Advances in molecular marker technologies have led to the development of high throughput genotyping techniques such as Genotyping by Sequencing (GBS), driving the application of genomics in crop research and breeding. They have also supported the use of novel mapping approaches, including Multi-parent Advanced Generation Inter-Cross (MAGIC) populations which have increased precision in identifying markers to inform plant breeding practices. In the first part of this thesis, a high density physical map derived from GBS was used to identify QTLs controlling key agronomic traits of wheat in a genome-wide association study (GWAS) and to demonstrate the practicability of genomic selection for predicting the trait values. The results from GBS were compared to a previous study conducted on the same association mapping panel using a less dense physical map derived from diversity arrays technology (DArT) markers. GBS detected more QTLs than DArT markers although some of the QTLs were detected by DArT markers alone. Prediction accuracies from the two marker platforms were mostly similar and largely dependent on trait genetic architecture. The second part of this thesis focused on MAGIC populations, which incorporate diversity and novel allelic combinations from several generations of recombination. Pedigrees representing a wild rice MAGIC population were used to model MAGIC populations by simulation to assess the level of recombination and creation of novel haplotypes. The wild rice species are an important reservoir of beneficial genes that have been variously introgressed into rice varieties using bi-parental population approaches. The level of recombination was found to be highly dependent on the number of crosses made and on the resulting population size. Creation of MAGIC populations require adequate planning in order to make sufficient number of crosses that capture optimal haplotype diversity. The third part of the thesis considers models that have been proposed for genomic prediction. The ridge regression best linear unbiased prediction (RR-BLUP) is based on the assumption that all genotyped molecular markers make equal contributions to the variations of a phenotype. Information from underlying candidate molecular markers are however of greater significance and can be used to improve the accuracy of prediction. Here, an existing Differentially Penalized Regression (DiPR) model which uses modifications to a standard RR-BLUP package and allows two or more marker sets from different platforms to be independently weighted was used. The DiPR model performed better than single or combined marker sets for predicting most of the traits both in a MAGIC population and an association mapping panel. Overall the work presented in this thesis shows that while these techniques have great promise, they should be carefully evaluated before introduction into breeding programmes.

A complex trait is a trait or disease that is controlled by both genetic and environmental factors, along with their interactions. Trait architecture encompasses the genetic variants and environmental causes of variation in the trait or disease, their effects on the trait or disease and the mechanism by which these factors interact at molecular and organism levels. It is important to understand trait architecture both from a biological viewpoint and a health perspective. In this thesis, I laid emphasis on exploring the influence of familial environmental factors on complex trait architecture alongside the genetic components. I performed a variety of studies to explore the architecture of anthropometric and cardio-metabolic traits, such as height, body mass index, high density lipoprotein content of blood and blood pressure, using a cohort of 20,000 individuals of recent Scottish descent and their phenotype measurements, Single Nucleotide Polymorphism (SNP) data and genealogical information. I extended a method of variance component analysis that could simultaneously estimate SNP-associated heritability and total heritability whilst considering familial environmental effects shared among siblings, couples and nuclear family members. I found that most missing heritability could be explained by including closely related individuals in the analysis and accounting for these close relationships; and that, on top of genetics, couple and sibling environmental effects are additional significant contributors to the complex trait variation investigated. Subsequently, I accounted for couple and sibling environmental effects in Genome- Wide Association Study (GWAS) and prediction models. Results demonstrated that by adding additional couple and sibling information, both GWAS performance and prediction accuracy were boosted for most traits investigated, especially for traits related to obesity. Since couple environmental effects as modelled in my study might, in fact, reflect the combined effect of assortative mating and shared couple environment, I explored further the dissection of couple effects according to their origin. I extended assortative mating theory by deriving the expected resemblance between an individual and in-laws of his first-degree relatives. Using the expected resemblance derived, I developed a novel pedigree study which could jointly estimate the heritability and the degree of assortative mating. I have shown in this thesis that, for anthropometric and cardio-metabolic traits, environmental factors shared by siblings and couples seem to have important effects on trait variation and that appropriate modelling of such effects may improve the outcome of genetic analyses and our understanding of the causes of trait variation. My thesis also points out that future studies on exploring trait architecture should not be limited to genetics because environment, as well as mate choice, might be a major contributor to trait variation, although trait architecture varies from trait to trait.

