Download Benefit of cycling strategies based on phenotype, clonal

Comparison of long-term
breeding strategies using
phenotype, clonal, progeny
testing for Eucalyptus
Darius Danusevičius1,2 and Dag Lindgren1
1-
Department of Forest Genetics and Plant Physiology,
Swedish University of Agricultural Sciences, S-901 83, Umeå,
Sweden.
2- Lithuanian Forest Research Institute, Girionys, LT-4312,
Kaunas reg., Lithuania.
Objective: comparing long term cycling
strategies based on phenotype, clone or
progeny testing by considering gain,
diversity, cost and time
Clone or progeny testing
Phenotype testing
N=50
N=50
(…n)
(…n)
(…n)
(…n)
(…m)(…m)(…m) (…m)(…m)(…m)
(…n), (…m) and selection age were optimized under a budget
constraint
Long-term benefit
1. Depth of
the pocket
2. Gain per
time (engine)
3. Diversity
potential
Other things,
e.g. to well
see the road
Long-term breeding
benefit
Group Merit per time =
(GAIN – C * DIVERSITY LOSS) / TIME
C
large breeding pop
Gain
Diversity
small breeding pop
The long-term program
Recurrent cycles of mating, testing and balanced selection
Mating
Within
family
selection
NS=50
Testing
Cycle time and cost
Under a budget
constraint
Time is
money
Cycle cost
•Recombination cost
•Cost per tested genotype (CL & PR)
•Cost per test plant (= 1’$’)
Cycle time
•Recombination time
•Time for production of test plants
•Testing time
Scenarios
Low
Main
High
Genetic parameters
lower
assumed
higher
Time components reasonable typical for reasonable
bound Ecalyptus bound
Cost components
While testing an alternative parameter value, the other
parameters were at main scenario values
And then we did the thing …
Results
Breeding cycler at
www.genfys.slu.se/staff/dagl.
Results
For all tested scenarios, clone strategy was superior
Phenotype
Clone
Progeny
1.4
GMG/Y, %
1.2
1.0
Phenotype ~ Clone;
0.8
0.6
Phenotype ~ Progeny
0.4
0.2
0.0
0
0.1 0.2 0.3 0.4 0.5 0.6
Narrow-sense heritability
If resemblance between genotype
and phenotype is high, there is less
need to test it.
Phenotype
Clone
Progeny
1.4
1.2
GMG/Y, %
1.0
0.8
0.6
0.4
0.2
0.0
4
6
8
10 12 14 16 18 20
Rotation age (years)
Short rotation (=high J-M correlation at normal
rotation), favored PH as it is cheap and the budget
constraint allows fast testing (= higher gain per
time).
Dominance variance had a minor effect
and was less favourable for Clone strategy.
Diversity loss had a minor effect (~ 25 or 80
individuals). BP can be sublined according to BV
of the members. For PH, cycles are shorter =
faster loss of diversity; and if BP is small, PH ~
PR.
Phenotype
0.8
0.8
0.7
0.7
GMG/Y, %
Progeny
Clone
0.636
0.6
0.6
0.5
0.5
0.4
0.4
0.331
0.3
0.3
0.2
0.2
0.1
0.1
0
1
2
3
4
5
Cost per genotype ($)
Effect of genotype cost was
small. Increasing the genotype
cost is an option only if other
benefits can be achieved.
6
0
1
2
3
Cost per plant ($)
Cost per test plant was
important, but less important
for phenotype strategy.
4
GMG/Y, %
Phenotype
1.4
0.8
1.2
0.7
1.0
0.6
0.8
0.5
0.6
0.4
0.4
0.3
0.2
0.0
0
5
10 15 20 25
Budget per year and parent ($)
Phenotype strategy is better
the lower the budget is, but at
high budget it is not superior
to Progeny strategy
Progeny
Clone
0.636
0.420
0.331
0.2
0.1
0
1
2
3
4
5
6
7
Time before establishment of selection
test (years)
At short Tbefore, PR ~ PH, thus,
for PR the first flowering could be
speeded up and at a high cost as
increase of genotype-dependent cost
was not so important.
Conclusions
Clonal testing is suggested to be the best testing
strategy.
Phenotype testing is most to its advantage at
high h2. If clone testing is not an option, it seems
preferable to progeny testing at short rotations and
low budget.
Progeny testing can be better than phenotype
testing when h2 is very low, flowering early, budget
high and rotation long.
Breeding Cycle Analyser
If your breeding plan is based on cycling and within family
selection, then which is the best testing strategy for selection
of the new parents? Find the answer by the aid of this
simulator which allows you to consider gain, diversity, cost
and time simultaneously. It is easy to use and is just a few
mouse clicks away from you at www.genfys.slu.se/staff/dagl
1.Set the parameters
common for all the
testing alternatives
2. Set specific
parameters for each
testing alternative
and find optimum
test size and time
3.Check the
final result