Genetic association analyses have successfully identified thousands of genetic variants contributing to complex disease susceptibility. However, these discoveries do not explain the full heritability of many diseases, due to the limited statistical power to detect loci with small effects, especially in regions with rare variants. The development of new and powerful methods is necessary to fully characterize the underlying genetic basis of complex diseases. Family history (FH) contains information on the disease status of un-genotyped relatives, which is related to the genotypes of probands at disease loci. Exploiting available FH in relatives could potentially enhance the ability to identify associations by increasing sample size. Many studies have very low power for genetic research in late-onset diseases because younger participants do not contribute a sufficient number of cases and older patients are more likely deceased without genotypes. Genetic association studies relying on cases and controls need to progress by incorporating additional information from FH to expand genetic research. This dissertation overcomes these challenges and opens up a new paradigm in genetic research. The first chapter summarizes relevant methods used in this dissertation. In the second chapter, we develop novel methods to exploit the availability of FH in aggregation unit-based test, which have greater power than other existing methods that do not incorporate FH, while maintaining a correct type I error. In the third chapter, we develop methods to exploit FH while adjusting for relatedness using the generalized linear mixed effect models. Such adjustment allows the methods to have well-controlled type I error and maintain the highest sample size because there is no need to restrict the analysis to an unrelated subset in family studies. We demonstrate the flexibility and validity of the methods to incorporate FH from various relatives. The methods presented in the fourth chapter overcome the issue of inflated type I error caused by extremely unbalanced case-control ratio. We propose robust versions of the methods developed in the second and third chapters, which can provide more accurate results for unbalanced study designs. Availability of these novel methods will facilitate the identification of rare variants associated with complex traits.

Aims: To elucidate the aetiopathological mechanisms underlying the IIMs, through a combination of genotyping, serotyping and clinical phenotyping in a large cohort of Caucasian idiopathic inflammatory myopathy (IIM) patients. Methods: A cross-sectional study of prevalent IIM cases, ascertained through the Adult Onset Myositis Immunogenetic Collaboration, was performed. Cases were confirmed as possessing myositis according to Bohan and Peter (Bohan and Peter 1975a; Bohan and Peter 1975b). IIM clinical subtypes studied included polymyositis (PM), dermatomyositis (DM) and myositis associated with other connective tissue disease (myositis/CTD-overlap). Genotyping of major histocompatibility complex genes, including HLA-B, -DR, -DQ, tumour necrosis factor alpha (TNF-α), was performed using commercial kits. Serotyping of a comprehensive range of myositis specific/associated antibodies (MSA/MAAs) was undertaken. Results: Clinical subsets are described within the serological groupings, suggesting that the classification of the IIMs appears to be better served by the serotype than by the clinical subgrouping of disease. The IIMs possess HLA class I and II haplotype associations and genetic differences observed between PM and DM are accounted for by serological differences. The TNF-308A association is not independent of HLA class I, due to the strong LD within the MHC, but does form part of a haplotype with these factors. An absence of routinely tested for MSA/MAAs makes cancer associated myositis (CAM) more likely, especially in the DM subgroup. An antibody against a 155 and 140kDa doublet is associated with the development of CAM. Outcome measures in the IIMs show construct validity. HLA-DRB1*07 appears to predict a milder clinical phenotype with less disability. No convincing gene-environmental interaction was found capable of altering disease susceptibility or clinical phenotype. Conclusions: Myositis disease subtypes therefore appear to be defined by specific haplotypes acting as risk factors for the development of various MSAs and MAAs.

<p>The Bayesian approach to model selection allows for uncertainty in both model specific parameters and in the models themselves. Much of the recent Bayesian model uncertainty literature has focused on defining these prior distributions in an objective manner, providing conditions under which Bayes factors lead to the correct model selection, particularly in the situation where the number of variables, <italic>p</italic>, increases with the sample size, <italic>n</italic>. This is certainly the case in our area of motivation; the biological application of genetic association studies involving single nucleotide polymorphisms. While the most common approach to this problem has been to apply a marginal test to all genetic markers, we employ analytical strategies that improve upon these marginal methods by modeling the outcome variable as a function of a multivariate genetic profile using Bayesian variable selection. In doing so, we perform variable selection on a large number of correlated covariates within studies involving modest sample sizes. </p> <p>In particular, we present an efficient Bayesian model search strategy that searches over the space of genetic markers and their genetic parametrization. The resulting method for Multilevel Inference of SNP Associations MISA, allows computation of multilevel posterior probabilities and Bayes factors at the global, gene and SNP level. We use simulated data sets to characterize MISA's statistical power, and show that MISA has higher power to detect association than standard procedures. Using data from the North Carolina Ovarian Cancer Study (NCOCS), MISA identifies variants that were not identified by standard methods and have been externally 'validated' in independent studies. </p> <p></p> <p>In the context of Bayesian model uncertainty for problems involving a large number of correlated covariates we characterize commonly used prior distributions on the model space and investigate their implicit multiplicity correction properties first in the extreme case where the model includes an increasing number of redundant covariates and then under the case of full rank design matrices. We provide conditions on the asymptotic (in <italic>n</italic> and <italic>p</italic>) behavior of the model space prior </p> <p>required to achieve consistent selection of the global hypothesis of at least one associated variable in the analysis using global posterior probabilities (i.e. under 0-1 loss). In particular, under the assumption that the null model is true, we show that the commonly used uniform prior on the model space leads to inconsistent selection of the global hypothesis via global posterior probabilities (the posterior probability of at least one association goes to <italic>1</italic>) when the rank of the design matrix is finite. In the full rank case, we also show inconsistency when <italic>p</italic> goes to infinity faster than the square root of <italic>n</italic>. Alternatively, we show that any model space prior such that the global prior odds of association increases at a rate slower than the square root of <italic>n<italic> results in consistent selection of the global hypothesis in terms of posterior probabilities.</p> / Dissertation

