Dr. Tim White Director, School of Forest Resources & Conservation

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2 CFGRP Cooperative Forest Genetics Research Program Administration Dr. Tim White Director, School of Forest Resources & Conservation CFGRP Staff Dr. Salvador Gezan Quantitative Genetics/Biometrics Dr. Matias Kirst Co-director, Quantitative Genetics Dr. Gary Peter Co-director, Genomics Mr. Greg Powell Co-director, Program Manager Associated Scientists Dr. John Davis Forest Biotechnology and Molecular Biology Dr. Dudley Huber Quantitative Genetics and Breeding Dr. Eric Jokela Silviculture and Forest Nutrition Dr. Timothy Martin Tree Physiology Dr. Jason Smith Forest Pathology

3 Fifty-fifth Annual Progress Report COOPERATIVE FOREST GENETICS RESEARCH PROGRAM 55 TH ANNUAL PROGRESS REPORT April 2013 School of Forest Resources and Conservation Institute of Food and Agricultural Sciences University of Florida Gainesville FL Prepared by Salvador Gezan Matias Kirst Patricio Munoz Gary Peter Greg Powell Jianxing Zhang 1

4 Cooperative Forest Genetics Research Program HIGHLIGHTS 3 rd Cycle Slash Pine Selection, Breeding and Testing (1) Eight seedling progeny tests were established with the 3 rd cycle slash pine breeding population. (2) Seedlings from the elite population were hedged for production of rooted cuttings for clonal testing. ACMF Clone Bank II (1) Grafting of 3 rd cycle slash pine selections continued at our new clone bank. Improved Method for Variance Partitioning (1) The partitioning of genetic variance between additive and non additive effects is improved with the inclusion of relationship matrices based on molecular markers. (2) Additive effects are overestimated with pedigree compared with molecular marker based relationships, suggesting that additive genetic gains might be overestimated. (3) Height has a substantial dominance component and strategies to capture this in breeding need to be utilized. Validation of Breeding Values with Full sib Block Plantings (1) For the seven common slash pine families tested, strong rank correlations between breeding values predicted from single tree plot polymix tests and plot volumes from full sib blocking plantings were observed. (2) Weak to moderate rank correlations were observed between breeding values for volume from the PMX I and PMX II tests with plot and tree level volume from the FSBP. 2

5 Fifty-fifth Annual Progress Report TABLE OF CONTENTS OVERVIEW & SUMMARY... 5 PROJECT REPORTS... 7 CFGRP 2 nd Cycle Full Sib Block Plots of Slash Pine (P 31)... 7 CFGRP 3 rd Cycle Slash Pine Selection, Breeding and Testing (P 72) Genomic Selection Methods (P 81) PROJECT UPDATES Introgression of Loblolly and Slash Pine Alleles, Well Field Planting (P 76) CFGRP Clone Bank II on the Austin Cary Memorial Forest (P 79) Active CFGRP Active Research Projects Graduate Students Involved with CFGRP Studies & Research RESEARCH PROJECT SUMMARIES Clonal GxE using CClones data (P 83) Seed Deployment (P 84) LITERATURE CITED

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7 Fifty-fifth Annual Progress Report OVERVIEW & SUMMARY Year in Review Core Activities: In the winter of 2011/12, the CFGRP installed eight seedling progeny tests with the 3 rd cycle slash pine breeding population. Five tests were established in the Florida panhandle, two in southern Georgia and one in southwest Mississippi. The growth measurements from these tests will provide rankings for the 3 rd cycle breeding population and material for the 4 th cycle forward selections. In the spring of 2012, grafting of the slash pine 3 rd cycle breeding population began at the Austin Cary Memorial Forest and will continue for one to two more years. After establishment of the progeny tests, the remaining progeny from the elite families were up potted and hedged at the UF greenhouse. A first round of cuttings was completed and a second, spring round is in progress for a clonal test. The CFGRP completed the 4 th year of breeding with the 2 nd cycle Florida loblolly population. Eight of the ten breeding groups are expected to be 100% complete, and we anticipate that this is the last year of breeding prior to test installation. Research Activities: Analytical methods that incorporate dominance matrices based on relatedness from molecular markers were developed (G BLUP) and evaluated with a clonal population. For tree height, the results demonstrate this new approach more precisely partitions genetic variance into additive and non additive effects. Dominance and epistasic interactions account for a significant proportion of the variance for height. This result suggests that additive gains based on breeding values might be overestimated, and that additional genetic gain from non additive components can be obtained with the proper strategy. Breeding values for volume at selection ages, from the PMX I and PMX II tests, were compared with tree and plot volume in the full sib block plot (FSBP) tests. When the same families are compared in the two PMX tests and in the PMX II and FSBP, the rank correlations are very strong. However, when the same parents are compared between the PMX and FSBP the rank correlation is significantly weaker. The CFGRP, in collaboration with the FBRC, installed a single site test with slash and loblolly backcrosses to the same loblolly x slash F1 hybrid parent. In 2013, the test was planted with the help of Plum Creek and ArborGen, and includes backcrosses of the F1 hybrid to loblolly and slash pine, and open pollinated species comparators. The test is designed to evaluate the fertilizer responsiveness of the loblolly and slash backcrosses by comparing growth with operational and intensive fertilizer treatments. Plans for Future Clonal tests for the elite slash pine are planned for planting in winter 2013/2014. The 2 nd cycle Florida loblolly tests are planned for sowing in spring of 2014 and planting in

