No description available.

Doctor of Philosophy / Department of Statistics / Haiyan Wang / High throughput screening technologies have generated a huge amount of biological data in the last ten years. With the easy availability of array technology, researchers started to investigate biological mechanisms using experiments with more sophisticated designs that pose novel challenges to statistical analysis. We provide theory for robust statistical tests in three flexible models. In the first model, we consider the hypothesis testing problems when there are a large number of variables observed repeatedly over time. A potential application is in tumor genomics where an array comparative genome hybridization (aCGH) study will be used to detect progressive DNA copy number changes in tumor development. In the second model, we consider hypothesis testing theory in a longitudinal microarray study when there are multiple treatments or experimental conditions. The tests developed can be used to detect treatment effects for a large group of genes and discover genes that respond to treatment over time. In the third model, we address a hypothesis testing problem that could arise when array data from different sources are to be integrated. We perform statistical tests by assuming a nested design. In all models, robust test statistics were constructed based on moment methods allowing unbalanced design and arbitrary heteroscedasticity. The limiting distributions were derived under the nonclassical setting when the number of probes is large. The test statistics are not targeted at a single probe. Instead, we are interested in testing for a selected set of probes simultaneously. Simulation studies were carried out to compare the proposed methods with some traditional tests using linear mixed-effects models and generalized estimating equations. Interesting results obtained with the proposed theory in two cancer genomic studies suggest that the new methods are promising for a wide range of biological applications with longitudinal arrays.

High-throughput genomic datasets obtained from microarray or sequencing studies have revolutionized the field of molecular biology over the last decade. The complexity of these new technologies also poses new challenges to statisticians to separate biological relevant information from technical noise. Two methods are introduced that address important issues with normalization of array comparative genomic hybridization (aCGH) microarrays and the analysis of RNA sequencing (RNA-Seq) studies. Many studies investigating copy number aberrations at the DNA level for cancer and genetic studies use comparative genomic hybridization (CGH) on oligo arrays. However, aCGH data often suffer from low signal to noise ratios resulting in poor resolution of fine features. Bilke et al. showed that the commonly used running average noise reduction strategy performs poorly when errors are dominated by systematic components. A method called pcaCGH is proposed that significantly reduces noise using a non-parametric regression on technical covariates of probes to estimate systematic bias. Then a robust principal components analysis (PCA) estimates any remaining systematic bias not explained by technical covariates used in the preceding regression. The proposed algorithm is demonstrated on two CGH datasets measuring the NCI-60 cell lines utilizing NimbleGen and Agilent microarrays. The method achieves a nominal error variance reduction of 60%-65% as well as an 2-fold increase in signal to noise ratio on average, resulting in more detailed copy number estimates. Furthermore, correlations of signal intensity ratios of NimbleGen and Agilent arrays are increased by 40% on average, indicating a significant improvement in agreement between the technologies. A second algorithm called gamSeq is introduced to test for differential gene expression in RNA sequencing studies. Limitations of existing methods are outlined and the proposed algorithm is compared to these existing algorithms. Simulation studies and real data are used to show that gamSeq improves upon existing methods with regards to type I error control while maintaining similar or better power for a range of sample sizes for RNA-Seq studies. Furthermore, the proposed method is applied to detect differential 3' UTR usage.

