Obesity is a serious public health crisis and recent estimates of its incidence are the highest in United States history, with 35% and 17% of American adults and children affected, respectively. The clinical definition of adult obesity is operationalized as a body mass index (BMI) greater than 30 kg/m2. Although the prevalence of common obesity has increased dramatically over the past 30 years–largely thought to be due to changes in the environment, such as high calorie diets and sedentary lifestyles—twin and family studies have shown consistently that relative body weight is under considerable genetic influence in both children and adults, with heritability estimates ranging from 40% to 90%. Elucidating the genetic and environmental liability to relative body weight is an important public health endeavor. To further our understanding of the genetics of BMI and common complex obesity, several studies are described that integrate clinical, twin, and genome-wide association (GWAS) methodology in the context of genetic risk scores, clinical risk prediction, development across adolescence into adulthood, and comorbidity with depression symptoms and smoking behavior. First, in two cross-sectional genetic association studies, the utility of genetic risk sum scores (GRSS) were assessed, which summarize the total number of risk alleles, as an alternative form of replication and for potential clinical utility for obesity risk prediction. Next, since there has been only limited research on when during development BMI-associated variants begin influencing BMI, a longitudinal twin study was utilized to assess the effects of adult-validated BMI-SNPs across adolescence into adulthood. In addition, obesity is comorbid with numerous medical conditions including cardiovascular disease, insulin-resistance and some forms of cancer, as well as, various psychiatric disorders including eating disorders, mood disorders, and substance use. The next series of studies aimed to understand phenotypic and genetic associations between BMI/obesity and binge eating disorder (BED), depression symptoms and smoking behavior. Using a clinical sample of overweight and obese women with and without BED, the relationship of BED, food intake and internalizing symptoms of depression and anxiety was examined. Next, twin study methodology was used to investigate if shared genetic and/or environmental liability was responsible for phenotypic associations found between BMI, depression symptoms, and impulsivity. Finally, a genetic association study aimed at investigating whether genetic variants were associated with multiple behaviors, body composition and smoking behavior, or were trait-specific is presented. By utilizing several samples and methodologies and by pursuing methods development, a comprehensive approach is presented that is hoped to represent a more powerful evidence-based strategy to understanding the genetic and environmental determinants of BMI and common complex obesity, along with associated depression symptoms and smoking behavior.

Diverses lésions ostéochondrales peuvent affecter les articulations des jeunes chevaux et réduire leurs futures performances en course. L’objectif de cette thèse est d’identifier les régions du génome, appelées locus à caractère quantitatif (QTL), associées avec des caractères mesurant l’ostéochondrose (OC) enregistrés dans le programme GENEQUIN sur une population de Trotteurs Français. Le génotypage a été réalisé à l’aide de la puce SNP Illumina BeadChip EquineSNP50, qui est dense et permet d’exploiter le déséquilibre de liaison par des analyses d’association. Ces analyses sont sujettes à certains problèmes en présence d’une structure familiale des données. Dans la première partie de la thèse, une comparaison de la puissance et de la robustesse d’un choix restreint de méthodes d’analyses est effectuée. L’originalité de ce travail réside dans la dérivation algébrique des moments des distributions des statistiques de test comparées, donnant ainsi plus de généralité à nos résultats et permettant une meilleure compréhension des différences. Les résultats peuvent notamment servir à l’optimisation du dispositif expérimental. La deuxième partie est consacrée à la cartographie des régions QTL des caractères mesurant l’OC en différents sites articulaires dans une population de 583 Trotteurs Français. Cette étude a permis de mettre en évidence plusieurs régions QTL d’effets moyens et faibles à un niveau significatif mais pas hautement significatif. Nous montrons que l’OC est un caractère polygénique et qu’aucun QTL, ayant un effet à la fois