1. Variación no-aditiva y selección genómica
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
Genotype
Frequency
Genotypic value
Additve value
Dominancdeviation AA p2 a
2qα
-2q2d Aa
2pq d
(q-p) α
2pqd aa q2 -a
-2pα
-2p2d
α=a+d(q-p)
σ2A =2pqα2
σ2D=(2pqd)2

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 (biallelic)
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
True
Infinitesimal
Finite number of loci (biallelic) 20 10 50 Ve 80
78.7
70.7
74.3
80.8 Va
18.6
18.5
19.8
21.4 Vd
14.4
21.1
15.8
7.2 D
-14.1
-11.1
-13.8
-14.7
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 (?)
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)
Species (breed)
Trait
Additive
Dominance
d2/h2
Dairy cattle (Holstein)
Milk yield
43.5
5.7
0,13
Fat yield
42.6
7.0
0,16
Protein yield
40.6
4.9
0,12
Stature
45.3
6.9
0,15
Strength
27.8
8.0
0,28
Body depth
34.5
9.8
Dairy form
23.4
5.3
0,22
Fore udder attachment
24.3
4.7
0,19
Swine (Yorkshire)
Number of born alive
8.8
2.2
0.25
21-day litter weight
8.1
6.3
0,78
Days to kg
33.1
10.3
0.31
Backfat at kg
43.6
4.8
0,11
Beef cattle (Limousin)
Postweaning gain
21.0
9.9
0,47
Salmon
Body weight
7.8
4.6
0,59
Chicken
Egg production
2.3 -
Body weigth
Age at 1st egg
7.4

12. Wild (Vd / Va) 1.17 life-history traits 1.06 physiological traits
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 dominanceB
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
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
- Then population was increased up to 500 males and 500 females during 3 generations
- These 3000 (generation 1001,1002 and 1003) individuals were genotyped and phenotyped and used as training population to estimate additive and dominance effects of SNPs
- Generation 1004 was formed from 25 sires and 250 dams of generation 1003

22. Simulation
Generation 1004 was formed from 25 sires and 250 dams selected from generation 1003
Three strategies of selection compared:
Mass selection using phenotypes with Random Mating
Genomic selection based on additive effects with Random Mating (GS)
Genomic selection based on additive effects and Optimal mating allocation based on dominance effects (GS+OP)

23. Simulation Genetic assumptions 10 chromosomes of 100 cM
10000 loci (9000 SNP and 1000 QTLs)
- Both SNPs and QTLs have two alleles
- Mutation rates were following Meuwissen et al. (2001) for SNPs and for QTLs (about 8000 SNP and 80 QTLs were segregating 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 BLUP: Animal Model solved with Gibbs sampling
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 h2 d2
Bayes A
Mate Sel
Bayes B
0.20
0.05
0.471
0.527
0.433
0.456
0.10
0.470
0.575
0.445
0.509
0.40
0,05
0.815
0.724
0.744
0.754
0.875
0.730
0.791

42. Genomic selection including or not dominance in the model (Bayes B)
Additive + Dominance model
Additive model
Advantage (%)
0.20
0.05
0.471
0.431
9.29
0.10
0.470
0.412
14.08
0.40
0,05
0.815
0.750
2.80
0.754
0.733
2.86

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