The epigenetic contribution to phenotype is now well established. Studies over the past decade have shown that proteins that are able to establish and propagate epigenetic modifications are essential for mammalian development. Some of the genes involved in these processes have been identified, but the roles of many remain unknown. The mutagenesis screens for modifiers of position effect variegation in Drosophila suggest that there are over 200 genes that are able to modify epigenetic variegation. We emulated this screen in the mouse to identify mammalian modifiers of a variegating transgene. The screen aimed to identify novel genes involved in epigenetic reprogramming, and to generate mouse models to study the impact of disruption to the epigenome. Inbred male mice carrying a variegating GFP transgene expressed in erythrocytes were mutagenised with ENU. Offspring were screened by flow cytometry and in the initial rounds of mutagenesis, 11 dominant mutant lines were identified. These lines were called MommeDs (Modifiers of murine metastable epialleles, dominant). This thesis describes the mapping and phenotypic characterisation of three Momme lines: MommeD7, MommeD8 and MommeD9. The MommeD9 mutation enhances variegation and was mapped to a 3.4 Mb interval on Chromosome 7. A mutation in a 5? splice site was found in the Trim28 gene. Analysis of Trim28 mRNA and protein in heterozygotes showed that the mutant allele was null. Homozygotes die before mid-gestation. Heterozygotes are viable but display variable and complex phenotypes, including infertility, obesity, behavioural abnormalities and premature death. Obese MommeD9 mice have liver steatosis, impaired glucose tolerance and other indicators of metabolic syndrome. This phenotype has not previously been reported for mice haploinsufficient for Trim28. There is considerable variability of phenotypes among inbred MommeD9 heterozygotes, which suggests a role for epigenetics in phenotypic noise or “intangible variation”. MommeD8 is a semi-dominant enhancer of variegation. Some homozygotes are viable but some die around birth. Viable homozygotes weigh less than wildtype littermates and have increased CpG methylation at the GFP transgene enhancer element. The mutation was mapped to a 4 Mb interval on chromosome 4. Extensive candidate gene sequencing failed to find a mutation and so DNA from mutant and wildtype individuals were sequenced across the entire linked interval by 454 Sequencing technology. MommeD8 individuals carry two point mutations, one is intergenic and the other lies in an intron of the Ppie gene. Analysis of Ppie mRNA in heterozygotes and homozygotes shows that mutants have reduced transcript levels, suggesting that a deficiency in Ppie causes the increased silencing of GFP. The Ppie gene has not been reported to be involved in epigenetic reprogramming and little is known about its function. Mice heterozygous for MommeD7 have a marked increase in expression of GFP. Heterozygotes have a range of hematopoietic abnormalities including splenomegaly, anaemia and reticulocytosis. Homozygotes die at birth and appear pale. The increased GFP in the peripheral blood appears to be the consequence of an increase in reticulocytes. The mutation is linked to a 1.5 Mb interval on Chromosome 7. MommeD7 mice appear to have hematopoietic abnormalities that affect the expression of the erythroid-specific GFP reporter transgene. MommeD7 mice serve as a reminder that, as well as discovering bona fide modifiers of epigenetic reprogramming, the ENU screen can also identify hematopoietic mutants.

No description available.

No description available.

This thesis presents genetic studies aiming at enlarging our knowledge regarding the genetic factors underlying two immune-mediated diseases, hypothyroidism and autoimmune Addison’s disease (AAD), in dogs and humans, respectively. Genetic and environmental factors are indicated to contribute to canine hypothyroidism, which can be considered a model for human Hashimoto’s thyroiditis (HT). In Paper I we performed the first genome-wide association (GWA) study of this disease in three high-risk dog breeds (Gordon Setter, Hovawart and Rhodesian Ridgeback). Using an integrated GWA and meta-analysis strategy, we identified a novel hypothyroidism risk haplotype located on chromosome 12 being shared by the three breeds. The identified haplotype, harboring three genes previously not associated with hypothyroidism, is independent of the dog leukocyte antigen region and significantly enriched across the affected dogs. In Paper II we performed a GWA study in another high-risk breed (Giant Schnauzer) and detected an associated locus located on chromosome 11 and conferring protection to hypothyroidism. After whole genome resequencing of a subset of samples with key haplotypes, we fine mapped the region of association that was subsequently screened for the presence of structural variants. We detected a putative copy number variant overlapping with the upstream region of the IFNA7 gene, which is located in a region of high genomic complexity. Remarkably, perturbed activities of type I Interferons have been extensively associated with HT and general autoimmunity. In Paper III we performed the first large-scale genetic study of human AAD, a rare autoimmune disorder characterized by dysfunction and ultimately destruction of the adrenal cortex. We resequenced 1853 immune-related genes comprising of their coding sequences, untranslated regions, as well as conserved intronic and intergenic regions in extensively characterized AAD patients and control samples, all collected in Sweden. We identified BACH2 gene as a novel risk locus associated with AAD, and we showed its independent association with isolated AAD. In addition, we confirmed the previously established AAD association with the human leukocyte antigen complex. The results of these studies will hopefully help increasing the understanding of such diseases in dogs and humans, eventually promoting their well-being.