8 Cooperative Forest Genetics Research Program Membership and Personnel It has been a pleasure for the UF CFGRP team to work with member cooperators: ArborGen, Florida Forest Service, Foley Timber and Land Company, Georgia Forestry Commission, Packaging Corporation of America, Plum Creek Timber Company, Rayonier, and Weyerhaeuser. The CFGRP management team consists of Directors Matias Kirst, Gary Peter and Greg Powell and Quantitative Genetics/Biometrician Salvador Gezan. Collaborating faculty include John Davis (fungal disease resistance), Eric Jokela (silviculture and forest nutrition), Tim Martin (tree physiology) and Jason Smith (forest pathology). 6

9 Fifty-fifth Annual Progress Report PROJECT REPORTS CFGRP 2 nd Cycle Full sib Block Plot Plantings of Slash Pine (P 31): Comparing Breeding Values for Volume from Single Tree Plots with Multiple Tree Plot Volume Background Forest tree breeders use single tree plots and early selection to improve program efficiency, achieve greater gains per unit time and maximize the number of genotypes tested for the least cost. However, operational deployment is typically planted in blocks of half and fullsib families. Empirical quantification of genetic gains in replicated family block plantings at full rotation and the correlation between predicted breeding values from early age, single tree trails and rotation age full sib blocks are vital information. This information is typically missing or incomplete, largely due to the time and costs associated with its collection. Table 1 summarizes the differences between block and single tree plot designs. Table 1. Multiple vs. Single Trees Plot Block Plot Single Tree Plot Test sites Large Small Number of families tested Small Large Replicates No / few Many Statistical precision Low High Survival/Volume estimates Accurate Biased To address these critical gaps, the Cooperative Forest Genetic Research Program (CFGRP), for its second cycle of slash pine improvement, established two polymix single tree series (P43 & P64) and one full sib block plot (FSBP, P 31) series of field tests. For the FSBP, survival, tree size and rust score data are available for a large number of plots for ages 5 and 8. Thus, the objective of this report is to compare and correlate, at selection ages, tree volume breeding values (BV) from polymix single tree plot tests with tree and plot volumes from multiple tree full sib block plot tests. Data and Methods Data The Polymix Series I (P 43, PMX I) was established in the winter of , and evaluated 140 families with eight sites in Florida and Georgia (Figure 1). The PMX I was a randomized complete block design with 20 blocks of single tree plots per location. Height, diameter and status (rust, survival, etc.), were collected at ages 5 and 8. Volume heritability (single tree volume at age 8 is calculated by equation (1)) was computed for every test site (Table 2). Test sites 766 and 767 had very good survival, with lower survival for test site 765. The volume heritability at age 8 was between and 0.460, and test site 765 had the largest average single tree volume (2.964 ft 3 ). Type B genetic correlation for volume was across tests, which meant very little genotype by environment interactions exists. 7

10 Cooperative Forest Genetics Research Program Vol 8= dbh8 ht (ht8-4.5) (1) dbh8 Table 2. Characteristics and Heritabilities for PMX I Test sites All tests # Families # trees 2,800 2,800 2,800 2,800 2,800 2,800 2,800 19,600 Survival % Volume h age 8 SE Mean Tree Volume(ft 3 ) The Polymix Series II (P 64, PMX II) was established in the winter of , and evaluated 201 families (174 were new polymix families, 25 were polymix families tested also present in PMX I, and 2 were polymix crossed cooperator orchard clones) and 43 elite full sib crosses (Figure 1). The field design of PMX II consisted of eight test sites in Florida and Georgia with 20 resolvable replications per site and 200 trees per replication. Within each replication there were 20 alpha lattice generated incomplete blocks of size 10. Measurements of height, diameter and status (rust, survival, etc.), were available for age 6. Age 6 heritabilities for volume (single tree volume at age 6 is calculated by equation (2)) are shown in Table 3. PMX II had better survival at age 6 than PMX I at age 8. Test site 773, located in central Florida, was the most southern one in the two test series and had excellent survival (94.1%), while survival at test site 769 was the lowest (62.35%). Overall, the volume heritabilities for PMX II were slightly higher than PMX I, with h 2 = at test site 771. The average tree volume still had a wide range. Type B genetic correlation for volume in this test series was 0.739, which meant slightly higher genotype by environment interactions than the PMX I. 2 Vol 6 = dbh (ht6-4.5) (2) Table 3. Characteristics and Heritabilities for PMX II Test sites All tests # Families # trees 4,000 4,000 4,000 4,000 4,000 4,000 4,000 4,000 32,000 Survival % Volume h age 6 SE Mean Tree Volume(ft 3 )

