Genotype x environment interaction of Eucalyptus globulus in Australia has similar patterns at the provenance and additive levels.

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1 2015 IUFRO Eucalypt Conference Zhanjiang City, Guangdong, China October 2015 Genotype x environment interaction of Eucalyptus globulus in Australia has similar patterns at the provenance and additive levels. Gregory W. Dutkowski 1, Brad M. Potts 2, David Pilbeam 3. 1 Greg.Dutkowski@plantplan.com, PlantPlan Genetics, P.O. Box 1811, MOUNT GAMBIER, South Australia, 5290, B.M.Potts@utas.edu.au, University of Tasmania. 3 DPilbeam@stba.com.au, Southern Tree Breeding Association. Abstract Genotype by environment interaction (GxE) in Eucalyptus globulus at the provenance level has been shown to relate to test site dryness using native forest open-pollinated individual tree seedlots. Advanced generation control-pollinated trials are needed to get estimates of GxE at the additive level, unconfounded by differential partial selfing and inbreeding depression, and at the (Specific Combining Ability) level. For advanced generation trials in a rolling front breeding scheme there is some parental overlap between trials in adjacent years, but little between years beyond that. This disconnection (and differences in measurement age) make GxE estimation difficult. Correlation estimates were derived from pairwise analysis of trials with more than 40 parents in common using within trial design features, and between trial additive and effects. Correlations were more variable (and had higher standard errors) when trial pairs had less than 100 parents in common. correlations were generally more variable, but slightly higher overall. All available trial pairs were used to adequately sample the range of possible environmental attributes in models of inter-site correlations based on long-term trial climatic attributes. Trials were split into groups based on critical values of environmental attributes which minimised model weighted error sum of squares. Age differences were accounted for using a Lambeth age ratio correlation model derived from within-trial multivariate models. correlations between ages were higher than additive correlations. For additive correlations, the best site groupings were based on the minimum temperature of the warmest month of the trial sites, and vapour pressure deficit within low temperature sites. However splitting into three site groups based on minimum monthly evaporation was almost as good a model and gave a sensible trend of decreasing correlation with differences in evaporation, with a correlation of 0.42 between the extreme classes. The average correlation between sites within each group was around 0.66, indicating that the model was grouping trials effectively, but that there was still substantial unexplained GxE. site type groups were dominated by wind speed, and then aridity within low wind speed areas. Using the same evaporation classes as used for additive correlations did not show any trend or lower between class correlations than within class correlations. Thus moisture availability was important for GxE at the provenance and additive levels, but was only of secondary importance at the level. Breeding values can now be estimated using these new site groups, rather than the previous site groups based on geographic or regional boundaries. Keywords: Eucalyptus globulus; genotype by environment interaction; breeding value prediction. Page 1 of 8

2 Eucalyptus globulus genotype by environment interaction for growth in Australia October 2015 Greg Dutkowski Brad Potts David Pilbeam Good News and Bad News Genotype by Environment interaction (GxE) has similar patterns at the provenance and additive levels A different pattern at the Specific Combining Ability level But we can still work with that Low overlap in advanced generation trials make recognition of GxE patterns difficult We are interested in GxE.. As we wish to use estimates of genetic value for different site types to make appropriate selections by Giving more weight to data from that site type While still using data from similar site types, but with less weight We just have to know What defines the site types (in a way that breeders and foresters can understand) What sort of weights to use Correlations give us the weights (Provenance effects rely on data within site types) Breeding Value and prediction uses site types and r a and r sca between them to model GxE Site types mean many fewer BVs to predict than treating every site as a different environment Breeders and foresters can understand site types Current site types based on ~ National Plantation Inventory regions Current Classification ~ NPI regions Not biology or performance Western Australia Green Triangle Region Gippsland Tasmania Complex correlation structure Gippsland Green Triangle Tasmania Western Australia Gippsland Green Ti Triangle Tasmania Western Australia Can we do better? Better discrimination between site types Page 2 of 8

