Deployment of related clo nes into seed orchards

1. I try to mark changes in red Darius my responce in green Deployment of related clones into seed orchards The road to efficient breeding DaDa work 2001-2007 Darius Danusevicius (LFRI), Dag Lindgren (SLU), Ola Rosvall (SkogForsk) Seed orchard conference, Umea, Sept. 26-29 2007

2. Main findings • Tools are developed to handle situations with related orchard candidates • Large diversity available: Make a subset of the best unrelated candidates and deploy proportionally to their BVs (“linear deployment”). • Small diversity: allow relatives in seed orchard and use computer optimization, e.g. (for small materials) Optimal proportionsin our EXCEL software “Orchard optimizer”

3. Ibreeding Problem How to deploy clones to seed orchards where candidates are tested relatives? How many fams? Individuals per fam.?, ramets from individual? Fam1 Progeny tests Fam2 Fam3 Fam4

4. Objective • To compare Net gain from alternative deployment strategies based on simulated data (“half sibs”) or real data (“full sibs”). • To develop methodology for seed orchard designers calculations to design seed orchards in an efficient way when candidates are related? • Half sibs: more general study, more scenarios and parameters. Full sibs: case-study with complex relatedness structure, but less scenarios and alternatives. • Optimal selection is best by definition, other alternatives are to check how inferior they are - if not very inferior they can be used as they are simpler?

5. M&M

6. Parameters • We want to maximize Net gain at a given status number • Net gain=average Breeding Value of seeds reduced by expected inbreeding depression • Status number= 0.5 /(group coancestry) • Group coancestry = Pair coancestry (among sibs) + selfcoancestry (within clone)

7. Simulated data with half-sibs • Selection forward in unrelated half sib families • Individual BVs generated from normal order statistics. BV expressedas std from mean 0 based on ND (Dag’s program)and expressed in units of CVa by x 10. BV=10*(SQRT(1/4)*O5+SQRT(3/4)*P5)+100 Among Within

8. Real data with full-sibs BV of 19 short listed field-tested clones from 11 fams (8 unrelated), max2 sibs per family Types of relatedness: - half sibsθ=0.125 -full sibsθ =0.25

9. Truncation Deployment Truncation related: individual above certain threshold are selected regardless of their relatives; equal number of ramets from each Truncation unrelated:one individual is selected from good families; equal number of ramets from each = common approach used today Fam1 Fam1 Fam2 Fam2 Fam3 Fam3 Fam4 Fam4 Fam5 Fam5

10. Linear deployment LD related: individuals above certain threshold are selected regardless of their relatives; no of ramets deployed proportionally the BV LD unrelated:the best clone is selected in the good families and deployed proportionally the BV Fam1 Fam1 Fam2 Fam2 Fam3 Fam3 Fam4 Fam4 pi = (gi – g0)/g0 where: pi =proportion of i-th individual; gi = breeding value of i-th individual g0= intercept of linear relationship (the threshold breeding value) Fam5 Fam5

11. Inputs Optimal proportions ·Individuals are deployed in the proportions which maximize the net gain at a given status number. give it all to a computer(”solver” in EXCEL) Fam1 Fam2 Fam3 Results Fam4 Fam5

12. Parameter Main Scenario Alternative scenarios Low budget scenario Number of half-sib families 24 6, 12, 48 12 Family size 40 20 5 Status number in seed orchard 12 3, 6, 24 12 Inbreeding depression Important Not Important Important Number of clones As found As found As found Scenarios (”half sibs”)

13. The simulator Seed orchard deployed (developed by Dag) Adjust the truncation BV to get the desired Ns, and see the resulting Net gain

14. Results (half-sibs)

15. Ordered by rank Optimal proportions Linear unrelatedTruncation unrelated Linear related Truncation related Strategies compared • “Optimal prop.” is best • “Linear unrelated” ~ “Otimal prop.” if fam. no > 20 • “Truncation related” & “Linear related” are inferior: they sample relatives when unrelated cand.’s with high BV are available. • Low diversity of candidates favors strategies which allow relatives

16. You phrase as we should use Linear unrelated when diversity is high. Linear unrelated is enough but it is by definition never better than optimal, and have we developed optimal we can use it always. So there is no need to develop optimal if diversity is high! But we should not directly recommend to forget about it, it is just unneeded. OK I have rephrased it Strategies compared • Increasing Ns means more diversity is needed in the orchard. • Net gain is lower as more inferior has to enter • “Optimal prop.” is at its best when the demand for diversity is high “Optimal prop.” is best; “Linear unrelated” may be used when diversity is high; Truncation to be left to the past Increasing demand of diversity