Zhang, Ji. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 100-104). / Abstracts in English and Chinese. / Chapter CHAPTER 1. --- Introduction / Chapter 1.1. --- Introduction of genetic association studies --- p.1 / Chapter 1.1.1. --- Application of genetic association studies in complex diseases --- p.3 / Chapter 1.1.2. --- Application of genetic association studies in type-2 diabetes --- p.4 / Chapter 1.2. --- Study design of genetic association studies --- p.7 / Chapter 1.3. --- Overview of statistical approaches in association studies --- p.10 / Chapter 1.3.1. --- Preliminary analyses --- p.10 / Chapter 1.3.1.1. --- HardýؤWeinberg equilibrium testing --- p.10 / Chapter 1.3.1.2. --- Inference of missing genotype data --- p.12 / Chapter 1.3.1.3. --- SNP tagging --- p.14 / Chapter 1.3.2. --- Single-point and multipoint tests for association --- p.15 / Chapter 1.4. --- Other relevant methods employed in this study --- p.20 / Chapter 1.4.1. --- Self-Organizing Maps (SOM) with further classification by K-means clustering --- p.20 / Chapter 1.4.2. --- Random forests --- p.27 / Chapter 1.5. --- Main objectives of this study --- p.31 / Chapter CHAPTER 2. --- Materials and methods / Chapter 2.1. --- Study cohort --- p.32 / Chapter 2.2. --- Study design --- p.34 / Chapter 2.2.1. --- Construction of sample sets for each stage using SOM and K-means clustering --- p.34 / Chapter 2.2.2. --- Stage 1 analysis by random forests --- p.37 / Chapter 2.2.3. --- Stage 2 analysis by chi-square test --- p.42 / Chapter 2.2.4. --- Two-stage genetic association study by chi-square test --- p.43 / Chapter 2.2.5. --- Comparison of results: random forests plus chi-square test versus chi-square test --- p.43 / Chapter 2.2.6. --- Validation of results in the whole sample set by allelic chi-square test --- p.44 / Chapter 2.2.7. --- Extensions of the study: cumulative effects of candidate SNPs on risk of type-2 diabetes --- p.45 / Chapter CHAPTER 3. --- Results / Chapter 3.1. --- Effects of sample classification by SOM and K-means clustering --- p.50 / Chapter 3.2. --- Genetic associations in stage 1 --- p.64 / Chapter 3.3. --- Genetic associations in stage 2 and validation of results --- p.69 / Chapter 3.4. --- Cumulative effects of candidate SNPs on risk of type-2 diabetes --- p.76 / Chapter CHAPTER 4. --- Discussion / Chapter 4.1. --- Overall strategy --- p.81 / Chapter 4.1.1. --- Effects of SOM and K-means clustering --- p.82 / Chapter 4.1.2. --- Effects of random forests in the first stage of association study --- p.83 / Chapter 4.1.3. --- Comparison of our method with traditional chi-square test --- p.84 / Chapter 4.1.4. --- Joint effects of candidate SNPs selected by the hybrid method --- p.86 / Chapter 4.2. --- Biological significance of candidate SNPs --- p.88 / Chapter 4.2.1. --- Gene CDKAL1 --- p.89 / Chapter 4.2.2. --- Gene KIAA1305 --- p.90 / Chapter 4.2.3. --- Gene DACH1 --- p.91 / Chapter 4.2.4. --- Gene FUCA1 --- p.92 / Chapter 4.2.5. --- Gene KCNQ1 --- p.93 / Chapter 4.2.6. --- Gene SLC27A1 --- p.94 / Chapter 4.3. --- Limits and improvement of this study --- p.96 / Chapter 4.4. --- Conclusion --- p.99 / REFERENCES --- p.100

