Plant shape is a major component of the visual quality of ornamental plants. It is the result of their architectural construction. It can be analyzed by breaking down the plant into entities (axis, ...metamer) that can be characterized morphologically, topologically and geometrically. Eight bush rose cultivars were selected for their contrasting shapes (from upright to spreading) and their architecture was digitized at two scales, the plant and the axis, differentiating between short and long axes. Thirty-five variables were measured. Measurement acquisition is nevertheless tedious and time-consuming and not really compatible with an analysis involving a large number of individuals. To diminish these constraints, our approach aimed at reducing the number of variables measured, limiting ourselves to the ones most relevant for describing the architecture. A selection of variables was made using the following criteria: to represent the different categories of variables describing the plant architecture; to explain the variability observed; to present the weakest correlation between them. Seven variables were selected: at the plant scale, the number of determined axes, the number of long axes of order 3 and the branching order number; at the long axis scale, the number of metamers and the length of the axis; and at the short axis scale, the basal diameter of the axis and the branching angle of the cord in relation to the vertical axis. Four architectural profiles were differentiated based on these seven variables. Moreover, a high correlation was revealed between some of these architectural variables and a shape descriptor.
The effect of genotype factors, year and their interaction was assessed on six architectural variables of eight cultivars of rose bush. Plants were grown in pots in a greenhouse in the spring of 2011 ...and 2012, two highly contrasted years in terms of the quantity of cumulative radiation, with a relative deviation (for 2012 compared to 2011) ranging from −24.6% (April) to +13.7% (March). Their architecture was digitized at two observation scales, the plant and the axis. Highly significant genotype (G) and year (Y) effects were revealed for all of the variables measured, as well as a G × Y interaction. Concerning the year effect, it was significantly higher in 2012 and for all of the variables measured. The G × Y interaction was due to (i) different genotype groupings according to year, (ii) difference response amplitudes between years according to genotype. Broad‐sense heritability was calculated for each of these variables. It was moderate to high, ranging from 48% for the length of long axes to 98% for the number of metamers on long axes.
Plant shape, and thereby plant architecture, is a major component of the visual quality of ornamental plants. We have been developing a new method for analyzing the entire plant architecture by 3D ...digitalization that allows an almost exhaustive description of rose bush architecture and generates a large number of variables, many of them inaccessible manually. We carried out a QTL analysis using this original phenotyping method. In order to evaluate a broader allelic variability as well as the effect of the genetic background on QTL detection, we used two connected, segregating, recurrent blooming populations. The number of QTLs per variable varied from three for the number of determined axes (NbDetA) to seven for the branching angle of order 2 long axes (AngLA2), the two populations taken together. Five new QTLs, located on the linkage groups (LGs) 2, 6, and 7, were detected for the branching angle of axes, and the QTL located on LG7 co-localized with
RhBRC1
, a branching repressor. Branching and stem elongation QTLs also co-located with
RhBRC1
, suggesting its pleiotropic nature. Year-specific QTLs were also revealed, that explained the genotype × year interactions observed for the number of order 3 short axes (NbSA3) and AngLA2 from a genetic point of view. We also evidenced an effect of the genetic background on QTL detection. This new knowledge should help to better reason the genetic improvement programs for rose bush architecture and, therefore, rose bush shape.
To integrate the gene pool of a wild species (primarily diploid) into a cultivated pool (primarily tetraploid), a crossing between a dihaploid cultivated rose and a hybrid of Rosa wichurana allowed ...to obtain interspecific diploid hybrids that produced 2n pollen grains. A return to a tetraploid level sought by breeders can then be considered using sexual polyploidization, obtained by crossing a tetraploid cultivated rose with these hybrids. Application of a high‐temperature regime led to a small but significant increase in the percentage of 2n pollen grains in these hybrids of up to 4.6%. This result was obtained by applying high temperatures close to 32°C during the day to plants cultivated in a glasshouse during recurrent cycles of bloom. Crosses were made between an unreleased tetraploid hybrid tea rose, as a female, and the diploid hybrid that produces the most 2n pollen grains. Tetraploid (42.1%) and triploid (57.9%) offspring were obtained. The use of these 2n pollen grains of the first division restitution type should facilitate the introgression of complex traits of interest.
