Marianne Joubert

Marie-Francoise Samson

Valerie Lullien-Pellerin

Caractérisation de Grains de blés tendres et de blés durs avec l'outil SKCS : discussions sur les données obtenues

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Corrélations entre paramètres SKCS et caractéristiques des grains

Matériels et méthodes

Résultats et discussion

Nombre de grains nécessaires à la mesure Le SKCS acquière les données grain à grain. Afin d'obtenir une bonne représentativité du lot étudié, il est conseillé de caractériser 300 grains. Dans le cas d'échantillonnages plus restreints, 150 grains pourraient cependant permettre une précision suffisante (Fig. 2).

Dispersion au sein des lots Cet outil présente l'avantage de permettre l'acquisition de données grain à grain qui renseignent sur la dispersion des caractéristiques au sein du lot (Fig. 3). Martin CR al., Transactions of the ASAE 36, 1399-1404 ;Osborne BG et Anderssen RS, Cereal Chem. 80, 613-622 (2003) ; Oury FX et al., Theor. Appl. Genet. 128, 913-922 (2015 )



  Bonne corrélation PMG-masse (R 2 = 0,8, n= 214)  Bonne corrélation PMG-diamètre (R 2 = 0,6-0,85; Fig. 4)  Pas de corrélation entre HI et dureté PSI ou NIRS  Pas ou faible corrélation entre HI et teneur en protéines Le SKCS permet de distinguer des lots de vitrosité contrastées au sein d'une même classe de dureté (Fig.5). et Récemment, nous avons acquis un nouvel outil de caractérisation de type «Single Kernel Characterization System», (SKCS 4100, Perten Instruments), décrit notamment par Martin et al. (1993) et Osborne et Anderssen (2003). Nous présentons ici des données obtenues sur des lots de grains de duretés contrastés : blés durs (Triticum durum, génome AABB) et de blés tendres (Triticum aestivum, génome AABBDD) de type «Soft» ou «Hard».

  Le SKCS permet d'acquérir, au sein d'un lot de blé, les données suivantes pour chaque grain : masse, teneur en eau, diamètre moyen et résistance mécanique (Hardness index ou HI). Il fonctionne selon le schéma Figure 1. Plusieurs lots de grains de blés durs et de blés tendres (hard et soft) de variétés/lignées, lieux et années différents ont été caractérisés avec le SKCS ou par des mesures PMG, teneur en protéines, dureté PSI, NIRS ou par marqueur génétique (présence et forme allélique gène Pinb-D1 sur le chromosome D), vitrosité farinotome de Pohl.

  -Remerciements : Léa Ollier (CDD INRA), P. Delobel (TR INRA)

Figure 1 :Figure

 1 Figure 1 : Schéma de fonctionnement du SKCS

  Figure 5 : Valeurs de dureté SKCS en fonction de la vitrosité des grains dans les différentes classes de dureté (Blés tendres, voir Oury et al., 2015 ; Blés durs, non publiés)


published or not. The documents may come

Towards Safer Plant Genetic Resources through improved viral diagnostics (SafePGR). Final outreach report

This document is to be filled out by the coordinator in collaboration with the project partners. It must be sent by the coordinator, within the 2 months after the official end of the project with a copy to all the funding bodies of the project It reports on the activity of all the project partners. All the partners must have a copy of the version sent to the JCS and to the funding bodies.

SafePGR

Date: 28 October 2015

A. MAIN OBJECTIVES OF YOUR OUTREACH ACTION

SafePGR: how to limit the risk of spreading viral diseases through the exchange of tropical plant germplasm

The general objective of the SafePGR project was to improve the knowledge of the diversity of viruses infecting the crops addressed by the partner's plant collections, in order to develop or optimize diagnostic techniques, ultimately permitting the sanitation and safe movement of plants between project partners and beyond.

