O objetivo deste estudo foi identificar e comparar os tipos fundamentais das impressões digitais em indivíduos comprovadamente alcoolistas e não alcoolistas. A pesquisa avaliou 152 doentes alcoolistas internados para desintoxicação alcoólica no Hospital Dr. Adolfo Bezerra de Menezes - São José Rio Preto - São Paulo ( Brasil ), comparando-os com 144 indivíduos não alcoolistas, integrantes do Exército Brasileiro na ativa, que constituíram o grupo-controle. Todos os resultados foram submetidos à criteriosa análise estatística pelos testes adequados a este estudo. Os tipos fundamentais de maior frequência nos alcoolistas foram as presilhas internas, seguidas de presilhas externas, verticilos e arcos. No grupo-controle foram as presilhas internas, seguidas de verticilos, presilhas externas e arcos. Constatou-se uma predominância das presilhas internas na mão esquerda e de presilhas externas na mão direita em ambos os grupos. Os padrões datiloscópicos encontrados em todos os dedos, analisados em conjunto e isoladamente foram concordantes com os dados da literatura mundial nos estudos de indivíduos da população normal e, parcialmente, concordantes com a literatura específica de dermatóglifos e alcoolismo. Não se encontrou um padrão datiloscópico característico que pudesse ser sugestivo ou considerado fator de risco para alcoolismo / The aim of this study was to identify and compare the main types of fingerprints between established alcoholic and non-alcoholic individuals. In this study, 152 patients who were admitted for alcohol detoxification at the Hospital Dr. Adolfo Bezerra de Menezes, São José Rio Preto, São Paulo, Brasil, were evaluated in comparison with the control group, which constituted 144 non-alcoholic individuals who were on active duty in the Brazilian Army. All the results were subjected to thorough statistical analysis using appropriate tests. The main fingerprint patterns with the highest frequencies were ulnar loops, followed by radial loops, whorls, and arches among the alcoholics; and ulnar loops, followed by whorls, radial loops, and arches among the controls. In both groups, inner loops predominated in the left hand; and radials loops, in the right hand. The dactyloscopic patterns found on the fingers analyzed together and separately were consistent with published data from studies with healthy individuals and partially concordant with specific studies on dermatoglyphics and alcoholism. No characteristic dactyloscopic pattern was found that could be suggestive of or considered as a risk factor of alcoholism

A estatística X2, quando na aplicação do teste de equilibrio genético de Hardy-weinberg, apresenta baixa eficiência, principalmente quando a amostra é de pequeno porte. Alguns procedimentos alternativos foram apresentados, com excelentes propriedades estatístcas: nivel de significância homogênio e não-viés. Esses procedimentos apresentam uma grande desvantagem prática: muitos pontos na região de rejeição são aleatorizados. No presente trabalho é apresentada uma nova propriedade, o máximo volume da função poder. Na busca do teste com essa propriedade, é sugerida uma forma de construção da região de rejeição, que apresenta o maior número de pontos, sem aleatorizações. Esse procedimento surge como uma adaptação da construção da região de rejeição com a propriedade de nível de significância homogêneo, sem a desvantagem de muitos pontos aleatorizados, apresentando a maior quantidade de combinações genotípicas, associadas ao não-equilibrio genético, qualquer que seja o nível de significância preestabelecido do teste. Tem-se, assim, uma alternativa prática que viabiliza propriedades teóricas desejáveis, a um teste de hipóteses. / The chi-square statistic, on the application of Hardy-Weinberg genetic equilibrium test, has low efficiency, mostly if the sample is scarce. Some alternative procedures have been presented, have valuable statistic proprieties: homogeneous significance level and unbiasedness. Those procedures have a pratical disadvantage: various points are parcially in the critical region. At the present work, it is shown a new approach, the maximum volume of the power function, as a that to construct a critical region, with the maximum number of points that not randomization. This approach is an adaptation of the critical region construction with homogeneous significance level propriety, but it does not have the disadvantage of many points parcially in the critical associated with Hardy-Weinberg genetic disequilibrium, whichever is the segnificance level in the test. Therefore, it is a pratical alternative which makes possible the propriety theory are desidered in a hypothesis test.