The shape and, therefore, the architecture of the plant are dependent on genetic and environmental factors such as water supply. The architecture determines the visual quality, a key criterion ...underlying the decision to purchase an ornamental potted plant. The aim of this study was to analyze genotypic responses of eight rose bush cultivars to alternation of water restriction and re-watering periods, with soil water potential of -20 and -10 kPa respectively. Responses were evaluated at the architectural level through 3D digitalization using six architectural variables and at the physiological level by measuring stomatal conductance, water content, hormones abscisic acid (ABA), auxin, cytokinins, jasmonic acid, and salicylic acid (SA), sugars (sucrose, fructose, and glucose), and proline. Highly significant genotype and watering effects were revealed for all the architectural variables measured, as well as genotype × watering interaction, with three distinct genotypic architectural responses to water restriction - weak, moderate and strong - represented by Hw336, 'Baipome' and 'The Fairy,' respectively. The physiological analysis explained, at least in part, the more moderate architectural response of 'Baipome' compared to 'The Fairy,' but not that of Hw336 which is an interspecific hybrid. Such physiological responses in 'Baipome' could be related to: (i) the maintenance of the stimulation of budbreak and photosynthetic activity during water restriction periods due to a higher concentration in conjugated cytokinins (cCK) and to a lower concentration in SA; (ii) a better resumption of budbreak during the re-watering periods due to a lower concentration in ABA during this period. When associated with the six architectural descriptors, cCK, SA and ABA, which explained the genotypic differences in this study, could be used as selection criteria for breeding programs aimed at improving plant shape and tolerance to water restriction.
Large complex network data have become common in many scientific domains, and require new statistical tools for discovering the underlying structures and features of interest. This thesis presents ...new methodology for network data analysis, with a focus on problems arising in the field of brain connectomics. Our overall goal is to learn parsimonious and interpretable network features, with computationally efficient and theoretically justified methods. The first project in the thesis focuses on prediction with network covariates. This setting is motivated by neuroimaging applications, in which each subject has an associated brain network constructed from fMRI data, and the goal is to derive interpretable prediction rules for a phenotype of interest or a clinical outcome. Existing approaches to this problem typically either reduce the data to a small set of global network summaries, losing a lot of local information, or treat network edges as a ``bag of features'' and use standard statistical tools without accounting for the network nature of the data. We develop a method that uses all edge weights, while still effectively incorporating network structure by using a penalty that encourages sparsity in both the number of edges and the number of nodes used. We develop efficient optimization algorithms for implementing this method and show it achieves state-of-the-art accuracy on a dataset of schizophrenic patients and healthy controls while using a smaller and more readily interpretable set of features than methods which ignore network structure. We also establish theoretical performance guarantees. Communities in networks are observed in many different domains, and in brain networks they typically correspond to regions of the brain responsible for different functions. In connectomic analyses, there are standard parcellations of the brain into such regions, typically obtained by applying clustering methods to brain connectomes of healthy subjects. However, there is now increasing evidence that these communities are dynamic, and when the goal is predicting a phenotype or distinguishing between different conditions, these static communities from an unrelated set of healthy subjects may not be the most useful for prediction. We present a method for supervised community detection, that is, a method that finds a partition of the network into communities that is most useful for predicting a particular response. We use a block-structured regularization and compute the solution with a combination of a spectral method and an ADMM optimization algorithm. The method performs well on both simulated and real brain networks, providing support for the idea of task-dependent brain regions. The last part of the thesis focuses on the problem of community detection in the general network setting. Unlike in neuroimaging, statistical network analysis is typically applied to a single network, motivated by datasets from the social sciences. While community detection has been well studied, in practice nodes in a network often belong to more than one community, leading to the much harder problem of overlapping community detection. We propose a new approach for overlapping community detection based on sparse principal component analysis, and develop efficient algorithms that are able to accurately recover community memberships, provided each node does not belong to too many communities at once. The method has a very low computational cost relative to other methods available for this problem. We show asymptotic consistency of recovering community memberships by the new method, and good empirical performance on both simulated and real-world networks.
