Many complex diseases are known to have a substantial genetically heritable component. Elucidation of these genetic risk factors provides increased knowledge of the biological mechanisms that result in the diseases while also presenting new potential targets for therapy. This thesis explores the methodology of mapping genetic loci using isolated populations in the context of quantitative trait analysis. Chapter 1 explores the rational for the project, discussing the benefits of using quantitative traits rather than binary disease status and the pros and cons of using isolated populations. This is followed by a brief history of genetic mapping with reference to type 2 diabetes mellitus (T2D) and related quantitative traits. Chapter 2 introduces the methods used in this thesis. This includes strategies to deal with medication, methods to determine kinship between individuals, linkage analysis, association analysis and meta‐analysis of multiple studies. Chapter 3 presents linkage analysis of T2D related traits carried out in 2 – 4 populations depending on availability of the traits and appropriate marker data. Chapter 4 presents the results of association analysis for T2D related traits in 3 – 5 populations using genome‐wide SNP data. The results using the alternate methods described in chapter 2 are compared using fasting glucose as this was the most widely measured phenotype. Chapter 5 introduces additional traits derived by pulse wave analysis and discusses their relevance to metabolic disease before presenting association analysis using the preferred method from chapter 4. An overall discussion of the strengths and weaknesses of the analysis is given in chapter 6.

Resolving the interplay of the genetic components of a complex disease is a challenging endeavor. Over the past several years, genome-wide association studies (GWAS) have emerged as a popular approach at locating common genetic variation within the human genome associated with disease risk. Assessing genetic-phenotype associations upon hundreds of thousands of genetic markers using the GWAS approach, introduces the potentially high number of false positive signals and requires statistical correction for multiple hypothesis testing. Permutation tests are considered the gold standard for multiple testing correction in GWAS, because they simultaneously provide unbiased Type I error control and high power. However, they demand heavy computational effort, especially with large-scale data sets of modern GWAS. In recent years, the computational problem has been circumvented by using approximations to permutation tests, but several studies have posed sampling conditions in which these approximations are suggestive to be biased. We have developed an optimized parallel algorithm for the permutation testing approach to multiple testing correction in GWAS, whose implementation essentially abates the computational problem. When introduced to GWAS data, our algorithm yields rapid, precise, and powerful multiplicity adjustment, many orders of magnitude faster than existing employed GWAS statistical software. Although GWAS have identified many potentially important genetic associations which will advance our understanding of human disease, the common variants with modest effects on disease risk discovered through this approach likely account for a small proportion of the heritability in complex disease. On the other hand, interactions between genetic and environmental factors could account for a substantial proportion of the heritability in a complex disease and are overlooked within the GWAS approach. We have developed an efficient and easily implemented tool for genetic association studies, whose aim is identifying genes involved in a gene-environment interaction. Our approach is amenable to a wide range of association studies and assorted densities in sampled genetic marker panels, and incorporates resampling for multiple testing correction. Within the context of a case-control study design we demonstrate by way of simulation that our proposed method offers greater statistical power to detect gene-environment interaction, when compared to several competing approaches to assess this type of interaction.

No description available.

A major challenge in medical genetics is to identify the mutations underlying heritable diseases. Dogs are excellent genetic models in the search for causative mutations, as they constitute a large library of naturally occurring heritable diseases many of which are analogous to those suffered by man. In addition, these animals have a genome structure well suited to gene mapping. The Shar-Pei dog has two breed-specific features; a strongly selected for wrinkled skin and a high predisposition to an autoinflammatory disease (AID). Abnormalities in the innate immune system cause this type of disease, presenting as spontaneous attacks of inflammation. Persistent inflammation puts an affected Shar-Pei at risk of amyloidosis, organ failure and premature death. In humans, similar AIDs occur and for a majority of cases, no underlying genetic cause has yet been identified. The aim of this thesis was to use the Shar-Pei as a genetic model for autoinflammation in order to find new genes and signalling pathways involved in disease. In paper I, a pleiotropic mutation was identified that could explain both the wrinkled skin and autoinflammation in Shar-Pei. The mutation is associated with an up-regulation of Hyaluronic Acid Synthase 2 (HAS2). Increased expression of HAS2 leads to abnormal depositions of hyaluronic acid (HA) in the skin, resulting in the wrinkled appearance. When fragmented, HA also function as a damage signal sensed by the innate immune system which then responds with inflammation. By selecting for the wrinkled skin, the autoinflammatory disease has inadvertently been enriched in the breed. In paper II, five different inflammatory signs could be associated with the same genetic risk factor, allowing the introduction of a new terminology: Shar-Pei autoinflammatory disease (SPAID) to describe the whole disease complex. In addition, a modifying locus containing several biologically attractive genes was suggested to contribute to varying incidence of amyloidosis in Shar-Pei. In paper III, signs of pathological changes in HA metabolism were investigated in human AID. HA concentration was found to be both higher in subjects with no molecular diagnosis and also associated to disease activity and severity. Taken together, this suggests HA is also involved in human AID.

