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.

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.

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).

No description available.

Patients with schizophrenia and their siblings typically show subtle changes of brain structures, such as a reduction of hippocampal volume. Hippocampal volume is heritable, may explain a variety of cognitive symptoms of schizophrenia and is thus considered an intermediate phenotype for this mental illness. The aim of our analyses was to identify single-nucleotide polymorphisms (SNP) related to hippocampal volume without making prior assumptions about possible candidate genes. In this study, we combined genetics, imaging and neuropsychological data obtained from the Mind Clinical Imaging Consortium study of schizophrenia (n = 328). A total of 743,591 SNPs were tested for association with hippocampal volume in a genome-wide association study. Gene expression profiles of human hippocampal tissue were investigated for gene regions of significantly associated SNPs. None of the genetic markers reached genome-wide significance. However, six highly correlated SNPs (rs4808611, rs35686037, rs12982178, rs1042178, rs10406920, rs8170) on chromosome 19p13.11, located within or in close proximity to the genes NR2F6, USHBP1, and BABAM1, as well as four SNPs in three other genomic regions (chromosome 1, 2 and 10) had p-values between 6.75×10−6 and 8.3×10−7. Using existing data of a very recently published GWAS of hippocampal volume and additional data of a multicentre study in a large cohort of adolescents of European ancestry, we found supporting evidence for our results. Furthermore, allelic differences in rs4808611 and rs8170 were highly associated with differential mRNA expression in the cis-acting region. Associations with memory functioning indicate a possible functional importance of the identified risk variants. Our findings provide new insights into the genetic architecture of a brain structure closely linked to schizophrenia. In silico replication, mRNA expression and cognitive data provide additional support for the relevance of our findings. Identification of causal variants and their functional effects may unveil yet unknown players in the neurodevelopment and the pathogenesis of neuropsychiatric disorders.

No description available.

Meningitis is an infection of the meninges, a layer of tissue surrounding the brain. In cases of pneumococcal meningitis (where the bacterium Streptococcus pneumoniae is the causat- ive agent) this causes severe inflammation, requiring intensive care and rapid antibiotic treatment. The contribution of variation in host and pathogen genetics to pneumococcal meningitis is unknown. In this thesis I develop and apply statistical genetics techniques to identify genomic variation associated with the various stages of pneumococcal meningitis, including colonisation, invasion and severity. I start by describing the development of a method to perform genome-wide association studies (GWAS) in bacteria, which can find variation in bacterial genomes associated with bacterial traits such as antibiotic resistance and virulence. I then applied this method to longitudinal samples from asymptomatic carriage, and found lineages and specific variants associated with altered duration of carriage. To assess meningitis versus carriage samples I applied similar analysis techniques, and found that the bacterial genome is crucial in determining invasive potential. As well as bacterial serotype, which I found to be the main effect, I discovered many independent sequence variants associated with disease. Separately, I analysed within host-diversity during the invasive phase of disease and found it to be of less relevance to disease progression. Finally, I analysed host genotype data from four independent studies using GWAS and heritability estimates to determine the contribution of human sequence variation to pneumococcal meningitis. Host sequence accounted for some variation in susceptibility to and severity of meningitis. The work concludes with a combined analysis of pairs of bacterial and human sequences from meningitis cases, and finds variation correlated between the two.

Human ageing is accompanied by characteristic metabolic and endocrine changes, including altered hormone profiles, insulin resistance and deterioration of skeletal muscle. Obesity and diabetes may themselves drive an accelerated ageing phenotype. Untangling the causal web between ageing, obesity and diabetes is a priority in order to understand their aetiology and improve prevention and management. The role of biological ageing in determining the risk of obesity and associated conditions has often been examined using mean leukocyte telomere length (LTL), a marker of replicative fatigue and senescence. However, considering phenotypes which represent different domains of biological and functional ageing as exposures for obesity and related traits could allow the elucidation of new understudied phenotypes relevant to cardio-metabolic risk in the wider population. This PhD considers the causal role of (1) hand grip strength (HGS), a marker of overall strength and physical functioning, and (2) resting energy expenditure, an indicator of overall energy metabolism and the major component of daily energy expenditure, in cardio-metabolic risk. I also characterise a new and readily-quantifiable marker of age-related genomic instability, mosaic loss of the Y chromosome (mLOY). Observational evidence implicates each of these phenotypes in cardio-metabolic conditions and intermediate phenotypes. However, it is not possible to infer causality from these observational associations due to confounding and reverse-causality. Mendelian randomisation offers a solution to these limitations and can allow the causal nature of these relationships to be investigated. Using population-based data including UK Biobank, this thesis presents the first large-scale genetic discovery effort for each trait and provides new biological insight into their shared and separate aetiology. I used identified variants to investigate the bidirectional causal associations of each trait with cardio-metabolic outcomes, intermediate phenotypes and other related traits such as frailty and mortality. In total I identified 16 loci for hand grip strength, 19 for mLOY, and one signal for REE. I have shown that HGS is likely to be causally linked to fracture risk, and I have identified the important shared genetic architecture between mLOY, glycaemic traits and cancer. I have also demonstrated that at least one known genetic variant contributing to obesity risk acts partially via reduced REE. Overall the findings of my PhD contribute to our wider understanding of the aetiological role of ageing processes in metabolic dysfunction, and have implications for both basic science and translational applications.