Genomic prediction of single-crosses is a promising tool in maize breeding, increasing genetics gains and reducing selection time. A strategy that can increase accuracy is applying multiple-trait genomic prediction using selection indices, which take into account the performance under optimal and stress conditions. Moreover, factors such as dominance, structural variants, and population structure can influence the accuracy of estimates of genomic breeding values (GEBV). Therefore, the objectives were to apply genomic prediction (i) including multi-trait models, (ii) incorporating dominance deviation and copy number variation effects, and (iii) controlling population structure in maize hybrids. Hence, we used two maize datasets (HELIX and USP), consisting of 452 and 906 maize single-crosses. The traits evaluated were grain yield, plant and ear height, stay green, and four selection indices. From multi-trait GBLUP and GK, using the combination of selection indices in MTGP is a viable alternative, increasing the selective accuracy. Furthermore, our results suggest that the best approach is predicting hybrids including dominance deviation, mainly for complex traits. We also observed including copy number variation effects seems to be suitable, due to the increase of prediction accuracies and reduction of model bias. On the other hand, adding four different sets of population structure as fixed covariates to GBLUP did not improve the prediction accuracy for grain yield and plant height. However, using nonmetric multidimensional scaling dimensions and fineSTRUCTURE group clustering increased reliability of the GEBV for GY and PH, respectively. / Predição genômica de híbridos simples é uma promissora ferramenta no melhoramento de milho, pois permite aumentar os ganhos genéticos por unidade de tempo, principalmente por reduzir o tempo de seleção. Uma estratégia que pode aumentar a acurácia das predições genômicas é realizar esta para múltiplos caracteres considerando os mesmos simultâneamente, ou utilizar índices de seleção, os quais captam a performance dos genótipos tanto em condições ótimas como em condições de estresse. Além disso, fatores como dominância, variantes estruturais, e estruturação populacional podem influenciar a acurácia de estimativas dos valores genéticos genômicos (VGG). Portanto, os objetivos foram aplicar predição genômica em híbridos de milho (i) incluindo modelos multi-trait, (ii) incorporando desvios de dominância e efeitos da variação no número de cópias, e (iii) controlando a estruturação populacional. Para isto, dois conjuntos de milho (HELIX e USP) foram utilizados, consistindo de 452 e 906 híbridos simples. Os caracteres avaliados foram produtividade de grãos, altura de planta e espiga, senescência, e quatro índices de seleção. A partir das análises multi-trait dos modelos GBLUP e GK, pôde-se concluir que a combinação dos índices é uma alternativa viável, aumentando a acurácia seletiva. Além disso, os resultados sugerem que o melhor método é a predição de híbridos incluindo desvios de dominância, principalmente para caracteres complexos. Observou-se também que incluir efeitos relacionados a variação no número de cópias indica ser adequado, devido ao aumento da acurácia e redução do viés nos modelos de predição genômica. Por outro lado, a acurácia de predição não aumentou quando se adicionou quatro diferentes conjuntos de estruturação como covariáveis fixas no modelo GBLUP. No entanto, usando o escalonamento multidimensional não métrico e o agrupamento do fineSTRUCTURE aumentaram a confiabilidade de estimação do VGG para produtividade de grãos e altura de plantas, respectivamente.

No description available.

The intestinal mucosa is made up of complex secreted mucus layer consist of mainly mucin 2 (MUC2) and antimicrobial components that defend the underlining cellular barrier from intrusion by luminal microbiota and toxins. In inflammatory bowel diseases (IBD), the mucosal integrity is compromised. This can result from a combination of altered host genetics, gut immune responses and environment factors. However, it is the presence of intestinal bacteria that is central to the pathogenesis of IBD. As part of the dynamic gut microbial flora, mucolytic bacteria produce a wide range of glycosidases that are able to remove the outer oligosaccharide chains of MUC2, which allow other luminal bacteria to further degrade the mucin. We hypothesised that increased mucolytic bacteria will cause excessive degradation of the mucus layer, which in turn, allow more luminal bacteria to be in close proximity to the underlining epithelial cells resulting in inflammation. Consistent with our group’s previous semi-quantitative bacterial 16S rRNA gene clone library analysis, we found increased Ruminococcus gnavus in non-inflamed ulcerative colitis (UC) mucosa. R. gnavus was previously isolated by others based on its mucolytic property. In this study, we quantify total mucosa-associated bacteria and mucolytic bacteria, namely, R. gnavus, R. torques, Akkermansia muciniphila and bifidobacteria. We were able to show quantitatively that total mucosa-associated bacteria were increased in IBD. There was also a population shift in the mucosa-associated mucolytic bacteria, which were increased overall. There was significantly more R. gnavus in non-inflamed IBD biopsies. For the first time, we were also able to demonstrate that R. gnavus can degrade human MUC2 in vitro. To examine whether the numerical association of R. gnavus in IBD does have functional influence on intestinal inflammation and Paneth cell antimicrobial peptide gene expression, we fed mice with R. gnavus. Interestingly, R. gnavus feeding did not result in histological or molecular evidence of gut inflammation; however, it was able to specifically induce Paneth cell cryptdins and lysozyme P genes expression in 3 week old, antibiotic pre-treated C57BL/6 mice. This demonstrated that R. gnavus is not a pathogenic bacterium, which will directly cause colitis. However, the increased Paneth cell response suggested the need for host innate defence when R. gnavus is increased. Other than bacterial degradation, altered host genetics will also influence the mucus barrier. There is evidence to suggest that the MUC2 gene is highly unstable and is susceptible to gene copy number variation (CNV). Therefore, we hypothesised that MUC2 CNV is present, which may result in altered oligomerisation of the MUC2 glycoprotein causing endoplasmic reticulum stress of the goblet cells that appears to be characteristic of UC. Currently, our data partly support the presence of MUC2 CNV. However, further investigation is required to verify the MUC2 CNV identity. Only then can a high throughput methodology be designed to screen a large population for any association with IBD.