The overall theme of this thesis work concerns the problem of handling measurement error and sources of variation in functional data models. The first part introduces a wavelet-based sparse principal component analysis approach for characterizing the variability of multilevel functional data that are characterized by spatial heterogeneity and local features. The total covariance of the data can be decomposed into three hierarchical levels: between subjects, between sessions and measurement error. Sparse principal component analysis in the wavelet domain allows for reducing dimension and deriving main directions of random effects that may vary for each hierarchical level. The method is illustrated by application to data from a study of human vision. The second part considers the problem of scalar-on-function regression when the functional regressors are observed with measurement error. We develop a simulation-extrapolation method for scalar-on-function regression, which first estimates the error variance, establishes the relationship between a sequence of added error variance and the corresponding estimates of coefficient functions, and then extrapolates to the zero-error. We introduce three methods to extrapolate the sequence of estimated coefficient functions. In a simulation study, we compare the performance of the simulation-extrapolation method with two pre-smoothing methods based on smoothing splines and functional principal component analysis. The third part discusses several extensions of the simulation-extrapolation method developed in the second part. Some of the extensions are illustrated by application to diffusion tensor imaging data.

The study of B and T cell receptor repertoires from high throughput sequencing is a recent development that allows for unprecedented resolution and quantification of the adaptive immune response. The immense diversity and long tailed distribution of these repertoires has up until now limited such studies to expanded clonal signatures or to analysis of imprecise signals with limited dynamic range collected by techniques such as radioactive and fluorescent labeling. This thesis presents a number of quantitative methods to characterize the repertoire and examine the questions of sequence diversity and inter-repertoire divergence of T cell repertoires. These approaches attempt to accurately parametrize the inherent distribution of T cell clones drawing from statistical tools derived from ecological literature and information theory. The methods presented are applied to T cell analyses of various tissue compartments of the human body, including peripheral blood mononucleocytes, thymic tissues, spleen, inguinal lymph nodes, lung lymph nodes and the brain. A number of applications are explored with strong implications for translational use in medicine. Novel insights are made into the mechanism of maintenance and compartmentalization of na{\"i}ve T cells from human donors of many different ages. Diversity and divergence of the tumor infiltrating sequence repertoire is measured in low grade gliomas and glioblastomas from cancer patients, and potential sequence based biomarkers are assessed for studying glioma phenotype progression. A careful investigation of the immune response to allogeneic stimulus reveals the effect of HLA on sequence sharing and diversity of the alloresponse, and quantifies for the first time using sequence data the fraction of T cells in a repertoire that are alloreactive. The use of repertoire sequencing and mathematical models within immunology is a new and emerging concept within the rapidly expanding field of systems immunology and will undoubtedly have a profound impact on the future of immunology research. It is hoped that the tools presented in this thesis will give insight into how to quantitatively explore the breadth and depth of the T cell receptor repertoire, and provide future directions for TCR repertoire analysis.

Perception is the result of more than just the unbiased processing of sensory stimuli. At each moment in time, sensory inputs enter a circuit already impacted by signals of arousal, attention, and memory. This thesis aims to understand the impact of such internal states on the processing of sensory stimuli. To do so, computational models meant to replicate known biological circuitry and activity were built and analyzed. Part one aims to replicate the neural activity changes observed in auditory cortex when an animal is passively versus actively listening. In part two, the impact of selective visual attention on performance is probed in two models: a large-scale abstract model of the visual system and a smaller, more biologically-realistic one. Finally in part three, a simplified model of Hebbian learning is used to explore how task context comes to impact prefrontal cortical activity. While the models used in this thesis range in scale and represent diverse brain areas, they are all designed to capture the physical processes by which internal brain states come to impact sensory processing.

This thesis has two overall themes, both of which involve the word functional, albeit in different contexts. The theme that motivates two of the chapters is the development of methods that enable a deeper understanding of the variability of functional data. The theme of the final chapter is the development of methods that enable a deeper understanding of the landscape of functionality across the human genome in different human tissues. The first chapter of this thesis provides a framework for quantifying the variability of functional data and for analyzing the factors that affect this variability. We extend functional principal components analysis by modeling the variance of principal component scores. We pose a Bayesian model, which we estimate using variational Bayes methods. We illustrate our model with an application to a kinematic dataset of two-dimensional planar reaching motions by healthy subjects, showing the effect of learning on motion variability. The second chapter of this thesis provides an alternative method for decomposing functional data that follows a Poisson distribution. Classical methods pose a latent Gaussian process that is then linked to the observed data via a logarithmic link function. We pose an alternative model that draws on ideas from non-negative matrix factorization, in which we constrain both scores and spline coefficient vectors for the functional prototypes to be non-negative. We impose smoothness on the functional prototypes. We estimate our model using the method of alternating minimization. We illustrate our model with an application to a dataset of accelerometer readings from elderly healthy Americans. The third chapter of this thesis focuses on functional genomics, rather than functional data analysis. Here we pose a method for unsupervised clustering of functional genomics data. Our method is non-parametric, allowing for flexible modeling of the functional genomics data without binarization. We estimate our model using variational Bayes methods, and illustrate it by calculating genome-wide functional scores (based on a partition of our clusters into functional and non-functional clusters) for 127 different human tissues. We show that these genome-wide and tissue-specific functional scores provide state-of-the-art functional prediction.

