Variación no-aditiva y selección genómica

1. Jornada de Selección Genómica, Zaragoza, 2009 Variación no-aditiva y selección genómica Miguel A. Toro y Luis Varona Dpto. Producción Animal, ETS Ingenieros Agrónomos, Madrid. Spain Dpto. Anatomía, Embriología y Genética. Facultad de Veterinaria, Zaragoza. Spain

2. Quantitative genetics models including dominance One locus model α=a+d(q-p) σ2D=(2pqd)2 σ2A =2pqα2

3. Quantitative genetics models including dominance Infinitesimal model Animal model y=Xb+Zu+Wd+e u~N(0, Aσ2A) d~N(0, Dσ2D)

4. One locus with dominance and inbreeding

8. Finite number of loci model - Instaed of using coancestry coefficients the model uses artificially created loci to model resemblance among relatives -The objective is to estimate variance components and to predict breeding values -Gibbs sampling It depends on the number of loci used in the analysis (Pong-Wong et al., 1997)

9. Estimation of non-additive effects is important • Ignore non-additive effects : • will produce biased estimates of breeding values • It should have an effect on ranking breeding values • Include non-additive effects : • It will produce more correct statistical • It will produce more genetic response Varona & Misztal about 10% low h2 high d2 low i high % full-sibs (>20%) • It is associate to inbreeding depression

10. Dominance effect have been rarely included in genetic evaluation -computational complexity (?) -inaccurate estimation of variance components (?) 20-100 more data >20% full-sibs -little evidence of non-additive variance (?)

11. Estimates of additive and dominance variance respect to the phenotypic variance (Misztal et al., 1998)

12. Estimates of additive and dominance variance respect to the phenotypic variance (Crokrak & Roff, 1995) Wild (Vd / Va) 1.17 life-history traits 1.06 physiological traits 0.19 morphological traits Domestic (Vd / Va) 0.80

13. How to profit from dominance Any methodology that pretends to use non-additive effects: - it must contemplate TWO types of matings: - matings from which the population will be propagated - matings to obtain commercial animals This can be carried out in a single population: there is not need of having separated lines - selection should be applied not to individuals but to matings (mate selection)

14. Mating plans (Mating allocation) are used in Animal Breeding • To control inbreeding • When economic merit is not linear • When there is an intermediate optimum (or restricted traits) • To increase connection among herds • To profit from dominance genetic effects

15. How to profit from dominance A B CROSSBREEDING AB B A AB B A

16. How to profit from dominance RECIPROCAL RECURRENT SELECTION A B AB B A AB B A

17. How to profit from dominance Progeny test with inbreeding ♀ ♂ Fullsib Mating Random ♀ N M Selection ProgenyTest

18. How to profit from dominance Progeny test scheme M♂ M♀ Generation t Minimum Coancestry M x n N♀ N♂ Maximum Coancestry M♀ Generation t + 1 M♂ M x n

19. How to profit from dominance A MATING ALLOCATION A C A C

20. Genomic Selection • Availability of very dense panels of SNPs. • Methods to use dominance variation should be revisited. • Mating allocation could the easier option.

21. Simulation - Population of Ne=100 during 1000 generations - Thenpopulationwasincreased up to 500 males and 500 femalesduring 3 generations - These 3000 (generation 1001,1002 and 1003) individualsweregenotyped and phenotyped and used as training populationtoestimateadditive and dominanceeffects of SNPs - Generation 1004 wasformedfrom 25 sires and 250 dams of generation 1003

22. Simulation Generation 1004 wasformedfrom 25 sires and 250 damsselectedfromgeneration 1003 Threestrategies of selectioncompared: MassselectionusingphenotypeswithRandomMating GenomicselectionbasedonadditiveeffectswithRandomMating (GS) Genomicselectionbasedonadditiveeffects and Optimalmatingallocationbasedondominanceeffects (GS+OP)

23. Simulation Geneticassumptions • 10 chromosomes of 100 cM • 10000 loci (9000 SNP and 1000 QTLs) - BothSNPs and QTLshavetwoalleles - Mutationrateswere 0.0025 followingMeuwissen et al. (2001) forSNPs and 0.00005 forQTLs (about 8000 SNP and 80 QTLsweresegregating in generation 1000)

24. Simulation Genetic effects • Additive effects sampled from N(0,1) • Dominance effects sampled from • N(0,1) Residual effects • Residuals were samples from a N(0,1) and rescaled according the desired heritability

25. Evaluation SNP estimation BLUP: Animal Model solved with Gibbs sampling Genomic selection: Estimation of additive and dominance effects with method Bayes A Genomic selection (method Bayes A) and Optimal mating based on dominance effects with simulated annealing

26. Bayes A

27. Bayes B., 2003

28. Optimal Mate Allocation From the 6250 (25 x 250) possible matings, we choose the best 250 based on the dominance prediction of the mating, and we obtain 4 new individuals for each mate. The algorithm of searching was a simulated annealing.

29. First generation 11.89%

30. First generation 22.34%

31. First generation 5.71%

32. First generation 16.05%

33. First generation 5.31%

34. First generation 14.38%

35. First generation 2.76%

36. First generation 8.36%

37. Bayes A vs Bayes B

42. Genomic selection including or not dominance in the model (Bayes B)

43. REMARKS -Including dominance effects in the model of genomic evaluation increases the accuracy of additive genomic evaluation up to 15% for h2=0.20 and d2=0.10 • Dominance can be used to improve phenotypic performaces through mating allocation • The increase of response of GSOM (Genomic Selection and Optimal Mating) vs. GS (Genomic Selection) is about 6-22%

44. BUT • Selection suffers a fast and deep decay of response after the first generation • Decay of linkage disequilibrium between markers and causal genes • Increase of linkage disequilibrium among causal genes due to selection • Advantage of mating allocation disappear after one generation of response

45. Gracias