Statistical methods that integrate between-individual (IA) and within-family (FA) genetic association analyses can increase statistical power to identify disease susceptibility genes, however combining IA and FA is valid only when the IA are free of population stratification bias (PSB). Existing methods initially test for PSB by comparing IA and FA results using an arbitrary testing level αPSB, typically 5%. Combined analyses are performed if no significant PSB is detected, otherwise analyses are restricted to FA. As a novel alternative, we propose a weighted (WGT) framework that combines the estimate from the most powerful analysis subject to PSB with the most powerful robust FA estimate, using weights based on the p-value from the PSB test. The WGT approach generalizes existing methods by using a continuous weighting function that depends only on the observed PSB p-value instead of a binary one that also depends on specification of an arbitrary PSB testing level αPSB. Simulations of quantitative trait and case-control data show that in comparison to existing methods, the WGT approach has 5% type I error closer to the nominal level, increased (decreased) accuracy for larger (smaller) PSB levels, and overall increased positive predictive value. The resulting PSB correction is SNP-specific and provides a good compromise between type I error control and power in candidate gene or confirmation studies limited to few loci, when PSB is likely and there are no additional empirical data available to correct PSB. We applied the WGT approach to a case-control study of childhood leukemia and a study of diabetes complications with time-to-event outcomes derived from repeated measurements obtained over 17 years of follow-up. To directly analyze the longitudinal measurements without specification of event thresholds, we developed fully Bayesian latent change-point time (LCPT) models for IA and FA. In analogy with the WGT approach, we also considered an extended LCPT model incorporating PSB evidence in analyses combining IA and FA.

Statistical methods that integrate between-individual (IA) and within-family (FA) genetic association analyses can increase statistical power to identify disease susceptibility genes, however combining IA and FA is valid only when the IA are free of population stratification bias (PSB). Existing methods initially test for PSB by comparing IA and FA results using an arbitrary testing level αPSB, typically 5%. Combined analyses are performed if no significant PSB is detected, otherwise analyses are restricted to FA. As a novel alternative, we propose a weighted (WGT) framework that combines the estimate from the most powerful analysis subject to PSB with the most powerful robust FA estimate, using weights based on the p-value from the PSB test. The WGT approach generalizes existing methods by using a continuous weighting function that depends only on the observed PSB p-value instead of a binary one that also depends on specification of an arbitrary PSB testing level αPSB. Simulations of quantitative trait and case-control data show that in comparison to existing methods, the WGT approach has 5% type I error closer to the nominal level, increased (decreased) accuracy for larger (smaller) PSB levels, and overall increased positive predictive value. The resulting PSB correction is SNP-specific and provides a good compromise between type I error control and power in candidate gene or confirmation studies limited to few loci, when PSB is likely and there are no additional empirical data available to correct PSB. We applied the WGT approach to a case-control study of childhood leukemia and a study of diabetes complications with time-to-event outcomes derived from repeated measurements obtained over 17 years of follow-up. To directly analyze the longitudinal measurements without specification of event thresholds, we developed fully Bayesian latent change-point time (LCPT) models for IA and FA. In analogy with the WGT approach, we also considered an extended LCPT model incorporating PSB evidence in analyses combining IA and FA.

