16 e Séminaire des thésards du Département de Génétique Animale Amboise, 2 & 3 avril 2013
INSTITUT NATIONAL DE LA RECHERCHE AGRONOMIQUE CENTRE de Toulouse • SAGA
Auzeville BP 52627• 31326 Castanet Tolosan cedex • Tél : +33(0) 561 285 182• Courriel : sophie.brard@toulouse.inra.fr http://www.toulouse.inra.fr/
Sophie Brard 1 , Anne Ricard 2 3
1 INRA, UR 631, 31326 Castanet-Tolosan, France, 2 INRA, UMR 1313, 78352 Jouy-en-Josas, France,
3 IFCE, Recherche et Innovation, 61310 Exmes, France
Emplacement photo qualité 150dpi minimum
Emplacement photo qualité 150dpi minimum
Emplacement photo qualité 150dpi minimum
Which theoretical and practical framework for genomic selection in horse breeding?
Funding
50% Méta-programme SelGen (INRA), 50% Institut Français du Cheval et de l’Equitation
Supervisor
Anne Ricard: ICPEF (INRA and Institut Français du Cheval et de l’Equitation)
Education
Engineer’s diploma of Ecole Nationale Supérieure des Sciences Agronomique de Bordeaux
Genomic selection has deeply changed the way of selecting animals. In dairy cattle, genetic value of a bull can be accurately known at his birth, long before the first
lactations of his daughters. Such an advance should be applied with success to other species. But in sport horses, the efficiency of genomic evaluation was proved to be
weak compared to results in other species: correlation between de-regressed EBVs for performance in jumping and BLUP is 0.36. Between de-regressed EBVs and
genomic BLUP the gain is low: 0.39.
What could explain this result :
2 nd step Work on genomic evaluation by comparing different methods and different training and validation populations.
1 st step
Study the population structure: characteristics of the genomic
relationship matrix, analysis of linkage disequilibrium, crosses in the population, position of SNPs on chromosomes.
3 rd step Develop a software able to apply the Bayes Cπ method to haplotypes (now works with SNPs).
4 th step Introduce the parameters studied (linkage disequilibrium, population characteristics) in the calculation of accuracy.
5 th step Calculate the properties of estimators of effects of SNPs and of the value of genomic evaluation in the different situation studied.
6 th step
Compare the results of the different steps using the data and simulation from the data. Depending on the result, propose a solution for
application of genomic selection in horse for different traits.
Dairy cattle Horses
Large population Small population
We need a large population to estimate well QTLs effects.
Large offspring per bull
Small offspring per stallion
A stallion with a high reliability may not have sons with a high reliability.
No sub-
population
Structured population
Accuracy of genomic selection is weak if reference population and evaluated population are too different.
Selection on progeny
Selection on own performances
Some of the candidates for selection which were not retained are not known, whereas all candidates are known in dairy cattle.
Furthermore
Depending on studies, methods differ on points like :
• estimation of variance attributable to SNPs,
• distribution of SNPs effects (normal distribution, gamma distribution),
• considering dominance effects or using only additive effects.
Objectives and strategy
Like most of stallions in activity in France are already genotyped, the size of our population won’t increase. The steps of my thesis will be:
Manhattan plot of –log10(P-value) for performance in jumping with a single-SNP mixed model (1 010 genotyped horses, 44 424 markers)
Significant Suggestive
Position by chromosome -log10(P-value)