Deciphering viral diversity in conserved tropical plant germplasm and refining diagnostic tools

Biological Resources Centres (BRCs) conserve and distribute plant germplasm for research and development purposes. They provide breeding programs with genitors that are critical for crop adaptation to ongoing environmental and societal changes. In order to prevent the spread or emergence of diseases, BRCs must guarantee the sanitary status of the resources they distribute. Guadeloupe, Madeira, Azores and Réunion BRCs conserve banana and plantain, sugarcane, yam, sweet potato, garlic and vanilla germplasm. These crops are vegetatively propagated and are prone to the accumulation of viruses, due to the lack of sexual reproduction which would act as a natural sanitation process since most plant viruses are not seed transmitted. Our knowledge of the viruses infecting these crops is also only partial. Sanitation methods exist for recovering virus-free plants but they require sensitive, polyvalent and reliable diagnosis tests. The general objective of the SafePGR project was to improve the knowledge of the diversity of viruses infecting the crops addressed by the partner's BRCs, in order to develop or optimize diagnostic techniques, ultimately permitting the safe movement of plants between project partners and beyond.

Combining classical molecular biology and next generation sequencing approaches to explore viral diversity

To reach its objective, the project combines classical molecular biology and next generation sequencing (NGS) approaches, leading to unprecedented virus discovery in the targeted crops. The NGS-based approaches have the advantage of not assuming any prior information and of therefore very broadly targeting viral genomes, even in the case of novel agents. These NGS-based approaches (so-called metagenomics approaches) are based on random screens of the plant viromes. Because they allow to detect viruses that would have escaped classical detection methods that use known viral sequences as targets, these approaches are complementary to the traditional PCR-based approaches that are widely used in forensics and diagnostics. The innovative approaches developed in the SafePGR project have enabled unprecedented virus discovery in the target crops, from which specific primers were designed, leading in fine to a systematic, optimized and efficient screening of the plants maintained in the partners BRCs. Examples:

B. PARTNERS AND AUDIENCE

Is it pertinent to remove all the viruses from the conserved germplasm, as beneficial viruses may occur in plants? Do you know if there is a correlation between interesting traits in plants, and the presence of some given viruses?

Which viruses need absolutely to be removed and which viruses may add qualities to the plants?

What about the viruses which are integrated in the plant genome? Are the new diagnostic methods developed during SafePGR discriminating them from viral particles causing viral diseases?

Are some plant species more vulnerable than other to viral diseases?

Will the CRBs be more useful for local development and regional exchanges when the results will be transferred?

These questions underlined an interesting topic, if we continue this project. It deals with the role played by some known or new viruses in the targeted crops. Pathogens agents or beneficial organisms?

D. TOOLS

Did you consider using the following tools:

• meetings with policy makers and civil society ? Yes • production of videos ? No

• website ? Yes

• other : Press kits

E. STAKEHOLDERS INVOLVEMENT

Could the outreach have been improved with a higher involvement of stakeholders during the design and implementation of the project?

Yes. In fact, the project was only with scientific actors, and a coconstruction would have generated more interest than an a posteriori communication.

  feed back from external actors ?Yes, during the meeting with the end users of Guadeloupe. Many interesting questions emerged, both scientific and on the possible benefits of the project.

PROCESS OF OUTREACH: DESCRIBE YOUR OUTREACH ACTIONS

	Territory	Who is	Level of	Who	Level of involvement *
		impacted ?	impact *	contributes ?
	Guadeloupe	Growers	Low		Low
	Guadeloupe	Extension	Fair	Chambre	Fair
		services		d'Agriculture,
				Institut technique
				IT2
	Guadeloupe	Media	High	TV, radio,	High
				newspapers
	Guadeloupe,	Research	High		High
	La Reunion,	teams
	Madeira,	(diagnostic
	Azores	lab)
	La Reunion	Growers	Low		Low
	La Reunion	Extension	Fair	Provanille,	Low
		services		Plantation Vanille
				Roulof
	Azores	Growers	Low		Low
	Azores	Extension	Fair		Fair
		services
	Madeira	Growers	Low		Low
	Madeira	Extension	High	Centre of	High
		services		Bananiculture,
				Online
				pubication of
				Directorat for
				Agriculture and
				Development
	Please be as specific and precise as possible
	A few examples:
	Website
	Scientific publications
	Other publications
	Video, radio program, etc …. (with date)
	Actions specific to a given territory

*: indicate: low, fair, high … C.

Did you meet specific difficulties during the outreach process ?

