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Pleiotropy and cryptic genetic variation govern the phenotypic space in Arabidopsis response to water
deficit
Denis Vile, François Vasseur, Sebastien Tisne, Christine Granier
To cite this version:
Denis Vile, François Vasseur, Sebastien Tisne, Christine Granier. Pleiotropy and cryptic genetic variation govern the phenotypic space in Arabidopsis response to water deficit. 23rd International Conference on Arabidopsis Research (ICAR) 2012, Jul 2012, Vienna, Austria. 2012. �hal-01190041�
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Chromosome 3
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Chromosome 5
Degree of similarity on 3 PCs
Position (cM)
Pleiotropy and cryptic genetic variation govern the
phenotypic space in Arabidopsis response to water deficit
Denis Vile, Sébastien Tisné, François Vasseur and Christine Granier
Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux, INRA-SupAgro, Montpellier, France.
Email: denis.vile@supargo.inra.fr
Pleiotropic genes have been identified to control simultaneously different processes involved in key functions such as plant growth and reproduction.
We developed a modeling approach to uncover the variation of whole rosette development of A. thaliana, a fitness related trait.
Literature cited
Plant material and growth conditions
Disentangling multivariate genetic architecture
Reduced path model of plant growth
- QTL of bivariate relationships: genome-wide detection of relationship QTL2,3 (rQTL).
We analyzed the effects of the mutation found at ERECTA in the Landsberg erecta (Ler) accession in two populations of recombinant inbred lines.
PHENOPSIS: an automated phenotyping platform
7Daily growth dynamics
120 RI lines (480 individuals, n = 4) per population were grown in PHENOPSIS under highly controlled climatic conditions (12 h photoperiod;
20-21 °C; 70% air RH; 220 µmol m-2 s-1 PAR), and under well-watered (WW)
and water deficit (WD) conditions. At flowering:
- Rosette area = Σ individual leaf area - Number of vegetative leaves
- Epidermal cell density of 6th leaf
→ cell number and cell area
Rosette area
Epidermal cell number
Leaf area Leaf number
Days to
flowering Epidermal
cell size
Rosette area
Epidermal cell number
Leaf area Leaf number
Days to flowering
Epidermal cell size Plant
size Organ
number Organ
size
Cell
number Cell size
Proliferation
of stem cells Cell
expansion Organ
production Duration of production
A structural equation model (SEM) of plant growth
- QTL detection
- Detection of QTL affecting multivariate
relationships: genome-wide variance-covariance
(G-)matrices comparisons4,5.
Incorporating genetic architecture into SEM
6A modeling approach to uncover phenotypic variation of plant growth
Multi-scale quantification of rosette development
Growth is a multi-level integrated trait. The genetic determinisms of plant growth occurring at each underlying level can lead to cryptic genetic
variation, i.e. standing genetic variation which is not observed when looking at the integrated complex trait
1. This results in a neutral phenotypic space in the absence of genetic or environmental perturbation.
Mutations of pleiotropic genes are suspected to sensitize such multi-
level systems to produce new phenotypes, out of the neutral phenotypic space, in interaction with standing cryptic genetic variation.
ERECTA has similar effects of on cell patterning whatever the population or soil water status, but significant variance differences are visible.
QTL analysis detected common and population-specific genetic architecture in response to water deficit
rQTL effect of ERECTA: comparable under well-watered conditions, but population specific under water deficit.
Measurements of traits
Ler x An-1 (LA) Ler x Cvi (LC)
Well-watered LA:WW LC:WW
Water deficit LA:WD LC:WD
The interactive role of ERECTA on plant growth and plant response to water deficit
The ERECTA gene has marked effects on development and physiology, and on plasticity due to its interactive effects with the environment and other genes8.
In response to water deficit, ERECTA affects the coordinated response of rosette development, in interaction with other QTL, depending on the population in which the mutation segregates8 (Vasseur et al. in prep.).
