Dans un contexte de changement climatique, la caractérisation des variétés de blé tendre en réponse à des évènements de sécheresse et de stress thermique est un des défis de l’agriculture. Cette thèse, issue d’un partenariat -public entre Arvalis-Institut du Végétal, Biogemma et l’INRA (Institut National de la Recherche Agronomique), avait pour but de développer des connaissances et des outils nécessaires à l’identification de variétés tolérantes à la sécheresse et au stress thermique et à la création de variétés répondant à cette exigence. Pour ce faire, nous avons analysé un panel de 220 variétés commerciales, génotypées avec 280K SNP et testées dans 35 environnements variés (combinaison d’année, lieu et régime hydrique), plus une expérimentation en conditions contrôlées où un stress thermique a été appliqué pendant le remplissage du grain. La complexité de l’étude de la tolérance à la sécheresse nous a conduit à présenter cette thèse en séparant, dans un premier temps, l’étude des stress hydriques et thermiques, puis de prospecter une méthode d’analyse multi-stress. Nous avons montré que même si la sélection a amélioré la performance des variétés en condition hydrique optimale, le progrès génétique doit être accéléré et mieux réparti en fonction des différents types de stress. Nous proposons pour cela plusieurs déterminants génétiques qui pourraient permettre un gain dans des environnements stressants. Nos résultats et méthodes sont discutés au regard des besoins en préconisation et amélioration variétale. Des pistes de recherche complémentaires et des améliorations ont aussi été suggérées. / In a context of climate change, the characterization of wheat varieties in response to drought and heat stress events is one of the major challenges of agriculture. This PhD thesis, resulting from a private-public partnership between Arvalis ‘Institut du Végétal’, Biogemma and INRA (“Institut National de la Recherche Agronomique”), aimed at providing necessary knowledge and tools to identify drought or heat-tolerant varieties and breed for varieties that meet these requirements. Analyses were conducted using a panel of 220 commercial varieties, genotyped with 280K SNP and tested in 35 environments (combination of year, location and water regime) and an experiment under controlled conditions where heat stress was applied during grain filling. The complexity of the study of drought and heat tolerance led us to present this thesis by first separating hydric and thermal stresses, and then to explore a multi-stress analysis method. Even if breeding has improved the performance of varieties under optimal water conditions, we showed that genetic progress must be accelerated and better distributed according to different stress scenarios. We propose several genetic determinants that could allow genetic gain in stressful environments. Our results and methods are discussed in view of the needs for varietal recommendation and improvement. Additional research strategies and methods improvements were also suggested.

<p>In every living cell, millions of different types of molecules constantly interact and react chemically in a complex system that can adapt to fluctuating environments and extreme conditions, living to survive and reproduce itself. The information required to produce these components is stored in the genome, which is copied in each cell division and transferred and mixed with another genome from parent to child. The regulatory mechanisms that control biological systems, for instance the regulation of expression levels for each gene, has evolved so that global robustness and ability to survive under harsh conditions is a strength, at the same time as biological tasks on a detailed molecular level must be carried out with good precision and without failures. This has resulted in systems that can be described as a hierarchy of levels of complexity: from the lowest level, where molecular mechanisms control other components at the same level, to pathways of coordinated interactions between components, formed to carry out particular biological tasks, and up to large-scale systems consisting of all components, connected in a network with a topology that makes the system robust and flexible. This thesis reports on work that model and analyze complex biological systems, and the signals and noise that regulate them, at all different levels of complexity. Also, it shows how signals are transduced vertically from one level to another, as when a single mutation can cause errors in low level mechanisms, disrupting pathways and create systemwide imbalances, such as in type 2 diabetes. The advancement of our knowledge of biological systems requires both that we go deeper and towards more detail, of single molecules in single cells, as well as taking a step back to understand the organisation and dynamics in the large networks of all components, and unite the different levels of complexity.</p>

