GWAS (Genome wide association studies) have aided in the discovery of various novel variants associated with diabetes. However, a detailed study is required to uncover the role of these genes and to determine how their dysfunction affects pathophysiology. Previous work in the lab has been successful in establishing zebrafish as an efficient model to characterise the effects of these candidate genes. Consequently, efforts have been also made to establish zebrafish as an efficient model system for drug screening as well. The current POP (Proof of principle) study aims to find whether treatment with tolbutamide drug in zebrafish carrying MODY (Maturity onset diabetes of the young) mutations has the similar effects in humans. The study employed zebrafish carrying five (gck, hnf1a, hnf1ba, hnf1bb, pdx1) CRISPR induced MODY orthologues. The zebrafish larvae were supplemented with tolbutamide drug from 5dpf till 10dpf (day post fertilisation). At 10dpf, larvae were screened for various glycaemic traits, whole body glucose and lipids as well body size. CRISPR-CAS9- induced mutations were quantified using paired end sequencing. The results showed that treatment with tolbutamide had a significant effect on the hyperglycaemic outcome induced by hnf1bb, hnf1a, and pdx1 mutations which was in line with the known effects of the drug in humans. In conclusion, the POP study proved to be successful in leveraging zebrafish as an efficient model system for, in vivo characterisation of drugs and can likely help to identify novel targets for therapeutic interventions.

Warfarin is one of the most widely used anticoagulants in the world. Treatment is complicated by a large inter-individual variation in the dose needed to reach adequate levels of anticoagulation i.e. INR 2.0 – 3.0. The objective of this thesis was to evaluate which factors, mainly genetic but also non-genetic, that affect the response to warfarin in terms of required maintenance dose, efficacy and safety with special focus on warfarin dose prediction. Through candidate gene and genome-wide studies, we have shown that the genes CYP2C9 and VKORC1 are the major determinants of warfarin maintenance dose. By combining the SNPs CYP2C9 *2, CYP2C9 *3 and VKORC1 rs9923231 with the clinical factors age, height, weight, ethnicity, amiodarone and use of inducers (carbamazepine, phenytoin or rifampicin) into a prediction model (the IWPC model) we can explain 43 % to 51 % of the variation in warfarin maintenance dose. Patients requiring doses < 29 mg/week and doses ≥ 49 mg/week benefitted the most from pharmacogenetic dosing. Further, we have shown that the difference across ethnicities in percent variance explained by VKORC1 was largely accounted for by the allele frequency of rs9923231. Other novel genes affecting maintenance dose (NEDD4 and DDHD1), as well as the replicated CYP4F2 gene, have small effects on dose predictions and are not likely to be cost-effective, unless inexpensive genotyping is available. Three types of prediction models for warfarin dosing exist: maintenance dose models, loading dose models and dose revision models. The combination of these three models is currently being used in the warfarin treatment arm of the European Pharmacogenetics of Anticoagulant Therapy (EU-PACT) study. Other clinical trials aiming to prove the clinical validity and utility of pharmacogenetic dosing are also underway. The future of pharmacogenetic warfarin dosing relies on results from these ongoing studies, the availability of inexpensive genotyping and the cost-effectiveness of pharmacogenetic driven warfarin dosing compared with new oral anticoagulant drugs.

The human genome contains slightly more than 20 000 genes that are expressed in a tissue specific manner. Transcription factors play a key role in gene regulation. By mapping the transcription factor binding sites genome-wide we can understand their role in different biological processes. In this thesis we have mapped transcription factors and histone marks along with nucleosome positions and RNA levels. In papers I and II, we used ChIP-seq to map five liver specific transcription factors that are crucial for liver development and function. We showed that the mapped transcription factors are involved in metabolism and other cellular processes. We showed that ChIP-seq can also be used to detect protein-protein interactions and functional SNPs. Finally, we showed that the epigenetic histone mark studied in paper I is associated with transcriptional activity at promoters. In paper III, we mapped nucleosome positions before and after treatment with transforming growth factor  β (TGFβ) and found that many nucleosomes changed positions when expression changed. After treatment with TGFβ, the transcription factor HNF4α was replaced by a nucleosome in some regions. In paper IV, we mapped USF1 transcription factor and three active chromatin marks in normal liver tissue and in liver tissue of patients diagnosed with alcoholic steatohepatitis. Using gene ontology, we as expected identified many metabolism related genes as active in normal samples whereas genes in cancer pathways were active in steatohepatitis tissue. Cancer is a common complication to the disease and early signs of this were found. We also found many novel and GWAS catalogue SNPs that are candidates to be functional. In conclusion, our results have provided information on location and structure of regulatory elements which will lead to better knowledge on liver function and disease.

First published online: November 1, 2010 / 名古屋大学博士学位論文 学位の種類 : 博士(医学)(課程) 学位授与年月日:平成23年3月25日 久島周氏の博士論文として提出された

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.

This dissertation aims at investigating the association between genotypes and phenotypes in human. Both common and rare regulatory variants have been studied. The phenotypes include disease risk, clinical traits and gene expression levels. This dissertation describes three different types of association study. The first study investigated the relationship between common variants and three sub-clinical traits as well as three complex diseases in the Center for Health Discovery and Well Being study (CHDWB). The second study is GWAS analysis of TNF-α and BMI/CRP conducted as a contribution to meta-GWAS analyses of these traits with investigators at the University of Groningen in the Netherlands, and the 1000 Genomes Consortium. The third study was the most original contribution of my thesis as it assessed the association between rare regulatory variants in promoter regions and gene expression levels. The results clearly show an enrichment of rare variants at both extremes of gene expression. This dissertation provides insight into how common and rare variants associate with broadly-defined quantitative phenotypes. The demonstration that rare regulatory variants make a substantial contribution to gene expression variation has important implications for personalized medicine as it implies that de novo and other rare alleles need to be considered as candidate effectors of rare disease risk.