The research of my dissertation studies the methods of designing and analyzing sequential multiple assignment randomized trial (SMART) for comparing multiple adaptive interventions. As a SMART typically consists of numerous adaptive interventions, inferential procedures based on pairwise comparisons of all interventions may suffer substantial loss in power after accounting for multiplicity. I address this problem using two approaches. First, I propose a likelihood-based Wald test, study the asymptotic distribution of its test statistics, and apply it as a gate-keeping test for making an adaptive intervention selection. Second, I consider a multiple comparison with the best approach by constructing simultaneous confidence intervals that compare the interventions of interest with the truly best intervention, which is assumed to be unknown in inference; an adaptive intervention with the proposed interval excluding zero will be declared as inferior to the truly best with a pre-specified confidence level. Simulation studies show that both methods outperform the corresponding multiple comparison procedures based on Bonferroni's correction in terms of the power of test and the average width of confidence intervals for estimation. Simulations also suggest desirable properties of the proposed methods. I apply these methods to analyze two real data sets. As part of the dissertation, I also develop a user-friendly R software package that covers many statistical work related to SMART, including study design, data analysis and visualization. Both proposed methods can be implemented by using this R package. In the end of the dissertation, I show an application of designing a SMART to compare multiple patient care strategies for depression management based on one of the proposed methods.

Computational genomics is at the intersection of computational applied physics, math, statistics, computer science and biology. With the advances in sequencing technology, large amounts of comprehensive genomic data are generated every year. However, the nature of genomic data is messy, complex and unstructured; it becomes extremely challenging to explore, analyze and understand the data based on traditional methods. The needs to develop new quantitative methods to analyze large-scale genomics datasets are urgent. By collecting, processing and organizing clean genomics datasets and using these datasets to extract insights and relevant information, we are able to develop novel methods and strategies to address specific genetics questions using the tools of applied mathematics, statistics, and human genetics. This thesis describes genetic and bioinformatics studies focused on utilizing and developing state-of-the-art computational methods and strategies in order to identify and interpret de novo mutations that are likely causing developmental disorders. We performed whole exome sequencing as well as whole genome sequencing on congenital diaphragmatic hernia parents-child trios and identified a new candidate risk gene MYRF. Additionally, we found male and female patients carry a different burden of likely-gene- disrupting mutations, and isolated and complex patients carry different gene expression levels in early development of diaphragm tissues for likely-gene-disrupting mutations. To increase the power to detect risk genes and risk variants, we developed a deep neural network classifier called MVP to accurately predict the pathogenicity of missense variants. MVP implemented an advanced structure of ResNet model and based on two independent data sets, MVP achieved clearly better results in prioritizing pathogenic variants than other methods. Additionally, we studied the genetic connection between developmental disorders and cancer. We found that in developmental disorder patients predicted deleterious de novo mutations are more enriched in cancer driver genes than non cancer driver genes. A Hidden Markov Model was implemented to discover cancer somatic missense mutation hotspots and we demonstrated many cancer driver genes shared a similar mode of action in developmental disorders and caner. By improving ability to interpret missense mutations and leveraging cancer genomics data, we can improve risk gene inference in developmental disorders.