Understanding complex traits is a fundamental challenge in plant genetics and a prerequisite for molecular breeding. Tools for trait dissection are markers, maps, and quantitative trait locus (QTL) analysis. Marker-assisted selection (MAS) is an application that integrates these tools. In this thesis research, a new sequence-based marker was evaluated, maps were constructed and used, and QTLs were detected using two types of populations. Marker-assisted selection was used to develop a novel class of barley. Restriction-site Associated DNA (RAD), a sequence based-marker technology, allows for simultaneous high-density single nucleotide polymorphism (SNP) discovery and genotyping. We assessed the value of RAD markers for linkage map construction using the Oregon Wolfe Barley (OWB) mapping population. We compared a RAD-based map to a map generated using Illumina GoldenGate Assay (EST-based SNPs). The RAD markers generated a high quality map with complete genome coverage. We then used the RAD map to locate QTL for agronomic fitness traits. A paper describing this research was published (Chutimanitsakun et al., 2011). Marker-assisted selection was used to rapidly develop fall-sown barley germplasm for human food uses. The target traits were high grain β-glucan, vernalization sensitivity (VS) and low temperature tolerance (LTT). The target loci were WX and VRN-H2. Marker-assisted selection was effective in fixing target alleles at both loci and waxy starch led to increase in grain β-glucan. Unexpected segregation at VRN-H1 and VRN-H3, revealed by genome-wide association mapping (GW-AM), led to unanticipated phenotypic variation in VS and LTT. We found that GW-AM is an efficient and powerful method for identifying the genome coordinates of genes determining target traits. Precise information is obtained with perfect markers; additional research may be needed when multiple alleles are segregating at target loci and significant associations are with markers in linkage disequilibrium (LD) with the target loci. A paper describing this research will be submitted for publication. / Graduation date: 2012

This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models. In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision.  Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction. A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes.  The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).

In every living cell, millions of different types of molecules constantly interact and react chemically in a complex system that can adapt to fluctuating environments and extreme conditions, living to survive and reproduce itself. The information required to produce these components is stored in the genome, which is copied in each cell division and transferred and mixed with another genome from parent to child. The regulatory mechanisms that control biological systems, for instance the regulation of expression levels for each gene, has evolved so that global robustness and ability to survive under harsh conditions is a strength, at the same time as biological tasks on a detailed molecular level must be carried out with good precision and without failures. This has resulted in systems that can be described as a hierarchy of levels of complexity: from the lowest level, where molecular mechanisms control other components at the same level, to pathways of coordinated interactions between components, formed to carry out particular biological tasks, and up to large-scale systems consisting of all components, connected in a network with a topology that makes the system robust and flexible. This thesis reports on work that model and analyze complex biological systems, and the signals and noise that regulate them, at all different levels of complexity. Also, it shows how signals are transduced vertically from one level to another, as when a single mutation can cause errors in low level mechanisms, disrupting pathways and create systemwide imbalances, such as in type 2 diabetes. The advancement of our knowledge of biological systems requires both that we go deeper and towards more detail, of single molecules in single cells, as well as taking a step back to understand the organisation and dynamics in the large networks of all components, and unite the different levels of complexity.