Ces dix dernières années, l’investigation des maladies génétiques a été bouleversée par l’émergence des techniques de séquençage haut-débit. Celles-ci permettent désormais de ne plus séquencer les gènes un par un, mais d’avoir accès à l’intégralité de la séquence génomique ou transcriptomique d’un individu. La difficulté devient alors d’identifier les variants causaux parmi une multitude d’artefacts techniques et de variants bénins, pour ensuite comprendre la physiopathologie des gènes identifiés.L’application du séquençage haut débit est particulièrement prometteuse dans le champ de la génétique de l’infertilité masculine car il s’agit d’une pathologie dont l’étiologie est souvent génétique, qui est génétiquement très hétérogène et pour laquelle peu de gènes ont été identifiés. Mon travail de thèse est donc centré sur la l’infertilité et comporte deux parties majeures : l’analyse des données issues du séquençage haut débit d’homme infertiles et de modèles animaux et la caractérisation moléculaire d’un phénotype spécifique d’infertilité, laglobozoospermie.Le nombre de variants identifiés dans le cadre d’un séquençage exomique pouvant s’élever à plusieurs dizaines de milliers, l’utilisation d’un outil informatique performant est indispensable. Pour arriver à une liste de variants suffisamment restreinte pour pouvoir être interprétée, plusieurs traitements sont nécessaires. Ainsi, j’ai développé un pipeline d’analyse de données issues de séquençage haut-débit effectuant de manière successive l’intégralité des étapes de l’analyse bio-informatique, c’est-à-dire l’alignement des reads sur un génome de référence, l’appel des génotypes, l’annotation des variants obtenus ainsi que le filtrage de ceux considérés comme non pertinents dans le contexte de l’analyse. L’ensemble de ces étapes étant interdépendantes,les réaliser au sein du même pipeline permet de mieux les calibrer pour ainsi réduire le nombre d’erreurs générées. Ce pipeline a été utilisé dans cinq études au sein du laboratoire, et a permis l’identification de variants impactant des gènes candidats prometteurs pouvant expliquer le phénotype d’infertilité des patients.L’ensemble des variants retenus ont ensuite pu être validés expérimentalement.J’ai également pris part aux investigations génétiques et moléculaires permettant la caractérisation du gène DPY19L2, identifié au laboratoire et dont la délétion homozygote entraine une globozoospermie, caractériséepar la présence dans l’éjaculât de spermatozoïdes à tête ronde dépourvus d’acrosome. Pour cela, j’ai contribué à caractériser les mécanismes responsables de cette délétion récurrente, puis, en utilisant le modèle murin Dpy19l2 knock out (KO) mimant le phénotype humain, j’ai réalisé une étude comparative des transcriptomes testiculaires de souris sauvages et de souris KO Dpy19l2-/-. Cette étude a ainsi permis de mettre en évidence la dérégulation de 76 gènes chez la souris KO. Parmi ceux-ci, 23 sont impliqués dans la liaison d’acides nucléiques et de protéines, pouvant ainsi expliquer les défauts d’ancrage de l’acrosome au noyau chez les spermatozoïdes globozoocéphales.Mon travail a donc permis de mieux comprendre la globozoospermie et de développer un pipeline d’analyse bioinformatique qui a déjà permis l’identification de plus de 15 gènes de la gamétogenèse humaine impliqués dans différents phénotypes d’infertilité. / In the last decade, the investigations of genetic diseases have been revolutionized by the rise of high throughput sequencing (HTS). Thanks to these new techniques it is now possible to analyze the totality of the coding sequences of an individual (exome sequencing) or even the sequences of his entire genome or transcriptome.The understanding of a pathology and of the genes associated with it now depends on our ability to identify causal variants within a plethora of technical artifact and benign variants.HTS is expected to be particularly useful in the field infertility as this pathology is expected to be highly genetically heterogeneous and only a few genes have so far been associated with it. My thesis focuses on male infertility and is divided into two main parts: HTS data analysis of infertile men and the molecular characterization of a specific phenotype, globozoospermia.Several thousands of distinct variants can be identified in a single exome, thereby using effective informatics is essential in order to obtain a short and actionable list of variants. It is for this purpose that I developed a HTS data analysis pipeline performing successively all bioinformatics analysis steps: 1) reads mapping along