Major Depressive Disorder (MDD) is a complex and chronic disease that ranks fourth as cause of disability worldwide. Thirteen to 14 million adults in the U.S. are believed to have MDD and an estimated 75% attempt suicide making MDD a major public health problem. Recently several genome-wide association (GWA) studies of MDD have been reported; however, few GWA studies focus on the analysis for MDD related phenotypes such as neuroticism and age at onset of MDD. The purpose of this study is to determine risk factors for MDD, identify genome-wide genetic variants affecting neuroticism and age at onset as quantitative traits, and detect gender differences influencing neuroticism. Bivariate and multiple logistic regression analyses were performed on 1,738 MDD cases and 1,618 non-MDD controls to determine phenotypic risk factors for MDD. Multiple linear regression analyses in PLINK software were used for GWA analyses for neuroticism and age at onset of MDD with 437,547 Single Nucleotide Polymorphisms (SNPs). Gender (OR: 1.43; 95% CI: 1.24 - 1.64) and a family history (OR: 2.88; 95% CI: 2.48 - 3.35) were significantly associated with an increased risk of MDD, which supports the findings of prior studies. Through GWA analysis 34 SNPs were identified to be associated with neuroticism (p < 10-4). The best SNP was rs4806846 within the TMPRSS9 gene (p = 7.79 x10-6). Furthermore, 46 SNPs were found showing significant gene x gender interactions for neuroticism with p<10-4. The best SNP showing gene x gender interaction was rs2430132 (p = 5.37x10-6) in HMCN1 gene. In addition, GWA analysis showed that several SNPs within 4 genes (GPR143, ASS1P4, MXRA5 and MAGEC1/2) were significantly associated with age at onset of MDD (p < 5x10-7). This study confirmed previous findings that MDD is associated with an increased prevalence in women (about 43% more compared to men) and is highly heritable among first degree relatives. Several novel genetic loci were identified to be associated with neuroticism and age at onset. Gender differences were found in genetic influence of neuroticism. These findings offer the potential for new insights into the pathogenesis of MDD.

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Despite the rapid advancement of sequencing technologies and although the wide diffusion of Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES) led to an increase in the diagnoses of diseases (A. C. Lionel, et al. 2018; D. J. Stavropoulos, et al. 2016; J. C. Taylor, et al. 2015) most genetic variants remain without a clear interpretation. One of the main difficulty related with the assessment of sequencing results is the abundance of Single Nucleotide Variant (SNV), around 4 million, that each healthy individual carries. Nearly all of these mutations will not produce any phenotype, that is equal to say that they have a benign or neutral effect. Only handful of those variants are potentially pathogenic, namely disease-causing. That is why computational Variant Effect Predictor (VEP) tools are used to prioritize variants worth investigating for medical consideration. Furthermore, the evidence of computational tools is considered among the different sources for variant effect assessment according to the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP) guidelines. In addition, those software tools can be recognized as medical devices according to the second article of the Medical Device Regulation (MDR) of the European Union (Regulation (EU) 2017/745). That is why building a computational tool that predicts with high accuracy variant pathogenicity might have a direct impact on the healthcare system. Since 2001 more than 100 VEPs tools have been developed. Yet, their thresholds to classify a variant as pathogenic are often set for high sensitivity, that results in high false positive rate, namely misclassification of benign variants (C. Cubuk, et al. 2021). During my PhD, I developed Deciphering Mutations in Actionable Genes (DeMAG), a supervised classifier for interpreting missense mutations, namely SNVs that alter the protein sequence, in a list of 59 actionable genes as identified by the ACMG Secondary Findings (SF) v2.0 list (S. S. Kalia, et al. 2017). DeMAG is a supervised classifier trained with a Gradient boosting machine (GBM) model that employs only 13 conservation-based and structural features derived from AlphaFold 3D models and manually curated Multiple Sequence Alignment (MSA). DeMAG yields the best performance on clinical data among other popular VEP tools, balancing sensitivity and specificity, reaching the highest Matthews Correlation Coefficient (MCC). The advancement of DeMAG is due to the assembling of a balanced and high-quality training set and to the design of the partners score, a feature that captures epistasis, both in the sequence and in the 3D space of the protein. Here, epistasis refers to residues co-evolution in the sequence and residues spatial proximity in the 3D structure of the protein. The feature is a probabilistic score obtained with a mixture discriminant analysis that predicts pathogenicity based on the phenotypic effect of co-evolving and spatially close