17. When is OK with Linear Deploy. Optimal deployment strategy Dependence of the no. of deployed clones on the diversity available for deployment (expressed as ratio Nsa / Nsd). When number of deployed clones is constant, one best individual was selected from certain number of superior families and then “Optimal prop” =“Linear unrelated”; 0 No of fams with one individual Here I will not go into details just will show the slide and say that it may be possible for particular cases to find a threshold value when to use what 5 14 18 Increase of Nsa/Nsd ration means high diversity available for selection

18. Effect of parameters Inbreeding depression had weak effect on Net gain, because the gene diversity in of candidates was high enough to maintain low degree of relatedness in the deployed population. No chance you can remember legends, but it is enough to give legends for the two main competers. The conclusion on the bottom is more important and easier to see than the one on the top, can you in some way change the emphasis I moved the bottom sentence to the slide 15 last paragraph, because namely there strategies are compared and it is shown that reduction of diversity of candidates favors strategies allowing relatives. Here we just show the effect of parameters.

19. Family size Ranking of deployment strategies is independent of family size.

20. Increasing diversity leads to selection of one best from ca. half of best fams Short lists of fam. members OP strategy

21. Results (“full-sibs”)

22. Linear unrelated is as good as Optimal proportions= it does not want more than one from certain no. best fams! Thus often we do not need to try to apply the higher degree of sophistication! Comparison of strategies

23. Model to optimize deployment To keep track of gene diversity Group coancestry = Pair coancestry (among relatives) + self coancestry(within clone) Nsd= 0.5 /(group coancestry) Candidates Deployment by Optimal prop or – if group coancestry low - Linear Deployment Unrelated Orchard Net gain is the parameter to be maximized Forests

24. Do we need different conclusions for half and full sibs, is it not enough with one conclusion slide? Yes you are right they duplicate each other I left this one Conclusions • Truncation is usually inefficient, thus today most advanced seed orchards are probably not efficiently established • If large number of unrelated individuals (half sibs) available: short list the single best candidate from the best families and deploy with ramet number linearly related to breeding values. • If such large reduction of diversity is not tolerable or the short-list tend to be related, optimize with the Optimal proportions.

25. End

26. Conclusion: “full sibs” Linear deployment of unrelated is an efficient approach when there is limited relatedness among the candidates If there is much relatedness among candidates a sophisticated program is needed to get high efficiency

27. Continents of 15 min • Deployment of full sibs (real data) (Dag Lindgren, Ola Rosvall, Darius D.) • Deployment of half sibs (Dag and Darius) (simulated data)

28. Parameters Group coancestry = Pair (among h-sibs) + self (among ramets) Ns= 0.5 /(group coancestry) Parameter to maximize: average BV of seeds produced from the orchard with a deduction for the expected inbreeding Net gain pigi- BV of clone (g) and its proportion (p, ramets) ID is a weight coef. for diversity: if ID=1 and all are inbreed (Op =1), then Net gain = 0. Nsa (for large HS fam) ~ 4

29. Spare slides full sibs How deployment strategies cope with increased demand for diversity in the orchard

30. How Nstatus is related to Ns? Helps to understand the concepts For Unrelated Ncensus=Ns; For related , they depart when relatives are sampled

31. Spare slides half sibs

32. Why Optimal prop. is efficient Variation in pair coancestry in the deployed material depending on Nsd. Increase of pair-coancestry - selection of relatives. LD samples individuals with high BV regardless of relatedness leading to high pair-coancestry. OP favors less sampling of relatives (relatively less relatives from the few top ranking families). When Nsd is increased, OP deploys more individuals from better fams and optimizes their proportions to maximize Net gain (leads to increase of pair-coancestry) TR & LR sample the top ranking individuals from the families of a lower rank, which are unrelated to the already sampled. This causes reduction in pair-coancestry. (Nsd) Increased demand on diversity

33. Self coancestry Is coancestry among the ramets of one clone Included in desired Ns and effective clone no. Not included in Net gain, but assumed that its negative effect can be handled by placing the ramets some 30 m apart from, each other

34. Proportion of selected fams (b) how much diversity we need for Nsd of 12 (usual case)? When Nsa is for 4 times greater than Nsd, all families are needed (12 half-sibs families at Nsd of 12).

35. Deployment strategies reduce relatedness Note, there is large diversity to select form and that deployment strategies are aimed to maximize net gain so they trend to reduce relatedness. Therefore, relatedness figures are rather small

36. 4: BP size optimised Seminar 2004.03.02 3: Ph/Prog amplified (pine), effect of J-M. 1-2: Best testing strategy The Road to this semianr Hungry shark Breeding cycler

37. Time compnents

38. Why selection forward? • The study is thought to serve for long term breeding • Long term breeding is usually based on selection forward • If select back say ½ best mothers based on OP fam. trial, the selections may miss good recombinant progeny which may return higher gain (Dag do you know reference??)