Pain is a major public health issue throughout the world. Increased understanding of the different forms of pain and identification of susceptibility genes could contribute to improved treatments. The main aims of this thesis were to identify the underlying genetic causes of pain by studying two large families affected with migraine and pain insensitivity, respectively. Migraine is one of the most common neurovascular disorders, affecting over 12% of the western population. The genetic contribution to migraine is about 50% according to family and twin studies. To identify novel susceptibility loci for migraine, we performed a genome-wide screen in a large family with migraine from northern Sweden. Linkage analysis revealed significant evidence of linkage (LOD=5.41) on chromosome 6p12.2-p21.1. A predisposing haplotype spanning 10 Mb was inherited with migraine in all affected members of the pedigree. Further fine-mapping of multiple SNP markers restricted the disease critical region to 8.5 Mb. Nine candidate genes were sequenced, revealing no disease-associated polymorphisms in SLC29A1, CLIC5, PLA2G7, IL17, SLC25A27 and TNFRSF21, but rare novel polymorphisms segregating with the disease haplotype in EFHC1, RHAG and MEP1A. EFHC1 has recently been shown to be involved in epilepsy, which is interesting considering the link between migraine and epilepsy. However, association analysis of EFHC1 revealed no difference between patients and controls, suggesting that this gene is not a risk factor for migraine. The combination of the two polymorphisms in RHAG and MEP1A could, however, not be found in any control individuals, indicating that they might be involved in genetic predisposition to migraine in this family. Disorders with reduced pain sensitivity are very rare, since pain perception is essential for survival. A number of disorders have still been identified with pain insensitivity and peripheral nerve degeneration as major clinical signs, including the hereditary sensory and autonomic neuropathies (HSAN). In order to identify novel susceptibility genes for HSAN V, we performed a genome-wide screen in a large consanguineous pedigree from a small village in northern Sweden. A homozygous region identical-by-descent was identified on chromosome 1p11.2-p13.2 in the three most severely affected patients. Subsequent analysis of candidate genes revealed a missense mutation in a conserved region of the nerve growth factor beta (NGFB) gene, causing a drastic amino acid change (R211W) in the NGF protein. NGF is important for the development and maintenance of the sympathetic and sensory nervous system and is therefore likely to be involved in disease. Functional analysis revealed that mutant NGF failed to induce neurite outgrowth and cell differentiation in PC12 cells. Furthermore, almost no mutant NGF was secreted by COS-7 cells, indicating that the processing and/or secretion of the protein might be disrupted. In conclusion, these findings present a novel migraine locus on chromosome 6 and identification of two rare polymorphisms that might be risk factors for migraine. Furthermore, a mutation in NGFB was found to cause complete loss of deep pain perception, which represents a very interesting model system to study pain mechanisms.

Lynch syndrome (Hereditary Nonpolyposis Colorectal Cancer, HNPCC) is the most common hereditary syndrome predisposing to colorectal cancer, accounting for 1-3% of all colorectal cancer. This multi-organ cancer predisposition syndrome is caused by mutations in the mismatch repair (MMR) genes, especially MLH1 and MSH2, and to lesser extents MSH6 and PMS2, which lead to widespread genetic instability and thus microsatellite instability (MSI). Hereditary cancer often manifests in two or more tumours in a single individual; 35-40% of Lynch syndrome patients have synchronous or metachronous tumours of the two major Lynch syndrome-related cancers: colorectal and endometrial. The main purposes of the work underlying this thesis were to identify persons at risk of Lynch syndrome or other types of hereditary colorectal cancer, to estimate the cancer risks associated with these predispositions and to identify the underlying genetic causes. A population-based cohort of 78 persons with double primary colorectal or colorectal and endometrial cancer was identified. Cancer risks in their 649 first-degree relatives were estimated in relation to tumour MSI status (positive or negative) and age at diagnosis (before or after 50 years of age) in the probands. The overall standardised incidence ratio was 1.69 (95% CI; 1.39-2.03). The highest risks for Lynch syndrome-associated cancers: (colorectal, endometrial, ovarian and gastric) were found in families with young MSI-positive probands, likely representing Lynch syndrome families. Importantly, no overall risk was found in families with old probands, irrespective of MSI status. Blood samples were available from 24 MSI-positive patients for mutation screening of MLH1, MSH2 and MSH6. Sequence variants or rearrangements predicted to affect protein function were found in 16 patients. Six novel variants were found: two large rearrangements, two truncating and two missense mutations. The missense mutations were found to segregate in the families. Studies of allele frequencies, MSI and loss of immunostaning in tumours from family members further supports the hypothesis that these missense changes play a role in Lynch syndrome, as do the non-conservative nature and evolutionary conservation of the amino acid exchanges. Five families had mutations in MLH1, five in MSH2, and six in MSH6. The unexpectedly large impact of MSH6 was in genealogical studies shown to be due to a founder effect. Cumulative risk studies showed that the MSH6 families, despite their late age of onset, have a high lifetime risk for all Lynch syndrome-related cancers, significantly higher in women (89% by age 80 years) than in men (69%). The gender differences are in part due to high endometrial (70%) and ovarian cancer risk (33%) in addition to the high colorectal cancer risk (60%). These findings are of great importance for counselling and surveillance of families with MSH6 mutations. Finally, in a large family with MSI-negative hereditary colorectal cancer for which the MMR genes and APC had been excluded as possible causes, a genome-wide linkage analysis was performed, resulting in a suggested linkage to chromosome 7. Conclusions: Relatives of probands with MSI-positive, double primary colorectal and endometrial cancer diagnosed before the age of 50 years have significantly increased risks of Lynch syndrome-related cancers. MSH6 mutations, which have unusually high impact in this study population due to a founder effect, confer high cumulative risks of cancer despite the generally late age of onset.

