Learning Center
Plans & pricing Sign in
Sign Out

Inbalanced breeding


									      Unbalances in Tree Breeding

                Dag Lindgren, SLU, Sweden

Let’s have the discussion on the net instead of oral, this
show and a discussion site is available at:
An effort was done to discuss at a workplace but that is now
        “Genetic unbalances” are:
•   The basis for evolution;
•   Natural – balance is extremely unnatural;
•   Unavoidable;
•   Essence of breeding.
Genetic contributions varies.
Natural selection favors some and disfavors others,
(survival of the fittest), thus some contributions will
increase, other decrease
      Selection means always unbalance,
       not selected components get no contribution


       Not selected

          Contribution of component
I talk about more sophisticated unbalance
Different types of unbalances…
• Unbalance in genetic components
• Unbalance in resources;
• Structure of breeding population (e.g.
  mating probability, PAM)

Often they come together, thus “Nucleus” (elite-main)
has all three components of unbalance
             Why unbalance?
Breeding has to consider:
• Gain;
• Gene diversity;
• Cost;
• Time;
• Interaction breeding → seed orchards.

Unbalances may make the breeding system more optimal
  and efficient.
Unbalances offer more degrees of freedom for optimization
  (balance is a form of simplification fundamentalism)
   Reasons against unbalance?
• Unbalance may just make things worse if not done wisely and
• It may overshoot and be too much unbalance!
• Unbalance requires competence!;
• Unbalance is more demanding on management skills;
• Often the tools for handling unbalance are badly developed!
• Seldom transparent;
• Sometimes the advantage is small, usually limited (3-8%) and
  seldom drastic (20%);
• Advantages are often calculated for an ideal situation, and are
  usually somewhat less in the real world!
• Historically unbalances were difficult to manage, thus all traditional
  wisdom is against, now computers can do everything!!!?
• It requires calculations to be done.
Unbalance is a black box!
  Requires lots of competence!

                     Output: Gain,
                     effective number,
                     remaining ramets
                     per clone
             Why balance?

•   Simpler;
•   More transparent;
•   Less demanding on competence;
•   Less demanding on skilful management;
•   More fail-safe.
      More reasons for unbalance
• Even a limited extra gain (e.g. 5% increase in gain)
  means enormous economic returns for some extra
• Although all extra gain predicted may not be reached, it
  is unlikely it will not give an extra gain (using some
  common sense);
• Unbalance may offer fast gain. That is more worth than
  options some centuries ahead;
• Competence can be increased by education and
• Complete balance is an extreme alternative, that makes
  it unlikely to be optimal;
• Complete balance is practically unrealistic! Unbalance
  must anyway be managed, so why not do it efficient!
     Quantitative evaluation of
 unbalance often overestimates the
         practical benefit!
• Genetic parameters (genetic correlations) change over
  time and environment;
• Environment changes;
• Unreliable parameter estimations;
• Breeding goals change and are not exactly predictable;
• Planned unbalances are influenced by unplanned;
• This is likely to lead to overestimates of the practical
  benefits of unbalances and that optimum is missed.
       Apply unbalance, but with
• I suggest to often apply unbalances;
• But do it with moderation and not too drastic;
• It might often be a good idea to try
  compromising between balance and the
  predicted optimal unbalance;
• After gaining experience of unbalance, a larger
  share of predicted advantages may be utilized.
      Unbalances in production
• Simplest case, only unbalances in different
  contributions (e.g. clones, parents) matter.
                     Equal (balanced) contribution of clones!

Breeding value of clone


                          A    B                     A     B
                                   C    D                       C D
                                             E                        EFG

                                Contribution of clone

                          More clones with different contributions can result
                          in both more gain and more diversity!
Linear deployment is optimal for

• Relate contribution linearly to breeding value;
• No other deployment combines higher gain with
  higher effective number.
 Linear deployment works with
        constraints also

At thinning ramets cannot be added,
   just withdrawn. Thus there is a
      highest number of ramets!
            The “Swedish” model
• In the following, many of presented figures intend to be
  relevant for Sweden or the Swedish breeding strategy;
• Heading for a number of long term “breeding
  populations”, each of size 50;
• Heading for balance: Within family selection; Each
  parent get two full sib families; One selection per family.
• Start with tested plus trees (typical 200 per breeding
• Test recruitment population (clone-testing or progeny-
• Genetic parameters, costs and time estimates should be
Unbalanced contributions at the
initiation of a tree improvement

