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The United States is currently experiencing an epidemic of opioid use, use disorder, and overdose-related deaths. While studies have identified several loci that are associated with opioid use disorder (OUD) risk, the genetic basis for the ability to discontinue opioid use has not been investigated. Furthermore, very few studies have investigated the non-genetic factors that are predictive of opioid cessation or their predictive ability. In this thesis, I studied a novel phenotype–opioid cessation, defined as the time since last use of illicit opioids (< 6 months ago as not cease, >1 year ago as cease) among persons meeting lifetime DSM-5 criteria for opioid use disorder (OUD). In chapter two, I identified novel genetic variants and biological pathways that potentially regulate opioid cessation success through a genome wide study, as well as genetic overlap between opioid cessation and other substance cessation traits. In chapter three, I identified multiple non-genetic risk factors specific to each racial group that are predictive of opioid cessation from the same individuals analyzed in chapter two by applying several linear and non-linear machine learning techniques to a set of more than 3,000 variables assessed by a structured psychiatric interview. Factors identified from this atheoretical approach can be grouped into opioid use activities, other drug use, health conditions, and demographics, while the predictive accuracy as high as nearly 80% was achieved. The findings from this research generated more hypotheses for future studies to reference. In chapter four, I performed differential gene expression and network analysis on mice with different oxycodone (an opioid receptor agonist)-induced behaviors and compared the significantly associated genes and network modules with top-ranked genes identified in humans. The pathway cross-talks and gene homologs identified from both species illuminate the potential molecular mechanism of opioid behaviors. In summary, this thesis utilized statistical genetics, machine learning, and a computational biology framework to address factors that are associative with opioid cessation in humans, and cross-referenced the genetic findings in a mouse model. These findings serve as references for future studies and provide a framework for personalizing the treatment of OUD.

The influence of pollinators on the evolution of flower morphology has been extensively explored. Yet, the effect of other ecological factors, such as genetic drift, environmental filtering, and allometric constraints, gained less attention. In this study, we addressed the importance of those drivers in a predominantly selfing species. 400 worldwide Arabidopsis thaliana accessions were gathered and grown in semi-controlled climatic settings to explore the association between flower organ size, genotypes, and habitats. In our dataset, petal area was the most variable trait. Petal size was phenotypically and genetically correlated with other flowering structures, but no genetic allometry constraints were found to affect petal size evolution. The negative correlation of petal size with fitness and the traces of selective constraints in petal associated genes suggest that petal size is currently under selection in this species. We found paucity of genotypes harbouring large petals at low suitability regions, which points to the presence of environmental filtering. The novelty of this project relies on the pluralistic integration of factors studied and highlights the role of the climate on flower size evolution. Our results suggest that resource allocation is an important driver of flower size evolution in self-fertilising species but that its effect is largely determined by local environmental pressures.

In the last years, Genome-Wide Associations Studies (GWAS) found many variants associated with complex diseases. However, the biological and molecular links between these variants and phenotypes are still mostly unknown. Also, even if sample sizes are constantly increasing, the associated variants do not explain all the heritability estimated for many traits. Many hypotheses have been proposed to explain the problem: from variant-variant interactions, the effect of rare and ultra-rare coding variants and also technical biases related to sequencing or statistic on sexual chromosomes. In this thesis, we mainly explore the hypothesis of variant-variant interaction and, briefly, the rare coding variants hypothesis while also considering possible molecular effects like allele-specific expression and the effects of variants on protein interfaces. Some parts of the thesis are also devoted to explore the implementation of efficient computational tools to explore these effects and to perform scalable genotyping of germline single nucleotide polymorphisms (SNPs) in huge datasets. The main part of the thesis regards the development of a new resource to identify putative variant-variant interactions. In particular, we integrated ChIP-seq data from ENCODE, transcription factor binding motifs from several resources and genotype and transcript level data from GTeX and TCGA. This new dataset allows us to formalize new models, to make hypothesis and to find putative novel associations and interactions between (mainly non-coding) germline variants and phenotypes, like cancer-specific phenotypes. In particular, we focused on the characterization of breast cancer and Alzheimer’s Disease GWAS risk variants, looking for putative variants’ interactions. Recently, the study of rare variants has become feasible thanks to the biobanks that made available genotypes and clinical data of thousands of patients. We characterize and explore the possible effects of rare coding inherited polymorphisms on protein interfaces in the UKBioBank trying to understand if the change in structure of protein can be one of the causes of complex diseases. Another part of the thesis explores variants as causal molecular effect for allele-specific expression. In particular, we describe UTRs variants that can alter the post-transcriptional regulation in mRNA leading to a phenomenon where an allele is more expressed than the other. Finally, we show those variants can have prognostic significance in breast cancer. This thesis work introduces results and computational tools that can be useful to a broad community of researcher studying human polymorphisms effects.

Preferential fat accumulation in visceral vs. subcutaneous depots makes obese individuals more prone to metabolic complications. Body fat distribution (FD) is regulated by genetics. FD patterns vary across ethnic groups independent of obesity. Asians have more and Africans have less visceral fat compared with Europeans. Consequently, Asians tend to be more susceptible to type 2 diabetes even with lower BMIs when compared with Europeans. To date, genome-wide association studies (GWAS) have identified more than 460 loci related to FD traits. However, the majority of these data were generated in European populations. In this review, we aimed to summarize recent advances in FD genetics with a focus on comparisons between European and non-European populations (Asians and Africans). We therefore not only compared FD-related susceptibility loci identified in three ethnicities but also discussed whether known genetic variants might explain the FD pattern heterogeneity across different ancestries. Moreover, we describe several novel candidate genes potentially regulating FD, including NID2, HECTD4 and GNAS, identified in studies with Asian populations. It is of note that in agreement with current knowledge, most of the proposed FD candidate genes found in Asians belong to the group of developmental genes.

