Combining genetic gain, gene diversity, time components, cost components when optimizing breeding strategies
Dag Lindgren
Seminar at the research school of forest genetics and breeding
Umeå 08-09-15
Two parts:
1. Published 2002-2005 (many heard the essentials 2004, but not the research school doctorands)
2. In press (galley proof received) or preparation (breeders heard parts of it in May, but it is improved calculations, also for meeting tomorrow).

Consider all!

The program “Breeding cycler” studies what happens during one complete breeding cycle
Selection
Mating
Long-term breeding Testing

Breeding cycler explores selection of selecting the best within a full sib family!
Acknowledgement: Large thanks to Swedish breeding for the justification to construct a reasonable simple breeding cycler!
Breeding population size is set to 50 and each is parent to two full sib families and from each the best individual is recruited to the next breeding population! Thus - to optimize
Swedish breeding - it is usually sufficient to consider a single full sib family. That is the first part.
Acknowledgement also to Ola Rosvall, who thought outside the box, and is spiritual father of thinking outside the 50 and a single generation. That is the second part.

• Genetic variance components like:
– Additive genetic variation in goal character (goal character is value for forestry but numerically volume production at mature age other characters unchanged)
– Juvenile-mature correlation
• Time components (duration for different actions) like:
– Testing (age at selection)
– Crossing
– Wait for flowering
• Cost components like:
– Cost for additional plants
– Cost for cycling the breeding population
– Cost for additional parents used in crosses
• Strategy: Structuring components e.g.:
– Wait for flowering - mate - establish field test - select

• Genetic Gain (breeding value) and Gene
Diversity combined into “Group Merit” (the sum).
• Cost expressed per founder and cycle
• Time and Cost combined to “annual cost”
(yearly budget)
•“Group Merit progress at annual cost” combines all issues into a single measure, which can be studied and maximized.

•
Input parameters
• Get results
• Use “trial and error” to find what is best
Results

Inputs – commonly used
•
Chosen to be relevant for to discussions about breeding of Swedish (=Nordic)
Norway spruce and Scots pine (we have also made studies on poplars and
Eucalyps)
•
Many alternative scenarios evaluated to home in on optimum
• Annual budget 10 “test plant equivalents per year and founder”
• Cycling cost 30 per founder
• Crossing possible at age 10-15 for pine and 20 for spruce
• Clone testing possible for spruce but not pine
•
CVAm = 14% (additive variation in value (volume) among trees at mature age)
• Dominance variance ¼ of additive
• Heritability almost 0.2 (within family heritability =0.1)
• Note than in breeding cycler papers 2000-2005 is the population considered a single full sib family, thus variance components are within family. That is explained and correct, but may still be misleading. In coming papers we will give it for whole population.
• Selection gain is created by selecting the best individual from each full sib or through one offspring per grandparent.
• Diversity loss with balanced 50 founder breeding, “group coancestry”, is punished as percent of forest production (probably high estimate)
•
Rotation age 60
•
Juvenile-mature correlation according to Lambeth (1981).

Comparison of main testing strategies
– best genotypes are selected based on
• Progeny – trees with good progeny
• Clone – trees with good vegetative copies
• Phenotype – trees with good appearance

0.6
0.5
0.4
0.3
0.2
0.1
0.0
Clone
Phenotype
Progeny
0 0.1
0.2
0.3
0.4
0.5
0.6
Narrow-sense within family heritability

• Clone was superior for all realistic scenarios.
• Swedish breeding uses clone testing with near optimal design (acc to breeding cycler) for Norway spruce and has initiated it for lodgepole pine. Development with clone testing is initiated for Scots pine. These approaches are strongly supported by BREEDING
CYCLER results.
• The optimal scenario for Norway spruce suggests later selection age in field trials than Swedish breeding heads for (20 instead of 15 years). The late sexual maturity of
Norway spruce is an argument.

Phenotypic selection may be slightly superior to Progeny-testing, for the most realistic scenarios

Annual Group Merit , %
0.20
0.15
Progeny
Phenotype
0.10
0.05
0 3 6 9 12 15 18
Delay before establishment of selection test (years)
Early flowering improves the efficiency of progeny-testing only marginally.
Early flowering may make progeny-testing marginally superior to selection on phenotype

0.3
0.2
Phenotype
0.1
Progeny
0.0
0 5 10
Budget per year and parent
15 20 25
Phenotype get more superior at low budget, progeny slightly superior at high budget

As phenotypic and progeny are similar, it seems logic to combine them in a two-stage strategy.
Pre-selection of phenotypes, which are progeny-tested and the best reselected.
Waiting time to sexual maturity is used for phenotypic testing!

