Proc. Assoc. Advmt. Anim. Breed. Genet. 17: 33-36
OPTIMISING BETWEEN AND WITHIN FAMILY SELECTION FOR HARVEST WEIGHT
IN PRAWNS WITH RESTRICTED HARVEST SIZE
M. Macbeth
Department of Primary Industries and Fisheries, Animal Research Institute, Yeerongpilly Q 4105
SUMMARY
Manual grading of prawns restricts the number that can be harvested. A restricted harvest size places
a limit on the opposing within family and between family sources of selection pressure. A simulation
study with inbreeding constrained at 0.5% per generation, a harvest size of 2000, heritability of 0.3,
common family environmental effect of 0.1, indicates that maximum response to selection could be
achieved with as few as 40 families. Increasing the number of families above 80 may reduce total
selection response. It is important to be aware that increasing the number of families may not always
yield a greater genetic response.
INTRODUCTION
Prawns (Penaeus sp.) are highly fecund with 40,000 to 1,000,000 nauplii per spawn (Macbeth et al.
2007, Arcos et al. 2004, Menasveta et al. 1994). In Australia ponds can be stocked with over 50,000
prawns per pond which in theory could result in very high selection pressures. Unfortunately
mechanical grading of mature prawns is not yet successful as they are sexually dimorphic in size
(Kenway et al. 2006) and have protruding appendages. Harvesting is usually achieved by draining
ponds. Hand grading at this time becomes a logistical challenge with perhaps only 2000 prawns
manually graded in one day with a team of 10 or more volunteers. Prawns can be easily tagged at 2
grams with families identified through visual implant elastomer (VIE) tags (Northwest Marine
Technologies) prior to communal pond rearing to achieve a harvest population size of 2000.
Handling this number for both between family selection and within family selection may be an
achievable practical objective.
When selecting solely on growth rate at a given harvest size, increasing the number of families
will potentially enable a higher degree of between family selection but at the same time this is
expected to reduce the number of animals available for within family selection. By fixing the rate of
inbreeding at a given harvest size, a comparison of total selection response at different family sizes is
achieved in this simulation study.
MATERIALS AND METHODS
The genetic values G of foundation parents were generated by computer simulation for each sire and
dam from sampling the genetic variates from normal distributions with variance σ G2 :
Gsire ~ N(0, σ G2 )
and
Gdam ~ N(0, σ G2 )
The genetic values of progeny were corrected for a reduction in mendelian sampling variance from
the average inbreeding of both parents F as:
Ganim ~ (Gsire+ Gdam)/2+ N(0, σ G2 ) (1 − F ) / 2
33
Breeding Program Design Initiatives
with phenotypic values P determined by sampling the common environmental variance ( σ C2 ) for each
family, and by sampling the animal error variance ( σ E2 ) for each individual animal as:
Panim ~ Ganim + N(0, σ C2 ) + N(0, σ E2 )
The number of families were varied from 25 to 120 with the number of animals within each
generation restricted to 2000, i.e. harvest size H=2000. Inbreeding was also restricted to
approximately 0.5% per generation through a binary search algorithm while maximising selection
response by altering the percentage of between family selection. The binary search on inbreeding
was assessed at 30 generations over 10 iterations. Within each iteration the average inbreeding from
12 replicates was assessed by holding the percentage of between family selection constant.
Between family selection was based on the average phenotypic value of family members with
the lowest C percent of families culled on average growth rate. Within family selection was based on
the heaviest animals within each family being selected. Mating was non-random with the best
selected families mated to each other. The simulation assumed a survival and mating success of
100%. The evaluation period was thirty generations of selection.
The program was written using the c programming language running under the Linux operating
system. Inbreeding was determined using algorithms within the ainv.c program from the Animal
Breeders Toolkit (Golden et al. 1995). Simulations were based on a common environmental variance
(c2) of 0.1 and a harvest weight heritability (h2) of 0.3.
RESULTS
The binary search was sufficient to achieve inbreeding levels close to the desired 0.5% and by
random sampling they varied from 0.585% to 0.453% (Table 1). The percentage of between family
selection ranged from 0% with 25 families through to 60% with 120 families. As the number of
families increased the number of animals available within each family (Nw) decreased (Table 1).
Table 1. Iterative solutions of family culling rate (C) and inbreeding (F) at given family sizes,
and the number of animals within each family (Nw).
Family size
C (%)
F (%)
Nw
25
30
35
37
40
43
46
50
60
0
2
5
10
15
20
20
29
35
0.585 0.469 0.453 0.507 0.504 0.516 0.486 0.478 0.516
80
67
57
54
50
47
43
40
33
80 120
43
60
0.49 0.473
25
17
Figure 1 illustrates the reduced response to within family selection as the number of families
increased from 25 to 120. As the percentage of family culling (C) increased (Table 2) the rate of
genetic gain due to between family selection also increased (Figure 1). The two components of
selection response gave a net response curve which yielded a plateau between 40 families and 80
families with a slight reduction in response from 80 to 120 families.
34
Proc. Assoc. Advmt. Anim. Breed. Genet. 17: 33-36
14
12
Response (sd)
10
8
6
4
Within (W)
2
Betw een (B)
B+W
0
20
40
60
80
100
120
Number of families
Figure 1. Response to selection after 30 generations with 2000 animals selected per generation,
h2=0.30, c2=0.10 and inbreeding F=0.005 per generation.