a reference genome, 2) genotype calling, 3) variant annotation and 4) the filtering of the variants considered as non-relevant for the analysis. Performing all these independent steps within a single pipeline is a good way to calibrate them and therefore to reduce the number of erroneous calls. This pipeline has been used in five studies and allowed the identification of variants impacting candidate genes that may explain the patients’ infertility phenotype. All these variants have been experimentally validated using Sanger sequencing.I also took part in the genetic and molecular investigations which permitted to demonstrate that the absence of the DPY192 gene induces male infertility due to globozoospermia, the presence in the ejaculate of only round-headed and acrosomeless spermatozoa. Most patients with globozoospermia have a homozygous deletion of the whole gene. I contributed to the characterization of the mechanisms responsible for this recurrent deletion, then, using Dpy19l2 knockout (KO) mice, I realized the comparative study of testicular transcriptome of wild type and Dpy19l2 -/- KO mice. This study highlighted a dysregulation of 76 genes in KO mice. Among them, 23 are involved in nucleic acid and protein binding, which may explain acrosome anchoring defaults observed in the sperm of globozoospermic patients.My work allowed a better understanding of globozoospermia and the development of a HTS data analysis pipeline. The latter allowed the identification of more than 15 human gametogenesis genes involved in different infertility phenotypes.

Submitted by MARCOS VINICIUS ANTUNES DE LEMOS (marcoslemoszootec@gmail.com) on 2017-06-02T15:32:45Z No. of bitstreams: 1 Tese_Marcos_VA_Lemos1.pdf: 3226396 bytes, checksum: 1f97ad6fd98b95977fbdc4ba573a83bb (MD5) / Approved for entry into archive by Luiz Galeffi (luizgaleffi@gmail.com) on 2017-06-02T17:51:15Z (GMT) No. of bitstreams: 1 lemos_mva_dr_jabo.pdf: 3226396 bytes, checksum: 1f97ad6fd98b95977fbdc4ba573a83bb (MD5) / Made available in DSpace on 2017-06-02T17:51:15Z (GMT). No. of bitstreams: 1 lemos_mva_dr_jabo.pdf: 3226396 bytes, checksum: 1f97ad6fd98b95977fbdc4ba573a83bb (MD5) Previous issue date: 2017-05-05 / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Objetivou-se identificar regiões no genôma de bovinos da raça Nelore que apresentam variações no número de cópias (CNV) e, associar estes CNV com o perfil de ácidos graxos da carne. Além disso, objetivou-se realizar associação genica ampla utilizando os método de single step (GWASss) a fim de detectar regiões genômicas associadas aos ácidos graxos dos grupos saturados, mono e poliinsaturados, assim como os omegas 3, 6 e sua relação. O estudo de caracterização e distribuição dos CNVs ao longo do genoma de bovinos Nelore, foi realizado através do software PennCNV utizando dados genotípicos de 3.794 aniamais, resultando em 399.361 CNVs identificados. Após controle de qualidade, 2.902 foram mantidos nas analises, resultando em 195.873 CNVs, com tamanho medio de 54,744 pb, maximo de 8.7 Mb e minimo 3 kb. As regiões de CNV foram geradas pela sobreposição dos CNVs através do software CNVRuler. Os cromossomos que mostraram maior incidencia de CNVR foram BTA19 (24,26%), BTA23 (18,68%) e BTA25 (18,05%). Ja os que mostraram menor incidencia foram BTA29 (1,63%), BTA13 (9,72%) and BTA8 (9,72%). As 9.805 regiões da CNV estimadas no presente estudo cobrem aproximadamente 13.05% do genoma bovino e sobrepõem-se a 5.495 genes conhecidos que envolvem processos biológicos que poderiam estar envolvidos na adaptação ambiental da subespécie a áreas tropicais. O estudo de GWASss identificou 115 janelas que explicaram mais de 1% da variação genética aditiva para os 22 ácidos graxos estudados. A identificação destas regiões e seus genes genes, tais qual ELOVL5, ESRRG, PCYT1A e os genes do grupo ABC (ABCA5, ABCA6 e ABCA10) são genes que estão relacionados direto e inderamente ao metabolismo lipídico. O GWAS entres os fenótipos de AG e os CNVs resultaram em um total de 186 CNVR siginificantivos para os grupos dos ácidos graxos saturados (43), monosaturados (42), poliinsaturados (66) e omegas (35), nas quais foram identificados 278 genes com funçao descrita. Estes resultados apontaram genes associados a AG de varias saturações, podendo ser destacados os genes SAMD8 e BSCL2, os quais estão relacionados ao metabolism lipidico; e o gene RAPGEF6, relacionado ao metabolism energetico. Assim os inúmeras regiões genômicas encontradas neste estudo, bem como os genes identificados nas mesmas, devem contribuir para a formação de uma base genética do