Thesis advisor: Gabor T. Marth / Structural variations (SVs), like single nucleotide polymorphisms (SNPs) and short insertion-deletion polymorphisms (INDELs), are a ubiquitous feature of genomic sequences and are major contributors to human genetic diversity and disease. Due to technical difficulties, i.e. the high data-acquisition cost and/or low detection resolution of previous genome-scanning technologies, this source of genetic variation has not been well studied until the completion of the Human Genome Project and the emergence of next-generation sequencing (NGS) technologies. The assembly of the human genome and economical high-throughput sequencing technologies enable the development of numerous new SV detection algorithms with unprecedented accuracy, sensitivity and precision. Although a number of SV detection programs have been developed for various SV types, such as copy number variations, deletions, tandem duplications, inversions and translocations, some types of SVs, e.g. copy number variations (CNVs) in capture sequencing data and mobile element insertions (MEIs) have undergone limited study. This is a result of the lack of suitable statistical models and computational approaches, e.g. efficient mapping method to handle multiple aligned reads from mobile element (ME) sequences. The focus of my dissertation was to identify and characterize CNVs in capture sequencing data and MEI from large-scale whole-genome sequencing data. This was achieved by building sophisticated statistical models and developing efficient algorithms and analysis methods for NGS data. In Chapter 2, I present a novel algorithm that uses the read depth (RD) signal to detect CNVs in deep-coverage exon capture sequencing data that are originally designed for SNPs discovery. We were one of the early pioneers to tackle this problem. In Chapter 3, I present a fast, convenient and memory-efficient program, Tangram, that integrates read-pair (RP) and split-read (SR) signals to detect and genotype MEI events. Based on the results from both simulated and experimental data, Tangram has superior sensitivity, specificity, breakpoint resolution and genotyping accuracy, when compared to other recently published MEI detection methods. Lastly, Chapter 4 summarizes my work for SV detection in human genomes during my PhD study and describes the future direction of genetic variant researches. / Thesis (PhD) — Boston College, 2013. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

Copy Number Variants (CNVs) are defined as DNA segments of 1kb or more in length and present in a variable number of copies in the human genome. It has been recently shown that many human genetic diseases including organ malformations are caused by CNVs in a patient's genome. However, the genetic and molecular basis for Renal Agenesis (RA), which is a medical condition whereby unilateral or bilateral fetal kidneys fail to develop, has not yet been extended to CNV studies. By using array-based Comparative Genomic Hybridization, we are analyzing DNA from patients who have RA in order to identify CNVs that are causative for RA; genes within the CNVs will then be assessed for their potential involvement in RA by altering their dose in Xenopus embryos.

Non-small cell lung cancer (NSCLC) constitutes a clinically, histologically, and genetically heterogeneous disease entity that represents a major cause of cancer-related death. Early-stage patients, who undergo surgery with curative intent, experience high recurrence rates and the effect of adjuvant treatment is modest. Prognostic biomarkers would be of particular relevance to guide intensified treatment depending on expected outcome and moreover often infer a biological role in tumourigenesis. This thesis presents a translational study approach to establish a well-characterised NSCLC frozen-tissue cohort and to obtain a profile of each specimen with regard to genome-wide copy number alterations, global gene expression levels and somatic mutations in selected cancer-related genes. Furthermore, the generation of a formalin-fixed, paraffin-embedded tissue microarray enabled validation of findings on the protein level using immunohistochemistry. The comprehensive molecular characterisation, combined with data on clinical parameters, enabled the analysis of biomarkers linked to disease outcome. In Paper I, single nucleotide polymorphism arrays were applied to assess copy number alterations in NSCLC and associations with overall survival in adenocarcinoma and squamous cell carcinoma were described. In Paper II, we evaluated expression levels of selected stromal proteins in NSCLC using immunohistochemistry and the adhesion molecule CD99 was identified as an outcome-related biomarker in two independent cohorts. Paper III presents a strategy for prognostic biomarker discovery based on gene expression profiling, meta-analysis, and validation of protein expression on tissue microarrays, and suggests the putative tumour suppressor CADM1 as a candidate biomarker. In Paper IV, we propose a prognostic role for tumour-infiltrating IGKC-expressing plasma cells in the local tumour microenvironment, indicating an involvement of the humoral immune response in anti-tumor activity. In Paper V, we combined next-generation deep sequencing with statistical analysis of the TP53 database to define novel parameters for database curation. In summary, this thesis exemplifies the benefits of a translational study approach, based on a comprehensive tumour characterisation, and describes molecular markers associated with clinical outcome in NSCLC.