We had to adapt our messages to non scientific audience. How could the Netbiome network be used to capitalize and build on the work done ?

G. FURTHER ACTIONS

After the end of the project, what are the key elements for extending the impact of your project?

To implement diagnostic and sanitation strategies in the BRCs. New projects derived from the methodologies developed in the Safe-PGR:

• French Etandard project involving several partners of Safe PGR • Regional project on cassava sanitation in Reunion, Mayote and Comoros (PVBMT)

• NGS indexation plateforme in Guadeloupe (CPER-PO project).

• Project of a Yam quality seed production sector, with scientific, extension services and economic actors in Guadeloupe (RITA network project).

Other comments:


Facundo Muñoz

You can use and distribute breedR for any purpose You can modify it to suit your needs we encourage to! please consider contributing your improvements you can distribute your modified version under the GPL However, breedR makes (intensive) use of the BLUPF90 suite of Fortran programs, which are for free but not free (remember CRAN?)

Fitting Linear Mixed Models

y =Xβ + Zu + ε u ∼N (0, G) ε ∼N (0, R)
A quantitative variable y is modelled as a linear function of fixed effects β and random effects u, with unaccounted residuals ε The function remlf90() yields a REML fit of a model to a dataset Additional functions (e.g. summary(), fixef(), ranef(), plot(), etc.) extract and present specific results Predicted Breeding Values by Family

γ(h) = 1 2 V [Z(u) -Z(v)], dist(u, v) = h
The empirical isotropic variogram is built by aggregating all the pairs of points separated by h, no matter the direction.

γ(x, y) = 1 2 V [Z(u) -Z(v)], dist(u, v) = (x, y)
The empirical row/col variogram is built by aggregating all the pairs of points separated by exactly x rows and y columns.

γ(x) = 1 2 V [Z(u) -Z(v)], u = v ± x
The empirical anisotropic variogram is built by aggregating all the pairs of points in the same direction separated by |x|. Each, can be scaled using a coefficient {u j B j (x)}

And summed to a linear combination f (x) = j u j B j (x)

Figure 12 f (x) = j u j B j (x) provides a spline representation of a wide family of curves, in terms of a vector of coefficients u For any set of points x = {x i }, the vector of values f (x i ) can be written as a matrix operation f = B j (x i ) u breedR extends this to two dimensions and defines a random effect Bu, u ∼ N (0, σ 2 s R s )

u is the vector of spline effects B is the matrix of spline bases evaluated at the observations σ 2 s is the spatial variance parameter R s imposes a fixed positive correlation between coefficients of neighbouring spline bases

The smoothness of the spatial surface can be controlled modifying the number of base functions This is directly determined by the number of knots (nok) in each dimension If not explicitly set, it is determined heuristically by breedR as a function of the number of observations 0 250 500 750 1000

x nok An AR1(ρ) on the line is a collection of random variables {x i } where

x t = ρx t-1 + ε t , ε t ∼ N (0, 1), |ρ| < 1
A few random simulations with ρ = 0.5:

breedR extends this model to the plane using and defines a component

Zu, u ∼ N (0, σ 2 s R AR )
u is the vector of random effects for each individual location on a regular grid Z is an indicator matrix such that Z[i, j] = 1 if the observation i is at site j σ 2 s is the spatial variance parameter R AR defines a separable correlation structure based on the kronecker product of two AR1 processes

The smoothness of the AR effects can be controlled by the autoregressive parameters (ρ x , ρ y ) in each dimension They can be given explicitly Otherwise, breedR fits a model for each combination of parameters in a default grid and returns the most likely

ω i (α) = k∈∂i z ik (α)u c,k where z ik (α) ∝ 1/d α ik , such that k∈∂i z ik (α) 2 = 1.
This condition is variance-estabilizer ensuring ∀i:

Var(ω i ) = Var(u c ) = σ 2 c
The decay parameter

The decay parameter α controls the relative intensity of competition of the neighbours

α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = 1 α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = α = 0 1 2 3 4 0 1 2 3 4
Relative distance

Relative weight

Figure 20

The weights z ik are scale-invariant e.g. a tree twice as far is weighted 1/2 α as much higher values of α concentrate the weights on the closest trees