The theme of this dissertation is to develop statistical methods for genetic studies with family history of diseases. Family history of disease is a major risk factor for many health outcomes. To study diseases that aggregate in the families of patients, genetic epidemiological studies recruit independent study participants, often referred to as probands. Probands also provide information on their relatives through a family health history interview. However, due to the high cost of in-person collection of blood samples or death of a relative, dense genotypes are often collected only in probands but not in their family members. In these designs, estimating genetic risk of a disease or identifying genetic risk factors for a complex disease is challenging due to unavailable genotypes in relatives as well as correlation presented among family members' phenotypes. This dissertation contains three parts to tackle these barriers in family studies: (1) develop methods to estimate the genetic risk of a disease more precisely; (2) develop methods to test for association between genetic markers and correlated phenotypes; and (3) develop methods to control population substructure and familial relatedness in genome-wide association studies (GWAS). In the first part of the dissertation, we propose a method to estimate the age-specific disease risk of genetic mutation in family studies that permits the adjustment for multiple covariates and interaction effects in the presence of unobserved genotypes in relatives. Compared to our previous nonparametric approaches that do not control covariates, our semiparametric estimation method allows controlling for individual characteristics such as sex, ethnicity, environmental risk factors, and genotypes at other loci. Moreover, gene-gene interactions and gene-environment interactions can also be handled within the framework of a semiparametric model. The analyses may provide insights on whether demographics or environmental variables play a role in modifying the genetic risk of a disease. We examine the performance of the proposed methods by simulations and apply them to estimate the age-specific cumulative risk of Parkinson's disease (PD) in relatives predicted to carry the LRRK2 G2019S mutation. The utility of the estimated carrier risk is demonstrated through designing a future clinical trial under various assumptions. The second part of the dissertation is motivated by extending the single genetic variant set up in the first part to genome-wide genotype data, but focuses on the genetic association tests. Here, we propose a computationally efficient multilevel model to analyze the association of a genetic marker with correlated binary phenotypes in family studies. Our method accounts for both random polygenic effects as well as shared non-genetic familial effects while handling unavailable genotypes in relatives. To discover genetic variants of a complex disorder that aggregates in the families of patients, we consider the combined data of probands with genome-wide genotypes and family history of diseases in relatives (GWAS+FH). To allow for large-scale genetic testing in GWAS+FH, we handle the unobserved genotypes as well as estimate the random effects with reduced computational cost through fast and stable EM-type algorithm as well as score test. Through simulations, we demonstrate that our method of incorporating family history of disease improves efficiency as well as power of detecting disease-associated genetic variants over the methods of using probands data alone, which emphasizes the importance of family studies. Lastly, we apply these methods to discover genetic variants associated with the risk of Alzheimer's disease (AD) for GWAS+FH collected in Washington Heights-Inwood Columbia Aging Project (WHICAP) Caribbean Hispanics. We identified several genetic variants which would not have been discovered by GWAS using proband data alone. In the third part of the dissertation, we build on the previously introduced random effects to propose a method for genetic association tests in order to control confounding due to familial relatedness in GWAS. It is critical to correct for confounding due to familial relatedness in GWAS in order to minimize spurious associations as well as maximize power to detect true association signals. With available pedigree data, our method uses the polygenic effects as well as the shared non-genetic familial effects in order to control confounding due to familial relatedness in GWAS. Through application to the WHICAP Caribbean Hispanic probands, we show that our method of using the polygenic effects as well as the shared familial effects achieves similar or better performance of controlling the familial relatedness compared to using principal components in GWAS. Notably, our method allows for controlling the confounding due to using family history data, but without requiring dense genotypes in the relatives. We conclude this dissertation by discussing future extensions of this work.

The overall theme of this thesis focuses on methods for functional regression and nonlinear mixed-effects models with applications to PET data. The first part considers the problem of variable selection in regression models with functional responses and scalar predictors. We pose the function-on-scalar model as a multivariate regression problem and use group-MCP for variable selection. We account for residual covariance by "pre-whitening" using an estimate of the covariance matrix, and establish theoretical properties for the resulting estimator. We further develop an iterative algorithm that alternately updates the spline coefficients and covariance. Our method is illustrated by the application to two-dimensional planar reaching motions in a study of the effects of stroke severity on motor control. The second part introduces a functional data analytic approach for the estimation of the IRF, which is necessary for describing the binding behavior of the radiotracer. Virtually all existing methods have three common aspects: summarizing the entire IRF with a single scalar measure; modeling each subject separately; and the imposition of parametric restrictions on the IRF. In contrast, we propose a functional data analytic approach that regards each subject's IRF as the basic analysis unit, models multiple subjects simultaneously, and estimates the IRF nonparametrically. We pose our model as a linear mixed effect model in which shrinkage and roughness penalties are incorporated to enforce identifiability and smoothness of the estimated curves, respectively, while monotonicity and non-negativity constraints impose biological information on estimates. We illustrate this approach by applying it to clinical PET data. The third part discusses a nonlinear mixed-effects modeling approach for PET data analysis under the assumption of a compartment model. The traditional NLS estimators of the population parameters are applied in a two-stage analysis, which brings instability issue and neglects the variation in rate parameters. In contrast, we propose to estimate the rate parameters by fitting nonlinear mixed-effects (NLME) models, in which all the subjects are modeled simultaneously by allowing rate parameters to have random effects and population parameters can be estimated directly from the joint model. Simulations are conducted to compare the power of detecting group effect in both rate parameters and summarized measures of tests based on both NLS and NLME models. We apply our NLME approach to clinical PET data to illustrate the model building procedure.