The shape and, therefore, the architecture of the plant are dependent on genetic and environmental factors such as water supply. The architecture determines the visual quality, a key criterion ...underlying the decision to purchase an ornamental potted plant. The aim of this study was to analyze genotypic responses of eight rose bush cultivars to alternation of water restriction and re-watering periods, with soil water potential of -20 and -10 kPa respectively. Responses were evaluated at the architectural level through 3D digitalization using six architectural variables and at the physiological level by measuring stomatal conductance, water content, hormones abscisic acid (ABA), auxin, cytokinins, jasmonic acid, and salicylic acid (SA), sugars (sucrose, fructose, and glucose), and proline. Highly significant genotype and watering effects were revealed for all the architectural variables measured, as well as genotype x watering interaction, with three distinct genotypic architectural responses to water restriction - weak, moderate and strong - represented by Hw336, 'Baipome' and 'The Fairy,' respectively. The physiological analysis explained, at least in part, the more moderate architectural response of 'Baipome' compared to 'The Fairy,' but not that of Hw336 which is an interspecific hybrid. Such physiological responses in 'Baipome' could be related to: (i) the maintenance of the stimulation of budbreak and photosynthetic activity during water restriction periods due to a higher concentration in conjugated cytokinins (cCK) and to a lower concentration in SA; (ii) a better resumption of budbreak during the re-watering periods due to a lower concentration in ABA during this period. When associated with the six architectural descriptors, cCK, SA and ABA, which explained the genotypic differences in this study, could be used as selection criteria for breeding programs aimed at improving plant shape and tolerance to water restriction.
While statistical analysis of a single network has received a lot of attention in recent years, with a focus on social networks, analysis of a sample of networks presents its own challenges which ...require a different set of analytic tools. Here we study the problem of classification of networks with labeled nodes, motivated by applications in neuroimaging. Brain networks are constructed from imaging data to represent functional connectivity between regions of the brain, and previous work has shown the potential of such networks to distinguish between various brain disorders, giving rise to a network classification problem. Existing approaches tend to either treat all edge weights as a long vector, ignoring the network structure, or focus on graph topology as represented by summary measures while ignoring the edge weights. Our goal is to design a classification method that uses both the individual edge information and the network structure of the data in a computationally efficient way, and that can produce a parsimonious and interpretable representation of differences in brain connectivity patterns between classes. We propose a graph classification method that uses edge weights as predictors but incorporates the network nature of the data via penalties that promote sparsity in the number of nodes, in addition to the usual sparsity penalties that encourage selection of edges. We implement the method via efficient convex optimization and provide a detailed analysis of data from two fMRI studies of schizophrenia.
While statistical analysis of a single network has received a lot of attention in recent years, with a focus on social networks, analysis of a sample of networks presents its own challenges which ...require a different set of analytic tools. Here we study the problem of classification of networks with labeled nodes, motivated by applications in neuroimaging. Brain networks are constructed from imaging data to represent functional connectivity between regions of the brain, and previous work has shown the potential of such networks to distinguish between various brain disorders, giving rise to a network classification problem. Existing approaches tend to either treat all edge weights as a long vector, ignoring the network structure, or focus on graph topology as represented by summary measures while ignoring the edge weights. Our goal is to design a classification method that uses both the individual edge information and the network structure of the data in a computationally efficient way, and that can produce a parsimonious and interpretable representation of differences in brain connectivity patterns between classes. We propose a graph classification method that uses edge weights as predictors but incorporates the network nature of the data via penalties that promote sparsity in the number of nodes, in addition to the usual sparsity penalties that encourage selection of edges. We implement the method via efficient convex optimization and provide a detailed analysis of data from two fMRI studies of schizophrenia.
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