Closing the breeding population is irreciprocal and
can not be undone! Argument to play on the safe
   Generalizations from Wei PhD
        thesis (Wei 1995)
Method developed for “optimal selection” in a population
  with family structure (can be visualized as unrelated full
  sibs with plus tree parents);
• Optimal selection among individuals with a family
  structure is close to linear deployment from parent
• Seems reasonable to start with crosses from about 150
  plus trees to start up a breeding population;
• Differences from current Swedish program: no testing
  (“phenotypic selection”), thus heritability not high; no
  initial knowledge of plus tree breeding values.
 Unbalances in setting up the first
     recruitment population
• Generalized from Ruotsalainen (2002)
  PhD thesis;
• An approximation to linear deployment
The same resources, the same resulting
           gene diversity.
  Unbalanced (60 founders)    Balanced (50 founders)
  Rank of plus    Progenies   Rank of plus   Progenies
     tree                        tree
  1-10            3           1-50           2
  11-30           2           -

  31-60           1           -

  61-200          0           51-200         0
  Gain            1.368                      1.271
  = selection

Eight percent more gain with unbalance in the F1
            recruitment population!
Unbalance, eight percent more gain than balance!

Share of represented founders   Progenies per
(tested plus trees)             founder

Best    1/6                     3

Medium 1/3                      2

Bottom 1/2                      1
Result Andersson PhD thesis 1999

 Unbalanced selection is superior to balanced
 in the initiation of a breeding program
         Unbalance by refreshing in F1
  Inspired from Andersson PhD thesis (1999)

Unbalance could perhaps be introduced in F 1 by refreshing:
• In model-calculations it was favorable to replace 5-10%
  of founders at F1 with new plus trees;
• That indicates that it may sometimes be beneficial to
  replace one or a few of bottom ranking BP members with
  new founders in the Swedish breeding;
• The introduced founders may have slightly lower BV, but
  the Group merit of the BP could increase. That would
  mean that a few F1 BP would be crossed with new
  founders to form the next BP generation;
• The bottom ranking selected founders are only slightly
  superior to the best non-selected candidates.
Unbalances in long term breeding
 Unbalances in long term breeding
• Wei (1995) demonstrated the possible
  disastrous effects to use the strongly
  unbalanced selection resulting from maximizing
  breeding value in each generation;
• When gene diversity is exhausted, genetic gain
• Sanchez (2000) studied the effect of a slight
  unbalance with quantitative simulation and small
  populations. It was noted that a slight unbalance
  often was more favorable in breeding than
  complete balance.
   Results generalized from PhD
      thesis Rosvall (1999)
• Used POPSIM (tree improvement
  simulator) to study different aspects of
  long term breeding with simulation of a
  program similar to Swedish Norway
  spruce breeding.
• The capacity of the breeding population to
  support a seed orchard was used as a
                                                           Genetic gain
               Breeding population size =48, SPM , progeny size = 50, GMS selection, high heritability, after five generations





                        Breeding population
30                                                             Seed orchard (production population)
20       * Gene diversity in the breeding population makes it
         more able to support production populations
          Gene diversity (status number) in the breeding population                                                 based on Rosvall 1999

     0              2               4                6                8               10               12               14                  16
Unbalances in long term breeding?
• Some advantage of unbalance is found,
  but so marginal and uncertain (Rosvall
  1999) that it seems doubtful applying
  unbalance in the Swedish long term
   Some reasons Rosvall (1999)
found little advantage of unbalance
• The benefit of the breeding is measured as its ability of supporting
  seed orchards;
• Less sophisticated selection for advanced generation seed orchards
  than will be used in practice;
• Testing of the recruitment population (clonal or progeny), thus high
  heritability (Swedish pine breeding may turn to phenotypic selection
  next cycle, when unbalance may appear more favorable);
• Not exactly optimal unbalance, constraints in the simulator makes it
  hard to use optimum;
• Intensively selected breeding populations (Bulmer effect);
• Distinct generations, that will not be so!;
• Breeding population size is small, that makes conservation of diversity
  relatively important;
• Mainly a closed breeding population.