sur l’OC du jarret et l’OC du boulet, n’est détectable dans ce protocole QTL, ce qui infirme l’hypothèse simple d’une cause génétique commune de la sensibilité à cette maladie sur les différents sites anatomiques. Suite à ces travaux, l’identification des gènes candidats et des mutations causales devrait clarifier la physiopathologie moléculaire de l’OC et ainsi permettre de développer des stratégies efficaces pour l’évaluation des risques. Pendant ce temps, les marqueurs peuvent être utilisés dans un contexte de sélection assistée par marqueurs afin d’améliorer la santé et le bien-être du cheval. / Osteochondral lesions are commonly observed in young horses and may be responsible for reduced performances in racing. The purpose of the PhD thesis was to identify genome regions, called quantitative trait loci (QTL), associated with various traits measuring osteochondrosis (OC) and recorded in the GENEQUIN program in a population of French Trotters horses. Genotyping was performed using the EquineSNP50 Illumina high density chip, which allows to exploit the linkage disequilibrium with genome-wide association studies. These analyses are subject to several problems in presence of family structure. We hence first proposed a comparison of power and robustness of a limited choice of models for this type of analysis. The originality of this work lies in the algebraic derivation of the distribution moments of the test statistics compared, making the outcome of this comparison more general and allowing a better understanding of differences. The results can be used to establish an experimental design. The second part was devoted to the QTL fine mapping of traits that measure OC in different joint sites. This study highlighted several significant QTL with low and medium effects but none of them were highly significant. We showed that OC is a polygenic trait and we were not able to identify QTL affecting both OC on the hock and the fetlock, rejecting the hypothesis of a single genetic determinism of susceptibility to this desease accross anatomical sites. Further studies will now focus on the identification of candidate genes and screening for mutation in an attempt to clarify the molecular physiopathology of OC and develop efficient strategies for risk assessment. Meanwhile, markers could be used in a marker-assisted selection context to improve horse health and welfare.

We developed a pipeline to identify the copy number polymorphisms (CNPs) in the Northern Swedish population using whole genome sequencing (WGS) data. Two different methodologies were applied to discover CNPs in more than 1,000 individuals. We also studied the association between the identified CNPs with the expression level of 438 plasma proteins collected in the same population. The identified CNPs were summarized and filtered as a population copy number matrix for 1,021 individuals in 243,987 non-overlapping CNP loci. For the 872 individuals with both WGS and plasma protein biomarkers data, we conducted linear regression analyses with age and sex as covariance. From the analyses, we detected 382 CNP loci, clustered in 30 collapsed copy number variable regions (CNVRs) that were significantly associated with the levels of 17 plasma protein biomarkers (p < 4.68×10-10).

Métodos de predição podem aumentar consideravelmente a eficiência dos programas de melhoramento de milho. O objetivo deste estudo foi predizer a performance de 250 híbridos simples de milho avaliados em múltiplos ambientes utilizando a informação de marcadores moleculares. Para isso, 50 linhagens endogâmicas provenientes de diferentes populações foram cruzadas com cinco linhagens elite, também endogâmicas, para obtenção dos 250 híbridos simples. As matrizes moleculares das linhagens e dos híbridos foram obtidas a partir da genotipagem das 55 linhagens com 614 marcadores AFLP. Os híbridos simples foram avaliados para produção de grãos em 13 ambientes. A predição dos híbridos foi realizada utilizando o modelo misto BLUP considerando diferentes coeficientes de parentesco e similaridade no estado na predição dos efeitos das capacidades geral e específica de combinação dos genitores. As médias preditas dos híbridos a partir de cada coeficiente foram correlacionadas com as médias fenotípicas para obtenção da acurácia de predição. A predição também foi realizada utilizando o modelo de seleção genômica ampla RR-BLUP. Nesse caso, a matriz molecular dos híbridos foi utilizada diretamente no modelo misto de estimação dos efeitos dos marcadores e da contribuição de cada um deles para o valor genético dos híbridos. Foram realizadas