DNA copy number variations (CNVs), which involve the deletion or duplication of subchromosomal segments of the genome, have become a focus of genetics research. This dissertation develops Bayesian HMMs for finding CNVs from single nucleotide polymorphism (SNP) arrays. A Bayesian framework to reconstruct the DNA copy number sequence from the observed sequence of SNP array measurements is proposed. A Markov chain Monte Carlo (MCMC) algorithm, with a forward-backward stochastic algorithm for sampling DNA copy number sequences, is developed for estimating model parameters. Numerous versions of Bayesian HMMs are explored, including a discrete-time model and different models for the instantaneous transition rates of change among copy number states of a continuous-time HMM. The most general model proposed makes no restrictions and assumes the rate of transition depends on the current state, whereas the nested model fixes some of these rates by assuming that the rate of transition is independent of the current state. Each model is assessed using a subset of the HapMap data. More general parameterizations of the transition intensity matrix of the continuous-time Markov process produced more accurate inference with respect to the length of CNV regions. The observed SNP array measurements are assumed to be stochastic with distribution determined by the underlying DNA copy number. Copy-number-specific distributions, including a non-symmetric distribution for the 0-copy state (homozygous deletions) and mixture distributions for 2-copy state (normal), are developed and shown to be more appropriate than existing implementations which lead to biologically implausible results. Compared to existing HMMs for SNP array data, this approach is more flexible in that model parameters are estimated from the data rather than set to a priori values. Measures of uncertainty, computed as simulation-based probabilities, can be determined for putative CNVs detected by the HMM. Finally, the dissertation concludes with a discussion of future work, with special attention given to model extensions for multiple sample analysis and family trio data.

DNA copy number variations (CNVs), which involve the deletion or duplication of subchromosomal segments of the genome, have become a focus of genetics research. This dissertation develops Bayesian HMMs for finding CNVs from single nucleotide polymorphism (SNP) arrays. A Bayesian framework to reconstruct the DNA copy number sequence from the observed sequence of SNP array measurements is proposed. A Markov chain Monte Carlo (MCMC) algorithm, with a forward-backward stochastic algorithm for sampling DNA copy number sequences, is developed for estimating model parameters. Numerous versions of Bayesian HMMs are explored, including a discrete-time model and different models for the instantaneous transition rates of change among copy number states of a continuous-time HMM. The most general model proposed makes no restrictions and assumes the rate of transition depends on the current state, whereas the nested model fixes some of these rates by assuming that the rate of transition is independent of the current state. Each model is assessed using a subset of the HapMap data. More general parameterizations of the transition intensity matrix of the continuous-time Markov process produced more accurate inference with respect to the length of CNV regions. The observed SNP array measurements are assumed to be stochastic with distribution determined by the underlying DNA copy number. Copy-number-specific distributions, including a non-symmetric distribution for the 0-copy state (homozygous deletions) and mixture distributions for 2-copy state (normal), are developed and shown to be more appropriate than existing implementations which lead to biologically implausible results. Compared to existing HMMs for SNP array data, this approach is more flexible in that model parameters are estimated from the data rather than set to a priori values. Measures of uncertainty, computed as simulation-based probabilities, can be determined for putative CNVs detected by the HMM. Finally, the dissertation concludes with a discussion of future work, with special attention given to model extensions for multiple sample analysis and family trio data.

Somatic copy number alterations (SCNAs) are a class of alterations that lead to deviations from diploidy in developing and established tumors. A feature that distinguishes SCNAs from other alterations is their genomic footprint. The large genomic footprint of SCNAs in a typical cancer's genome presents both a challenge and an opportunity to find targetable vulnerabilities in cancer. Because a single event affects many genes, it is often challenging to identify the tumorigenic targets of SCNAs. Conversely, events that affect multiple genes may provide specific vulnerabilities through "bystander" genes, in addition to vulnerabilities directly associated with the targets. We approached the goal of understanding how the structure of SCNAs may lead to dependency in two ways. To improve our understanding of how SCNAs promote tumor progression we analyzed the SCNAs in 4934 primary tumors in 11 common cancers collected by the Cancer Genome Atlas (TCGA). The scale of this dataset provided insights into the structure and patterns of SCNA, including purity and ploidy rates across disease, mechanistic forces shaping patterns of SCNA, regions undergoing significantly recurrent SCNAs, and correlations between SCNAs in regions implicated in cancer formation. In a complementary approach, we integrating SCNA data and pooled RNAi screening data involving 11,000 genes across 86 cell lines to find non-driver genes whose partial loss led to increased sensitivity to RNAi suppression. We identified a new set of cancer specific vulnerabilities predicted by loss of non-driver genes, with the most significant gene being PSMC2, an obligate member of the 26S proteasome. Biochemically, we found that PSMC2 is in excess of cellular requirement in diploid cells, but becomes the stoichiometric limiting factor in proteasome formation after partial loss of this gene. In summary, my work improved our understanding of the structure and patterns of SCNA, both informing how cancers develop and predicting novel cancer vulnerabilities. Our characterization of the SCNAs present across 5000 tumors uncovered novel structure in SCNAs and significant regions likely to contain driver genes. Through integrating SCNA data with the results of a functional genetic screen, we also uncovered a new set of vulnerabilities caused by unintended loss of non-driver genes.