Holistic and growth stage-specific screening is needed for identifying tolerant genotypes and for formulating strategies to mitigate the negative effects of abiotic stresses on crops. The objectives of this study were to characterize the genetic variability of 100 rice lines for early-season vigor, growth and physiological plasticity, and drought and temperature tolerance. Five studies were conducted to accomplish these objectives. In study 1 and 2, 100 rice genotypes consisting of several cultivars and experimental breeding lines were characterized for early-season vigor using several shoot and root morphological, physiological, and yield related traits. In study 3, low- and high-temperature tolerance assessed on select rice cultivars/hybrids during early-season. In study 4, genotypic variability in response to drought stress tolerance using morpo-physiological traits including roots was assessed under pot-culture conditions in a mini-greenhouse conditions. In study 5, the 100 rice genotypes were used to identify and validate SNP markers, and genome-wide association study (GWAS) to generate genotypic and phenotypic data with the objective of identifying new genetic loci controlling drought stress traits. Significant variability was recorded among rice genotypes and treatments for many traits measured. Early-season cumulative vigor response indices (CVRI) developed by summing individual responses indices for each trait varied among the rice genotypes, 21.36 (RU1404196) to 36.17 (N-22). Based on means and standard deviation of the CVRI, rice genotypes were classified as low- (43) and moderately low- (33), high- (16), and very high-vigor (5) groups. Total low-temperature response index values ranged from 18.48 to 23.15 whereas total high-temperature responses index values ranged from 42.01 to 48.82. Antonio, CLXL 745, and Mermentau were identified as sensitive to cold- and heat, and XL 753 was highly cold and heat tolerant genotypes tested. A cumulative drought stress response index (CDSRI) values varied between 14.7 (CHENIERE) and 27.9 (RU1402174) among the genotypes tested. This preliminary analysis of GWA indicated that substantial phenotypic and genotypic diversity exists in the 100 rice genotypes, despite their narrow genetic pool. The stress tolerant and high vigor rice genotypes will be valuable for rice breeders for developing new genotypes best suited under growing environments prone to early-season drought and temperature.

<p>Complex disorders are caused by multiple genetic, environmental, and lifestyle factors, and their interactions. Most human diseases are complex, including many psychiatric, autoimmune, neurodegenerative, and cardiovascular disorders. Understanding their genetic background is an essential step toward developing effective preventive and therapeutic interventions for these disorders. In this dissertation, we present an overview of state-of-the-art methodology that is used to help elucidate the genetic basis of complex diseases and apply these methods to understand the genetic background of different complex disorders. First, we carried out a GWAS for myasthenia gravis (MG), a rare autoimmune disorder, and detected a novel risk locus, AGRN, which encodes a protein, involved in neuromuscular junction activation. Additionally, we observed significant genetic correlation between MG and ADs, and variants with pleiotropic effects. Second, we explored the genetic and phenotypic relationships among 11 different autoimmune disorders (ADs), using GWAS results o to calculate polygenic risk scores (PRS) and performing a PRS- phenome-wide association study (PheWAS) analysis with 3,281 phenotypes available in the UK Biobank. We observed associations of ADs PRS with phenotypes in multiple categories, including lifestyle, biomarkers, mental and physical health. We also explored the shared genetic components among the ADs, through genetic correlation and cross-disorder meta-analysis approaches, where we</p> <p>identified pleiotropic variants among the correlated ADs. Finally, we performed a meta-analysis GWAS of Tourette Syndrome (TS) followed by post-GWAS analyses including biological annotation of the results, and association tests of TS PRS with brain volumes. We detected a novel locus, NR2F1, associated with TS, supported by eQTL and Hi-C data. TS PRS was significantly associated with right and left thalamus volumes and right putamen volume. Overall, our work demonstrates the power of GWAS and related methods to help disentangle the genetic basis of complex disease and provides important insights into the genetic basis of the specific disorders that are the focus of our studies.</p>

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The oral microbiome is a key component of the human body and has been associated with several habits and diseases. Despite its important role in health, it remains relatively understudied, compared to the gut microbiome. To deepen our understanding of the oral microbiome and its links to host conditions, the main aim of my PhD thesis was to characterize the lifestyle, environmental and genetic determinants of the salivary microbiome using data from CHRISMB, a convenience sample within the Cooperative Health Research in South Tyrol (CHRIS) study. With more than 1,900 samples, CHRISMB is one of the largest salivary microbiome data resources in the world. First, I studied the association between the salivary microbiome and smoking status and degree of exposure both from the compositional and predicted metabolism perspective. I found associations with 44 genera, 11 of which were also proportionally affected by the degree of exposure to tobacco. Intriguingly, these associations highlight a novel role of salivary microbiome metabolism in cardiovascular diseases through periodontium degeneration via the nitrate reduction and extracellular matrix degradation pathways. My second contribution focused on the role of geography, family relatedness, and genetics in shaping CHRISMB diversity. I investigated the associations between household, municipality and altitude of residence, heritability, and genetic marker associations (mbGWAS). I confirmed that cohabitation is a strong driver of microbiome similarity, while municipality and altitude of residence did not show strong associations. Siblings living apart had a more similar microbiota than unrelated and non-cohabiting individuals. Sixteen out of 142 taxa had a significant heritability component, while 34 had a significant household component. A mbGWAS Gene-level analysis resulted in one association between rare variants in the SRFBP1 and LOX genes locus and Selenomonas noxia. This work confirmed that host genetics and familial relationships has a modest but significant association with the salivary microbiome composition and that the environment and lifestyle are strongly associated. In summary, this thesis deepens our understanding of population-level factors associated with salivary microbiome variability, which can help design future hypothesis driven studies.