Analyse des bases génétiques et moléculaires de la tolérance à la sécheresse chez le maïs par génétique d'association

Le maïs est l'une des céréales les plus cultivées au monde. C'est une espèce majeure sur le plan économique qui est particulièrement sensible aux déficits hydriques. Il est donc très important d'améliorer la tolérance du maïs à cette contrainte du milieu. La génétique d'association est l'une des approches qui permettent de détecter des polymorphismes impliqués dans la tolérance à cette contrainte abiotique, ou des polymorphismes en déséquilibre de liaison avec ces derniers. Cette thèse est subdivisée en deux parties distinctes. La première partie du travail avait pour objectif de mieux comprendre l'effet des méthodes de contrôle de la structuration sur les résultats d'association. Un panel composé de 342 lignées publiques caractérisées pour le poids de mille grains et pour la précocité de floraison mâle a été utilisé. Ce panel, génotypé pour plus de 12 000 Single Nucleotide Polymorphism (SNPs), comporte une structuration interne très complexe. Cette dernière a été estimée en utilisant des méthodes de réduction dimensionnelle et le logiciel Structure (approche bayésienne) pour différents nombres de groupes. Les tests d'association ont montré une grande sensibilité des résultats aux modèles de contrôle de la structuration utilisés. Pour sélectionner les loci les plus robustes, une méta-analyse des tests d'association a donc été réalisée en se basant sur les résultats de tous les modèles de structuration testés. La deuxième partie du projet avait pour objectif de rechercher des polymorphismes impliqués dans le déterminisme de la tolérance au stress hydrique. Deux panels de lignées représentatifs de deux groupes hétérotiques différents ont été utilisés. Ces deux panels ont été phénotypés pour différents caractères en conditions d'irrigation normale ou restrictive ; ils ont été génotypés pour plus de 68 000 SNPs. Dans un premier temps, les données de génotypage ont été utilisées pour estimer la diversité génétique, la structuration et le déséquilibre de liaison dans chacun des deux panels. Puis, dans un deuxième temps, les données phénotypiques (dates de floraison et composantes du rendement) issues d'un dispositif multi-lieux et multi-années, ont été ajustées aux différents effets environnementaux à l'aide de modèles spatiaux. A partir des valeurs génétiques estimées, des indices de tolérance au stress hydrique ont été calculés. Pour finir, les loci significativement associés à la tolérance au stress hydrique ont été recherchés par génétique d'association. Ainsi, 47 zones impliquées spécifiquement dans le déterminisme de la tolérance au stress hydrique ont été identifiées dans les deux panels travaillés.Maize is one of the most cultivated cereals worldwide. This economically important species is very sensitive to water stress. It is thus important to improve the tolerance of maize to this abiotic stress. Association mapping is one of the approaches that make it possible to map loci underlying the tolerance to water stress or loci in linkage disequilibrium with the causal ones. This PhD project was divided in two separate parts. The first part aimed at evaluating the effect of structure models on association mapping results. A real maize data set of 342 inbred lines, genotyped with more than 12 000 single nucleotide polymorphisms (SNPs), was used to carry out this study. These inbred lines were phenotyped for both male flowering dates and thousand kernel weight. The complex structure of this panel was estimated with both dimensional reduction methods and STRUCTURE software (Bayesian approach) for several group numbers. Our results show a significant effect of the structure model used on association mapping tests. This effect depends on both the method and the number of groups used to control the panel structure. In order to select the most robust loci, a meta-analysis approach was carried out, making use of the results of association mapping tests with all structure models. The second part of the project aimed at mapping loci and genomic regions involved in maize drought tolerance. Two panels of elite inbred lines, representative of two heterotic groups were used. These panels were phenotyped under well-watered and water-stressed conditions. All inbred lines were genotyped with about 68 000 SNPs. The genotypic data was used to estimate the genetic diversity, the structure and the linkage disequilibrium within each panel. At the same time, the phenotypic data (flowering dates and yield related traits) were used to estimate genetic values which made it possible to calculate drought tolerance indices. To finish, association mapping tests were carried out between all polymorphic SNPs and drought tolerance indices. They allowed for the identification of 47 genomic regions that are specifically involved in drought tolerance

The pattern and distribution of deleterious mutations in maize

Most non-synonymous mutations are thought to be deleterious because of their effect on protein sequence. These polymorphisms are expected to be removed or kept at low frequency by the action of natural selection, and rare deleterious variants have been implicated as a possible explanation for the "missing heritability" seen in many studies of complex traits. Nonetheless, the effect of positive selection on linked sites or drift in small or inbred populations may also impact the evolution of deleterious alleles. Here, we made use of genome-wide genotyping data to characterize deleterious variants in a large panel of maize inbred lines. We show that, in spite of small effective population sizes and inbreeding, most putatively deleterious SNPs are indeed at low frequencies within individual genetic groups. We find that genes showing associations with a number of complex traits are enriched for deleterious variants. Together these data are consistent with the dominance model of heterosis, in which complementation of numerous low frequency, weak deleterious variants contribute to hybrid vigor

