<p>Systemic lupus erythematosus (SLE) is a complex autoimmune disease with unknown etiology. The aim of this thesis was to identify susceptibility regions through genetic mapping, using model-based linkage analysis on nuclear and extended SLE multicase families.</p><p>In the first paper we performed a genome scan on 19 genetically homogenous Icelandic and Swedish families. One region at 2q37 was identified with a significant linkage with contribution from both populations (Z=4.24). Five other regions 2q11, 4p13, 9p22, 9p13 and 9q13 showed suggestive linkage (Z>2.0).</p><p>In the second paper, 87 families from 10 different countries were analysed only for chromosome 1. One region at 1q31 showed significant linkage (Z=3.79) with contribution from families from all populations, including Mexicans and Europeans. Four other regions 1p36, 1p21, 1q23, and 1q25, showed levels of suggestive linkage. Linkage for most regions was highly dependent on what population was used, which indicated strong genetic heterogeneity in the genetic susceptibility for SLE.</p><p>In the two last papers, we used the positional candidate gene strategy, in order to investigate candidate genes in two regions linked to SLE. For the Bcl-2 gene (at 18q21) we could not detect any association with SLE using three different markers. However, when we investigated the tightly linked low-affinity family of FcγR genes (at 1q23), we could find association for two risk alleles in the FcγRIIA and FcγRIIIA genes. The risk alleles were transmitted to SLE patients on one specific haplotype and therefore are not independent risk alleles.</p><p>The results show that model-based linkage analysis is a strong approach in the search for susceptibility genes behind complex diseases like SLE.</p>

Systemic lupus erythematosus (SLE) is a complex autoimmune disease with unknown etiology. The aim of this thesis was to identify susceptibility regions through genetic mapping, using model-based linkage analysis on nuclear and extended SLE multicase families. In the first paper we performed a genome scan on 19 genetically homogenous Icelandic and Swedish families. One region at 2q37 was identified with a significant linkage with contribution from both populations (Z=4.24). Five other regions 2q11, 4p13, 9p22, 9p13 and 9q13 showed suggestive linkage (Z&gt;2.0). In the second paper, 87 families from 10 different countries were analysed only for chromosome 1. One region at 1q31 showed significant linkage (Z=3.79) with contribution from families from all populations, including Mexicans and Europeans. Four other regions 1p36, 1p21, 1q23, and 1q25, showed levels of suggestive linkage. Linkage for most regions was highly dependent on what population was used, which indicated strong genetic heterogeneity in the genetic susceptibility for SLE. In the two last papers, we used the positional candidate gene strategy, in order to investigate candidate genes in two regions linked to SLE. For the Bcl-2 gene (at 18q21) we could not detect any association with SLE using three different markers. However, when we investigated the tightly linked low-affinity family of FcγR genes (at 1q23), we could find association for two risk alleles in the FcγRIIA and FcγRIIIA genes. The risk alleles were transmitted to SLE patients on one specific haplotype and therefore are not independent risk alleles. The results show that model-based linkage analysis is a strong approach in the search for susceptibility genes behind complex diseases like SLE.

Human complex diseases, like Diabetes and Cancer, affect many people worldwide today. Despite existing knowledge, many of these diseases are still not preventable. Complex diseases are known to be caused by a combination of genetic factors, as well as environmental and life style factors. The scope of this investigation covered the genomics of Type 1 Diabetes (T1D). There are 49 human genomic regions that are known to carry markers (disease-associated single nucleotide mutations) for T1D, and these were extensively studied in this research. The aim was to find out in how far this disease may be caused by problems in gene regulation rather than in gene coding. For this, the genetic factors associated with T1D, including the single point mutations and susceptibility regions, were characterised on the basis of their genomic attributes. Furthermore, mutations that occur in binding sites for transcription factors were analysed for change in the conspicuousness of their binding region, caused by allele substitution. This is called SNP (Single nucleotide polymorphism) sensitivity. From this study, it was found that the markers for T1D are mostly non-coding SNPs that occur in introns and non-coding gene transcripts, these are structures known to be involved in gene regulatory activity. It was also discovered that the T1D susceptibility regions contain an abundance of intronic, non-coding transcript and regulatory nucleotides, and that they can be split into three distinct groups on the basis of their structural and functional genomic contents. Finally, using an algorithm designed for this study, thirty-seven SNPs that change the representation of their surrounding region were identified. These regulatory mutations are non-associated T1D-SNPs that are mostly characterised by Cytosine to Thymine (C-T) transition mutations. They were found to be closer in average distance to the disease-associated SNPs than other SNPs in binding sites, and also to occur frequently in the binding motifs for the USF (Upstream stimulatory factor) protein family which is linked to problems in Type 2 diabetes.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

The aetiology of common diseases is shaped by the effects of genetic and environmental factors. Big efforts have been devoted to unravel the genetic basis of disease with the hope that it will help to develop new therapeutic treatments and to achieve personalized medicine. With the development of high-throughput genotyping technologies, hundreds of association studies have described many loci associated to disease. However, the depiction of disease architecture remains incomplete. The aim of this work is to perform exhaustive comparisons across human populations to evaluate pressing questions. Our results provide new insights in the allele frequency of risk variants, their sharing across populations and the likely architecture of disease / La etiología de las enfermedades comunes está formada por factores genéticos y ambientales. Se ha puesto mucho empeño en describir sus bases genéticas. Este conocimiento será útil para desarrollar nuevas terapias y la medicina personalizada. Gracias a las técnicas de genotipado masivo, centenares de estudios de asociación han descrito una infinidad de genes asociados a enfermedad. Pese a ello, la arquitectura genética de las enfermedades no ha sido totalmente descrita. Esta tesis pretende llevar a cabo exhaustivas comparaciones entre poblaciones para responder diversas preguntas candentes. Nuestros resultados dan pistas sobre la frecuencia de los alelos de riesgo, su presencia entre poblaciones y la probable arquitectura de las enfermedades.