validações cruzadas entre e dentro de ambientes e entre e dentro de grupos de híbridos relacionados a fim de verificar os efeitos do tamanho da população de treinamento (N), número de marcas (NM), interação híbridos x ambientes (H x A) e da estrutura da população na estimativa da acurácia de predição. A predição genômica foi comparada com a seleção fenotípica quanto à eficiência em identificar híbridos superiores em um esquema de melhoramento de milho. Todos os coeficientes de parentesco e similaridade no estado apresentaram elevadas estimativas de acurácia, contudo foi possível observar considerável superioridade dos coeficientes Wang e Rogers Modificado tanto na predição quanto na seleção dos híbridos superiores, demonstrando o potencial dessas metodologias como ferramentas a serem utilizadas nos programas de melhoramento de milho. Os resultados da predição utilizando o modelo de seleção genômica ampla indicaram que o aumento de N e NM não alteraram significativamente as estimativas de acurácia. As estimativas da acurácia na validação cruzada dentro de ambientes foram superiores às obtidas entre ambientes, inferindo que o efeito da interação H x A foi expressivo. Também foram observadas estimativas de acurácia expressivamente maiores para populações de treinamento e validação compostas por híbridos relacionados. Em todos os casos, as estimativas de acurácia apresentaram amplos intervalos em função da amostra de híbridos utilizada nas populações de treinamento e validação, indicando que a seleção genômica pode não ser eficiente dependendo da população amostrada. Os resultados deste estudo sugerem que a predição genômica é uma ferramenta para aumentar a eficiência da seleção nos programas de melhoramento se utilizada de forma adequada pelo melhorista, considerando os efeitos de estrutura de população e interação H x A de forma a maximizar a acurácia e, consequentemente, o sucesso da predição. / Prediction using molecular markers information can greatly increase the efficiency of maize breeding programs. This study aimed to predict the performance of maize single-crosses evaluated in multiple environments and using molecular markers information. Five inbred lines used as testers were crossed to 50 inbred lines from multiple populations to obtain 250 maize single-crosses. 614 AFLP markers were used to asses molecular matrices of the inbred lines and single-crosses. The 250 single-crosses were evaluated for grain yield in 13 environments. Genomic prediction was performed using the mixed model BLUP considering different genomic relationship and similarity in state coefficients to predict the effect of general and specific combining abilities of the parents. Predicted means from each coefficient were correlated with phenotypic means for obtaining prediction accuracy. Genomewide prediction was also performed using the linear regression model RR-BLUP in the estimation of markers genotypic values and its contribution to hybrids genetic values. Cross-validations between and within environments and between and within groups of related single-crosses were performed to verify the effects of training population size (N), number of markers (NM), genotype-by-environment interaction (G x E) and population structure in estimating accuracy. Genomic prediction was compared with phenotypic selection in efficiency of selecting better hybrids in a maize breeding program. All relationship coefficients and similarity in state coefficients showed high values of accuracy, however we observed superiority of Wang relationship coefficient and Modified Rogers similarity coefficient both in predicting and in identifying the best single-crosses, showing the potential of these approaches as tools to be used in maize breeding programs. Genomewide prediction results showed that increasing N and NM did not led to higher accuracy estimates. Predicted accuracies of cross validation analysis within environments were higher than between environments, indicating that the effect of G x E interaction was significant. Greater accuracies were achieved when training and validation set were from related single-crosses. In all scenarios, wide intervals of accuracy were found, meaning that genomic prediction may not be effective depending on the sample used. Therefore, the results of this study suggest that genomic prediction is a tool to increase the efficiency of selection in breeding programs if used properly by breeders, considering the population structure and G x E interaction effect so as to reduce sample problems and maximize accuracy and hence the success of prediction.

The selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals by increasing accuracy of prediction and reducing generation interval, especially for difficult to measure traits, such as feed efficiency. Feed efficiency is the most important trait in animal production due to its impacts on cost of production and environmental factors. Many metrics measure the feed efficiency, such as ratio of gain to feed (FER), the ratio of feed to gain (FCR) and residual feed intake (RFI). Nevertheless, in ovine, no study with the aim of understand the genetic variants or the accuracy of genomic estimated breeding value (GEBV) for feed efficiency traits was published yet. Moreover, before to apply the genomic information, it is necessary to understand and characterized the population structure, for instance, by linkage disequilibrium (LD). Both genome-wide association studies (GWAS) and genomic selection (GS) leverage LD between marker and causal mutation. Based on the above considerations, the aim of this study was to map LD in ovine, characterized by Brazilian Santa Inês sheep; to search genetic variants for feed efficiency traits (FER, FCR and RFI) through GWAS; and to verify the accuracy of GEBV for RFI. In total, 396 samples (animals) of Longissimus dorsi muscle were collect. A high-density panel of SNP (Illumina High-Density Ovine SNP BeadChip®) comprising 54,241 SNPs was used to obtain the genotyping data. The phenotype data was comprised of 387 animals. The average LD between adjacent markers for two LD metrics, r² and |D\'|, were 0.166 and 0.617, respectively. The degree of LD estimated was lower than reported in other species and it was characterized by short haplotype blocks. Consequently, for genomic analyses, high-density panels of marker are recommended. Many markers were associated to feed efficiency traits in GWAS, mainly to RFI trait. Few candidate genes were reported in this study, highlighting NRF-1 (nuclear respiratory factor 1), which controls mitochondrial biosynthesis, the most important process responsible by a great fraction of the produced energy. Finally, we verified the accuracy of GEBV for RFI using few Bayesian regression models, and we found low accuracy, ranging from 0.033 (BayesB with π=0.9912) to 0.036 (BayesA), which might be explained by the low relationship among animals and small training population. / A seleção com base nos valores genéticos genômicos preditos pode aumentar substancialmente a taxa de ganho genético em animais por meio do aumento da acurácia de predição e redução do intervalo de gerações, especialmente para características de difícil e/ou onerosa mensuração, como eficiência alimentar. A eficiência alimentar é uma das características mais importantes na produção animal devido principalmente aos seus impactos econômicos e ambientais. Muitas métricas representam a eficiência alimentar, por exemplo: a relação do ganho de peso e consumo alimentar (EA), a proporção do consumo alimentar e ganho de peso (CA) e o consumo alimentar residual (CAR). Em ovinos, nenhum estudo com o objetivo de buscar variantes genéticas ou verificar a acurácia do valor genético genômico estimado para eficiência alimentar foi publicado. Adicionalmente, antes de aplicar a informação genômica, é necessário compreender e caracterizar a estrutura da população, como por meio do desequilíbrio de ligação (LD). O estudo de associação genômica (GWAS) e seleção genômica (GS) consideram o LD entre marcador e a mutação causal. Com base nas considerações acima, o objetivo deste estudo foi mapear o LD em ovinos, caracterizado pela raça ovina Santa Inês; localizar variantes genéticas para as características de eficiência alimentar (EA, CA e CAR) utilizando a abordagem GWAS; e verificar a acurácia da estimação dos valores genéticos genômico para o CAR. No total, foram coletadas 396 amostras (animais) do músculo Longissimus dorsi, para posterior genotipagem utilizando o painel de alta densidade (Illumina High-Density Ovine SNP BeadChip®), compreendendo 54.241 SNPs. O banco fenotípico é composto por 387 animais. O LD médio entre marcadores adjacentes para duas métricas de LD, r² e |D\'|, foram 0,166 e 0,617, respectivamente. O grau de LD estimado foi menor que o relatado em outras espécies e foi caracterizado por blocos de haplótipos curtos. Consequentemente, para as análises genômicas são recomendados painéis de marcadores de alta densidade. No GWAS, foram encontrados muitos marcadores associados aos fenótipos, em especial, à característica CAR. Alguns genes candidatos foram relatados neste estudo, destacando-se o NRF-1 (fator respiratório nuclear 1), que controla a biossíntese mitocondrial, o processo mais importante responsável por grande parte da produção de energia. Finalmente, verificamos a acurácia do valor genético genômico estimado para o CAR usando modelos de regressão Bayesiana, e encontramos baixos valores para acurácia (0,033 a 0,036) o que pode ser explicado pelo baixo grau de relacionamento entre os indivíduos e tamanho reduzido da população de treinamento.

