,011
0,529 ± 0,013error
0,602 ± 0,0450,683 ± 0,0400,049 ± 0,006
0,536 ± 0,0200,084 ± 0,004
0,086 ± 0,004Horse trainer
0,738 ± 0,0220,775 ± 0,0200,411 ± 0,016
0,672 ± 0,0250,610 ± 0,014
0,385 ± 0,015animal
JumpingGaitsCharacterComponent
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1. Variance component ratios are in good agreement between the different models and with that observed in the first 7 editions
2. High traits heritability
3. High accuracy (>95%) in the training period scores for all 3 traits.
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Heritability and genetic correlation in the finals scores
0,804 ± 0,0490,911 ± 0,0270,541 ± 0,034
0,863 ± 0,0390,429 ± 0,036
0,330 ± 0,041III) multiple trait
0,461 ± 0,0350,441 ± 0,0560,355 ± 0,054II) single trait
0,486 ± 0,0350,461 ± 0,0360,368 ± 0,039I) sigle trait with unrelated animals
jumpinggaitscharacterModel
Unlike what is found in previous editions (Silvestrelli et al.) were be able to obtain multivariate estimates for the final professional riders scores.
(HIGH NUMBER OF OBSERVATIONS)
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Data analysis shows that estimates of heritability between single and multiple traits models are relatively concordant
mixed model not used for the estimation of the final scores (deviate from riders mean)
Estimates of the “gaits” heritability 0,58±0,04 (first 7th
final scores) goes to opposite way
Estimates of the “character” heritability (first 7th final scores)
0,23±0,14 close to the estimate during training period.
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CORRELATION
A. Between the complessive indexes of the training period (single trait models with relationship or unrelated animals) 98,2%
B. Between sigle trait model with unrelated animals and multiple trait model 98,1
C. Between single and multiple trait BLUP models 99,9%
on RANKS
A. 98,2%,
B. 97,9%
C. 99,8%
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In this edition the use of a Single Trait BLUP model instead the old evaluation method gives an exchange of consecutive positions in the ranking between 2 stallions.
Furthermore with the Multiple Trait BLUP model 3 couple of not approved horses swap their consecutive position.
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CONCLUSION
genetic indices of the ancestors could help in choosing candidates
Dataset collected in 10 editions of the Performance Test permitted BLUP evaluation
low impact on estimate accuracy
comparation between stallions approved in different edition are now possible
low impact on ratings within edition
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