2. DNA profiles in DUS testing of grasses A new UPOV model ? Henk Bonthuis Naktuinbouw Aanvragersoverleg Rvp Wageningsche Berg 19 oktober 2015

3. Lolium perenne (perennial ryegrass) Genetically diverse: –Obligate outcrossing species –genetically heterogenic populations –Synthetic Varieties: –created by polycross of selected individual clones (3-20) Morphologically diverse –Relative uniformity (in relation to existing varieties) Additional diversity: –Genotype x location interactions –Environmental effects (winterhardiness,drought, stress) –Random experimental errors

4. Challenges Make DUS testing of Grasses more efficient –DUS testing of grasses is labour-intensive –testing based on single plants –measured characteristics mainly –large reference collection Make DUS testing of Grasses more predictable –Unpredictable morphological differences at the start of DUS –Therefore ref. collection needs to be measured completely each year –Low discriminative power due to uncontrolled environmental variation –Many negative DUS reports as a result

5. Pilot study (2014) Making DUS testing of grasses more efficient by using UPOV Model 2 approach: –combining morphological and molecular distances for the management of the reference collection. Making DUS testing of grasses more predictable by creating molecular database(s): –to be used by (all) Examination Offices –to be used by breeders for DUS screening beforehand Objectives

6. Approved UPOV model 2 approach Setting a Molecular threshold for reference varieties to be excluded from the field trial

7. UPOV-BMT Model 2 Grasses today: growing the full reference collection Variety pairs to be tested in the field Morphological distance Molecular distance 0

8. UPOV-BMT Model 2 Facts well known: Morphological threshold for distinctness Variety pairs above morph. threshold were actually redundant Morphological distance Molecular distance 0

9. UPOV-BMT Model 2 Additional information from molecular profile: Molecular distance of variety pairs Morphological distance Molecular distance 0 Can varieties with large molecular distances be excluded from the field trial ?

10. UPOV-BMT Model 2 Morphological distance Molecular distance 0 Area of Concern probability of incorrect decisions on excluding reference varieties from the field trial Area of concern Area of concern

11. UPOV-BMT Model 2 Morphological distance Molecular distance 0 Incorrect decisions can be avoided by setting the molecular threshold at a safe level for morphological distance. Variety pairs to be excluded from the field trial

12. Purple area = varieties which can be excluded from the field trial, based on Rogers distance and morphological GAIA distance UPOV model 2 in Maize (France)

13. Based on validated data of 183 varieties (16653 pairs) 5 pairs not distinct: Mutants and/or closely related varieties UPOV model 2 in potato (NL) Rest of 16653 pairs were all distinct

14. Threshold for molecular distance based on 16,653 pairs (minus 5) corresponds with threshold previously found in the SSR database project (900 varieties > 400,000 variety pairs), confirmed by present database (1953 varieties = 1,906,128 variety pairs) 0,05 Thresholds for distinctness: Morphological distance: Cityblock 0,05 Molecular distance: Jaccard 0,15 0,15

15. High-lighted area: above distinct plus thresholds low risk for wrong decisions on reference varieties to be excluded from the growing trial 0,05 Varieties which can be excluded from the growing trial: Cityblock distance > 0,10 and Jaccard distance > 0,20 0,10 0,15 0,20 Distinct Plus More distinct than just distinct

16. Pilot study on Grasses Lolium perenne (perennial ryegrass) Phenotypical data of 20 amenity-type varieties 20 varieties make (20x19/2 =) 190 variety pairs –standard UPOV characteristics – TG/4/8 –16 morphological traits –measurements of 60 individual plants per variety –Complete dataset over 3 years (2010 – 2012)

17. Trait summary & weights used in distance calculation Trait descriptionminmeanmaxrangeweight Growth habit4.55.15.51.01 Intensity of green colour4.95.67.32.41 % flowering in autumn1.61.72.40.81 Heading date4757.865189 Flagleaf length (mm)91.3111.4125.934.66 Flagleaf width (0.1 mm)34.638.244.59.94 Flagleaf length/width ratio24.229.232.78.51 Flagleaf area3019.13737.44585.71566.61 Plant height 30 days after heading58.663.470.411.86 Length upper internode180.3220.2244.964.66 Inflorescence: length137.2148.4166.729.51 Length of longest stem323.5372.7411.5886 Inflorescence: number of spikelets1819.720.72.76 Inflorescence density7.17.78.71.61 Length outer glume (mm)6.78.19.83.14 Length basal glume (mm)10.611.613.32.74

