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.

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Yes / The majority of Mycobacterium tuberculosis isolates resistant to isoniazid harbour a mutation in katG. Since these mutations cause a wide range of minimum inhibitory concentrations (MICs), largely below the serum level reached with higher dosing (15 mg/L upon 15–20 mg/kg), the drug might still remain partly active in presence of a katG mutation. We therefore investigated which genetic mutations predict the level of phenotypic isoniazid resistance in clinical M. tuberculosis isolates. To this end, the association between known and unknown isoniazid resistance-conferring mutations in whole genome sequences, and the isoniazid MICs of 176 isolates was examined. We found mostly moderate-level resistance characterized by a mode of 6.4 mg/L for the very common katG Ser315Thr mutation, and always very high MICs (≥19.2 mg/L) for the combination of katG Ser315Thr and inhA c-15t. Contrary to common belief, isolates harbouring inhA c-15t alone, partly also showed moderate-level resistance, particularly when combined with inhA Ser94Ala. No overt association between low-confidence or unknown mutations, except in katG, and isoniazid resistance (level) was found. Except for the rare katG deletion, line probe assay is thus not sufficiently accurate to predict the level of isoniazid resistance for a single mutation in katG or inhA. / European Research Council (Starting Grant INTERRUPTB 311725 to CM, LR and BdJ), The Damien Foundation

Genetic association studies usually include related individuals. Meanwhile, high-throughput sequencing technologies produce data of multiple genetic variants. Due to linkage disequilibrium (LD) and familial relatedness, the genotype data from such studies often carries complex correlations. Moreover, missing values in genotype usually lead to loss of power in genetic association tests. Also, repeated measurements of phenotype and dynamic covariates from longitudinal studies bring in more opportunities but also challenges in the discovery of disease-related genetic factors. This dissertation focuses on developing novel statistical methods to address some challenging questions remaining in genetic association studies due to the aforementioned reasons. So far, a lot of methods have been proposed to detect disease-related genetic regions (e.g., genes, pathways). However, with multiple-variant data from a sample with relatedness, it is critical to account for the complex genotypic correlations when assessing genetic contribution. Recognizing the limitations of existing methods, in the first work of this dissertation, the Adaptive-weight Burden Test (ABT) --- a score test between a quantitative trait and the genotype data with complex correlations --- is proposed. ABT achieves higher power by adopting data-driven weights, which make good use of the LD and relatedness. Because the null distribution has been successfully derived, the computational simplicity of ABT makes it a good fit for genome-wide association studies. Genotype missingness commonly arises due to limitations in genotyping technologies. Imputation of the missing values in genotype usually improves quality of the data used in the subsequent association test and thus increases power. Complex correlations, though troublesome, provide the opportunity to proper handling of genotypic missingness. In the second part of this dissertation, a genotype imputation method is developed, which can impute the missingness in multiple genetic variants via the LD and the relatedness. The popularity of longitudinal studies in genetics and genomics calls for methods deliberately designed for repeated measurements. Therefore, a multiple-variant genetic association test for a longitudinal trait on samples with relatedness is developed, which treats the longitudinal measurements as observations of functions and thus takes into account the time factor properly. / PHD / It has been widely recognized that complex diseases are results of poor habits and genetic predisposition. Though people can make their own choices about lifestyle, the mysterious genome language seems to be unchangeable and inevitable. Decoding the messages delivered by DNA can help with prevention, prediction and treatment of diseases. This work focuses on developing novel statistical methods that can make contributions to the detection of disease-related genetic factors. Specifically, given the genotype data and phenotype (e.g., fasting glucose level) data on a sample of individuals where some could be relatives and the rest may be not, three challenges are addressed in this work: (1) how to detect if a genetic region (such as a gene) is significantly associated with the phenotype, while non-genetic information (such as demographic data) is taken into account; (2) how to deal with missing values in genotype data via the relatedness among individuals as well as the similarity among genetic variants; (3) if the phenotype is measured over time for every individual, how to take advantage of the abundant information to discover genes with time-related effects on the phenotype. To address question (1), a hypothesis test is proposed, which is proved being able to successfully detect genes already discovered being associated with a specific trait in previous studies. To address question (2), an imputation method is developed and it is shown that this method can improve the power of association tests. For the third challenge, a second hypothesis test is proposed and it is verified to be able to identify genes contributing to the pattern of a longitudinal trait.

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.