2.02.53.03.5
Rosette area
0.60.81.01.21.41.6
Leaf number
-1.0-0.50.0
Leaf 6 area
1.51.61.71.8
Days to flowering
-1.4-1.0-0.6
Cell area
2.0 2.5 3.0 3.5
-0.8-0.40.0
0.6 0.8 1.0 1.2 1.4 1.6 -1.0 -0.5 0.0 1.5 1.6 1.7 1.8 -1.4 -1.2 -1.0 -0.8 -0.6 -0.8 -0.6 -0.4 -0.2 0.0 0.2
Cell number
1.5 2.0 2.5 3.0 3.5 0.8 1.0 1.2 1.4 1.6 -2.0 -1.5 -1.0 -0.5 0.0 1.5 1.6 1.7 1.8 1.9 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -1.5 -1.0 -0.5 0.0
1.52.53.50.81.01.21.41.6-2.0-1.00.01.51.61.71.81.9-1.6-1.2-0.8-1.5-1.0-0.50.0
Multivariate relationships between growth-related variables: differences between RI lines populations and response to water deficit
Genetic as well as ecophysiological relationships are explicitly integrated into path models. Our results show that the effects of major effect mutations are highly dependent on the genetic background.
-20 -10 0 10 20 30
LOD score
LerxCvi-0_WW LerxAn-1_WW LerxCvi-0_WD LerxAn-1_WD
-30 -20 -10 0 10 20 30
Chromosome
LOD score
1 2 3 4 5
LerxCvi-0_WW LerxAn-1_WW LerxCvi-0_WD LerxAn-1_WD
ERECTA
Rosette areaEpidermal cell area
Rosette area
Epidermal cell number
Leaf area Leaf number
Days to
flowering Epidermal
cell size
QTLtop5.2
QTLERECTA QTLtop1
QTLtop5.1
QTLmid4
Ler x An-1 Ler x Cvi
Common effects
Towards path models including QTL effects and interactions
(example under well-watered conditions)
Comparison of variance- covariance matrices
A genome-wide analysis of the multidimensional data underlying plant growth revealed similar and contrasted levels of similarity depending on the allelic values in the two populations and on the watering conditions. This suggests background- dependent responses to environmental stresses.
LA:WD
LA:WW LC:WD
LCxWW
ROSETTE AREA
LEAF NUMBER LEAF AREA
DAYS TO FLOWERING CELL AREA CELL NUMBER
Principal component analysis
PC1 (58%)
PC2 (29%)
0.0 0.5 1.0 1.5 2.0
0 50000 100000 150000
Epidermal cell number
Leaf 6 area (mm²) Well-watered
Le r@ERECTA An-1 or Cvi@ERECTA
Ler x An-1 Ler x Cvi
0.0 0.5 1.0 1.5 2.0
Leaf 6 area (mm²) Water deficit
Ler x An-1 Ler x Cvi
Well-watered Water deficit An-1 Ler An-1 Ler
0 1000 2000 3000 4000
Epidermal cell density (mm2 )
Well-watered Water deficit Cvi Ler Cvi Ler
0 500 1000 1500
Epidermal cell density (mm2 )
1Gibson G and Dworkin I. (2004). Nature Rev. Gen. 5: 681-690.
2Tisné S et al. (2008). Plant Physiol. 148: 1117-1127.
3Pavlicev M et al. (2008). Evolution 62: 199-213.
4Stinchcombe JR et al. (2009). Genetics 182: 911-922.
5Jouan-Rimbaud D. et al. (1998). Chemometr. Intell. Lab., 40, 129-144.
6Li R et al. (2006). PLoS Genetics 2: e114.
7Granier C, et al. (2006). New Phytol. 169: 623-35.
8Tisné S et al. (2010). Plant Cell & Environ. 33: 1875-1887.
Relationship QTL
Gene 1
Trait 2
Trait 1 Trait 3
Gene 3
Trait j Trait k PLEIOTROPY
EPISTASIS
Phenotypic space
Genotypic space Gene 2
Trait n
LD Environment
Integrating pleiotropy and epistatis into the modeling of plant growth will provide valuable information into the processes underlying plant performance in response to environmental constraints.