18. Genotyping-by-Sequencing (GBS) 20 varieties of amenity grasses –Genotyped by AgriBio lab (Centre for AgroBioscience, Bundoora, Victoria, Australia) –1000 seeds/variety - representing variety (population) –DNA extraction of bulk sample (DNeasy Plant kit from Qiagen) –Profiles based on allele frequencies –Targeted amplification step –Ligation using bar-coded synthetic DNA adapters –Sequencing with Illumina MiSeq –295 SNP-markers retained

19. Methods: calculating distances Distances between varieties based on morphological traits: – Euclidean, Cityblock, Minkowski, Divergence, etc. Distances between varieties based on SNPs: – Euclidean, Jaccard, Rogers, Nei, etc. –∑ k { w k (x ik, x jk ) s k (x ik, x jk ) } / ∑ k { w k (x ik, x jk ) } X ik, X jk = value of the data variate k in unit i or unit j resp. S k = contribution function (depending on the variate range) W k = weight function (1 for all QN-variates) For further details see: Gower, 1971/1985

20. Data Analysis Calculated different distance measures for morphological traits (Euclidean, Cityblock, etc) based on range and weights Calculated different distance measures for SNPs: Euclidean, Jaccard Considered combination of the two types of distances (UPOV-Model 2) Selected SNPs with higher correlation to morphological traits Selected 111 SNPs with a correlation >0.5 with a trait

21. Results: Genetic relationships Varieties genetically sufficiently distinct (based on Nei’s coëff for SNPs). Nautica most divergent. Greenway and Hayley most similar. (Trojan and Nagano are control varieties)

22. Combining Y: Molecular distance (Euclidean) and X: Morphological distances (Cityblock) and Ndiff (Number of trait differences) for 190 variety pairs 27 pairs Molecular threshold

23. GxE interaction for morphology interfering with molecular threshold for distinctness 27 pairs Molecular threshold

24. Conclusions of Pilot (end 2014) UPOV Model 2 does not work for grasses Due to failing morphological model of Lolium perenne Morphology = limiting factor: too many GxE interactions, environmental effects and experimental errors involved.

25. Failing Morphological Model of grasses Varieties of perennial ryegrass should be distinct (by nature) ! – Obligate outcrossing species, genetically heterogenic populations – Synthetic varieties created by polycross of selected individual clones Too many GxE interactions and environmental effects – Observations on single plants, randomly picked leaves, seasonal effects, etc. O.P. Crops excluded from PBR failing to fulfill DUS criteria in 1960’s. – Sugar beet, Rye, Alfalfa, White Clover, Caraway, etc. (ZPW 1967). Narrowing genepools in grasses (since 1960’s) ? – Too much noise in relation to real genetic differences – puts additional pressure on morphological model of grasses

26. New approach presented by US experts from Monsanto at UPOV-BMT – Korea 2014 Candidates described in relation to Reference Varieties (based on molecular distance)

27. UPOV – BMT 2014 Molecular distances based on reference varieties added to the morphological description as additional traits

28. Distance application to genotypes: Identify reference varieties: by enlarging database – mapping (all) varieties in common knowledge Example: data Pilot project Phylogram illustrating separate genepools: tested EU cultivars – amenity types (in blue) and varieties from the Australian perennial ryegrass catalogue known at AgriBio Lab (mostly fodder types). (Control samples in red)

29. Estimate genetic variation representative for morphology excluding environmental influences New challenge ahead

30. Ongoing efforts on Lolium perenne at Naktuinbouw Expanding and Improving the set of SNPs for maximum differentiation –Ongoing GBS project financed by Rvp (2015 and 2016) – but limited resources –Create consortium of Labs, EO’s and breeders for maximum impact Identification of reference varieties –Reference varieties (i.e. additional traits) primarily needed for variety description –Reference varieties should be relevant for the area under consideration Define molecular thresholds for distinctness (crucial !) –Excluding environmental effects from morphological data –Requires genome-wide SNPs and Bio-informatics tools –Include datasets from different environments (estimating GE and e) –Associate phenotype and genotype by genomic prediction (training and target pop) ? –Calculate thresholds for distinctness (and distinct plus) –Molecular thresholds determine direct variety comparisons (target oriented testing) –Morphology remains ultimate test for distinctness

31. Ultimately … To make breeding more effective DUS testing of grasses should be more efficient and more predictable Showcase for other (cross-pollinated) crops ? New UPOV-BMT model ?

32. Acknowledgements: João Paulo (Biometris, Wageningen) Paul Goedhart (Biometris, Wageningen) Noel Cogan et al. (Biosciences Research, Bundoora, Australia)

33. Quality in Horticulture