This thesis presents genetic studies of atopic dermatitis (AD) and IgA deficiency in dogs. AD is a chronic inflammatory and pruritic skin disorder caused by allergic reactions against environmental allergens. Both genetic and environmental factors are involved in the development of Canine AD (CAD) and human AD. In Paper I, we performed genome-wide association studies (GWAS) and identified a locus on chromosome 27 significantly associated with CAD in German shepherd dogs (GSDs). The locus contains several genes and fine-mapping indicated strongest association close to the candidate gene PKP2. In Paper II, we performed additional fine-mapping and identified four highly associated SNPs located in regions with transcriptional regulatory potential in epithelial and immune cells. The risk alleles were associated with increased transcriptional activity and the effect on expression was cell-type dependent. These data indicate that multiple cell-type specific enhancers regulate the expression of PKP2, and/or the neighboring genes YARS2, DNM1L and FGD4, and predispose GSDs to CAD. IgA deficiency is the most common primary immune deficiency disorder in both humans and dogs, characterized by a higher risk of recurrent mucosal tract infections, allergic and other immune-mediated diseases. In Paper III, we performed the widest screening (to date) of serum IgA levels in dog breeds (Ndogs=1267, Nbreeds=22) and defined eight breeds as predisposed to low IgA levels. In Paper IV, we performed GWAS in four of the breeds defined as prone to low IgA levels. We used a novel percentile groups-approach to establish breed-specific cut-offs to perform analyses in a close to continuous manner. In total, 35 genomic loci were suggestively associated (p<0.0005) to IgA levels, and three genomic regions (including the genes KIRREL3 and SERPINA9) were genome-wide significantly associated with IgA levels in GSDs. A ~20kb long haplotype on chromosome 28, significantly associated to IgA levels in Shar-Pei dogs, was positioned within the first intron of the gene SLIT1 overlapping with a possible dog domestication sweep. This thesis suggests novel candidate genes involved in two immune-mediated disorders in the dog. Hopefully, these results will become an important resource for the genetic research of the corresponding human diseases.

Genetic association analyses have successfully identified thousands of genetic variants contributing to complex disease susceptibility. However, these discoveries do not explain the full heritability of many diseases, due to the limited statistical power to detect loci with small effects, especially in regions with rare variants. The development of new and powerful methods is necessary to fully characterize the underlying genetic basis of complex diseases. Family history (FH) contains information on the disease status of un-genotyped relatives, which is related to the genotypes of probands at disease loci. Exploiting available FH in relatives could potentially enhance the ability to identify associations by increasing sample size. Many studies have very low power for genetic research in late-onset diseases because younger participants do not contribute a sufficient number of cases and older patients are more likely deceased without genotypes. Genetic association studies relying on cases and controls need to progress by incorporating additional information from FH to expand genetic research. This dissertation overcomes these challenges and opens up a new paradigm in genetic research. The first chapter summarizes relevant methods used in this dissertation. In the second chapter, we develop novel methods to exploit the availability of FH in aggregation unit-based test, which have greater power than other existing methods that do not incorporate FH, while maintaining a correct type I error. In the third chapter, we develop methods to exploit FH while adjusting for relatedness using the generalized linear mixed effect models. Such adjustment allows the methods to have well-controlled type I error and maintain the highest sample size because there is no need to restrict the analysis to an unrelated subset in family studies. We demonstrate the flexibility and validity of the methods to incorporate FH from various relatives. The methods presented in the fourth chapter overcome the issue of inflated type I error caused by extremely unbalanced case-control ratio. We propose robust versions of the methods developed in the second and third chapters, which can provide more accurate results for unbalanced study designs. Availability of these novel methods will facilitate the identification of rare variants associated with complex traits.

yes / In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44–66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding.

Aims: To elucidate the aetiopathological mechanisms underlying the IIMs, through a combination of genotyping, serotyping and clinical phenotyping in a large cohort of Caucasian idiopathic inflammatory myopathy (IIM) patients. Methods: A cross-sectional study of prevalent IIM cases, ascertained through the Adult Onset Myositis Immunogenetic Collaboration, was performed. Cases were confirmed as possessing myositis according to Bohan and Peter (Bohan and Peter 1975a; Bohan and Peter 1975b). IIM clinical subtypes studied included polymyositis (PM), dermatomyositis (DM) and myositis associated with other connective tissue disease (myositis/CTD-overlap). Genotyping of major histocompatibility complex genes, including HLA-B, -DR, -DQ, tumour necrosis factor alpha (TNF-α), was performed using commercial kits. Serotyping of a comprehensive range of myositis specific/associated antibodies (MSA/MAAs) was undertaken. Results: Clinical subsets are described within the serological groupings, suggesting that the classification of the IIMs appears to be better served by the serotype than by the clinical subgrouping of disease. The IIMs possess HLA class I and II haplotype associations and genetic differences observed between PM and DM are accounted for by serological differences. The TNF-308A association is not independent of HLA class I, due to the strong LD within the MHC, but does form part of a haplotype with these factors. An absence of routinely tested for MSA/MAAs makes cancer associated myositis (CAM) more likely, especially in the DM subgroup. An antibody against a 155 and 140kDa doublet is associated with the development of CAM. Outcome measures in the IIMs show construct validity. HLA-DRB1*07 appears to predict a milder clinical phenotype with less disability. No convincing gene-environmental interaction was found capable of altering disease susceptibility or clinical phenotype. Conclusions: Myositis disease subtypes therefore appear to be defined by specific haplotypes acting as risk factors for the development of various MSAs and MAAs.