11 Fifty-fifth Annual Progress Report Figure 1. Test Locations for PMX I and PMX II test series From 1993 to 2003, CFGRP cooperators developed full sib seed and established 11 sets of tests. FSBPs were established at 11 locations (Figure 2) in Florida, Georgia and Mississippi. Each location contained 50 to 100 trees in unreplicated blocks, with a total of 884 full sib families planted. Each test consisted of several plantings established in different years for a grand total of 50 plantings. For every group of about 10 FSBPs, one 50 to 100 tree block plot of common improved and unimproved checks was also established at each site. In total, there were 107,012 measurement trees. Measurements of FSBP tests are available for ages 5 and 8, and are ongoing for ages 12 and 15, including the variables tree height, dbh, status, lean, rust and pitch canker. In the analysis, the year 8 data were used for comparison with the polymix series data. The single tree volume was calculated by equation (1), and the plot volume corresponded to the sum of the living tree volumes. Thirty four of the 50 plantings (71,631 trees) have been measured through age eight. The eighth year means were as follows: survival = 77.1 %, rust = 41.7%, height = 30.9 feet, dbh = 5.1inches, and volume outside bark = 2.316ft 3. The site index (age index 25 in feet) for these plantings based on dominant unimproved check trees ranged from 58.4 to 99.9 ft and averaged 81.1 ft. In the rank comparison among these three test datasets, volume was the variable of interest for this analysis (PMX I at age 8; PMX II at age 6; and FSBPs at age 8). Figure 2. Test Locations for FSBP Methods Methods for Ranking: In PMX I and II tests, the females were ranked based on the breeding value estimates according to the following model: Y Test Block Fem (Test_Fem) (3) ijk i j( testi ) k ik ijk In the FSBP tests, we ranked the families or females based on the estimated gains based on the following expression: Volume(fam) Volume(check) Estimated Gains 100% (4) Volume(check) 9

12 Cooperative Forest Genetics Research Program Rank Correlation: For PMX series, selected same females tested in both PMX I and II, and the rank values were compared between them. For FSBPs and PMX, same females tested in both FSBPs and PMX I (or FSBPs and PMX II), were selected, and the ranks were compared between them. Single tree level comparisons were obtained by contrasting the rank correlations between FSBP and PMX based on single tree volume for FSBP. In addition, plot level comparisons were obtained by contrasting the rank correlations between FSBP and PMX based on plot volume for FSBP. Results Comparisons between PMX I and PMX II For the subset of 25 common polymix families tested in both PMX I and PMXII, their rank correlation was (Figure 3), indicating that the same families tested in different single tree plot tests rank quite similarly. Note that this high correlation is more relevant given that some of the male parents differ between the series and confirm the stability of the polymix rankings. PMX2 ranking Corr=0.869 y= x PMX1 ranking Figure 3. Rank Correlation between PMX I and II 10 Comparisons between PMX I and FSBP At the plot level, only plots containing 50 to 100 trees were used, and 79 females were common for both PMX I and FSBPs; here, the rank correlation was At the single tree level, all plots were considered (i.e., plots with any size). Since PMX I tests were single tree plot tests, the average single tree volume (living trees) for each plot in FSBPs were computed. The rank correlations were compared, with 102 common females for both tests. Note that the correlation between ranks at the single tree level increased slightly (Table 4). Table 4. Comparison of PMX I and FSBP at Tree and Plot levels Correlation Matched Comparison females Correlation Plot level Tree level In the FSBP tests, some families had extremely low survival, and these might potentially bias the rank correlation calculations in the comparisons. Therefore, lower survival plots were deleted before comparisons were made (Table 5). For example, survival % cutoff >30 (Table 5) meant that plots with survival below 30% were removed from the comparison. Comparisons were done for three single test sites 762, 763 and 766 (higher heritability), and across all test sites in PMX I. Rank correlations increased as the survival cutoff were increased in a clear trend. As the mean survival in PMX I was 71.75%, we expect the survival cutoff of 70% in FSBP would have the strongest correlation.

13 Fifty-fifth Annual Progress Report Table 5. Comparison of PMX I and FSBP (plot level ranks) with Survival Cutoffs PMX I test 762 PMX I test 763 PMX I test 766 All PMX I tests survival % matched Rank matched Rank matched Rank matched Rank cutoff females correlation females correlation females correlation females correlation > > > > > > Comparisons between PMX II and FSBP At the plot level, 110 females tested in PMX II were also planted in FSBPs, and their rank correlation was (Table 6). At tree level, the correlation increased to with a total of 140 common females tested in both tests. Table 6. Comparison of PMX II and FSBP at Tree and Plot levels Correlation Matched Comparison females Correlation Plot level Tree level Similar survival cutoffs were used also in the comparison between PMX II and FSBPs. Comparisons were carried out for test sites 770, 771 and 772 (higher heritability) and across all test sites in PMX II (Table 7). In contrast with PMX I, there was no clear trend in rank correlations with oscillating values in PMX II. One explanation is that the ages used for these two test sets were different (PMX II: volume at age 6; FSBPs: volume at age 8). Interestingly, of the 43 elite full sib crosses tested in PMX II, 7 full sib crosses were also tested in the FSBPs. The rank correlation between these 7 full sib crosses was much stronger with a value of Thus, for this small set of the same families 11 tested in both single tree and multiple tree plots the rank correlations were very consistent. Discussion and Conclusions For the 25 half sib families tested in both PMXI and PMXII, a strong rank correlation between estimated BVs was obtained confirming the stability of the polymix rankings. In contrast, the rank correlations at the plot and tree levels between the FSBP and polymix tests were significantly weaker. Moreover, the plot level rank correlation between the FSBP and PMXII was about half that of PMXI. One possible reason for this weaker rank correlation between PMXII and the FSBP compared with that for PMXI might be the higher GxE interaction (type B of 0.74 vs. 0.88), observed across the PMX II test sites. In addition, several other reasons can explain the relatively weak correlations between PMX and FSBP. First, the genetic variability of the half sib families is significantly greater than the full sib families planted in the FSBP and dominance effects should not be ignored. Second, there was no replication in FSBPs, which not only gives low statistical accuracy but also doesn t allow for data adjustments that are present in the BVs from the PMX tests.

