Bridging landscape genomics and quantitative genetics for a regional adaptation of European

grasslands to climate change

GrassLandscape

INRA, IBERS, IPK, EPHE, ILVO

ERA-NET+ (FP7) – FACCE-JPI 2014 programme ‘Climate Smart Agriculture’

INRA UR P3F, Centre Poitou-Charentes, Lusignan, France JP Sampoux, P Barre, I Litrico, D Combes

UMR Ecologie et Ecophysiologie Forestières, Nancy, France V Badeau

IBERS IBERS - Aberystwyth University, Wales, UK M Hegarty , I Thomas

IPK Genebank Department, Satellite Collections North, Malchow-Poel, Germany K Dehmer, E Willner, A Roschanski

EPHE EPHE – CEFE, Montpellier, France S Manel

ILVO Plant Sciences Unit, Growth and development, Melle, Belgium I Roldan-Ruiz, T Ruttink, H Muylde

The GrassLandscape Consortium

Permanent grasslands Mainly natural diversity (grasses and legumes)

31% of total EU utilised agricultural area

Grasslands in Europe

Grazing, cut forage High stocking rate

Grazing, cut forage Low stocking rate

Food industry Dairy and meat products Turnover: 175 109 €

Breeding To improve usage value

Genetic resources

service

Temporary meadows Sown with grass and legume

cultivars released by breeding

5% of total EU utilised agricultural area

Artificial meadows Legume cultivars only

0,4% of total EU utilised agricultural area

→ Grasslands are also important for other services than feed production: - Ecosystemic services - Landscape amenity services

Many grassland taxa (Lolium perenne, Festuca arundinacea, Dactylis glomerata, Trifolium sp…) have broad climatic niche and wide distribution area

Area of natural expansion of perennial ryegrass (Lolium perenne)

→ Wide ecotype (genetic) differentiation enables climatic adaptation within these taxa → Distribution of adaptive diversity (alleles) along wide climatic gradients

Climatic adaptation in the natural diversity of grassland species

Local natural populations and cultivars of grassland species are likely to lack capacity to adapt to changing climate

Permanent grasslands: Local natural ecotypes may lack sufficient allelic diversity to evolve under climate change (e.g. heavy damages in 2003 and 2005 in Western Europe)

Temporary meadows: Narrow-based cultivars of breeders have been bred for adaptation to present mean climatic conditions

Consequences of lack of adaptive capacity: Loss of feeding potential Decrease of ecosystemic services of grasslands (biodiversity shelter, …) Decrease of mitigation potential of grasslands (decrease of carbon sink potential) Loss of potentially useful genetic resources Risk of replacement by invasive species with poor value for services

Climatic adaptation in the natural diversity of grassland species

Permanent grasslands Need to organise assisted migration of diversity Need to recombine adaptive features to face new combinations of climatic constraints Need to maintain large diversity in permanent grasslands (dynamic conservation of in situ

genetic resources)

Challenges for a genetic adaptation of grassland species to climate change

Temporary meadows Need to introgress new climatic adaptations in cultivars (while keeping high level of

agronomic performances) Need to recombine adaptive features to face new combinations of climatic constraints

Classical breeding methods are not well adapted to cope with these challenges (within the next 30 years)

Classical breeding based on extensive phenotype evaluation takes long time (5 to 7 years for a recurrent selection cycle, 10 years to create a new cultivar)

Classical breeding is not adapted to cope with the challenge of maintaining wide diversity

in permanent grasslands

New genome-wide genotyping methods (up to several 106 SNPs) based on massive sequencing technologies (NGS technologies) SNP micro-arrays GBS (genotyping by sequencing), RADseq

Already used to: - investigate genetic determinism (association genetics) - breed for quantitative traits (genomic selection)

We have the opportunity to implement new tools and new methods

New methods to detect adaptive diversity along environmental gradients → Landscape genomics (Manel et al., 2010) Combines population genetics methods (detection of signature of selection) and

statistical methods (associations genomic diversity × environmental variations)

Already implemented in several wild species

Challenges for a genetic adaptation of grassland species to climate change

Example: Multiple-population frequentist test based on FST distribution (Beaumont and Nichols, 1996)