La sévérité du paludisme est influencée par des interactions complexes entre de nombreux facteurs dont la génétique de l’hôte. Plusieurs études de liaison génétique menées dans différentes ethnies africaines ont montré une liaison entre le locus 6p21 et le paludisme simple. De plus, différents variants au sein des gènes TNF et NCR3, retrouvés dans ce locus, ont été indépendamment associés à ce phénotype au Burkina Faso.Ainsi, nous nous sommes tout d’abord intéressés aux polymorphismes du TNF. Nos résultats montrent que les variants TNF-308, TNF-244, et TNF-238 sont associés à la parasitémie maximale ou aux accès simples au Congo. Les approches moléculaires indiquent que le TNF-244 a un effet cis-régulateur avec une activité promotrice réduite en présence du variant A ainsi qu’une fixation altérée de protéines nucléaires en présence de ce même variant. Enfin, nos analyses bio-informatiques suggèrent que le TNF-244 et le TNF-238 agissent en synergie pour modifier le site de fixation d’au moins un facteur de transcription.Nous avons ensuite confirmé l’association du NCR3-412 avec le paludisme simple et le nombre d’accès fébrile au Congo. Les analyses fonctionnelles montrent que ce SNP a aussi un effet cis-régulateur avec une activité promotrice accrue en présence de l’allèle G et une liaison altérée de deux complexes protéiques en présence de l’allèle C. Les approches in silico et in vitro indiquent que les facteurs STAT4 et RUNX3 sont ceux dont la fixation est altérée.NCR3-412 altérant la résistance à la forme simple du paludisme, nous avons souhaité déterminer s’il est aussi impliqué dans la résistance au paludisme sévère mais nous n’avons détecté aucune association. / The severity of malaria is influenced by complex interactions between many factors including host genetics. Numerous genetic studies conducted in different African ethnic groups have shown a significant linkage between the 6p21 locus and mild malaria attack. In addition to their linkage, several polymorphisms found under the linkage peak, and more precisely within TNF and NCR3, were also independently associated with different sub-phenotypes of mild malaria in Burkina Faso.Thus, we first focused on TNF polymorphisms. Among the 4 polymorphisms analyzed, we found associations between TNF-238, TNF-244, TNF-308 and either mild malaria attack or maximum parasitemia. Molecular approaches showed that TNF-244 has a cis-regulatory effect. Indeed, we observe a decreased promoter activity and an altered binding of nuclear proteins in the presence of the A variant. In addition, our bioinformatics analyses suggested a cooperative effect of TNF-244 and TNF-238 in modifying the binding of at least one transcription factor.We then confirmed the association of NCR3-412 with both mild malaria and the number of febrile episodes in Congo. Functional analyses have shown that this SNP has also a cis-regulatory effect with a decreased promoter activity and an altered binding of two nuclear protein complexes in the presence of the C allele. Finally, in silico and in vitro approaches indicated that STAT4 and RUNX3 are the two transcription factors affected.As NCR3-412 is associated with resistance to mild malaria, we therefore investigated whether this SNP is also involved in severe malaria resistance, but we did not detect any association neither with severe anemia nor with cerebral malaria.

Introduction: Obsessive-compulsive disorder (OCD) is a complex disorder that is associated with significantly impaired functioning. The current prevailing model of OCD implicates dysfunction of the serotonergic neurotransmitter system and fronto-striatal neural networks, but challenges in replicating findings within OCD samples are often attributed to clinical heterogeneity. OCD symptoms that develop or worsen within the perinatal period appears to reflect a distinct subtype of the disorder, but the genetic and neurobiological factors that contributes to its presentation in women is poorly understood. In this dissertation, we aimed to review the literature on the genetic architecture of OCD, identify potential gene candidates for perinatal OCD and analyze one serotonin system gene according to OCD and possible subtypes using meta-analytic techniques. Based on these findings, we then tested the association of serotonergic candidate gene polymorphisms with the presence of infant-related obsessive-compulsive symptoms (OCS). Lastly, we investigated the cortical morphological features associated with perinatal OCD and OCS symptom severity in postpartum mothers. Results: From prior reports in the literature and our own meta-analytic investigation, polymorphic variants in genes coding for the serotonergic transporter and serotonin 2A receptor subtype (SLC6A4 and HTR2A, respectively) appear to be candidates for perinatal OCD due to their association in female samples. However, upon investigation in our perinatal sample (n=107), we found no evidence to support the association of the 5-HTTLPR polymorphism of SLC6A4 with perinatal-related OCS, but larger samples are needed to confirm this finding. Due to technical challenges, the HTR2A polymorphism remains to be tested. Our novel whole-brain explorations revealed distinct cortical morphology associated with symptom worsening across the perinatal period, irrespective of diagnosis. Cortical parameters were not able to differentiate mothers with and without OCD; however, OCD mothers displayed positive correlations between cortical surface area and symptom severity in widespread regions, including the frontal, parietal, temporal and occipital cortex. Conclusions: Overall, this body of work aimed to fill the gap in the literature by exploring the possible genetic and cortical correlates of perinatal-related OCS and OCD. While 5-HTTLPR or HTR2A are candidates for perinatal OCD, it is not yet clear whether they increase susceptibility for the development of infant-related OCS in the perinatal period. Distinct cortical alterations in surface area appeared alongside OCS exacerbation in the postpartum period in regions that extend beyond the frontoparietal network. This suggests that additional neural networks may be contributing to symptom severity and that the cortical plasticity that occurs across the perinatal period may predispose women for risk of OCD. Future studies should continue to use a multiple perspective approach, that utilizes genetic and neurobiological techniques, in order to provide greater insight into the etiology of perinatal OCD. / Dissertation / Doctor of Philosophy (PhD) / Women are at greater risk for the development of mental illness in the time surrounding pregnancy and postpartum, known as the perinatal period. In the case of perinatal obsessive-compulsive disorder (OCD), mothers may experience unique worries in regard to their parenting or fears that their baby may be harmed. While these worries are common, they can become disruptive when persistent and impact the mother’s mood and ability to bond with the infant. Our current understanding of OCD includes the influence of genetic factors and brain changes, but little is understood about what factors may increase risk for OCD in the perinatal period. In this thesis, we aimed to review whether certain alterations within DNA segments, known as gene variants, may be linked to the development of OCD in females and if these gene changes, as well as differences in brain structures in postpartum mothers, are associated with OCD symptoms during the perinatal period. The genes we examined are important for regulating a chemical signaling substance in the brain known as serotonin. Based on our results, we did not find a relationship between serotonin gene variants and OCD symptoms in perinatal women. We also found no differences when comparing the cortical brain structures between mothers with OCD and healthy mothers; however, we observed that measures of surface area across several cortical brain regions were related to symptom worsening from pregnancy to postpartum, and also with symptom severity in postpartum mothers with OCD. These results suggest that there are widespread brain changes during the postpartum period that may increase a mother’s risk for developing OCD. Overall, the work in this thesis provides the first glimpse into potential risk factors for perinatal OCD.

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.