Complex patterns of local adaptation in teosinte

Populations of widely distributed species often encounter and adapt to specific environmental conditions. However, comprehensive characterization of the genetic basis of adaptation is demanding, requiring genome-wide genotype data, multiple sampled populations, and a good understanding of population structure. We have used environmental and high-density genotype data to describe the genetic basis of local adaptation in 21 populations of teosinte, the wild ancestor of maize. We found that altitude, dispersal events and admixture among subspecies formed a complex hierarchical genetic structure within teosinte. Patterns of linkage disequilibrium revealed four mega-base scale inversions that segregated among populations and had altitudinal clines. Based on patterns of differentiation and correlation with environmental variation, inversions and nongenic regions play an important role in local adaptation of teosinte. Further, we note that strongly differentiated individual populations can bias the identification of adaptive loci. The role of inversions in local adaptation has been predicted by theory and requires attention as genome-wide data become available for additional plant species. These results also suggest a potentially important role for noncoding variation, especially in large plant genomes in which the gene space represents a fraction of the entire genome

Effect of population structure corrections on the results of association mapping tests in complex maize diversity panels

Association mapping of sequence polymorphisms underlying the phenotypic variability of quantitative agronomical traits is now a widely used method in plant genetics. However, due to the common presence of a complex genetic structure within the plant diversity panels, spurious associations are expected to be highly frequent. Several methods have thus been suggested to control for panel structure. They mainly rely on ad hoc criteria for selecting the number of ancestral groups; which is often not evident for the complex panels that are commonly used in maize. It was thus necessary to evaluate the effect of the selected structure models on the association mapping results. A real maize data set (342 maize inbred lines and 12,000 SNPs) was used for this study. The panel structure was estimated using both Bayesian and dimensional reduction methods, considering an increasing number of ancestral groups. Effect on association tests depends in particular on the number of ancestral groups and on the trait analyzed. The results also show that using a high number of ancestral groups leads to an over-corrected model in which all causal loci vanish. Finally the results of all models tested were combined in a meta-analysis approach. In this way, robust associations were highlighted for each analyzed trait

The pattern and distribution of deleterious mutations in maize.

Most nonsynonymous mutations are thought to be deleterious because of their effect on protein sequence and are expected to be removed or kept at low frequency by the action of natural selection. Nonetheless, the effect of positive selection on linked sites or drift in small or inbred populations may also impact the evolution of deleterious alleles. Despite their potential to affect complex trait phenotypes, deleterious alleles are difficult to study precisely because they are often at low frequency. Here, we made use of genome-wide genotyping data to characterize deleterious variants in a large panel of maize inbred lines. We show that, despite small effective population sizes and inbreeding, most putatively deleterious SNPs are indeed at low frequencies within individual genetic groups. We find that genes associated with a number of complex traits are enriched for deleterious variants. Together, these data are consistent with the dominance model of heterosis, in which complementation of numerous low-frequency, weak deleterious variants contribute to hybrid vigor

Data from: Complex patterns of local adaptation in Teosinte

Populations of widely distributed species encounter and must adapt to local environmental conditions. However, comprehensive characterization of the genetic basis of adaptation is demanding, requiring genome wide genotype data, multiple sampled populations, and an understanding of population structure and potential selection pressures. Here, we used SNP genotyping and data on numerous environmental variables to describe the genetic basis of local adaptation in 21 populations of teosinte, the wild ancestor of maize. We found complex hierarchical genetic structure created by altitude, dispersal events and admixture among subspecies that complicated identification of locally beneficial alleles. Patterns of linkage disequilibrium revealed four large inversion polymorphisms showing clinal patterns of frequency. Population differentiation and environmental correlations suggest that both inversions and intergenic polymorphisms are involved in local adaptation

Complex patterns of local adaptation in teosinte.

Populations of widely distributed species encounter and must adapt to local environmental conditions. However, comprehensive characterization of the genetic basis of adaptation is demanding, requiring genome-wide genotype data, multiple sampled populations, and an understanding of population structure and potential selection pressures. Here, we used single-nucleotide polymorphism genotyping and data on numerous environmental variables to describe the genetic basis of local adaptation in 21 populations of teosinte, the wild ancestor of maize. We found complex hierarchical genetic structure created by altitude, dispersal events, and admixture among subspecies, which complicated identification of locally beneficial alleles. Patterns of linkage disequilibrium revealed four large putative inversion polymorphisms showing clinal patterns of frequency. Population differentiation and environmental correlations suggest that both inversions and intergenic polymorphisms are involved in local adaptation

CNVmap: a method and software to detect and map copy number variants from segregation data

Single nucleotide polymorphisms (SNPs) are used widely for detecting quantitative trait loci, or for searching for causal variants of diseases. Nevertheless, structural variations such as copy-number variants (CNVs) represent a large part of natural genetic diversity, and contribute significantly to trait variation. Numerous methods and softwares based on different technologies (amplicons, CGH, tiling, or SNP arrays, or sequencing) have already been developed to detect CNVs, but they bypass a wealth of information such as genotyping data from segregating populations, produced, e.g., for QTL mapping. Here, we propose an original method to both detect and genetically map CNVs using mapping panels. Specifically, we exploit the apparent heterozygous state of duplicated loci: peaks in appropriately defined genome-wide allelic profiles provide highly specific signatures that identify the nature and position of the CNVs. Our original method and software can detect and map automatically up to 33 different predefined types of CNVs based on segregation data only. We validate this approach on simulated and experimental biparental mapping panels in two maize populations and one wheat population. Most of the events found correspond to having just one extra copy in one of the parental lines, but the corresponding allelic value can be that of either parent. We also find cases with two or more additional copies, especially in wheat, where these copies locate to homeologues. More generally, our computational tool can be used to give additional value, at no cost, to many datasets produced over the past decade from genetic mapping panels