DISCUSSION
The design of breeding programs entails a balance between managing risks and maximising
economic gains through selection. Maintaining additional families in aquaculture can be costly due
to additional labour and infrastructure costs particularly during the early establishment phase. This
study shows that when selection is on a single production trait, harvest size restricted to 2000, h2=0.3
and c2=0.10, there is very little to achieve in terms of selection response by increasing family
numbers above 40. Increasing family numbers above 80 may reduce total genetic response.
There are few papers in Penaeus sp. where the common environmental effect (c2) has been
determined with Gitterle et al. (2005) reporting estimates from 0 to 17%. The value of 0.10 was used
in this study as it was consistent with many other animal studies. An increase in c2 will make
between family selection less favourable (Macbeth 2005) resulting in a lower response from this
component of selection. The average heritability estimate from Kenway et al. 2006, Gitterle et al.
(2005), Arcos et al. 2004, Perez-Rostro et al. 2003, Argue et al. 2002, Hetzel et al. 2000 and Carr et
al. 1997 was above the 0.3 value used in this study. A larger heritability would increase the ratio of
gains made from the within family selection over the between family selection which may tend to
lower the number of families required.
General recommendations on the maximum rate of inbreeding range from 0.5% to 1% (Goddard
1992, Bentsen and Olesen 2002, Meuwissen and Woolliams 1994, Nicholas 1989). If the permissible
level of inbreeding per generation in this study was increased to 1%, then a higher level of between
family selection and resulting response would be achievable. Given Penaeus sp. have generation
lengths as low as 6 to 12 months a lower inbreeding rate may be more appropriate for long term
selection programs.
The model assumed a trait in which selection could be applied both within and between families
such as weight gain. There are traits that are best selected between families such as family survival,
family reproductive traits and possibly family meat quality traits. If additional between family
selection traits are included in an economic index then between family selection would become more
important in terms of response rate compared to within family selection.
35
Breeding Program Design Initiatives
The effective population size of within family selection is twice the number of parents with the
rate of inbreeding ∆ F = 1/(2Ne) (Falconer 1972). When only within family selection is applied to a
family size of 25, the expected rate of inbreeding is 0.5% per generation. For 25 families this study
obtained a value of 0.585%. The difference is expected to have been caused by non-random mating
and random chance.
This simulation study was intentionally made simple to show a point. In the real world
knowledge of specific husbandry, biology and selection objectives need to be taken into account
when designing aquaculture breeding programs. This study assumed a survival and reproductive rate
of 100%. Individual values of these parameters could easily be included as well as other specific
information to tailor a breeding program to meet individual circumstances.
CONCLUSIONS
When harvest size in aquaculture production is restricted at 2000 there may be little improvement in
selection response when the number of families is increased above 40. Increasing family size above
80 may reduce total selection response. It is important to be aware that increasing family numbers
may not always yield a greater genetic response. Individual situations will need to be considered in
detail before making any recommendations.
REFERENCES
Arcos, F.G., Racotta, I.S. and Ibarra, A.M. (2004). Aquaculture. 236:151.
Argue, B.J., Arce, S.M., Lotz J.M. and Moss, S.M., (2002) Aquaculture 204:447.
Bentsen HB, Olesen I (2002) Aquaculture 204:349.
Carr, W.H., Fjalestad, K.T., Godin, D., Swingle, J., Sweeny, J.N. and Gjedrem, T., (1997) “Book of
abstracts”, p 63, editors T.W Flegel and I.H. MacRae , World Aquaculture, Bangkok, Thailand.
Falconer, D.S. (1972) “Introduction to quantitative genetics”. Oliver and Boyd: Edinburgh.
Gillerle T., Rye M., Salte R., Cock J., Johansen H., Lozano C., Suarez J.A. and Gjerde B. (2005)
Aquaculture 243: 83.
Goddard, M.E. (1992) Journal of Dairy Science. 75:2902
Golden, B.L., Snelling, W.M., and Mallinckrodt C.H. (1992) “Animal breeder’s tool kit user’s guide
and reference manual”. Colorado State University, Agric. Exp. Sta. Bull. LTB92-2.
Hetzel, D.J.S., Crocos, P.J., Davis, G.P., Moore, S.S. and Preston, N.C., (2000) Aquaculture 181:215
Kenway, M., Macbeth, M., Salmon, M., McPhee, C., Benzie, J., Wilson, K. and Knibb W. (2006)
Aquaculture. 259:138.
Macbeth, M. (2005) Aust. J.Exp. Agric. 45:893
Macbeth, M., Kenway M., Salmon M., Benzie J., Knibb W. and Wilson K. (2007) Aquaculture. (In
press)
Menasveta, P., Sangpradub, S. and Piyatiratitivorakul S (1994) J. World Aqua. Soc. 25:41.
Meuwissen, T.H.E. and Woolliams J.A. (1994) Theor. App. Genet. 89:1019.
Nicholas, F.W. (1989) In “Evolution and animal breeding”, p 203, editors WG Hill and FC MacKay,
CAB International, Wallingford.
Perez-Rostro, C.I., and Ibarra, A.M., (2003) Aqua. Res. 34:543.
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