14 Cooperative Forest Genetics Research Program Table 7. Comparison of PMX II and FSBP (plot level) at with Survival Cutoff test 770 test 771 test 772 All tests survival % cutoff matched females Rank correlation matched females Rank correlation matched females Rank correlation matched females Rank correlation > > > > > > CFGRP 3 rd Cycle Slash Pine Selection, Breeding and Testing (P 72) Introduction This report summarizes the CFGRP 3 rd cycle slash pine selection, breeding and testing program. More information can be found in , 2011 and 2012 Progress Reports. The CFGRP 3 rd cycle began in 2003 with the ranking of our 2 nd cycle full sib block plot plantings and forward selection of improved 3 rd cycle slash pine, and will end with measurement of the 3 rd cycle slash pine full sib and clonal trials (Table 8). At the time of this writing, the selection and breeding phases are completed, the full sib seedling progeny tests have been established and first year survival measured. The CFGRP is also in the process of developing clonal seedlings from elite crosses for establishing 3 rd cycle elite clonal trials. Selection The 2 nd cycle base population contained: 1. 2,488 well tested firstgeneration selections (BACK1); 2. 1,017 well tested second generation selections (BACK2); and 3. 1,407 2 nd cycle full sib 12 families from CFGRP 2 nd cycle breeding (FORW3) and full sib slash seed from UDSA Forest Service breeding which were planted in the full sib trials (UDSA3). Data was used from 363 open pollinated and 265 full sib first generation progeny tests to identify selections from the first cycle (BACK1) and second cycle (BACK2) to bring forward into the third cycle. Data from 16 polymix tests planted in 1997 and 2001, and 50 full sib plantings established from 1994 to 2003, were used to identify potential 3 rd cycle forward selections (FORW3, USDA3). Three hundred and nine 3 rd cycle CFGRP slash pine selections (FORW3) and 15 USFS full sib infusions (USDA3) were made in the winters of 2003, 2004 and 2005 in the fullsib block plot plantings. In addition, 59 excellent 1 st cycle selections (BACK1) and 81 excellent 2 nd cycle selections (BACK2) were brought into the 3 rd cycle for a total of rd cycle slash pine selections in the base population. Breeding Breeding Structure These 464 selections were divided into 10 sublines with between 41 to 50 selections (Figure 4). Each CFGRP member was assigned a subline. All related

15 Fifty-fifth Annual Progress Report selections were assigned to the same subline. Whenever possible, BACK1 and BACK2 selections were assigned to the CFGRP member that had those selections grafted in a seed orchard or clone bank to limit the need for top grafting. Sublines were made to have near equal average breeding values. The 10 sublines were assigned to one of two superlines (5 sublines to the orange superline, 5 sublines to the blue superline, figure A). The top six selections in each subline (based on their growth and rust resistance breeding values) were identified as elite selections (ELITE3). Because the CFGRP lost International Paper Company in 2004, St. Joe Paper Company in 2005 and Smurfit Stone Container Corporation in 2010 as members, those three sublines were reassigned to UF. Top grafting In the winters of 2003, 2004 and 2005, each member and UF staff top grafted their 3 rd cycle forward (FORW3) and USFS infusion (USFS3) selections from their subline, into sexually mature seed orchard or clone bank trees. Backward selections (BACK1 and BACK2) were only top grafted if the member did not have their assigned BACK1 and BACK2 selections grafted in an sexually mature clone bank or seed orchard. Breeding The 3 rd cycle slash pine breeding phase covered six breeding seasons, winters of The original goal was to complete the breeding phase in three seasons; however, the hurricanes of 2004 and 2005 that sweep through the southeastern United State set this breeding effort back at least two years (see Figure 4). Mainline breeding was conducted within a subline whereas elite breeding was conducted across sublines, but within the same orange or blue superline (Figure 5). The mating design used for both the mainline and elite breeding was a circular diallel with each parent mated with the two previous and two following parents listed in the circular diallel for a target of four crosses per parent (see a six parent circular mating design example Figure 6). In addition, for elite breeding, elite selections were mated with one off diallel parent for a total of five crosses per elite per parent. Figure 4. First generation Florida Forest Service seed orchard after 2004 hurricane. 13

16 Cooperative Forest Genetics Research Program Figure 5. Stucture of the slash pine breeding population Parents X XX 2 XX X 3 XX XX 4 X X 5 X X 6 X X Figure 6. Circular mating design example showing the 4 planned crosses for parent 3 (XX). The final seed from the mainline and elite breeding efforts was collected in September of 2010 (Table 8). Mainline Breeding Mainline breeding was done within a subline and could include any selection within that subline (BACK1, BACK2, USFS3, FORW3 and ELITE3 selections). Targets for the mainline breeding was to breed 36 selections (including all 6 of the elite selections) per subline with two previous and two following parents listed in the circular diallel (four crosses per parent). Our goal was to produce 100 or more seed per cross. For each subline, a completed mating design has 72 crosses in the mainline including 12 crosses with elite selections. The following five circumstances helped with the 3 rd cycle breeding effort: 14