perfil de ácidos graxos da carne de bovino Nelore (Bos indicus), podendo contribuir para uma melhor seleção das características associadas à melhora da saúde humana. saúde. O conhecimento desses CNVs deverá melhorar a compreensão dos mecanismos genéticos e fisiológicos que contribuem para as características produtivas, bem como na seleção de animais mais produtivos e eficientes, contribuindo para o melhoramento genético das características produtivas. / The objective of this study was to identify genomic regions of Nellore cattle that present variations in the number of copies (CNV) and to associate these CNV with the fatty acid profile of the meat. In addition, the objective of this study was to carry out a genome-wid association using the single step method (GWASss) to detect genomic regions associated with saturated, mono and polyunsaturated fatty acids, as well as omega 3, 6 and their relationship. The study of the characterization and distribution of CNVs along the Nellore genome was performed by PennCNV software using genotypic data of 3,794 animals, resulting in 399,361 CNVs identified. After quality control, 2,902 were maintained in the analyzes, resulting in 195,873 CNVs, with an average size of 54,744 pb, maximum of 8.7 Mb and minimum 3 kb. The CNV regions were generated by the overlap of the CNVs by CNVRuler software. The chromosomes that showed the highest incidence of CNVR were BTA19 (24.26%), BTA23 (18.68%) and BTA25 (18.05%). Those with the lowest incidence were BTA29 (1.63%), BTA13 (9.72%) and BTA8 (9.72%). The 9,805 CNV regions estimated in the present study covered approximately 13.05% of the bovine genome and overlaped 5,495 genes known to envolve in biological processes that could be involved in the environmental adaptation of the subspecies to tropical areas. The GWASss study showed 115 windows that explained more than 1% of the additive genetic variation for the 22 fatty acids studied. The identification of these regions and their genes, such as ELOVL5, ESRRG, PCYT1A and the ABC group genes (ABCA5, ABCA6 and ABCA10) are genes directly and indirectly related to lipid metabolism. The GWAS between AG phenotypes and CNVs resulted in a total of 186 CNVRs that were significant for the saturated (43), monosaturated (42), polyunsaturated (66) and omegas (35) fatty acids groups, in which 278 genes with described function. These results pointed genes associated to AG of various saturations, and the genes SAMD8 and BSCL2, which are related to lipid metabolism; and the RAPGEF6 gene, related to energetic metabolism. Thus, the numerous genomic regions found in this study, as well as the genes identified in them, should contribute to the formation of a genetic basis of the Nellore beef (Bos indicus) fatty acid profile, contributing to a better selection of the traits associated with improvement of human health. The knowledge of these CNVs could improve the understanding of the genetic and physiological mechanisms that contribute to the productive traits, as well as the selection of more productive and efficient animals. / FAPESP: 2013/11853-4

Les variations du nombre de copies (CNVs) incluent les délétions et les duplications de régions chromosomiques d’une taille variant de 50 pb à plusieurs Mb. Depuis 2005, les études d’association pangénomiques (GWAS) ont permis d’associer certains larges CNVs à des maladies syndromiques associées à la déficience intellectuelle incluant les syndromes de DiGeorge, Williams, Angelman, etc. En fonction de la densité génique de la région d’intérêt et de la variabilité des phénotypes associés, l’étude de la physiopathologie des syndromes peut être extrêmement complexe. La modélisation murine offre de nombreux avantages pour l’identification des gènes candidats et la compréhension des mécanismes moléculaires associés à ces pathologies.Les travaux présentés dans ce manuscrit consistent en la caractérisation des modèles murins pour cinq maladies syndromiques associées aux CNVs : la monosomie 21 partielle ainsi que les réarrangements des régions 16p11.1 et 17q21.31. Les caractérisations anatomiques, métaboliques et comportementales des animaux nous ont permis d’évaluer un grand nombre de paramètres associés à la symptomatique humaine. Nous avons également réalisé des analyses électrophysiologiques et transcriptomiques en ciblant nos investigations sur l’hippocampe, structure cérébrale qui joue un rôle central dans les processus de mémoire et d’apprentissage. Ce projet de recherche s’inscrit dans une perspective plus large qui est l’identification des gènes candidats pour les phénotypes