Mitochondria are essential cellular organelles and the location of the electron transport chain, the site of the majority of energy production in the cell. Mitochondria contain their own circular genome approximately 16,000 base pairs in length. The mitochondrial genome (mtDNA) encodes 11 protein-coding genes essential for the electron transport chain, 22 tRNA genes, and two rRNA genes. Mitochondrial malfunction occurs in many diseases, and changes in the mitochondrial genome lead to numerous disorders. Multiple mitochondrial haplotypes and sequence features are associated with Alzheimer's disease. In this dissertation we utilized TreeScanning, an evolutionary-based haplotype approach to identify haplotypes and sequence variation associated with specific phenotypes: Alzheimer's disease case-control status, mitochondrial copy number, and 16 neuroimaging phenotypes related to Alzheimer's disease neurodegeneration. In the first two studies we utilized 1007 complete mitochondrial genomes from participants in the Cache County Study on Memory Health and Aging. First, individuals with mitochondrial haplotypes H6A1A and H6A1B showed a reduced risk of AD. Our study is the largest to date and the only study with complete mtDNA genome sequence data. Next, each cell contains multiple mitochondria, and each mitochondrion contains multiple copies of its own circular genome. The ratio of mitochondrial genomes to nuclear genomes is referred to as mitochondrial copy number. Decreases in mitochondrial copy number are known to occur in many tissues as people age, and in certain diseases. Three variants belonging to mitochondrial haplogroups U5A1 and T2 were significantly associated with higher mitochondrial copy number in our dataset. Each of these three variants was associated with higher mitochondrial copy number and we suggest several hypotheses for how these variants influence mitochondrial copy number by interacting with known regulators of mitochondrial copy number. Our results are the first to report sequence variation in the mitochondrial genome that lead to changes in mitochondrial copy number. The identification of these variants that increase mtDNA copy number has important implications in understanding the pathological processes that underlie these phenotypes. Lastly, we used an endophenotype-based approach to further characterize mitochondrial genetic variation and its relationship to risk markers for Alzheimer's disease. We analyzed longitudinal data from non-demented, mild cognitive impairment, and late onset Alzheimer's disease participants in the Alzheimer's Disease Neuroimaging Initiative with genetic, brain imaging, and behavioral data. Four clades were associated with three different endophenotypes: whole brain volume, percent change in temporal pole thickness, and left hippocampal atrophy over two years. This was the first study of its kind to identify mitochondrial variation associated with brain imaging endophenotypes of Alzheimer's disease. Together, these projects provide evidence of mtDNA involvement in the risk and physiological changes of Alzheimer's disease.

Frequently evidentiary items contain an insufficient quantity of DNA to obtain complete or even partial DNA profiles using standard forensic gentotyping techniques. Here, various methods of post PCR purification were evaluated for their effects on the sensitivity of fluophore-based allelic detection. A method of post PCR purification is described which increases the sensitivity of standard 28 cycle PCR such that low copy number DNA templates (<100 pg DNA) can be analyzed. Full profiles were consistently obtained with as little as 20 pg template DNA without increased cycle number. In mock case type samples with dermal ridge fingerprints, genetic profiles were obtained by amplification with 28 cycles followed by post-PCR purification whereas no profiles were obtained without purification of the PCR product. Allele drop-out, increased stutter, and contamination (allele drop-in) typical of LCN analysis were observed. A single incident of contamination was observed in a reagent blank (not duplicated upon re-amplification) however, no contamination was observed in negative amplification controls.

Unlike the autosomes, the Y chromosome in humans and other primates has few protein coding genes, with only a few dozen single-copy genes and several tandem duplicated gene families, called the "ampliconic" genes. The interaction of many biological and evolutionary factors is responsible for this structural heterogeneity among different parts of the genome. We sequenced and assayed the copy numbers of Y-linked, single-copy genes and ampliconic genes in a group of closely related macaque monkeys, then fit models of gene family evolution to this data along with whole genome data from human, chimpanzee, and rhesus macaque. Our results (i) recovered evidence for several novel examples of gene conversion in papionin monkeys, (ii) indicate that ampliconic gene families evolve faster than autosomal gene families and than single-copy genes on the Y chromosome, and that (iii) Y-linked singleton and autosomal gene families evolved faster in great apes than they do in other Old World higher Primates. These findings highlight the evolutionary eccentricity of duplicated genes on the Y chromosome and suggest an important role for natural selection and gene conversion in the evolution of Y-linked gene duplicates. / Thesis / Master of Science (MSc)

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Biological systems constantly evolve and adapt in response to changed environment and external stimuli at the molecular and genomic levels. Building statistical models that characterize such dynamic changes in biological systems is one of the key objectives in bioinformatics and computational biology. Recent advances in high-throughput genomic and molecular profiling technologies such as gene expression and and copy number microarrays provide ample opportunities to study cellular activities at the individual gene and network levels. The aim of this dissertation is to formulate mathematically dynamic changes in biological networks and DNA copy numbers, to develop machine learning algorithms to learn these statistical models from high-throughput biological data, and to demonstrate their applications in systems biological studies. The first part (Chapters 2-4) of the dissertation focuses on the dynamic changes taking placing at the biological network level. Biological networks are context-specific and dynamic in nature. Under different conditions, different regulatory components and mechanisms are activated and the topology of the underlying gene regulatory network changes. We report a differential dependency network (DDN) analysis to detect statistically significant topological changes in the transcriptional networks between two biological conditions. Further, we formalize and extend the DDN approach to an effective learning strategy to extract structural changes in graphical models using l1-regularization based convex optimization. We discuss the key properties of this formulation and introduce an efficient implementation by the block coordinate descent algorithm. Another type of dynamic changes in biological networks is the observation that a group of genes involved in certain biological functions or processes coordinate to response to outside stimuli, producing distinct time course patterns. We apply the echo stat network, a new architecture of recurrent neural networks, to model temporal gene expression patterns and analyze the theoretical properties of echo state networks with random matrix theory. The second part (Chapter 5) of the dissertation focuses on the changes at the DNA copy number level, especially in cancer cells. Somatic DNA copy number alterations (CNAs) are key genetic events in the development and progression of human cancers, and frequently contribute to tumorigenesis. We propose a statistically-principled in silico approach, Bayesian Analysis of COpy number Mixtures (BACOM), to accurately detect genomic deletion type, estimate normal tissue contamination, and accordingly recover the true copy number profile in cancer cells. / Ph. D.