Systemic lupus erythematosus (SLE) is a chronic and complex autoimmune disorder characterized by a failure in the mechanism of self-tolerance and production of autoantibodies, potentially affecting any organ in the body. The genetic factors behind the disease have been extensively studied in the past years and to date a list of more than 30 loci have been associated with SLE. However, very little is known about the functional significance of the risk variants. In this thesis, we focused on the analysis of SLE-associated variants in three genes: interferon regulatory factor 5 (IRF5), CD226 and the microRNA 146a. In paper I, we analyzed four polymorphisms in the IRF5 gene in a large set of individuals from different populations. We replicated a strong association of a promoter indel in our meta-analysis, but expression analysis indicated that it is rather another variant, SNP rs10954213 in the poly(A) signal of the gene that is in fact the major contributor to the altered gene expression in leukocytes. In manuscript II, we further characterized the regulation of IRF5 expression, showing that this gene can be up-regulated by estrogen in PBMCs and monocytes, regardless of the genotype, which could to some extent, explain the sex-bias of SLE. In paper III, we investigated the association of CD226 with SLE and the potential functional effect of the associated variants. The genetic analysis showed an association of a three-SNP-haplotype located at the 3’UTR region of the gene. The risk haplotype correlated with lower CD226 protein expression on the surface of cytotoxic and helper T cells, as well as in NK T cells. Reporter assays pointed to rs727088 in the 3’UTR as the main responsible variant for altered gene expression. In paper IV, we described the association of a variant in microRNA miR-146a, involved in the interferon pathway, with SLE in Europeans, which could in addition be correlated with decreased expression of both mature and primary miR-146a in leukocytes. In summary, we have investigated the genetic association of three genes with SLE in a large cohort of individuals and identified variants responsible for functional alterations of these genes, providing further insight into the pathogenesis of SLE.

<p>The Bayesian approach to model selection allows for uncertainty in both model specific parameters and in the models themselves. Much of the recent Bayesian model uncertainty literature has focused on defining these prior distributions in an objective manner, providing conditions under which Bayes factors lead to the correct model selection, particularly in the situation where the number of variables, <italic>p</italic>, increases with the sample size, <italic>n</italic>. This is certainly the case in our area of motivation; the biological application of genetic association studies involving single nucleotide polymorphisms. While the most common approach to this problem has been to apply a marginal test to all genetic markers, we employ analytical strategies that improve upon these marginal methods by modeling the outcome variable as a function of a multivariate genetic profile using Bayesian variable selection. In doing so, we perform variable selection on a large number of correlated covariates within studies involving modest sample sizes. </p> <p>In particular, we present an efficient Bayesian model search strategy that searches over the space of genetic markers and their genetic parametrization. The resulting method for Multilevel Inference of SNP Associations MISA, allows computation of multilevel posterior probabilities and Bayes factors at the global, gene and SNP level. We use simulated data sets to characterize MISA's statistical power, and show that MISA has higher power to detect association than standard procedures. Using data from the North Carolina Ovarian Cancer Study (NCOCS), MISA identifies variants that were not identified by standard methods and have been externally 'validated' in independent studies. </p> <p></p> <p>In the context of Bayesian model uncertainty for problems involving a large number of correlated covariates we characterize commonly used prior distributions on the model space and investigate their implicit multiplicity correction properties first in the extreme case where the model includes an increasing number of redundant covariates and then under the case of full rank design matrices. We provide conditions on the asymptotic (in <italic>n</italic> and <italic>p</italic>) behavior of the model space prior </p> <p>required to achieve consistent selection of the global hypothesis of at least one associated variable in the analysis using global posterior probabilities (i.e. under 0-1 loss). In particular, under the assumption that the null model is true, we show that the commonly used uniform prior on the model space leads to inconsistent selection of the global hypothesis via global posterior probabilities (the posterior probability of at least one association goes to <italic>1</italic>) when the rank of the design matrix is finite. In the full rank case, we also show inconsistency when <italic>p</italic> goes to infinity faster than the square root of <italic>n</italic>. Alternatively, we show that any model space prior such that the global prior odds of association increases at a rate slower than the square root of <italic>n<italic> results in consistent selection of the global hypothesis in terms of posterior probabilities.</p> / Dissertation