DNA profiles in DUS testing of grasses

A new UPOV model ?

Henk BonthuisNaktuinbouw

Aanvragersoverleg RvpWageningsche Berg 19 oktober 2015

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

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

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

Approved UPOV model 2 approach

Setting a Molecular threshold for reference varietiesto be excluded from the field trial

UPOV-BMT Model 2

• Grasses today: growing the full reference collection

Variety pairs

to be tested

in the field

Morphological distance

Molecular distance0

UPOV-BMT Model 2

• Facts well known:

• Morphological threshold for distinctness

• Variety pairs above morph. threshold were actually redundant

Morphological distance

Molecular distance0

UPOV-BMT Model 2

• Additional information from molecular profile:

Molecular distance of variety pairs

Morphological distance

Molecular distance0

• Can varieties with large molecular distances be excluded from the field trial ?

UPOV-BMT Model 2

Morphological distance

Molecular distance0

• Area of Concern

probability of incorrect decisions on excluding reference varieties from the field trial

Area of concern

UPOV-BMT Model 2

Morphological distance

Molecular distance0

• 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

UPOV model 2 in Maize (France)

Combining Morphological and Molecular distances

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Jaccard distance

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• 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

Combining Morphological and Molecular distances

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Jaccard distance

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• 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)

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Thresholds for distinctness:

Morphological distance:Cityblock 0,05

Molecular distance:Jaccard 0,15

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Combining Morphological and Molecular distances

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Jaccard distance

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• High-lighted area: above distinct plus thresholds

• low risk for wrong decisions on reference varieties to be excluded from the growing trial

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Varieties which can be excluded from the growing trial:

Cityblock distance > 0,10 and Jaccard distance > 0,20

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Distinct PlusMore distinct than just distinct

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)

Trait summary & weights used in distance calculation

Trait description min mean max range weight

Growth habit 4.5 5.1 5.5 1.0 1

Intensity of green colour 4.9 5.6 7.3 2.4 1

% flowering in autumn 1.6 1.7 2.4 0.8 1

Heading date 47 57.8 65 18 9

Flagleaf length (mm) 91.3 111.4 125.9 34.6 6

Flagleaf width (0.1 mm) 34.6 38.2 44.5 9.9 4

Flagleaf length/width ratio 24.2 29.2 32.7 8.5 1

Flagleaf area 3019.1 3737.4 4585.7 1566.6 1

Plant height 30 days after heading 58.6 63.4 70.4 11.8 6

Length upper internode 180.3 220.2 244.9 64.6 6

Inflorescence: length 137.2 148.4 166.7 29.5 1

Length of longest stem 323.5 372.7 411.5 88 6

Inflorescence: number of spikelets 18 19.7 20.7 2.7 6

Inflorescence density 7.1 7.7 8.7 1.6 1

Length outer glume (mm) 6.7 8.1 9.8 3.1 4

Length basal glume (mm) 10.6 11.6 13.3 2.7 4

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

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 { wk(xik, xjk) sk(xik, xjk) } / ∑k { wk(xik, xjk) }

• Xik , Xjk = value of the data variate k in unit i or unit j resp.• Sk = contribution function (depending on the variate range)• Wk = weight function (1 for all QN-variates)• For further details see: Gower, 1971/1985

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

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)

Combining Y: Molecular distance (Euclidean)and X: Morphological distances (Cityblock) and Ndiff

(Number of trait differences) for 190 variety pairs

27 pairs

Molecularthreshold

GxE interaction for morphology interfering with molecular threshold for distinctness

27 pairs

Molecularthreshold

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.

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

New approach

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

UPOV – BMT 2014

Molecular distances based on reference varieties added to the morphological description as additional traits

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)

Estimate genetic variation

representative for morphology

excluding environmental influences

New challenge ahead

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

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 ?

Acknowledgements:

João Paulo (Biometris, Wageningen)

Paul Goedhart (Biometris, Wageningen)

Noel Cogan et al. (Biosciences Research, Bundoora, Australia)

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