DNA profiles in DUS testing of grasses. A new UPOV model? Lolium perenne (perennial ryegrass) Pilot study (2014)

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1 2--25 DNA profiles in DUS testing of grasses A new UPOV model? Henk Bonthuis Naktuinbouw Aanvragersoverleg Rvp Wageningsche Berg 9 oktober 25 Lolium perenne (perennial ryegrass) Challenges Genetically diverse: Obligate outcrossing species genetically heterogenic populations Synthetic Varieties: created by polycross of selected individual clones (3-2) Morphologically diverse Relative uniformity (in relation to existing varieties) Additional diversity: Genotype x location interactions Environmental effects (winterhardiness,drought, stress) Random experimental errors 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 Pilot study (2) Approved UPOV model 2 approach Objectives 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 Setting a Molecular threshold for reference varieties to be excluded from the field trial

2 2--25 Grasses today: growing the full reference collection Variety pairs to be tested in the field Facts well known: Morphological threshold for distinctness Variety pairs above morph. threshold were actually redundant Additional information from molecular profile: of variety pairs Can varieties with large molecular distances be excluded from the field trial? Area of concern Area of Concern probability of incorrect decisions on excluding reference varieties from the field trial UPOV model 2 in Maize(France) 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 Purple area = varieties which can be excluded from the field trial, based on Rogers distance and morphological GAIA distance 2

3 2--25 UPOV model 2 in potato (NL) Combining Morphological and s Thresholds for distinctness:,7 Combining Morphological and s,, Cityblock distance,5,,3 Rest of 53 pairs were all distinct,2,5 Cityblock distance 5 pairs not distinct: Mutants and/or closely related varieties,7 : Cityblock,5,,3 : Jaccard,5,2,,,5,2,,,8 Jaccard distance,2,,,8 Jaccard distance Based on validated data of 83 varieties (53 pairs) Threshold for molecular distance based on,53 pairs (minus 5) corresponds with threshold previously found in the SSR database project (9 varieties >, variety pairs), confirmed by present database (953 varieties =,9,28 variety pairs) Pilot study on Grasses Combining Morphological and s Distinct Plus More distinct than just distinct,7, Cityblock distance,5 Varieties which can be excluded from the growing trial:, Cityblock distance >, and Jaccard distance >,2,3,2,, Lolium perenne (perennial ryegrass) Phenotypical data of 2 amenity-type varieties 2 varieties make (2x9/2 =) 9 variety pairs standard UPOV characteristics TG//8 morphological traits measurements of individual plants per variety Complete dataset over 3 years (2 22),5,2,,,8 Jaccard distance High-lighted area: above distinct plus thresholds low risk for wrong decisions on reference varieties to be excluded from the growing trial Trait summary & weights used in distance calculation Trait description min mean max range weight Growth habit Intensity of green colour % flowering in autumn Heading date Flagleaf length (mm) Flagleaf width (. mm) Flagleaf length/width ratio Flagleaf area Plant height 3 days after heading Length upper internode Inflorescence: length Length of longest stem Inflorescence: number of spikelets Inflorescence density Length outer glume (mm) Length basal glume (mm) Genotyping-by-Sequencing (GBS) 2 varieties of amenity grasses Genotyped by AgriBio lab (Centre for AgroBioscience, Bundoora, Victoria, Australia) 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 3

4 2--25 Methods: calculating distances Data Analysis 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 ( for all QN-variates) For further details see: Gower, 97/985 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 SNPs with a correlation >.5 with a trait Results: Genetic relationships Varieties genetically sufficiently distinct (based on Nei s coëff for SNPs). Combining Y: (Euclidean) and X: s(cityblock) and Ndiff (Number of trait differences) for 9 variety pairs Nautica most divergent. Greenway and Hayley most similar. Molecular threshold (Trojan and Nagano are control varieties) 27 pairs GxE interaction for morphology interfering with molecular threshold for distinctness Conclusions of Pilot (end 2) 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. Molecular threshold 27 pairs

5 2--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 New approach presented by US experts from Monsanto at UPOV-BMT Korea 2 Candidates described in relation to Reference Varieties (based on molecular distance) 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 9 s. Sugar beet, Rye, Alfalfa, White Clover, Caraway, etc. (ZPW 97). Narrowing genepools in grasses (since 9 s)? Too much noise in relation to real genetic differences puts additional pressure on morphological model of grasses UPOV BMT 2 Distance application to genotypes: Identify reference varieties: by enlarging database mapping (all) varieties in common knowledge s based on reference varieties added to the morphological description as additional traits 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) New challengeahead Ongoing efforts on Lolium perenne at Naktuinbouw Expanding and Improving the set of SNPs for maximum differentiation Estimate genetic variation representative for morphology excluding environmental influences Ongoing GBS project financed by Rvp (25 and 2) 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 5

6 2--25 Ultimately To make breeding more effective DUS testing of grasses should be more efficient and more predictable Showcase for other (cross-pollinated) crops? Acknowledgements: João Paulo (Biometris, Wageningen) Paul Goedhart (Biometris, Wageningen) Noel Cogan et al. (Biosciences Research, Bundoora, Australia) New UPOV-BMT model? Quality in Horticulture