Parmi les Prunus, genre botanique d’intérêt économique important, l’abricotier (Prunusarmeniaca L.) est une culture emblématique du Bassin Méditerranéen. Il y est soumis à des contraintes biotiques importantes, parmi lesquelles le dépérissement bactérien, causé par Pseudomonas syringae (Psy), peut mener à des phénomènes de mortalité en verger au niveau des régions à hivers froids et humides. La mise en évidence de différences variétales en verger offre potentiellement des perspectives de contrôle de la maladie à travers le levier génétique. Aussi, ce travail de thèse avait pour principaux objectifs (i) d’identifier chez la plante des régions génomiques liées à la résistance partielle à la bactérie et (ii) d’étudier un plan factoriel d’interaction entre les diversités de la plante et de la bactérie (GxG) afin d’apprécier la généricité de la résistance et sa durabilité. Afin de répondre au premier objectif, deux approches complémentaires ont été mobilisées : une cartographie de QRLs (Quantitative Resistance Loci) sur quatre populations biparentales dont trois sont issues du croisement avec un géniteur commun ainsi qu’une analyse d’association sur une core-collection. Les données phénotypiques mobilisées correspondent à des symptômes issus d’inoculations contrôlées ainsi que des notes de mortalité obtenues suite à infection naturelle en verger. Ces deux approches (analyse de liaison et d’association) ont permis de mettre en évidence 22 QRLs de résistance, parmi lesquels seuls 2 QRLs sur les chromosomes 6 et 7 colocalisent entre les deux approches. Deux régions majeures détectées en étude d’association sur les chromosomes 5 et 6 se sont révélées être en déséquilibre de liaison et contrôlent près de 26 et 43% de la variation des symptômes. Deux mécanismes complémentaires reposant sur le blocage de l’infection de Psy et sur la limitation de la progression locale de la bactérie dans les tissus ont été mis en évidence à travers la détection de QRLs sur les chromosomes 3, 6, 8 d’une part et 1,4et 6 d’autre part. Le second objectif a été abordé grâce à une étude d’un plan factoriel d’interaction entre 20 accessions d’abricotier et 9 souches de Psy, échantillonnées d’après la connaissance de l’épidémiologie de la maladie en verger. L’analyse statistique de ce dispositif mis en œuvre à la fois en verger et en laboratoire a démontré la prédominance de l’effet du facteur souche dans la variabilité des symptômes étudiés et la très faible importance du facteur d’interaction GxG, indiquant une potentielle généricité des facteurs de résistance et des perspectives favorables à leur durabilité en verger.Les résultats issus de cette thèse contribuent à offrir une meilleure compréhension des mécanismes de résistance partielle au dépérissement bactérien de l’abricotier et fournissent des marqueurs et haplotypes, potentiellement mobilisables dans le cadre de programmes d’innovation variétale. / Within the genus Prunus, that contains highly valuable species, apricot (Prunusarmeniaca L.) is an emblematic Mediterranean crop. But apricot cultivation is constrainedby many biotic stresses, among which bacterial canker caused by Pseudomonas syringae(Psy) is particularly severe and can lead to the death of the trees in regions with humidand cold winters. Differences of susceptibilities have been observed between cultivars inorchards and create opportunities for disease management through genetic improvement.This thesis aimed to (i) identify genetic determinants linked to partial resistance to thebacterium and to (ii) study a factorial interaction design between both diversities of theplant and the pathogen (GxG interaction) in order to assess resistance genericity anddurability. With regard to the first objective, two complementary approaches were used :QRL (Quantitative Resistance Loci) mapping over four biparental progenies, amongwhich three were obtained with a cross involving a common genitor, and a genome-wideassociation study on a core-collection. The phenotypic data mobilized in this work rely onsymptoms issued from controlled inoculations and on mortality notations followingnatural infections in the orchard. These approaches led to the detection of 22 QRLs amongwhich only 2 QRLs, located on chromosomes 6 and 7, co-localized between the twomethods. Two main regions detected in the association study, over the chromosomes 5and 6, appeared to be in linkage disequilibrium and controlled 26 and 43% of the variationof the symptoms. A complementarity between two mechanisms, one that involves blockingthe infection of Psy and the other that limits bacterial mobility in the tissues has beenrevealed through the detection of QRLs over chromosomes 3, 6, 8 for one mechanism and1,4, 6 for the other, respectively. The second objective was fulfilled with a study of afactorial interaction design between 20 apricot accessions and 9 Psy strains, which weresampled according to the previous knowledge of the disease epidemiology in the orchard.Statistical analyses of phenotypic data obtained both from the orchard and a laboratorytest showed a clear predominance of the strain effect on symptom variability and a weakimportance of the GxG interaction factor. This last result highlighted a potentialgenericity of the resistance factors and favorable perspectives of durability in the orchard.The results issued from this thesis contribute to a better understanding of the mechanismsunderlying partial resistance of apricot to bacterial canker. Moreover, it provide markersand haplotypes of interest which could be mobilized in breeding programs.

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.

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).

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).

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