17 Fifty-fifth Annual Progress Report Table 8. CFGRP 3 rd cycle slash pine program timeline overview Date Activity Make selections, top graft into mature seed orchard trees Conduct breeding Collect cones, extract and clean seed Winter 2011 Full sib test sites were selected and prepared May Nov2011 Seedlings were produced by ArborGen Nov 2011 Jan 2012 Established eight full sib tests Nov 2012 March 2013 First year survival measurement of full sib 2012 Produce elite clones in UF greenhouse TBA Select clonal test sites and prepared TBA Establish 4 clonal tests TBA First year survival measurement of clonal tests TBA Fourth year measurement of full sib tests TBA Fourth year measurement of clonal tests TBA Analysis data and make forth cycle selections. 1. FORW3 selections were top grafted into sexually mature seed orchard or clone bank trees where most started to produce flowers one year after grafting; 2. When possible, BACK1 and BACK2 selections were assigned to the subline of the member that had those clones already grafted in a mature seed orchard or clone bank, making the breeding on those selections much easier; 3. We used opportunistic breeding by adding selections to the circular diallel as they flowered; 4. When possible, BACK1 and BACK2 selections were placed in the circular diallel in every third or fourth position, making it easier to complete the breeding because cross opportunities were, in general, better for these selections; and 5. Our goal was to breed 36 of the 41 to 50 selections that were in a subline. So selections that grafted or flowered poorly were dropped from the breeding population. 15 Elite Breeding Elite breeding was conducted within a superline but across sublines. Six elites from each subline within a superline were bred in a circular mating design for a total of 30 elites breed per superline (6 elite selections x 5 sublines/superlines = 30 elite selections). Each elite selection was targeted to be breed with the two previous and two following parents listed in the circular diallel, plus one off diagonal cross of the members choosing, for a total of 5 crosses/elite selection and 75 crosses per superline. The total elite breeding effort target was 150 elites, 75 in the orange superline and 75 in the blue superline. As was with the mainline breeding, our target was to produce 100 or more seeds per cross. Breeding effort summary More than 290,000 seeds and 1,000 crosses were generated by the mainline and

18 Cooperative Forest Genetics Research Program elite breeding effort. The number of seed per cross ranged from 2 to 2,765. After eliminating crosses that produced less than 10 seed, duplicates and reciprocals crosses, 880 different crosses, 770 from mainline breeding and 110 from elite breeding were identified for use in the testing phase. Of those 880 crosses, 692 or 78.6% had more than the target number of 100 seed per cross. One hundred or more seeds were obtained from 89 of the 110 crosses of elite selections (80.1%). Testing The CFGRP 3 rd cycle slash pine testing program will include eight full sib seedling progeny tests and four elite selection clonal tests. The seed for these trials were produced in the third cycle slash pine breeding effort. Full sib tests In spring of 2011, each CFGRP member sent seed from their 3 rd cycle slash pine breeding effort to UF. Up to 120 seeds from the 880 crosses were given to ArborGen at the annual CFGRP meeting on April 27, 2011 for seedling production. Seedling production took place at the ArborGen Bellville SuperTree Nursery. First, the seeds were stratified May 9 23 rd. Then, on May th, trays were filled with SC10 Ray Leach super cell tubes and soil by ArborGen, Foley Timber and Land Company and the Georgia Forestry Commission. Up to 98 seeds per cross were sown by ArborGen, Plum Creek and Rayonier on May th. Almost 700 crosses were sown with 98 seeds. The seedling trays were placed into two ArborGen greenhouses for 16 germination and early growth. The seedlings were taken out of the greenhouse and double spaced (49 seedlings/tray) on August 2 3 rd. Finally, seedlings were top pruned by hand to 12 inches by Jerome Martin on August 31 st In early October, ArborGen inventoried the seedlings. The maximum number of seedlings needed for full representation in the eight tests was 40 (8 tests x 5 rep = 40 seedlings). And it was decided to only use crosses in the test that had 10 or more healthy seedling. Of the 880 crosses sown, 837 crosses had 10+ seedlings and 702 crosses having 40+ seedlings. This inventory was used by Salvador Gezan to generate eight randomized field trials using seedlings from the 837 different crosses. The test design is: single tree plots; alpha lattice design (4 rows x 7 seedlings = 28 plants); 5 replications; 784 planting positions per replication; and 3,920 trees per test. Tube tags were printed for each field trial by ArborGen and, from November 2011 through January 2012, members of the CFGRP traveled to the ArborGen s Bellville Georgia SuperTree Nursery with their test randomization sheets and 44 empty trays. Each member randomized and planted one test for a total of eight tests, the included 31,360 test trees, 5,000 border trees and approximately 55 total test acres (see Table 9). Five tests were planted in Florida, two in Georgia and one in Mississippi (Figure 7). Six of the tests were planted at on 6 x 10 foot spacing, one on 6 x 11 foot spacing and one on 5 x 16 foot spacing. Each test has at least two border rows and is approximately 6.2 to 9.5 acres in size (Table 9).