No description available.

Schizophrenia is a genetically complex neuropsychiatric disease. Myths and uncertainty about aetiology, and concerns about familial recurrence, may contribute to the significant stigma and burden on families. There has recently been concrete progress in understanding individual genetic causes of schizophrenia, which are now known to extend beyond 22q11.2 microdeletions to include other large rare copy number variations. However, there are limited data on issues germane to the translation of these genetic discoveries into clinical practice. The aim of this thesis was to evaluate the contemporary clinical applicability of genetic testing and genetic counselling in schizophrenia. First, general genetic counselling was provided to both adults with schizophrenia without individually relevant genetic test results and their family members. Pre-counselling, there was evidence of widespread misconceptions about schizophrenia aetiology and familial recurrence risks, which were associated with considerable psychological distress. Post-counselling, the myriad significant lasting benefits of genetic counselling included reductions in stigma. The results provided initial evidence of need for, and efficacy of, genetic counselling for schizophrenia. A first ever study was then conducted of the impact of providing a specific aetiological explanation for schizophrenia. Affected individuals and family members were found to value a molecular genetic diagnosis of a 22q11.2 microdeletion for its ability to explain the presence of stigmatized neuropsychiatric conditions. An investigation of transmission patterns and reproductive fitness associated with 22q11.2 microdeletions provided novel insights into the evolutionary biology and clinical correlates of this structural rearrangement. The results demonstrated the scientific and clinical benefits of identifying a genetic subtype of schizophrenia. Last, high resolution genome-wide microarrays were used to investigate rare copy number variations in a prospectively recruited community-based schizophrenia cohort. Clinically significant variants were greatly enriched in schizophrenia, even with 22q11.2 microdeletions a priori excluded. The collective prevalence of these genetic variants in a single community catchment area was high, approaching that seen in autism, where clinical microarray testing is now a first-tier diagnostic test. Collectively, the findings of these pioneering studies suggest a role for genetic testing and genetic counselling in the contemporary management of schizophrenia.

Schizophrenia is a genetically complex neuropsychiatric disease. Myths and uncertainty about aetiology, and concerns about familial recurrence, may contribute to the significant stigma and burden on families. There has recently been concrete progress in understanding individual genetic causes of schizophrenia, which are now known to extend beyond 22q11.2 microdeletions to include other large rare copy number variations. However, there are limited data on issues germane to the translation of these genetic discoveries into clinical practice. The aim of this thesis was to evaluate the contemporary clinical applicability of genetic testing and genetic counselling in schizophrenia. First, general genetic counselling was provided to both adults with schizophrenia without individually relevant genetic test results and their family members. Pre-counselling, there was evidence of widespread misconceptions about schizophrenia aetiology and familial recurrence risks, which were associated with considerable psychological distress. Post-counselling, the myriad significant lasting benefits of genetic counselling included reductions in stigma. The results provided initial evidence of need for, and efficacy of, genetic counselling for schizophrenia. A first ever study was then conducted of the impact of providing a specific aetiological explanation for schizophrenia. Affected individuals and family members were found to value a molecular genetic diagnosis of a 22q11.2 microdeletion for its ability to explain the presence of stigmatized neuropsychiatric conditions. An investigation of transmission patterns and reproductive fitness associated with 22q11.2 microdeletions provided novel insights into the evolutionary biology and clinical correlates of this structural rearrangement. The results demonstrated the scientific and clinical benefits of identifying a genetic subtype of schizophrenia. Last, high resolution genome-wide microarrays were used to investigate rare copy number variations in a prospectively recruited community-based schizophrenia cohort. Clinically significant variants were greatly enriched in schizophrenia, even with 22q11.2 microdeletions a priori excluded. The collective prevalence of these genetic variants in a single community catchment area was high, approaching that seen in autism, where clinical microarray testing is now a first-tier diagnostic test. Collectively, the findings of these pioneering studies suggest a role for genetic testing and genetic counselling in the contemporary management of schizophrenia.

Pleural solitary fibrous tumours (pSFTs) are uncommon soft tissue tumours of the pleura. that may recur and contribute to the patients’ demise. We analyzed a group of benign and malignant pSFTs for copy number alterations and for common mutations in oncogenes and tumour-suppressor genes. Malignant SFTs demonstrated more copy number alterations, especially 8q (c-myc) gain, 10q (include PTEN) loss, and 13q (Rb1) loss. Mutations were rare in this limited study.