Die Linksventrikuläre Noncompaction des Myokards (LVNC) ist eine seltene primäre Herzmuskelerkrankung. Es wird angenommen, dass es sich um eine embryonale Entwicklungsstörung des Myokards handelt. Mutationen in dem X-chromosomalen Gen TAZ sind verantwortlich für Fälle von frühkindlicher LVNC während die genetische Ursache autosomal-dominant vererbter adulter LVNC weitgehend unbekannt ist. In dieser Arbeit wurde die genetische Ursache der LVNC in der Familie LVNC-105 untersucht. Weiterhin wurden in einem großen Kollektiv von LVNC-Indexpatienten Kandidatengenanalysen durchgeführt. Bei der Familie LVNC-105 zeigte die genomweite Kopplungsanalyse nur signifikant hohe 2-Punkt-LOD-Werte auf Chromosom 11p15. Der maximale 2-Punkt-LOD-Wert betrug 5,06 bei D11S902 und der Lokus konnte auf 3,2 Mb (4,9 cM) eingeengt werden. Unter den 40 Genen des Erkrankungslokus war das Kandidatengen CSRP3, das bereits für 2 andere Kardiomyopathien, die dilatative und die hypertrophe Kardiomyopathie (DCM und HCM), als Krankheitsgen beschrieben wurde. Die Sequenzierung des genomischen Bereichs von CSRP3 zeigte keine Mutation bei den betroffenen Familienmitgliedern. Auch die Analyse von weiteren, im Lokus enthaltenen Gene ergab keine Mutation in kodierenden Exons. Auch Untersuchungen auf Transkriptebene offenbarten keine genetische Veränderung. Bei der Sequenzierung der LVNC-Kandidatengene LDB3, LMNA, Nkx2.5 und\linebreak BMP10 bei 63 erwachsenen Indexpatienten mit isolierter LVNC wurde nur eine Mutation in LDB3 gefunden. Erstmals wurden auch 7 Gene, die für sarkomere Proteine kodieren und als Krankheitsgene für HCM und DCM bekannt sind, mittels DHPLC untersucht. Es wurden Mutationen in einem großen Anteil der LVNC-Indexpatienten (19%) in MYH7, ACTC, TPM1 und TNNT2 identifiziert. Klinische Untersuchungen zeigten bei 7 von 12 Patienten mit Mutationen das Vorliegen einer familiären LVNC. In 4 autosomal-dominanten LVNC-Familien kosegregierten die MYH7 Mutationen mit der Erkrankung. MYH7 war mit einem Anteil von 13% das häufigste Krankheitsgen. Die Mutationen in MYH7 lagen vorwiegend in der ATP-Bindungsstelle. LVNC gehört damit zum Spektrum der Kardiomyopathien, die durch Mutationen in sarkomeren Proteinen hervorgerufen werden können. / Left ventricular noncompaction of the myocardium (LVNC) constitutes a rare primary cardiomyopathy. The mechanistic basis is assumed to be an arrest in embryonic cardiac development. Mutations in the X-linked TAZ gene are responsible for cases of infantile LVNC whereas the genetic base of late-onset LVNC in most patients is still unresolved. The objectives of this dissertation were to investigate the genetic defect in family LVNC-105 with autosomal dominant inherited LVNC and to screen a large cohort of patients with isolated LVNC for mutations in candidate genes. In kindred LVNC-105 genome wide linkage analysis revealed significant two-point LOD scores only at chromosome 11p15. A peak 2-point LOD score of 5.06 was obtained with marker D11S902 and a critical interval of 3.2 Mb (4.9 cM) was determined. Among the 40 genes within the disease region one candidate gene was CSRP3, a disease gene for hypertrophic (HCM) and dilated (DCM) cardiomyopathy. Sequence analysis of the genomic CSRP3 region did not reveal mutations in affected family members. Also, analysis of the coding region of further candidate genes contained within the disease locus did not show mutations. Investigations of the genes on transcript level did not detect alterations. Candidate gene analysis of LDB3, LMNA, Nkx2.5 and BMP10 in 63 index patients with isolated LVNC only one mutation was detected in LDB3. For the first time 7 genes encoding sarcomere proteins, known as disease genes for HCM and DCM, were screened for mutations by DHPLC in LVNC patients. Mutations were found in a significant proportion of the cohort of LVNC index patients (19%) in MYH7, ACTC, TPM1 and TNNT2. Clinical evaluations demonstrated familial disease in 7 of 12 probands with sarcomere gene mutations. MYH7 mutations segregated with the disease in 4 autosomal dominant LVNC kindreds. MYH7 was identified as the most prevalent LVNC disease gene (13%) in this cohort. Modified residues in MYH7 were mainly located within the ATP binding site. In conclusion, LVNC belongs to the spectrum of cardiomyopathies originating in molecular defects of the sarcomere.