19 Fifty-fifth Annual Progress Report Approximately 6 to 8 weeks after test planting each member assessed survival. Some members elected to replant dead seedlings. Dead seedlings where replaced with seedlings from the same family whenever possible. First year survival was taken in the winter of First year survival of the eight tests was excellent and ranged from 93.9% to 98.4% with an overall test series first year survival of 95.6% (see Table 9). CFGRP members will maintain their tests with good management and measure them for height, DBH and status when the tests are deemed large enough to analyze for ranking the third cycle breeding population and provide material for making forth cycle forward selections. Clonal tests Advantages of clonal over seedling progeny tests include improved precision of breeding value prediction, increase in genetic gain from within family selections, estimates of total genetic value, ability to separate additive from dominance and epistasic interactions, and reducing the Figure 7. CFGRP 3 rd cycle slash pine test locations costs for developing markers for genomic selection model development. Despite higher test costs and potential for a small increase in the time necessary to complete the breeding cycle, we decided to use elite cross seedlings that were left from the eight slash pine full sib seedling tests, to develop hedges for collecting cuttings to develop clonal seedlings for planting elite clonal slash pine trials. Table 9. CFGRP 3 rd cycle slash pine progeny tests. Test State County Established Spacing Acres 1 st Year Survival 830 Florida Gadsden Jan. 25, X Florida Santa Rosa Dec , X Florida Taylor Dec. 13, X Georgia Dooly Dec. 28, X Florida Hamilton Dec , X Georgia Wayne Dec. 5 9, X Florida Nassau Dec , X Mississippi Pearl River Nov. 20, X

20 Cooperative Forest Genetics Research Program From 55 elite crosses, 25 to 35 seedlings were selected for a total of 1,784 seedlings and transported from the ArborGen Bellville SuperTree Nursery to Gainesville Florida. The seedlings were up potted from SC10 Ray Leach super cell tubes to 3.5 gallon pots. These plants were hedged and approximately 7,500 cuttings from these hedge plants (see photos on back cover of this report) were dipped into a rooting media and then stuck into rooting soil in SC10 Ray Leach super cells tubes. The stickings were then placed into a misting chamber where they are misted for seconds every 20 minutes during daylight hours. Once rooting is complete, they will be moved out of the misting chamber and placed onto flood benches for seedling development. At least one more sticking event will take place to generate enough plants for the clonal trials. On September 25 th 2012, the UF staff hosted a CFGRP science meeting at the Austin Cary Forest located just north of Gainesville Florida. At this meeting we discussed clonal propagation and clonal test design. It was decided to establish four tests. Each of the four tests will be prepared, established, maintained and measured by two members. They will share the responsibility of preparing the site, planting and measuring the test. The test parings are: 1. Florida Forest Service and Georgia Forestry Commission; 2. Foley Timber and Land Company and Packaging Corporation of America; 3. Plum Creek and Weyerhaeuser; and 4. Rayonier and ArborGen. The test design, size and estab lishment date is dependent on the timing and number of clonal seedlings that are generated. The general idea is to plant six ramets from each progeny per test. Measurements are scheduled for age 4 and/or 6 years and each test will be approximately 15 to 18 acres in size. These tests will be planted in the winter of or , depending on the success in generating rooted cuttings. Summary In , the CFGRP third cycle slash pine program started with the selection of 464 new forward selections, and will finish with the measurement of the full sib seedling and clonal tests. These test measurements, analyzed and used for ranking the third cycle breeding population and provide material for making fourthcycle forward selections. During the course of the next four or five years the CFGRP members will maintain and measure the eight full sib trials, establish and measure the four clonal trials. Also, CFGRP members and UF staff will be developing the CFGRP slash pine fourth cycle strategy for selection, breeding and testing. This fourth cycle program will most likely include traditional and genomic marker based tree improvement techniques. Congratulations to the members of the CFGRP on the completion of the selection, grafting, breeding and establishment of the 3 rd cycle full sib progeny tests. Your hard work and attention to details is truly amazing! 18

21 Fifty-fifth Annual Progress Report Unraveling Additive from Nonadditive Effects using Genomic Relationship Matrices (P 81) Background Quantitative genetics and its applications in plant and animal breeding have largely focused on additive models. Under idealized conditions, such as those described by Cockerham (1954) and Kempthorne (1954), genetic values due to additive and non additive effects are orthogonal to each other. However, these conditions are often not met, particularly in breeding populations, with the consequence that genetic values due to additive and non additive effects are not orthogonal. Under these conditions a large proportion of variance due to interactions of alleles (dominance and epistasis) can manifest as additive variance (Hill et al. 2008). With standard pedigree models, variance estimates of these elements are highly correlated, reflecting confounding effects (Lynch and Walsh 1998; Hill 2010). The proportion of additive variance attributable to interactions of alleles largely depends on the distribution of allele frequencies at causal loci (Lu et al. 1999; Zuk et al. 2012, Hill et al. 2008). This affects the estimation of variance components and breeding value (BV) predictions (Palucci et al. 2007; Vanderwerf and Deboer 1989), as well as our ability to dissect the genetic architecture of the trait at the causal level. Understanding the genetic architecture of a trait is also essential for defining breeding strategies and for maximizing genetic gains. For instance, individual genetic differences due to non additive effects could be employed to design optimal mating schemes and would be very important if family or clonal propagation is possible in a breeding program. Appropriate separation of additive and dominance genetic components with use of standard pedigree based models involves mating designs with large numbers of close, typically full sibs, relatives. Furthermore, partitioning epistasis requires, in addition, either inbred or vegetatively propagated (clonal) populations. In perennial plants inbred are not used because of the long generation length and inbreeding depression is often a problem, which leaves clonal populations as the only alternative to explore the full genetic architecture in these species (Foster and Shaw 1988). Several studies aimed at partitioning genetic variance into components using data from clonal populations and traditional pedigree based quantitative genetics methods (Foster and Shaw 1988; Mullin et al. 1992; Wu 1996; Isik et al. 2003, 2005; Costa e Silva et al. 2004, 2009; Baltunis et al. 2007, 2008, 2009; Araujo et al. 2012) estimated small values for dominance variation and often null values for epistasis variation. This does not necessarily imply that interaction between alleles within (i.e. dominance) or between (i.e. epistasis) loci are not important. As the relative importance of non additive effects may be masked by effects due to the distribution of allele frequencies (e.g. Hill et al. 2008), or, alternatively, the above mentioned results may reflect the limitations imposed by family structure available or the genetic information used (pedigrees), which only allow use of the expected degree of genetic similarity. Current available genotyping and sequencing technologies can provide a high level of certainty of the actual fraction of allele sharing between pairs of individuals. In pedigree based genetic relationships, each element in the numerator relationship 19