Multi-locus genotyping

Computation of observed FST for each locus

Many coalescent simulations of allelic distributions under a neutral model

Theoretical distribution of FST from simulations conditional to heterozygosity

Outlier loci FST < lower bound of neutral distribution balancing selection

Outlier loci FST > upper bound of neutral distribution directional selection

Landscape genomics: detection of signature of selection

Landscape genomics: Association between genomic diversity and environmental variations

Theoretical background: Luikart et al., 2003; Holderegger et al., 2008; Manel et al., 2010

Requirements: - Geo-referenced individuals/populations over an area of interest - Environmental data at collection sites - Genome-wide genotyping of genetic material

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Collection sites

Allelic frequencies at marker loci

f11 . . . f1j . . . f1l

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Environmental parameters

Statistical model for each locus: E(fij) = function (Sak xik) Models significantly different from null model

Landscape genomics: a straightforward mean to detect adaptive diversity

Spatial distribution of an SNP involved in fowering earliness in Arabidopsis Thaliana (Banta et al., 2012)

Association models Genomic diversity X environmental variations

Models of environmental distribution of adaptive alleles

Spatial distribution of adaptive alleles

Distribution models can be projected into climate change scenarios to foresee possible shifts in the range of suitability of adaptive alleles

→ Where to find genetic resources (today) for future climatic conditions → Assisted migration of diversity → Markers to breed for future climatic conditions

We can use the Landscape genomics frame to cope with the challenges of genetic adaptation to climate change

GrassLandscape → Implementing the Landscape genomics approach for a genetic adaptation of perennial ryegrass (Lolium perenne) to climate change

Background Perennial ryegrass is a major component of grasslands in Europe (tolerate grazing and trampling)

Background Extensive collections of natural populations of perennial ryegrass during the past decades

In situ collection Seed increase in genebank fields Seed storage in genebank cold rooms

Round 3500 natural populations of perennial ryegrass (accessions) are maintained in European genebanks

GrassLandscape → Implementing the Landscape genomics approach for a genetic adaptation of perennial ryegrass (Lolium perenne) to climate change

The GrassLandscape design

→ The genebank set: Extraction of a seed sample from 500 accessions of European genebanks to represent the natural diversity of perennial ryegrass over its area of primary expansion

Contributions to the genebank set: - Genebanks from 15 European countries - USDA GRIN

The GrassLandscape design

→ The genebank set: Assessment of agronomic performances of the 500 accessions in field trial sown in three locations (INRA, IPK, ILVO – sown in 2015)

Record of phenotypic features → Phenological and morphological traits → Desease resistance, persistency → Water soluble carbohydrates, %N, fiber composition → Isotopic discrimination of C (d13C) → Canopy temperature

Accessions sown in 1m² micro-plots with 3 replicates per location

The GrassLandscape design

→ The genebank set: Fine measurement of morphogenesis parameters on a high-throughput

phenotyping platform (IPK Gatersleben, done in April – May 2015)

Monitoring of early morphogenesis for 3 individuals per accession on the LemnaTec Platform of IPK, Gatersleben

The GrassLandscape design

→ The genebank set: Documenting the environmental conditions at collection sites of accessions (in process)

Climatic data extracted from grids of 14 days norms over the 1989-2010 period (0.05° resolution) → Tmin, tmax, rainfall from EURO4M MESAN → Global radiation from EUMETSAT CM SAF → Computed ETCCDI indices

Mining of other geo-referenced environmental databases → soil and sub-soil → land cover, land use

Databases of genebank centres → Environmental data reported by collectors of perennial ryegrass populations

Solar surface radiation: mean over 1989-2010

The GrassLandscape design

→ The genebank set: Two genotyping approaches based on high-throughput sequencing (in process)

1. Genotyping based on the sequencing of a great number of random DNA fragments → GBS technology implemented by IBERS → Target: 40 000 to 70 000 SNPs across perennial ryegrass nuclear genome 2. Resequencing in a limited number of known nuclear and chloroplastic candidates genes → Carried out by INRA with contribution of ILVO → Resequencing of 500bp fragments in round 20 candidate genes For both approaches, we use an innovative protocol

Sequencing pooled DNAs of individuals from a same population → Direct assessment of allelic frequencies in populations