Genetic mapping of complex traits has been successful over the last decade, with over 2,000 regions in the genome associated to disease. Yet, the translation of these findings into a better understanding of disease biology is not straightforward. The true promise of human genetics lies in its ability to explain disease etiology, and the need to translate genetic findings into a better understanding of biological processes is of great relevance to the community. We hypothesized that integrating genetics and protein- protein interaction (PPI) networks would shed light on the relationship among genes associated to complex traits, ultimately to help guide understanding of disease biology. First, we discuss the design, testing and implementation of a novel in silico approach (“DAPPLE”) to rigorously ask whether loci associated to complex traits code for proteins that form significantly connected networks. Using a high-confidence set of publically available physical interactions, we show that loci associated to autoimmune diseases code for proteins that assemble into significantly connected networks and that these networks are predictive of new genetic variants associated to the phenotypes in question. Next, we study variation in the electrocardiographic QT-interval, a heritable phenotype that when prolonged is a risk factor for cardiac arrhythmia and sudden cardiac death. We show that a large proportion of QT-associated loci encode proteins that are members of complexes identified by immunoprecipitations in mouse cardiac tissue of proteins known to be causal of Mendelian long-QT syndrome. For several of the identified proteins, we show they affect cardiac ion channel currents in model organisms. Using replication genotyping in 17,500 individuals, we use the complexes to identify genome-wide significant loci that would have otherwise been missed. Finally, we consider whether PPIs can be used to interpret rare and de novo variation discovered through recent technological advances in exome-sequencing. We report a highly connected network underlying de novo variants discovered in an autism trio exome-sequencing effort, and we design, test and implement a novel statistical framework (“DAPPLE/SEQ”) to analyze rare inherited variants in the context of PPIs in a way that significantly boosts power to detect association.

Genome-wide association studies (GWAS) of common diseases have identified hundreds of genomic regions harboring disease-associated variants. Translating these findings into an improved understanding of human disease requires identifying the causal variants(s) and gene(s) in the implicated regions which, to date, has only been accomplished for a small number of associations. Several factors complicate the identification of mutations playing a causal role in disease. First, GWAS arrays survey only a subset of known variation. The true causal mutation may not have been directly assayed in the GWAS and may be an unknown, novel variant. Moreover, the regions identified by GWAS may contain several genes and many tightly linked variants with equivalent association signals, making it difficult to decipher causal variants from association data alone. Finally, in many cases the variants with strongest association signals map to non-coding regions that we do not yet know how to interpret and where it remains challenging to predict a variants likely phenotypic impact. Here, we present a framework for the genetic and functional study of intergenic regions identified through GWAS and describe application of this framework to chromosome 9p21: a non-coding region with associations to type 2 diabetes (T2D), myocardial infarction (MI), aneurysm, glaucoma, and multiple cancers. First, we compare methods for genetic fine-mapping of GWAS associations, including methods for creating a more comprehensive catalog of variants in implicated regions and methods for capturing these variants in case- control cohorts. Next, we describe an approach for using massively parallel reporter assays (MPRA) to systematically identify regulatory elements and variants across disease-associated regions. On chromosome 9p21, we fine-map the T2D and MI associations and identify, for each disease, a collection of common variants with equivalent association signals. Using MPRA, we identify hundreds of regulatory elements on chromosome 9p21 and multiple variants (including MI- and T2D-associated variants) with evidence for allelic effects on regulatory activity that can serve as a foundation for further study. More generally, the methods presented here have broad potential application to the many intergenic regions identified through GWAS and can help to uncover the mechanisms by which variants in these regions influence human disease.

No description available.

Taking the advantage of the high-throughput Single Nucleotide Polymorphism (SNP) genotyping technology, Genome-Wide Association Studies (GWASs) are regarded holding promise for unravelling complex relationships between genotype and phenotype. GWASs aim to identify genetic variants associated with disease by assaying and analyzing hundreds of thousands of SNPs. Traditional single-locus-based and two-locus-based methods have been standardized and led to many interesting findings. Recently, a substantial number of GWASs indicate that, for most disorders, joint genetic effects (epistatic interaction) across the whole genome are broadly existing in complex traits. At present, identifying high-order epistatic interactions from GWASs is computationally and methodologically challenging. My dissertation research focuses on the problem of searching genome-wide association with considering three frequently encountered scenarios, i.e. one case one control, multi-cases multi-controls, and Linkage Disequilibrium (LD) block structure. For the first scenario, we present a simple and fast method, named DCHE, using dynamic clustering. Also, we design two methods, a Bayesian inference based method and a heuristic method, to detect genome-wide multi-locus epistatic interactions on multiple diseases. For the last scenario, we propose a block-based Bayesian approach to model the LD and conditional disease association simultaneously. Experimental results on both synthetic and real GWAS datasets show that the proposed methods improve the detection accuracy of disease-specific associations and lessen the computational cost compared with current popular methods.