22 Cooperative Forest Genetics Research Program matrix (A matrix) is defined as the expected fraction of shared alleles assuming an infinitesimal model. However, due to Mendelian sampling, the values from the realized genomic relationships (A G matrix), constructed with molecular marker information, deviate from their expected value (VanRaden 2008; Hill and Weir 2011). One way of incorporating molecular marker information for prediction of genetic values consists of replacing, in a BLUP analysis, the pedigree based relationship matrices (P BLUP) with marker based counterparts (known as Genomic Best Linear Unbiased Predictor or G BLUP) (VanRaden 2008). Similar to P BLUP, G BLUP can be extended to account for non additive effects by replacing pedigree based relationship matrices due to non additive effects (Mrode 2005) with their markerbased counterpart. This is because; dominance and epistatic interactions (e.g. additive by additive, dominance bydominance and additive by dominance) relationship matrices can also be constructed using molecular information, as is currently done with A G. Evidence suggests that use of the realized genomic similarity (A G ) increases (relative to A) the ability of the model to uncover genetic signal from phenotypic data (Resende et al 2012b; Munoz et al 2013). However, it is not clear whether the power to partition genetic variance into additive and non additive components can also be improved by the use of the realized genomic relationships. If so, this would lead to a finer dissection of the genetic architecture of complex traits that could have profound impacts on the future design and implementation of breeding strategies. The objective of this study is to assess the extent to which the use of markerbased additive and non additive relationship matrices improves the precision of partitioning genetic variance into its components. For this assessment, tree height from a clonal population of Pinus taeda L. was evaluated with a sequence of models that account for additive, dominance and 1st order epistatic interactions (additive by additive, dominance by dominance, and additive bydominance) implemented with either pedigree or molecular marker information. Material and Methods Data: Field data from a single experimental trial from the CCLONES population (see Baltunis et al. 2007, Resende et al. 2012a for details) was used in this study. The response variable total tree height (HT, m) was used. A subset of the CCLONES population, composed of 951 individuals from 61 families, were genotyped using the Illumina Infinium platform (Illumina, San Diego, CA (Eckert et al. 2010) with 7,216 SNPs, each representing a unique pine EST contig. A subset of 4,853 SNPs were polymorphic and were used for further analyses. Relationship Matrices: A marker based additive relationship matrix (A G ) was constructed following the method from Powell et al. (2010) and adjusted as recommended by Yang et al. (2010) to lessen estimation error. The resulting A G was used to correct the original pedigree as detailed in Munoz et al. (2013). In addition, a molecular marker based dominance relationship matrix (D G ) was constructed. To build a dominance relationship matrix, we created an incidence matrix (S) for effects due to dominance S = {s ij }, where s ij was parameterized to be coded 1 if the genotype was heterozygous and 0 if the marker genotype was homozygous for 20