observés et le développement de premières stratégies thérapeutiques pouvant potentiellement aboutir à l’amélioration des capacités cognitives des patients. / Copy number variations (CNVs) include deletions and duplications of chromosomal regions ranging in size from 50bp to several Mb. Since 2005, genome-wide association studies (GWAS) have associated some large CNVs to syndromic diseases linked to intellectual disability including DiGeorge, Williams, Angelman syndroms, etc. Depending on the gene density of the region of interest and the variability of symptoms, the study of the pathophysiology of syndromes can be extremely complex. Mouse modeling show many advantages for the identification of candidate genes and the understanding of molecular mechanisms associated with these diseases.The work presented in this manuscript consists of the characterization of mouse models of five syndromic diseases associated with CNVs: partial monosomy 21 and rearrangements of 16p11.2 and 17q21.31 regions. Anatomical, metabolic and behavioral characterizations of animals allowed us to evaluate a broad number of parameters associated with human phenotypes. We also performed electrophysiological and transcriptomic analysis focusing our investigation on the hippocampus which has a major role in learning and memory processes. This project is part of a wider perspective which is the identification of candidate genes for the different phenotypes we observe in the mouse and the development of first treatment strategies which can potentially lead to the improvement of cognitive capacity of patients.

Brain disorders present a significant burden on affected individuals, their families and society at large. Existing diagnostic tests suffer from a lack of genetic biomarkers, particularly for substance use disorders, such as alcohol dependence (AD). Numerous studies have demonstrated that AD has a genetic heritability of 40-60%. The existing genetics literature of AD has primarily focused on linkage analyses in small family cohorts and more recently on genome-wide association analyses (GWAS) in large case-control cohorts, fueled by rapid advances in next generation sequencing (NGS). Numerous AD-associated genomic variations are present at a common frequency in the general population, making these variants of public health significance. However, known AD-associated variants explain only a fraction of the expected heritability. In this dissertation, we demonstrate that systems biology applications that integrate evolutionary genomics, rare variants and structural variation can dissect the genetic architecture of AD and elucidate its heritability. We identified several complex human diseases, including AD and other brain disorders, as potential targets of natural selection forces in diverse world populations. Further evidence of natural selection forces affecting AD was revealed when we identified an association between eye color, a trait under strong selection, and AD. These findings provide strong support for conducting GWAS on brain disorder phenotypes. However, with the ever-increasing abundance of rare genomic variants and large cohorts of multi-ethnic samples, population stratification becomes a serious confounding factor for GWAS. To address this problem, we designed a novel approach to identify ancestry informative single nucleotide polymorphisms (SNPs) for population stratification adjustment in association analyses. Furthermore, to leverage untyped variants from genotyping arrays – particularly rare variants – for GWAS and meta-analysis through rapid imputation, we designed a tool that converts genotype definitions across various array platforms. To further elucidate the genetic heritability of brain disorders, we designed approaches aimed at identifying Copy Number Variations (CNVs) and viral insertions into the human genome. We conducted the first CNV-based whole genome meta-analysis for AD. We also designed an integrated approach to estimate the sensitivity of NGS-based methods of viral insertion detection. For the first time in the literature, we identified herpesvirus in NGS data from an Alzheimer’s disease brain sample. The work in this dissertation represents a three-faceted advance in our understanding of brain disease etiology: 1) evolutionary genomic insights, 2) novel resources and tools to leverage rare variants, and 3) the discovery of disease-associated structural genomic aberrations. Our findings have broad implications on the genetics of complex human disease and hold promise for delivering clinically useful knowledge and resources.