This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models. In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision.  Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction. A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes.  The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).

L’approche GWAS est un outil essentiel pour la découverte de gènes associés aux maladies, mais elle pose des problèmes de puissance statistique quand il est impossible d’échantillonner génétiquement des dizaines de milliers de sujets. Les résultats présentés ici—ALDsuite, un programme en utilisant une correction nouvelle et efficace pour le déséquilibre de liaison (DL) ancestrale de la population locale, en permettant l'utilisation de marqueurs denses dans le MALD, et la démonstration que la méthode simpleM fournit une correction optimale pour les comparaisons multiples dans le GWAS—réaffirment la valeur de l'analyse en composantes principales (APC) pour capturer l’essence de la complexité des systèmes de grande dimension. L’APC est déjà la norme pour corriger la structure de la population dans le GWAS; mes résultats indiquent qu’elle est aussi une stratégie générale pour faire face à la forte dimensionnalité des données génomiques d'association. / GWAS is an essential tool for disease gene discovery, but has severe problems of statistical power when it is impractical to genetically sample tens of thousands of subjects. The results presented here—a novel, effective correction for local ancestral population LD allowing use of dense markers in MALD using the ALDsuite and the demonstration that the simpleM method provides an optimum Bonferroni correction for multiple comparisons in GWAS, reiterate the value of PCA for capturing the essential part of the complexity of high- dimensional systems. PCA is already standard for correcting for population substructure in GWAS; my results point to it’s broader applicability as a general strategy for dealing with the high dimensionality of genomic association data.

The regulation of gene expression at the right time, place, and degree is crucial for many cellular processes such as proliferation and development. In addition, in order to maintain cellular life, cells must rapidly and appropriately respond to various environmental stimuli. Sequence-specific transcription factors (TFs) can recognize functional regulatory DNA elements in a sequence-specific manner so that they can regulate only a specific group of genes, a process which enables cells to cope with diverse internal and external stimuli. Human has approximately 1,400 sequence-specific TFs whose aberrant expression causes a wide range of detrimental consequences including developmental disorders, diseases, and cancers; therefore, it is pivotal to identify the binding sites of each sequence-specific TF in order to unravel its roles in and mechanisms of gene regulation. Even though some TFs have been intensively studied, the majority of TFs still remain to be studied, particularly the tasks of identifying their genome-wide target genes and deciphering their biological roles in specific cellular contexts. Many questions remain unanswered: how many sites on the human genome a sequence-specific TF can bind; whether all TF-bound sites are functional; how a TF achieves binding specificity onto its targets; how and to what extent a TF is involved in gene regulation. Comprehensive identification of the binding sites of sequence-specific TFs and follow-up molecular studies including gene expression microarrays will provide close answers to these questions. Chromatin Immunoprecipitation coupled with recently developed high-throughput sequencing (ChIP-seq) allows us to perform genome-scale unbiased identification of the binding sites of sequence-specific TFs. Here, to gain insight into gene regulatory functions of TFs as well as their influences on gene expression, we conducted, in diverse cell lines, genome-wide identification of the binding sites of several sequence-specific TFs (CTCF, E2F4, MYC, Pol II) that are involved in a wide range of biological functions, including cell proliferation, development, apoptosis, genome stability, and DNA repair. Analysis of ChIP-seq data provided not only comprehensive binding profiles of those TF across the genome in diverse cell lines, but also revealed tissue-specific binding of CTCF, MYC, and Pol II as well as combinatorial usage among these three factors. Analyses also showed that some CTCF binding sites were inherited from parents to children and regulated in an individual-specific as well as allele-specific manner. Finally, genome-wide target identification of several TFs will broaden our understanding of the gene regulatory roles of these sequence-specific TFs. / text