/
Stage1: Phenotype test and pre-selection
Stage 2: .Sexual

Phenotype / Progeny strategy superior to either single stage strategy for most relevant cases
At high budget, successful flower stimulation and low heritability, progeny test is superior.
At low budget, high heritability, low cycling cost and low penalty for diversity, phenotypic selection is preferable.

Result: 2-stage seems preferrable
• Early flowering at to Scots pine age 3 can make progeny-test compatible with
2-stage
0.6
Main scenario
0.3
Pheno /Progeny
• Sexual maturity at age
10 seems optimal, but no marked loss if sexual maturity occur first at age
15.
Progeny
0.0
0 5 10 15 20 25
Age of mating for progeny test (years)

Suggested good scenario for Scots pine
(for one family at given annual budget)
Cycle time= 26
Gain=8%
Select the best when progenytest is 10 years
Mating
3 years
Long-term breeding
Stage 1: Test
70 phenotypes
Stage 2. Progenytest with 30 offspring
Cross
Polymix
(3 years)
“Preselect”
Select 5 at age 10

• The size of the breeding population (50) has been chosen because it is “sufficient”.
We tried to optimize with breeding cycler!;
• 50 (as used in Sweden) seemed about right for spruce and pine in Sweden;
• May be a little lower for Norway spruce;
• Maybe a little higher for Scots pine;

The rest of the stuff is not published
(one study in press, one in manus)
• Idea : Keep balance by monitoring grandparents instead of parents. Each grand-parent give the same contribution but parents/grandparent is an input.
• “Grandparents” can be regarded a synonym to “founders” for the situation in
Sweden (50 founders/bpop).

• Scots pine in central Sweden with 50 founders (=grandparents)
• New method (balance among grandparents, 300 parents)
• Earlier assumed best strategy ( phenotypic selection strategy, 50 parents,
Hannrup et al 2007)
• Comparison assuming the same budget and gene diversity
0.3
0.25
0.2
0.15
0.1
0.05
0
}
= 54%
New Current

• Well-documentet (CJFR by Swedish breeders
• Advantage of 2-stage not large
• Special case of granparental balance with parents/grandparent = 1.

SPM with parental balance
(almost current Swedish program)
(…)
Grand parents
(=founders), F
0
Mating grand parents
Select and mate 2 best sibs
F
1
(…)
(…)
F
2
(…)

Green trees show pedigree
F
0
(…)
Grandparents
=founders
Cross 4 best sibs
F
1
(…)
F
2
1 st rank family
(…)
3 rd rank family
(…) n th rank family
Cross e.g. 4 best sibs in the 2 best families (2 parents per grandparent)
(…)
2 nd rank family
(…)

• Note that retrospectively SPM and multiple
SPM have identical pedigrees, thus identical increase of coancestry.
• Simple SPM (phenotypic selection) is a special case of multiple SPM (“combined selection”) with 1 parent per grandparent.

• Heading to be relevant for Scots pine in central Sweden
• Costs, field trial plant is the unit, cost components are derived from Hannrup et al. 2007. Genetic variance components derived from Rosvall et al 2001.
• Annual budget 50/grandparent
• Scots pine, cycling cost 100/grandparent
• Added parent 50
• 6 parents/grandparent (near optimal)
• Rotation time 70 years
• Field test selection at 15 (optimal slightly lower but marginal reduction)
• Breeding cycle length 20 years.
• Juvenile-mature correlations for Scots pine derived from
Jansson et al (2003).

0.5
0.4
0.3
0.2
Jansson et al. 2003
Lambeth & Dill 2001
Gwase et al. 2000
Lambeth 1980
0.1
0.0
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Time, years
Jansson et al. 2003 is for Scots pine in southern Sweden, which is most relevant. 15 year testing time seem near optimal.

Genetic gain at different parents per grandparent
0.30
Parent cost=50
0.25
Optimum at 6
0.20
Phenotypic strategy
0.15
0 1 2 3 4 5 6 7 8 9 10111213141516
Number of parents per grandparent
54 % better

0.30
C parent
=0
0.25
0.20
C parent
=100
C parent
=50
0.15
0 1 2 3 4 5 6 7 8 9 10111213141516
Number of parents per grandparent
Optimum is not strongly dependent on parent cost

16
14
C parent
=0
12
10
10.5
C parent
=50
8
6
6
C parent
=100
4
4.5
2
0
10 50
100
Annual budget per grandparent
Optimum
P/GP rises linearly with budget

This study supports that the suggested strategy 5 (which will be more discussed 090816) is the best way to breed Scots pine, and suggest the strategy is substantially better than other alternatives.
The details of the strategy as suggested in end of December
2007 seem optimal.
This is however further investigated.

Note that the start (“phenotypic preselection) is identical to the two stage strategy, final decision need to be done first at crossings