Pour lutter contre les agents phytopathogènes, la sélection de plantes résistantes est la stratégie la plus rentable et la plus écologique. Les résistances quantitatives, basées sur des mécanismes de résistances complexes, sont connues pour être sujettes à l’érosion, en cas d’évolution de l’agressivité des agents pathogènes. L’objectif de ce travail basé sur le pathosystème peuplier – rouille du peuplier (Melampsora larici-populina) est d’évaluer le potentiel évolutif des caractères d’agressivité et morphologiques du parasite par des approches de génétique quantitative et d'identifier les bases génétiques par génétique d'association. Pour estimer la plasticité, l’héritabilité et les compromis évolutifs d’un ensemble de caractères quantitatifs, nous avons précisément mesuré leurs variations dans quatre populations contrastées du champignon. Nous avons montré que le volume des spores est un caractère héritable qui évolue rapidement. La quantité de mycélium in planta est aussi héritable mais très conservée car sous sélection stabilisante dans les populations étudiées. Le temps de latence, la taille des lésions et le taux de sporulation présentent une héritabilité faible, ce qui explique l’absence d’évolution observées au cours du temps pour ces trois caractères. Les caractères liés à la fonction de sporulation semblent être les plus plastiques le long d’un gradient de maturité foliaire. Cependant, l’absence de mise en évidence de compromis évolutifs ne nous a pas permis d’identifier des caractères d’agressivité qui seraient les meilleures cibles pour les résistances quantitatives chez le peuplier. Si aucune base génétique de ces caractères quantitatifs n’a été mise en évidence, nous avons localisé un locus d’avirulence potentiel (Avr7) sur lequel une caractérisation fonctionnelle est envisagée / To control plant pathogens, breeding resistant plants is the most cost-effective and ecological strategy. Quantitative resistances, which are based on complex plant mechanisms, are known to be exposed to erosion through an increase of pathogens aggressiveness. Through the study the poplar – poplar rust (Melampsora larici-populina) pathosystem, this work aims to estimate the evolutionary potential of aggressiveness and morphological traits using quantitative genetic approaches and to identify molecular bases through genome-wide association study. To estimate plasticity, heritability, and trade-offs for a set of quantitative traits, we precisely measured their variation in four contrasted pathogen populations. It appeared that spore volume is highly heritable and evolved rapidly. In planta mycelium quantity is also heritable but constant because of stabilizing selection occurring in the studied populations. Latent period, lesion size and sporulation rate exhibit low heritability, which explains the absence of evolution during the studied time period. Traits involved in the sporulating function seem to be the most plastic ones along a leaf maturity gradient. However, the lack of evidence of trade-offs did not allow us to identify aggressiveness traits that would be the best targets for the construction of durable resistance in poplar. No genetic underpinning has been found for quantitative traits, but we have identified a potential avirulence locus (Avr7), opening the way for its functional characterization

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