<p>Predisposition to autoimmune diseases such as, rheumatoid arthritis, diabetes, and multiple sclerosis, is caused by the effect of multiple genes and a strong influence from the environment. </p><p>In this study, I have investigated genetic factors that confer susceptibility to rheumatoid arthritis in a rat model. This work has led to the identification of several chromosomal regions, containing uncharacterized genes that directly or indirectly are associated to the arthritis development in these rats. We have observed that timing, gender, and genetic interactions are features that play a part in the effect that these genetic factors exert. </p><p>Unarguably, animal models for human disorders display differences to the human form of disease. An important fact is however that the same chromosomal regions are identified in both rodent and human studies, which suggests that there are genetic factors that we have in common, which are involved directly or indirectly with an autoimmune response. </p><p>Focusing the interest on these similarities, and on the possibility to apply a wide set of genetic tools, make animal models an invaluable, and probably necessary, instrument to dissect the genetic component of complex disorders. To fully comprehend the genetic basis for a complex disorder like this, will require understanding of how multiple genes interact with each other to cause disease. </p><p>We have been able to demonstrate that chronic arthritis, in a rat model for rheumatoid arthritis, is regulated by several genes and that these act during different temporal phases of the disease. These findings will hopefully contribute to our understanding of the etiology and progression of rheumatoid arthritis.</p>

Predisposition to autoimmune diseases such as, rheumatoid arthritis, diabetes, and multiple sclerosis, is caused by the effect of multiple genes and a strong influence from the environment. In this study, I have investigated genetic factors that confer susceptibility to rheumatoid arthritis in a rat model. This work has led to the identification of several chromosomal regions, containing uncharacterized genes that directly or indirectly are associated to the arthritis development in these rats. We have observed that timing, gender, and genetic interactions are features that play a part in the effect that these genetic factors exert. Unarguably, animal models for human disorders display differences to the human form of disease. An important fact is however that the same chromosomal regions are identified in both rodent and human studies, which suggests that there are genetic factors that we have in common, which are involved directly or indirectly with an autoimmune response. Focusing the interest on these similarities, and on the possibility to apply a wide set of genetic tools, make animal models an invaluable, and probably necessary, instrument to dissect the genetic component of complex disorders. To fully comprehend the genetic basis for a complex disorder like this, will require understanding of how multiple genes interact with each other to cause disease. We have been able to demonstrate that chronic arthritis, in a rat model for rheumatoid arthritis, is regulated by several genes and that these act during different temporal phases of the disease. These findings will hopefully contribute to our understanding of the etiology and progression of rheumatoid arthritis.

Single Nucleotide Polymorphisms (SNPs) are the most frequent genomic variations and the main basis for genetic differences among individuals and many diseases. As genotyping millions of SNPs at once is now possible with the microarrays and advanced sequencing technologies, SNPs are becoming more popular as genomic biomarkers. Like other high-throughput research techniques, genome wide association studies (GWAS) of SNPs usually hit a bottleneck after statistical analysis of significantly associated SNPs, as there is no standardized approach to prioritize SNPs or to select representative SNPs that show association with the conditions under study. In this study, a java based integrated system that makes use of major public databases to prioritize SNPs according to their biological relevance and statistical significance has been constructed. The Analytic Hierarchy Process, has been utilized for objective prioritization of SNPs and a new emerging methodology for second-wave analysis of genes and pathways related to disease associated SNPs based on a combined p-value approach is applied into the prioritization scheme. Using the subset of SNPs that is most representative of all SNPs associated with the diseases reduces the required computational power for analysis and decreases cost of following association and biomarker discovery studies. In addition to the proposed prioritization system, we have developed a novel feature selection method based on Simulated Annealing (SA) for representative SNP selection. The validity and accuracy of developed model has been tested on real life case control data set and produced biologically meaningful results. The integrated desktop application developed in our study will facilitate reliable identification of SNPs that are involved in the etiology of complex diseases, ultimately supporting timely identification of genomic disease biomarkers, and development of personalized medicine approaches and targeted drug discoveries.

Thesis (M.Sc (Clinical Pathology))--Mahidol University, 1982.