Multiple health issues are associated with obesity and numerous factors are causative of the disease. The role of genetic factors is well established, as is the knowledge that dietary and sedentary behavior promotes weight gain. Although there is strong suspicion towards the role of epigenetics as a driving force toward disease, this field remains l in the context of obesity. DNA methylation correlation networks were profiled from blood samples of 69 adolescents of two distinct weight-classes; obese (n=35) and normal-weight (n=34). The network analysis revealed major differences in the organization of the networks where the network of the obese had less modularity compared to normal-weight. This is manifested by more and smaller clusters in the obese, pertaining to genes of related functions and pathways, than the network of the normal-weight. Consequently, this suggests that biological pathways have a lower order of coordination between each other in means of DNA methylation in obese than normal-weight. Analysis of highly connected genes, hubs, in the two networks suggests that the difference in coordination between biological pathways may be derived by changes of the methylation pattern of these hubs; highly connected genes in one network had an intriguingly low connectivity in the other. In conclusion, the results suggest differential regulation of transcription through changes in the coordination of DNA methylation in overweight and normal weighted individuals. The findings of this study are a major step towards understanding the role of DNA methylation in obesity and provide potential biomarkers for diagnosing and predicting obesity.

The worldwide increasing number of persons affected by largely preventable diseases like diabetes demands better prevention and treatment. Insulin is required for effective utilisation of circulating nutrients. Impaired responsiveness to insulin (insulin resistance, IR) is a hallmark of type 2 diabetes and independently raises the risk of heart attack and stroke. The pathophysiology of IR is incompletely understood. High-throughput measurement of large numbers of circulating biomarkers may provide new insights beyond established risk factors. The aims of this thesis were to (i) use proteomics, metabolomics and genomics methods in large community samples to identify biomarkers of IR; (ii) assess biomarkers for risk prediction and insights into aetiology and consequences of IR; and (iii) use Mendelian randomisation analysis to assess causality. In Study I, analysis of 80 circulating proteins in 70-to-77-year-old Swedes identified cathepsin D as a biomarker for IR and highlighted a tentative causal effect of IR on raised plasma tissue plasminogen activator levels. In Study II, nontargeted fasting plasma metabolomics was used to discover 52 metabolites associated with glycaemic traits in non-diabetic 70-year-old men. Replication in independent samples of several thousand persons provided evidence for a causal effect of IR on reduced plasma oleic acid and palmitoleic acid levels. In Study III, nontargeted metabolomics in plasma samples obtained at three time points during an oral glucose challenge in 70-year-old men identified associations between a physiologic measure of IR and concentration changes in medium-chain acylcarnitines, monounsaturated fatty acids, bile acids and lysophosphatidylethanolamines. Study IV provided evidence in two large longitudinal cohorts for causal effects of type 2 diabetes and impaired insulin secretion on raised coronary artery disease risk. In conclusion, the Studies in this thesis provide new insights into the pathophysiology and adverse health consequences of IR and illustrate the value of combining traditional epidemiologic designs with recent molecular techniques and bioinformatics methods. The results provide limited evidence for the role of circulating proteins and small molecules in IR and require replication in separate studies and validation in experimental designs.

El treball presentat en aquesta tesi és un estudi de la diversitat genètica en un conjunt de gens implicats en funcions neurològiques ("Gens cerebrals"). Hom ha examinat vint-i-dos gens implicats en els sistemes de neurotransmissió dopaminèrgic, serotoninèrgic i glutamatèrgic. L'objectiu de l'estudi té dos vessants: per una banda l'anàlisi de la diversitat genètica en un conjunt de gens implicats en malaltia humana, en aquest cas en malaltia psiquiàtrica, en poblacions humanes mundials, amb la intenció d'assentar les bases per propers esforços de mapatge genètic; i per altra banda analitzar la diversitat genètica en aquest conjunt de gens per tal de descobrir evidències d'esdeveniments històrics, incloent possibles evidències de selecció. / The work presented in this thesis is a study of the genetic variation in a set of genes related to neurological function ('Brain genes'). Twenty two genes are examined, all of which are involved in either the Dopaminergic, Serotonergic or the Glutamatergic systems of neurotransmission.The objective of the study has two aspects: on the one hand the analysis of genetic variation in a set of genes which are implicated in human disease, in this case psychiatric disease, across global human populations, towards the end of providing some new insight for gene mapping efforts, and on the other hand the study of genetic variation in this set of genes may reveal traces of the population history events undergone, including possible evidence for selection.