Random-effect representation

Z d u d + Z c (α)u c , u d u c ∼ N 0, Σ a ⊗ A , Σ a = σ 2 d σ dc σ dc σ 2 c
Each set of BVs is modelled as a zero-mean random effect with structure matrix given by the pedigree and independent variances σ

Random-Regression model

For each observation of an individual i at j

y ij =X i β + Σ ord k=0 a ik L k (j) + ε ij (a 0 , . . . , a ord ) ∼N (0, Σ ⊗ A) ε ∼N (0, σ 2 e )
The This can be used to leverage genomic information (GBLUP)

The Generic model Practical case 1

GxE study

C13 =orig + site + fam + 3 e=1 f e 1 e + ε fam ∼N 0, σ 2 f I (f 1 , f 2 , f 3 ) ∼N 0, Σ G×E ⊗ I ε ∼N 0, D 3 ⊗ I
One global family effect (fam) One group of three site-specific family effects (f i , i = 1, 2, 3) Jointly, they represent the G × E interaction with genetic cross-covariation Σ G×E

Y 1 = Xβ 1 + Zu 1 + ε 1 Y 2 = Xβ 2 + Zu 2 + ε 2 , (u 1 , u 2 ) ∼ N (0, Σ u ⊗ G) (ε 1 , ε 2 ) ∼ N (0, Σ ⊗ I n ).

  implementing statistical models specifically tailored to the analysis of forest genetic resources A inference tool for Linear Mixed Models, with facilities for typical needs breedR acts as an interface providing the means to: 1 Combine any number of prefabricated model components into a larger model 2 Compute automatically incidence and covariance matrices from a few input parameters 3 Fit the model 4 Plot data and results, and perform model diagnostics

Figure 1 :

 1 Figure 1: GPL-3

  Figure 2

  Figure 3

  Figure10

  Figure 16: spatial-effects

Figure

  Figure 18: Competition model

2 d and σ 2 c

 2 Figure 21

  Figure 26: random-regression

  This additional component allows to introduce a random effect ψ with arbitrary incidence and covariance matrices Z and Σ:Zψ, ψ ∼ N 0, σ 2 ψ Σ ψ Applications:include additional not-predefined components e.g. Dominance, Hybrid populations, Genomic evaluation, etc. GBLUP Zu, u ∼ N 0, σ 2 G G Use markers to compute a relationship matrix G for individuals Several methods available e.g. VanRaden et al. 2009 G = XX / 2p(1 -p) Replace the additive-genetic model, which uses the pedigree-based relationship matrix A with a generic model with a genomic relationship matrix G Z is an indicator matrix linking observations with individuals Predicts genetic value of individuals, not markers Improved accuracy wrt pedigree-pased evaluation

Figure 27 :Figure 28

 2728 Figure 27: relationship-matrices

Figure 29 :Figure

 29 Figure 29: GxE-interaction

Figure

  Figure 32: spatialxE

Σ

  u and Σ either diagonal or fully-parameterized 2 × 2 matrices Some of the fixed or random effects can affect only a subset of the traits e.g. fixed effect of operator breedR's simulation function Simulating data under competition Simulate datasets of any size, from any most supported models See ?simulation for details on the syntax Source: local data frame [500 x 14]

  Variables not shown: BV2 (dbl), wnc (dbl), pec (dbl), wnp (dbl), resid (dbl), phenotype (dbl)

								1.0738194 -1.023654
	2	42	18	33	1	18	22 1.6654315 2.477488
	3	43	11	31	1	19	19 0.7047348 1.961305
	4	44	5	38	1	1	23 0.4698724 1.207112
	5	45	16	24	1	7	12 -0.7233131 -1.919742
	6	46	9	38	1	3	5 -0.1197804 1.174054
	..	...	...	...	...	...	...	...	...

http://famuvie.github.io/breedR/

package to analyse genetic data (WP6)

What is longitudinal data

Parallel computing

After you submit a job, you are free to submit more (specially with multiple-processor servers)

Query the status of all jobs with breedR.qstat() Kill some job with breedR.qdel(res) or all jobs with breedR.qnuke()