3 Provenance effects linked to dryness Significant drivers GxE (all correlated) TWQ mean temperature of the warmest quarter PWQ precipitation of the warmest quarter RWQ radiation of the warmest quarter LPM lowest period moisture index Costa e Silva et al 2006 Provenance differences related to drought damage Drought damage Low Race performance differs with environment 0 high Relative e performance High Dutkowski 1995 & unpubl. data Tolerant Tolerant dry wet Rainfall in warmest quarter Costa e Silva et al low The next generation Provenance estimates will always be based on the first generation trials correlations first generation trials boosted by universal poor performance of partially selfed families, so cannot be used CP advanced generation trials will give correlation estimates within provenance (Breeding Values) Specific Combining Ability The next generation Base population First generation OP trials Next generation CP trials 40 CP trials with more than 40 parents Estimating GxE There are many methods Many rely on data with almost complete balance in parents or families across sites Page 3 of 8

4 CP trial linkage poorer Better at parental level than family level Parents Families Overlap variable Want to use all trials for genetic value prediction Account for historic selection Use all ages to maximise harvest gain Maximise amount of information Maximise genetic gain Enough overlap Correlation modelling can include all trial pairs with enough linkage How do our trials vary? Climate is variable Environmental information Atlas of Living Australia Long term weather parameters Some interpolated soils information Local soil survey information Forestry company pre planting soil surveys Collated but sparse and hard to interpret Database of DBH genetic correlations Type A: Within site (between trait and age) (26 sites) B: Between site pairs with > 40 parents in common by age (39 sites) Effects within provenance (Breeding Value) Specific Combining Ability Data Correlation SE Subjective quality indicator Age 2 Age 4 Age 8 Page 4 of 8

5 Correlations variable No trial pairs < 40 parents in common used Most correlations age 4 Correlations more variable with less overlap Probably need >80 parents and 100 families in common for good estimates Many nsd 1 and/or 1 even more variable Correlation modelling Can use a broader range of trials as not all need to be well connected connected in sub sets is OK. So can sample a broader range of sites Can use a broader range of age classes Just have to cope with the idiosyncrasies of correlations Can relate directly to environmental variables Modelling tool Model Minimises Wt ESS to estimate Maximise match of model to database Model : Est = r t:t *r a:a (*r e:e ) r t:t Traits(+site types) r a:a Lambeth age:age correlation model r e:e Environmental effect auto regressive model Weighted by SE (with limits) Subjective quality indicator (Use) Number of times the trial appears (nuse) Eigen values (E: for a nicely behaved correlation matrix) Minimise WtESS Wt ESS = Sum(W a *W use /W se /W nuse (Est r a ) 2 + W b *W use /W se /W nuse (Est r b ) 2 +W e E) Correlations between and within site types (*100) Lambeth age ratio model for age:age correlations Split continuous variables Split groups of categorical variables Split again Split again Page 5 of 8

6 Age:Age stronger for Wt Model ESS 269 Base Better NPI Estimates Eigen Weighting 213 Est 199 Real NPI Wt Model ESS 153 Base less improvement in WtESS Eigen Weighting Different patterns 141 Est 137 Real NPI Still substantial unexplained GxE Diagonals are within site type average correlations Should be 1! Means we can do better Less so for Page 6 of 8

7 Wt ESS Dryness is better for Similar to provenance!!!! Substantial within site type GxE but r within >r between 199 Real NPI 163 Adefi: Maximum month precipitation deficit is a problem Wind speed is the best discriminator, seems strange Regions differ 162 Evapi: Minimum month evaporation Combine the two site type definitions BLUP is flexible Constraints to reflect detected patterns Add in the variance ratios So what should we get? So what do we get? Provenance effects driven by moisture availability Breeding values (Provenance+ effects) driven by moisture availability Specific family values di driven by moisture availability (Provenance+ effects) and effects driven by wind speed.. Page 7 of 8

8 So where to? With the current results Do we believe result? Can have different GxE drivers at different genetic strata Look at predicted effects and see if they make sense Questions Soils information help discrimination, but difficult to get. Actual weather (not long term averages) may help. Look at increments rather than total size. Age trends with GxE? Eucalyptus globulus GxE Low overlap in advanced generation programs makes estimation of GxE by any means problematic Site pairwise correlation database can use all of your trials and ages Correlation modelling can relate directly to environmental characteristics GxE drivers may differ at different genetic strata but analysis can cope with that. Page 8 of 8