Statistical association tests of quantitative traits have been widely used in the past decade, to locate loci associated with a disease trait. For instance, Genome Wide Association Studies (GWAS) have led to tremendous success in finding susceptible genes or associated loci. However, most of the past studies were based on unrelated samples focusing on quantitative or qualitative traits. The analysis of polychotomous traits in family samples is very challenging. This dissertation describes three projects related to methods to conduct association tests beyond continuous traits, such as multinomial traits, bivariate traits, and tests involving haplotypes. The first project focuses on developing a statistical approach to test the association between common or low-frequency variants with a multinomial trait in family samples. It is an important issue because there is no computer efficient software available for this type of question. We employ Laplace approximation in conjunction with an efficient grid-search strategy to obtain an approximate maximum log-likelihood function and the Maximum Likelihood Estimate (MLE) of the variance component. We also successfully incorporate the kinship matrix to adjust for the familial correlation, based on a regression framework. Extensive simulation studies are performed to evaluate the type-I error rate and power in scenarios with causal variant with different Minor Allele Frequency (MAF). In the second project, we propose an approach to test the association between a genetic variant and a bivariate trait arising from a combination of a quantitative and a binary trait in family samples, based on Extended Generalized Estimating Equations (EGEE). Multiple phenotype-genotype association tests are often reduced to univariate tests, decreasing efficiency and power. Our approach is shown to be much more powerful and efficient than univariate association tests adjusted for multiple testing. The third project involves the development of a general framework for meta-analysis of haplotype association tests, applicable to both unrelated and family samples. Although meta-analysis has been widely used in single-variant and gene-based tests, there are few existing methods to meta-analyze haplotype association tests. A predominant advantage of our novel approach is that it accommodates cohort-specific haplotypes as well as haplotypes common to all cohorts. The cohort participants may be either related or unrelated. Our approach consists of two stages: in the first stage, each cohort performs a haplotype association test, reports the estimates of effect size, variance, haplotypes, and their frequency. In the second stage, a generalized least square method is applied to combine the results of all the cohorts into one vector of meta-analysis coefficients. Our approach is shown to have the correct type-I error rate in scenarios with different between and within cohort variation. We also present an application to exome-chip data from a large consortium. Through the three projects, we are able to tackle the problem of conducting association tests for non-continuous traits in family samples. All the approaches achieve the correct type-I error rate and are computationally efficient. We hope these approaches will not only facilitate analyses of categorical traits in family samples, but will also provide a basis for future methodological development of statistical approaches for non-continuous traits.

Investigators have discovered thousands of genetic variants associated with various traits using genome-wide association studies (GWAS). These discoveries have substantially improved our understanding of the genetic architecture of many complex traits. Despite the striking success, these trait-associated loci collectively explain relatively little of disease risk. Many reasons for this unexplained heritability have been suggested and two understudied components are hypothesized to have an impact in complex disease etiology: rare variants and gene-environment (GE) interactions. Advances in next generation sequencing have offered the opportunity to comprehensively investigate the genetic contribution of rare variants on complex traits. Such diseases are multifactorial, suggesting an interplay of both genetics and environmental factors, but most GWAS have focused on the main effects of genetic variants and disregarded GE interactions. In this dissertation, we develop statistical methods to detect GE interactions for rare variant analysis for various types of outcomes in both independent and related samples. We leverage the joint information across a set of rare variants and implement variance component score tests to reduce the computational burden. First, we develop a GE interaction test for rare variants for binary and continuous traits in related individuals, which avoids having to restrict to unrelated individuals and thereby retaining more samples. Next, we propose a method to test GE interactions in rare variants for time-to-event outcomes. Rare variant tests for survival outcomes have been underdeveloped, despite their importance in medical studies. We use a shrinkage method to impose a ridge penalty on the genetic main effects to deal with potential multicollinearity. Finally, we compare different types of penalties, such as least absolute shrinkage selection operator and elastic net regularization, to examine the performance of our second method under various simulation scenarios. We illustrate applications of the proposed methods to detect gene x smoking interaction influencing body mass index and time-to-fracture in the Framingham Heart Study. Our proposed methods can be readily applied to a wide range of phenotypes and various genetic epidemiologic studies, thereby providing insight into biological mechanisms of complex diseases, identifying high-penetrance subgroups, and eventually leading to the development of better diagnostics and therapeutic interventions.