Advances in high-throughput genomic technologies have facilitated the collection of DNA information for thousands of individuals, providing unprecedented opportunities to explore the genetic architecture of complex disease. One important finding has been that the majority of variants in the human genome are low in frequency or rare. It has been hypothesised that recent explosive growth of the human population afforded unexpectedly large amounts of rare variants with potentially deleterious effects, suggesting that rare variants may play a role in disease predisposition. But, importantly, rare variants embody a source of information through which we may learn more about our recent evolutionary history. In this thesis, I developed several statistical and computational methods to address problems associated with the analysis of rare variants and, foremost, to leverage the genealogical information they encode. First, one constraint in genome-wide association studies is that lower-frequency variants are not well captured by genotyping methods, but instead are predicted through imputation from a reference dataset. I developed the meta-imputation method to improve imputation accuracy by integrating genotype data from multiple, independent reference panels, which outperformed imputations from separate references in almost all comparisons (mean correlation with masked genotypes r²&GT;0.9). I further demonstrated in simulated case-control studies that meta-imputation increased the statistical power to identify low-frequency variants of intermediate or high penetrance by 2.2-3.6%. Second, rare variants are likely to have originated recently through mutation and thereby sit on relatively long haplotype regions identical by descent (IBD). I developed a method that exploits rare variants as identifiers for shared haplotype segments around which the breakpoints of recombination are detected using non-probabilistic approaches. In coalescent simulations, I show that such breakpoints can be inferred with high accuracy (r²&GT;0.99) around rare variants at frequencies &LT;0.05%, using either haplotype or genotype data. Third, I show that technical error poses a major problem for the analysis of whole-genome sequencing or genotyping data, particularly for alleles below 0.05% frequency (false positive rate, FPR=0.1). I therefore propose a novel approach to infer IBD segments using a Hidden Markov Model (HMM) which operates on genotype data alone. I incorporated an empirical error model constructed from error rates I estimated in publicly available sequencing and genotyping datasets. The HMM was robust in presence of error in simulated data (r²&GT;0.98) while nonprobabilistic methods failed (r²&LT;0.02). Lastly, the age of an allele (the time since its creation through mutation) may provide clues about demographic processes that resulted in its observed frequency. I present a novel method to estimate (rare) allele age based on the inferred shared haplotype structure of the sample. The method operates in a Bayesian framework to infer pairwise coalescent times from which the age is estimated using a composite posterior approach. I show in simulated data that coalescent time can be inferred with high accuracy (rank correlation &GT;0.91) which resulted in a likewise high accuracy for estimated age (&GT;0.94). When applied to data from the 1000 Genomes Project, I show that estimated age distributions were overall conform with frequency-dependent expectations under neutrality, but where patterns of low frequency and old age may hint at signatures of selection at certain sites. Thus, this method may prove useful in the analysis of large cohorts when linked to biomedical phenotype data.

Type 2 diabetes (T2D) has a genetic component which is only partially understood. The majority of genetic variance in disease susceptibility is unaccounted for, whilst the precise transcripts and molecular mechanisms through which most risk variants exert their effect is unclear. A complete understanding of T2D susceptibility mechanisms could have benefits in risk prediction, and in drug discovery through the identification of novel therapeutic targets. Work presented in this thesis aims to define relevant transcripts and disease mechanisms at known susceptibility loci, and to identify disease association with classes of genetic variation other than common single nucleotide polymorphisms (SNPs). KCNQ1 contains intronic variants associated with T2D susceptibility and β-cell dysfunction, but only maternally-inherited alleles confer increased disease risk. It maps within an imprinted domain with an established role in congenital and islet-specific growth phenotypes. Using human adult islet and foetal pancreas samples, I refined the transcripts and developmental stage at which T2D susceptibility must be conferred by demonstrating developmentally plastic monoallelic and biallelic expression. I identified a potential risk mechanism through the effect of T2D risk alleles upon DNA methylation. The disease-associated regions identified through genome-wide association (GWA) studies often contain multiple transcripts. I performed mRNA expression profiling of genes within loci associated with raised proinsulin/insulin ratios in human islets and metabolically relevant tissues. Some genes (notably CT62) were not expressed and therefore excluded from consideration for a risk effect, whilst others (for example C2CD4A) were highlighted as good regional candidates due to specific expression in relevant tissues. GWA studies for T2D risk have focused predominantly upon common single nucleotide polymorphisms. As part of a consortium conducing GWA analysis for copy number variation (CNV) and T2D risk, I optimised and compared alternative methods of CNV genotyping, before using this information to validate two signals of disease association. I genotyped three rare single nucleotide variants emerging from an association study with T2D risk based on imputed data, providing an indication of imputation accuracy and more powerful disease association analysis. These data underscore the challenge of translating association signals to causal mechanisms, and of identifying alternative forms of genomic variation which contribute to T2D risk. My work highlights candidates for functional analysis around proinsulin-associated loci, and makes significant progress towards uncovering risk mechanisms at the KCNQ1 locus.