23 Fifty-fifth Annual Progress Report either class. The matrix S was further standardized to have mean zero by using: s ij = 1 2p j q j if the individual is heterozygous s ij = 0 if the individual has a missing data s ij = 0 2p j q j otherwise. Using the above we constructed D G as where the denominator is the sum of the variances of s ij under Hardy Weinberg equilibrium. Pedigree based relationship matrices for additive (A) and effects due to dominance (D) were computed using standard methods (Lynch and Walsh 1998; Mrode 2005). Following existing theory (Cockerham 1954; Kempthorne 1954; Henderson 1885; Gianola and de los Campos 2008), the co variance matrices due to 1 st degree epistatic terms were computed using Hadamard products (i.e. cell by cell product denoted as #) of the following form: (i) additive by additive interactions (A#A or A G #A G ); (ii) dominanceby dominance interactions (D#D or D G #D G ); and (iii) additive by dominance interactions (A#D or A G #D G ) for pedigree and markerbased methods, respectively. Genetic Analyses: All analyses were carried out in the software ASReml v3.0 (Gilmour et al. 2009). Five models were fit using the pedigreebased matrices (models 1 to 5) and five using the marker based matrices (models 6 to 10). The full model, including all terms (i.e. model 5 or 10) was # where y is the phenotypic HT response, is a vector of fixed effects (i.e. silvicultural treatment and replicate), i~n(0, I 2 i) is a vector of the random incomplete block effects within replication, ~N(0, C 1 2 a) is a vector of random additive effects of individuals, here C 1 is a relationship matrix due to additive effects either from pedigree (A) or markers (A G ), t 1 ~N(0, C 1 I 2 t1) is a vector of random additive by silviculture type interactions, d~n(0, C 2 2 d) is a vector of random individual dominance effects, here C 2 is a relationship matrix due to dominance effects that was computed either from pedigree (D) or markers (D G ), t 2 ~N(0, C 2 I 2 t2) is a vector of random dominance by silviculture type interactions, # ~N(0, C 1 #C 1 2 iaa) is either a vector of random additive by additive interaction, a vector of random dominance by dominance interactions # ~N(0, C 2 #C 2 2 idd) or a vector of random additive by dominance interactions # ~N(0, C 1 #C 2 2 iad) and e~n(0, I 2 e) is a vector of random residual effects. Above, matrices X and Z 1 Z 6, are incidence matrices for fixed and random effects, respectively, I denotes an identity matrix, and # represent the Kronecker and Hadamard (cell by cell) product, respectively. Under the above model the narrow sense heritability can be estimated as, the dominance to total variance ratio as, the epistatic to total variance ratio as, and the broadsense heritability as, where is the estimated additive variance, is the estimated dominance variance, and, and are the total phenotypic, epistatic and total genetic variance, respectively, that changed accordingly to the model being fit (Table 10). 21

24 Cooperative Forest Genetics Research Program Model Comparisons: Models were compared using the Akaike Information Criterion (AIC, Akaike 1974). Precision and dependency between estimates of variance components was assessed using the asymptotic variance covariance matrix of estimates of variance parameters (V) and the asymptotic sampling correlation matrix of estimates (F) computed as, where L is a diagonal matrix containing the diagonal elements of V. Inspection of the off diagonal elements of the F matrix allows assessing sampling correlation of variance estimates. In addition, in order to have an overall assessment of dependency between the estimates, eigenvalues of F were examined. Table 10. Summary of models, fitted effects and relationship matrices used in the study. Relationship matrix used Model (Information Used, A,D=Pedigree, A G,D G =Markers) Number Code Additive Dominance Epistasis 1 P_A A D 6 M_A A G D G 2 P_AD A D 7 M_AD A G D G 3 P_A#A A D A#A 8 M_A#A A G D G A G #A G 4 P_D#D A D D#D 9 M_D#D A G D G D G #D G 5 P_A#D A D A#D 10 M_A#D A G D G A G #D G The predictive ability of each model was calculated as the correlation between the phenotype (phe) with the breeding value (BV) derived from each model; and with the genetic value (GV) derived from each model, calculated as the sum of BV, dominance deviation and epistatic effect. In addition, the prediction stability of the different models was evaluated as the correlation between the BV and the predicted BV (PBV) from a 10 fold cross validation (Kohavi 1995) with a random subsampling partitioning, fixed for all models. The mean square error (MSE) was calculated between BV and PBV with standard methods. Finally, the capacity of the model to predict ranking position of the top 10% of the individuals, simulating a selection scenario, was evaluated as the correlation between the ranking position using the BV and using the PBV. Results and Discussion The variance components, genetic parameters and goodness of fit statistics, estimated with each alternative model are summarized in Table 11. Both P_A and M_A models had narrow sense heritability (h 2 ) over After including the dominance effect in the pedigree based model (P_AD) h 2 decreased by approximately 26% and the dominance ratio (d 2 ) estimate was small (0.07) and non significant (2 SE(d 2 ) > 0.08). This result is expected because depending on the distribution of allele frequencies, a sizable proportion of variance due to nonadditive effects can be manifest as additive variance (Lu et al. 1999; Zuk et al. 2012). In addition, when pedigree based models included non additive effects, the conclusions were not different from the common observation that non additive effects represent a small fraction of the total genetic variation (Isik et al. 2003; Costa e Silva et al. 2004; Baltunis et al. 2007; Araujo et al. 2012). 22

25 Fifty-fifth Annual Progress Report Table 11. Estimates of variance components, genetic parameters (standard errors in parenthesis) and goodness of fit measures. P_A M_A P_AD M_AD P_A#A P_D#D M_D#D P_A#D M_A#D Incomplete block (Iblk) Additive (Add) Dominance (Dom) Epistasis Add x Add 0.01 Epistasis Dom x Dom Epistasis Add x Dom Culture x Add Culture x Dom Culture x (Add x Add) Culture x (Dom x Dom) Culture x (Add x Dom) Residual Total Variance h 2 SE(h 2 ) (0.018) (0.021) (0.059) (0.039) (0.058) (0.058) (0.041) (0.058) (0.043) d SE(d 2 na na ) (0.044) (0.032) (0.043) (0.042) (0.039) (0.043) (0.040) i SE(i 2 na na na na ) (0.000) (0.000) (0.034) (0.000) (0.039) H 2 SE(H 2 ) (0.018) (0.021) (0.023) (0.020) (0.023) (0.023) (0.021) (0.024) (0.021) LogLikelihood AIC