Arendonk, J.A.M., van

Blonk, R.J.W.

Komen, J.

English

Wageningen Yield 

*Animal Breeding and Genomics Centre, Wageningen University, PO Box 338, 6700 AH 
Wageningen, the Netherlands.  
Minimizing Genotyping In Breeding Programs With Natural 
Mating 
R.J.W. Blonk*, J. Komen* and J.A.M. van Arendonk* 
 
Introduction 
In many aquaculture populations highly skewed parental contributions are observed when 
natural mating in groups is used to obtain offspring (Bekkevold 2006; Fessehaye, El-Bialy, 
Rezk et al., 2006; Blonk, Komen, Kamstra et al., 2009). In such situations, pedigree 
information is missing and mass selection will result in high rates of inbreeding (Blonk, 
Komen, Kamstra et al., 2009). Relationships can be reconstructed using DNA markers such 
as microsatellites or SNP’s. However, with large numbers of selection candidates, this can be 
very expensive. Sonesson (2005) showed that for populations with a controlled mating 
design and contribution of parents, the number of genotyped selection candidates can be 
restricted by using a two stage selection scheme with optimal contribution selection on a 
phenotypically pre-selected fraction of the population. However, in situations where natural 
mating in groups is used to obtain offspring, rates of inbreeding and response to selection are 
difficult to predict and can be highly fluctuating depending on superiority of the highest 
contributing families. In this paper, we use stochastic simulation to determine the relation 
between nucleus size, genotyped fraction and achieved response and rate of inbreeding in 
two stage selection schemes with natural mating and skewed contributions of parents. 
Materials and methods 
Two single trait selection schemes were simulated: 1) mass selection, 2) mass selection 
followed by genotyping and optimal contribution selection (2-Stage selection). A population 
of N = 8000 selection candidates with nucleus size n = 200 or n = 300 (nsires = ndams = ½ n) 
was simulated. Heritability for the trait was 0.2 (simulations with heritability 0.5 were also 
performed but not included in the paper). In all simulations, the nucleus was divided in four 
groups and the number of parents was kept constant over all generations. In 2-Stage selection, 
different fractions (comprising 3, 4, 5, 6, 8,10, 12 or 15% of the population) with the best 
phenotypes were selected using mass selection. Assuming that these animals were pedigreed 
by genotyping, ASReml (Gilmour, Gogel, Cullis et al., 2006) was used to obtain BLUP of 
breeding values. A fixed number of parents (n) was then selected under a restriction of ∆F of 
1% per generation using optimal contribution selection with GENCONT software 
(Meuwissen 1997). Offspring of all full sib families were pooled for each generation and 
response and rate of inbreeding per generation were calculated. 
 
Parental contributions. To construct skewed parental contributions (c), nsires and ndams 
random numbers were drawn from a gamma distribution (x ~ Γ(0.75, 1/3)) and transformed 
to relative contributions. Only sires and dams with c > 0 (ncsires and ncdams) were used to 
contribute to the next generation. Parental contributions were not correlated to selected trait. 
Using the relative contributions, a full factorial mating design with dimensions ncsires by ncdams 
was set up with the size of full sib families (cFS) as the kronecker product of the parental 
contributions. To reflect that some full fib families are not contributing, a maximum number 
of possible families (nmaxfam = ncsires + ncdams) was randomly drawn from the full factorial 
mating design. Sire and dam breeding values in the founder population were simulated as 
( )22 phz σ⋅  with z as a normal deviate generated from a simulated normal distribution 
(N~(0,1)) and phenotypic variance 2pσ  set to 1. For each full sib family, phenotypes of N·cFS 
offspring were calculated using the parental breeding values, a Mendelian sampling term 
including inbreeding coefficients of the parents and a residual. 
Results and Discussion  
For each selection scheme and h2 = 0.2, results of ten generations selection (averaged over 50 
replicates) are presented in figure 1. With only mass selection (see figure 1) and nucleus size 
n = 200, ∆F after 10 generations was estimated on 1.52% per generation while ∆G was 35% 
per generation. For n = 300, ∆F and ∆G were respectively 1.01% and 33.7%. This is 
considerably higher than in populations with random mating and equal contributions. 
Prediction of ∆F and ∆G using Selaction software (Bijma, Van Arendonk and Woolliams 
2001) for the same selection scheme yields a ∆F of 0.56% and ∆G of 41% for n = 200. For n 
= 300, a ∆F of 0.35% and a ∆G of 38% are predicted. These results show that normal mass 
selection will lead to excessive rates of inbreeding in populations where natural mating in 
groups is used to obtain offspring. 
In a 2-stage breeding program, ∆F is restricted while ∆G is maximized. As expected, an 
overall lower ∆F and ∆G is obtained in a 2-stage breeding program when compared to mass 
selection. In a 2-stage breeding program, ∆F has a U-shaped trend for increasing genotyped 
fractions (figure 1). For fractions 3% to 5% (for n = 200) and fractions 4% to 5% (for n = 
300), ∆F decreases. This is probably due to fixed nucleus sizes and limited availability of 
families in the relatively small genotyped fractions. In such cases, optimal contribution 
routines can not hold the set restriction and are expected to yield relatively high ∆F. As the 
genotyped fractions increase, more families become available, yielding lower ∆F. 
 