 I guess the advantage of unbalance is slightly greater
 in “real world”, and in particular in initiation!
Gain at a given “diversity”.                h2=0.25 and P=0.1
                                             Modified From Lindgren and Wei 1993

          Combined index=estimated
            BV (maximizes gain)


                      Between family
                    (exhausts diversity)
                                                Within family
                                             (preserves diversity)
                0                         0.5                                      1
                                       Relative diversity
Within family selection does not look efficient. Information from sibs was used
for estimating breeding values (selection index). Infinite normal populations
were assumed.
Distinct generations rolling synchronously
will not work! Rolling front breeding is more
   operational, and must be unbalanced!

• Of the Swedish breeding populations, which reached
  F1 in field, 75% are not synchronized in time. In spite
  of time lost in efforts!
• Mates can be selected in several ways. Trees in field
  trials can probably only function as pollen parents,
  while grafts may first be available as seed parents.
  Optimal use of such factors will force unbalances.
• It will be found optimal to utilize genotypes
  technically in different generations;
• Some materials will be remeasured at higher age,
  some not;
• The management of rolling front will be unbalanced
  anyway, so that balance is simple will be irrelevant.
                               Not only contributions but also
                                resource allocation matters!
                             More resources for improving larger components may
                             result in higher average gain!
Genetic value of component

                               Equal resources         More resources for better

                                      A          B               A

                                     Contribution of component
  More attention on the better may
         improve efficiency
• If the predicted best contributions get more
  attention (larger test families, more mating
  partners etc) the best contributions benefit
  more from breeding.
    Results interpreted from PhD thesis Lstiburek (2005).
   POPSIM simulations. Linear deployment of family sizes
related to their breeding value combined with PAM and within
  family selection boosts ability to select for high gain seed
                       Strong unbalance
                       gives 20 % more gain
   Family size

                                                          unbalance, 10%
                                                          more gain
                                After Mullin et al 2005

                 Low   Family breeding value               High
  Why not stronger unbalance?
• It cannot be good breeding economy to spend lots of
  resources to produce a family (including their parents)
  and when make the family size very small for some
• Test environments and optimal test criteria for
  “optimizing” family size is different from there families are
  deployed, thus the advantage will be reduced;
• The accepted conventional wisdom is the same family
  size, safer not to make too extreme changes, while
  experience and considerations accumulate successively
  stronger unbalance may be applied.
            Average selection intensity
 The selection gain by within family selection drops if the same total
 testing effort is unequally distributed among families, but marginally
 little if the unbalance is moderate.
                      Balanced        Moderate              Strong
                                      unbalance            unbalance

                  Size      i      Size        i         Size          i
Large family      5       1.163   6        1.267     9          1.485
Small family      5       1.163   4        1.029     1          0
Average                   1.163            1.148                0.742
% of balanced             100              98.7                 63.8
         Population structuring
      Ph-thesis Rosvall (1999), Lstiburek (2005)

• To structure a breeding population in elite
  and main offers advantages, but more
  advantages of the same type can be
  achieved by proper management of a
  single population.
           Stratified sublining
         From Ruotsalainen PhThesis (2001)

• Extends the PAM concept to several
• A better alternative to elite – main is to use
  many strata.
                     Stratified sublining

                                            F1 families



Taken from Finnish breeding strategy (Haapanen 2004)
               Stratified sublines
   – stratification allows prioritising testing and breeding efforts on the
     sublines which are most likely contribute trees to seed orchards
• Complete control of inbreeding
   – enables enough unrelated selections to be deployed in seed
• Flexible
   – sublines can be merged or entirely abandoned if desired

Taken from Finnish breeding strategy (Haapanen 2004)
 Slight overestimate of advantages
• Parents are paired for PAM or allocated to
  stratified sublines based on certain optimal
  indices, but the optimal indices will be
  different in the offspring (different
  environment, different genetic parameters,
  different desires), thus the positive effect
  of PAM and stratified sublines may be
  slightly overestimated.

To top