In biomolecules, the relationship among the sequence, molecular structure, and biological function are of very importance in the development of nanotechnology such as drug discovery. Previous studies involving multiple sequence alignment of biomolecules have demonstrated that interdependent or associated sites are indicative of the structural and functional characteristics of biomolecules, as an extension to methods such as consensus sequences analysis. In this thesis, a new method to detect associated sites in aligned sequence ensembles is proposed. It involves the use of multiple sub-tables (or levels) of two-dimensional contingency table analysis. The idea is to incorporate analysis by following an approach known as granular computing, which represents information at different levels of granularity or resolution. When associations of multiple sites in the sequence alignment converge, they reflect points of interrelatedness among the sites in the biomolecules. The study involves two different phases of analysis. The first phase includes labeling of the molecular sites in the p53 protein multiple sequence alignment according to the detected patterns. The sites are consequently labeled into three different types based on their site characteristics - conserved sites, associated sites, and hypervariate sites. To identify and label the associated sites, the proposed method is employed. In the second phase, the significance of the extracted site patterns is evaluated with respect to some of the structural and functional characteristics of the p53 protein. The results indicate that the extracted site patterns in combination with conserved sites are significantly associated with some of the known functionalities of p53 such as post translational modifications and the mutation frequency of the sites, hence establishing the link between these identified sites and the defined functionality. Furthermore, when these sites are aligned with p63 and p73, the homologs of p53, based on the common domains, the sites significantly discriminate between the human sequences of the p53 family. Therefore, the study confirms the importance of these detected sites that could indicate their differences in cancer suppressing property.

There is evidence from family studies for the importance of genetic factors in the development of autism spectrum disorders (ASDs) but the identification of major genes has not been achieved to date. There are several reports of deletions and duplications in individuals with ASDs, some of which are not unique to an individual. In most cases, the frequencies and relevance of these abnormalities are unknown, as they have been identified serendipitously in one or a few individuals. My overall hypothesis was that such rearrangements would facilitate the identification of “culprit” genes associated with ASDs by identifying a small chromosomal region for candidate gene testing. I molecularly characterized two overlapping 2p15-2p16.1 deletions detected in unrelated individuals with confirmed autistic disorder (Subject 1) or autistic features (Subject 2), a 1.4Mb deletion on chromosome Xp22 (Subject 1) and a duplication of chromosome 7q11.23, reciprocal to the Williams-Beuren Syndrome (WBS) deletion, in one individual with an ASD (Subject 3). Using real-time semi-quantitative PCR, I screened a total of 798 individuals with an ASD and 186 healthy controls for the presence of similar abnormalities. No additional cases were identified in either group. Subsequently, I selected 6 genes [Orthodenticle homolog 1 (OTX1), Variable charge, X-linked (VCX), Neuroligin 4, X-linked (NLGN4X), Syntaxin 1A (STX1A), Cytoplasmic linker 2 (CYLN2) and General transcription factor IIi (GTF2i)], based on their function and localization within or in the vicinity of the rearrangements and tested them for association with ASDs. Although there was no evidence for association for any marker or haplotype in most of the genes tested, this was not so for GTF2i. Haplotype transmission disequilibrium testing revealed an increased transmission, from healthy parents to their affected offspring, of the common alleles of one marker and one haplotype in GTF2i (P = 0.0010 and 0.0005, respectively). This gene encodes a brain-expressed transcription factor previously implicated in the mental retardation associated with WBS. Based on these findings, I propose that, although the genomic rearrangements reported herein are not a common cause of ASDs, the GTF2i gene within the WBS critical region is important in the aetiology of autism. / Thesis (Ph.D, Physiology) -- Queen's University, 2009-11-20 00:35:11.727

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Modern medical practice and science involve complex phenotypic definitions. Understanding patterns of association across this range of phenotypes requires co-analysis of high-dimensional association studies in order to characterise shared and distinct elements. In this thesis I address several problems in this area, with a general linking aim of making more efficient use of available data. The main application of these methods is in the analysis of genome-wide association studies (GWAS) and similar studies. Firstly, I developed methodology for a Bayesian conditional false discovery rate (cFDR) for levering GWAS results using summary statistics from a related disease. I extended an existing method to enable a shared control design, increasing power and applicability, and developed an approximate bound on false-discovery rate (FDR) for the procedure. Using the new method I identified several new variant-disease associations. I then developed a second application of shared control design in the context of study replication, enabling improvement in power at the cost of changing the spectrum of sensitivity to systematic errors in study cohorts. This has application in studies on rare diseases or in between-case analyses. I then developed a method for partially characterising heterogeneity within a disease by modelling the bivariate distribution of case-control and within-case effect sizes. Using an adaptation of a likelihood-ratio test, this allows an assessment to be made of whether disease heterogeneity corresponds to differences in disease pathology. I applied this method to a range of simulated and real datasets, enabling insight into the cause of heterogeneity in autoantibody positivity in type 1 diabetes (T1D). Finally, I investigated the relation of subtypes of juvenile idiopathic arthritis (JIA) to adult diseases, using modified genetic risk scores and linear discriminants in a penalised regression framework. The contribution of this thesis is in a range of methodological developments in the analysis of high-dimensional association study comparison. Methods such as these will have wide application in the analysis of GWAS and similar areas, particularly in the development of stratified medicine.