Figure 1: Mean ∆F (%) and response (%) for a two stage breeding program including 
optimal genetic contribution selection on different genotyped fractions. Results are 
shown for nucleus size 200 (circles) and 300 (triangles). Population size = 8000. 
Restriction for ∆F = 1% (dotted line). ∆F and Response for mass selection on a natural 
mating population are depicted for nucleus size 200 (star) and 300 (square).  
 With genotyped fractions higher than 5%, ∆F increases to 1.4 - 1.6% per generation. At these 
fractions, short term 1% ∆F restrictions are met at an early stage. Consequently, there is little 
room left to lower ∆F when unexpected high ∆F is reached due to natural mating with 
skewed contributions. Again, optimal contribution selection can not hold the set restriction of 
∆F, resulting in higher values. 
In many schemes, ∆F was above the restriction of 1%. This is probably due to the fixed 
number of selected parents (nucleus size). Optimal contribution selection can suppress ∆F 
once the number of parents to select is free. However. in practical circumstances, nucleus 
sizes are generally fixed for economic reasons. Also, nucleus size often has a constrained 
minimum set by the required reproductive output of fertilized eggs.  
 
Implications ∆G of a 2-stage breeding program with natural mating is relatively stable 
across increasing mass selected and genotyped fractions, i.e. genotyping more animals does 
not considerably increase ∆G. For example, with a genotyped fraction of 5% (400 animals), 
response is approximately 32%. This increases to approximately 35% with a genotyped 
fraction of 15% (1200 animals). It will depend on the economic importance of the trait 
whether this increase in response outweighs the increasing genotyping costs. However, with 
respect to ∆F, there is an optimum size of mass selected and genotyped fractions; rates of 
inbreeding exceed acceptable levels once too few or too many candidates are genotyped. 
Moreover, when mass selected and genotyped fractions comprise less than 10% of a 
population of 8000 animals, ∆F remains below values of mass selection.  
In conclusion, using mass selection to genetically improve stocks which are dependent on 
natural mating in groups, is likely to yield excessive rates of inbreeding, unless a large 
nucleus is maintained. However, when genotyping 5 to 10% of the selection candidates and 
applying optimal contribution selection, levels of inbreeding can be kept at or close to the 
generally accepted level of 1%. This means that there are good prospects for genetic 
improvement of aquaculture industry when natural mating in groups is used to obtain 
offspring. 
 
References 
Bekkevold, D.X. (2006). Journal of Fish Biology, 69: 945-950. 
Bijma, P., Van Arendonk, J.A. and Woolliams, J.A. (2001). J. Anim Sci., 79: 840-853. 
Blonk, R.J.W., Komen, J., Kamstra, A., et al. (2009). Aquaculture, 289: 26-31. 
Fessehaye, Y., El-Bialy, Z., Rezk, M.A., et al. (2006). Aquaculture, 256 : 148-158. 
Gilmour, A.R., Gogel, B.J., Cullis, B.R., et al. (2006). VSN International Ltd. Hemel 
Hempstead, HP1 1ES, UK. 
Meuwissen, T.H.E. (1997). Journal of Animal Science, 75: 934-940. 
Sonesson, A.A.K. (2009). Genet. Sel. Evol., 37: 587-599. 
 


Minimizing Genotyping In Breeding Programs With Natural Mating

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