A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine. / Genetic association studies simplify genotype‐phenotype relationship investigation by considering only the presence of a given polymorphism and the presence or absence of a given downstream phenotype. Although such associations do not indicate causation, collections of phenotypes sharing association with a single genetic polymorphism may provide valuable mechanistic insights. In this thesis we explore such genetic pleiotropy with Deep Phenotype Association Studies (DeePAS) using data from the Age‐Related Eye Study 2 (AREDS2). We also employ a novel text mining approach to extract pleiotropic associations from the published literature as a hypothesis generation mechanism. Is it possible to identify pleiotropic genetic associations across multiple published abstracts and validate these in data from AREDS2? Data from the AREDS2 trial includes 123 phenotypes including AMD features, other ocular conditions, cognitive function and cardiovascular, neurological, gastrointestinal and endocrine disease. A previously validated relationship extraction algorithm was used to isolate descriptions of genetic associations with these phenotypes in MEDLINE abstracts. Results were filtered to exclude negated findings and normalize variant mentions. Genotype data was available for 1826 AREDS2 participants. A DeePAS was performed by evaluating the association between selected SNPs and all available phenotypes. Associations that remained significant after Bonferroni‐correction were replicated in AREDS. LitWAS analysis identified 9372 SNPs with literature support for at least two distinct phenotypes, with an average of 3.1 phenotypes/SNP. PheWAS analyses revealed that two variants of the ARMS2‐HTRA1 locus at 10q26, rs10490924 and rs3750846, were significantly associated with sub‐retinal hemorrhage in AMD (rs3750846 OR 1.79 (1.41‐2.27), p=1.17*10‐7). This associated remained significant even in populations of participants with neovascular AMD. Furthermore, odds ratios for the development of sub‐retinal hemorrhage in the presence of the rs3750846 SNP were similar between incident and prevalent AREDS2 sub‐populations (OR: 1.94 vs 1.75). This association was also replicated in data from the AREDS trial. No literature‐defined pleiotropic associations tested remained significant after multiple‐testing correction. The rs3750846 variant of the ARMS2‐HTRA1 locus is associated with sub‐retinal hemorrhage. Automatic literature mining, when paired with clinical data, is a promising method for exploring genotype‐phenotype relationships.

Yes / Symptoms and signs (symptoms in brief) are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM). To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. The proposed method gets reliable gene rank list with AUC (area under curve) 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms. / NSFC Project (61105055, 81230086), China 973 Program (2014CB542903), The National Key Technology R&D Program (2013BAI02B01, 2013BAI13B04), the National S&T Major Special Project on Major New Drug Innovation (2012ZX09503-001-003), and the Fundamental Research Funds for the Central Universities.

A pigmentação humana é uma característica variável e complexa determinada por fatores genéticos e hormonais, exposição à radiação ultravioleta, idade, doenças, entre outros. Alguns polimorfismos em genes de pigmentação têm sido associados com a diversidade fenotípica de cor da pele, cabelo e olhos e em populações homogêneas. A técnica denominada Fenotipagem Forense pelo DNA (FDP) vem beneficiando a ciência forense em vários países e auxiliando investigações criminais por ser capaz de sugerir, com boa precisão, os possíveis fenótipos para as características externamente visíveis (EVCs) em amostras de origem desconhecida. No presente trabalho foram avaliadas as associações entre os SNPs presentes nos genes SLC24A5 (rs1426654; rs16960620; rs2555364), TYR (rs1126809) e ASIP (rs6058017) com cor de pele, cabelo e olhos em indivíduos da população brasileira para apontar o possível uso desses marcadores na prática forense em populações miscigenadas. Os voluntários responderam um questionário no qual fizeram a autodeclaração dessas características e estes dados foram usados para as comparações entre genótipos e fenótipos. Os resultados mostraram que para os SNPs rs2555364 e rs1426654 o alelo ancestral esteve associado com as características cor de pele negra, cabelos castanhos ou pretos e olhos castanhos. Além disso, o alelo ancestral do SNP rs6058017 foi significativamente associado com cor de pele negra e olhos castanhos. Inversamente, os alelos variantes destes SNPs são correlacionados com características de pigmentação clara para as EVCs avaliadas, corroborando os estudos prévios realizados em diferentes populações. Esses resultados mostram que a informação molecular pode ser útil para a inferência de EVCs, e a técnica de FDP é uma importante ferramenta para estudos forenses em amostra brasileira / Human pigmentation is a variable and complex trait determined by genetic and hormonal factors, exposure to ultraviolet radiation, age, diseases, among others. Some polymorphisms in pigmentation genes have been associated with the phenotypic diversity of skin, hair and eyes color in homogeneous populations. Forensic DNA Phenotyping (FDP) is benefiting forensic science in several countries, helping in criminal investigations due to its ability to suggest, with good accuracy, the possible phenotypes for externally visible characteristics (EVCs) in samples of unknown origin. Herein, we evaluated the associations between the SNPs present in the genes SLC24A5 (rs1426654; rs16960620; rs2555364), TYR (rs1126809) and ASIP (rs6058017) with skin, hair and eyes color in individuals of the Brazilian population in order to point out the possible use of these markers in forensic practice in admixed populations. The volunteers answered a questionnaire in which they self reported these characteristics for comparison between genotypes and phenotypes. The results showed that for the SNPs rs2555364 and rs1426654 the ancestral allele was associated with characteristics of black skin color, brown or black hair and brown eyes. In addition, the ancestral allele of the SNP rs6058017 was significantly associated with black skin color and brown eyes. Inversely, the variant alleles of these SNPs are correlated with fair pigmentation characteristics for the evaluated EVCs, corroborating the previous studies performed in different populations. These results show that molecular information may be useful for the inference of EVCs, and the FDP technique is an important tool for forensic studies in a Brazilian sample