The GrassLandscape design

→ The genebank set: From seed samples to landscape genomics

In-field phenotyping data Phenotyping platform data Sequencing data Environmental data

Genebank set = 500 accessions

Analyses of phenotypic diversity (2016-2017) → Associations phenotypic diversity × SNP diversity → Associations phenotypic diversity × environmental variations

Landscape genomics analyses (2016-2017) → Signature of selection → Associations SNP diversity × environmental variations

The GrassLandscape design

→ The in situ set: New collection of natural populations of perennial ryegrass in 55 sites across Europe (done in spring – summer 2015)

Expected to avoid drift or selection possibly occuring in accessions maintained in genebanks → However only feasable for a limited number of sites (55)

Lamayou, Aveyron, France

França, North Portugal

Idsegahuizum, The Netherlands

Breitnau, BadenWürt, Germany

The GrassLandscape design

→ The in situ set: New collection of natural populations of perennial ryegrass

In situ set = 50 populations

Sequencing data Data from in-field spaced-plant trial (planted 18 Sept 2015 at IPK)

Environmental data

Analyses of phenotypic diversity (2016-2017) → Associations phenotypic diversity × SNP diversity → Associations phenotypic diversity × environmental variations

Landscape genomics analyses (1016-2017) → Signature of selection → Associations SNP diversity × environmental variations

Models of environmental distribution of adaptive diversity

Fine-resolution spatial grids of climatic norms for 1989-2010 period

Fine-resolution spatial grids of climatic norms for several climate change scenarios at the 2050 and 2100 terms

Expected spatial distribution of adaptive diversity in the 1989-2010 period

Foreseen spatial distribution of adaptive diversity at the 2050 and 2100 terms

Foreseen shifts in the range of suitability of adaptive diversity

Identification of genomic markers of climatic adaptation to drive: - Assisted migration - Recombination of climatic adaptations

Models of association Phenotype x SNP

Genomic markers of agronomic value

Recombination of agronomic value and climatic adaptation

- Genetic pools to reseed permanent grasslands - Introgression of climatic adaptations into elite cvs

Corrected RCA4 – CORDEX model for AR5 scenarios RCP 4.5, 8.5 and possibly 2.6

Regional adaptation of perennial ryegrass to climate change

Regional adaptation of perennial ryegrass to climate change

From the GrassLandscape project to the restoration of permanent grasslands degraded by climatic disruptions

Integration of the GrassLandscape strategy into the agriculture socio-economy

Regional adaptation of perennial ryegrass to climate change

2015 2016 2017

WP1 Genetic resources and phenotyping

K Dehmer

1.1 E Willner Choice and phenotyping of a genebank set from European genebank accessions

- Choice of 500 populations (11-12/2014)

- DNA extraction of the genebank set

- LemnaTec phenotyping of the genebank set

- Field phenotyping of genebank set (3 loc.)

- Chemical phenotyping of the genebank

set

1.2 E Willner Choice and phenotyping of an in situ set from collections in European grasslands

- Collect 50 populations x 30 individuals

- Field phenotyping of the in situ set (1 loc.)

- DNA extraction of the in situ set

Work plan – WP1

Work plan – WP2

2015 2016 2017

WP2 Genotyping

M Hegarty

2.1 M Hegarty - RADseq of the genebank set

- RADseq of the in situ set

2.2 P Barre - Reseq. candidate genes in the genebank set

- Reseq. candidate genes in the in situ set

Work plan – WP3

2015 2016 2017

WP3 Genetic analyses

S Manel

3.1 V Badeau - Collection of environmental data

3.2 I Litrico - Signature of selection (genebank set)

- Signature of selection (in situ set)

3.3 I Litrico - Structure & phylogeography (two sets)

3.4 S Manel - Assoc. molecular X environ. (two sets)

3.5 K Dehmer - Assoc. molecular X phenot. (genebank set)

- Assoc. molecular X phenot. (in situ set)

Work plan – WP4

2015 2016 2017

WP4 Regional adaptation to climate change

JP Sampoux

4.1 V Badeau - Shifts in spatial ranges with climate change

4.2 JP Sampoux - Regional adaptation to climate change

4.3 JP Sampoux - Dissemination towards stakeholders

WP5 Coordination

JP Sampoux

- Consortium meetings