residues. The partners score feature is a general framework to study genotype and phenotype interactions. For example, those interactions might be between hetero or homoproteins forming a complex as tertiary structure and genetic variants occurring at interfaces, already known to be disease-causing, might be enriched for the same phenotypic effect. The framework of the partners score might not be limited to protein sequence, for example, interactions in the 3D genome might reveal regions enriched with the same phenotypic effect. DeMAG has been trained only on a small set of genes and yet, without further training, it generalizes well to additional 257 genes that have enough clinical data. Because for those new genes I did not manually curate MSA, I noted that the partners score from protein 3D models seems necessary for reaching high performance, while the contribution of the partners score obtained from long-range interactions, as derived from the co-evolution analysis, does not seem crucial for variant effect predictions. DeMAG is a supervised method especially designed for clinical translation purposes. That is why it focuses on clinically actionable genes and it balances its performance between the accuracy of the pathogenic and the benign class, acknowledging the importance of minimizing both the false negatives and false positives to avoid under and over diagnosis, critical to reduce health costs and patients psychological burden. Unsupervised general VEPs are powerful tools to investigate the functional effect of genetic variants as demonstrated by their higher correlation, over supervised tools, with data from Multiplexed Assay of Variant Effect (MAVE) and Deep Mutational Scanning (DMS) experiments. Nevertheless, for targeted clinical applications, I endorse the development of specialized tools that can leverage the existing wealth of data and knowledge available to minimize predictions errors. In order to make DeMAG readily available, I developed a web application available at https://demag.org/demag_app/ that provides predictions for all amino acids substitutions in the 59 and additional 257 genes together with training and testing datasets. Moreover, the app displays all the features of DeMAG highlighting the specific value annotated for the query mutation in relation to the distribution of the features for the pathogenic and benign mutations in the training set. This provides more insights than the minimalistic prediction label.

Die biotechnologischen Entwicklungen der letzten Jahre ermöglichen eine immer detailliertere Untersuchung von genetischen und molekularen Markern mit multiplen komplexen Traits. Allerdings liefern vorhandene statistische Methoden für diese komplexen Analysen oft keine valide Inferenz. Das erste Ziel der vorliegenden Arbeit ist, zwei neue statistische Methoden für Assoziationsstudien von genetischen Markern mit multiplen Phänotypen zu entwickeln, effizient und robust zu implementieren, und im Vergleich zu existierenden statistischen Methoden zu evaluieren. Der erste Ansatz, C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes), ermöglicht die Assoziation von genetischen Varianten mit multiplen Traits in einem gemeinsamen Copula Modell zu untersuchen. Der zweite Ansatz, CIEE (Causal Inference using Estimating Equations), ermöglicht direkte genetische Effekte zu schätzen und testen. C-JAMP wird in dieser Arbeit für Assoziationsstudien von seltenen genetischen Varianten mit quantitativen Traits evaluiert, und CIEE für Assoziationsstudien von häufigen genetischen Varianten mit quantitativen Traits und Ereigniszeiten. Die Ergebnisse von umfangreichen Simulationsstudien zeigen, dass beide Methoden unverzerrte und effiziente Parameterschätzer liefern und die statistische Power von Assoziationstests im Vergleich zu existierenden Methoden erhöhen können - welche ihrerseits oft keine valide Inferenz liefern. Für das zweite Ziel dieser Arbeit, neue genetische und transkriptomische Marker für kardiometabolische Traits zu identifizieren, werden zwei Studien mit genom- und transkriptomweiten Daten mit C-JAMP und CIEE analysiert. In den Analysen werden mehrere neue Kandidatenmarker und -gene für Blutdruck und Adipositas identifiziert. Dies unterstreicht den Wert, neue statistische Methoden zu entwickeln, evaluieren, und implementieren. Für beide entwickelten Methoden sind R Pakete verfügbar, die ihre Anwendung in zukünftigen Studien ermöglichen. / In recent years, the biotechnological advancements have allowed to investigate associations of genetic and molecular markers with multiple complex phenotypes in much greater depth. However, for the analysis of such complex datasets, available statistical methods often don’t yield valid inference. The first aim of this thesis is to develop two novel statistical methods for association analyses of genetic markers with multiple phenotypes, to implement them in a computationally efficient and robust manner so that they can be used for large-scale analyses, and evaluate them in comparison to existing statistical approaches under realistic scenarios. The first approach, called the copula-based joint analysis of multiple phenotypes (C-JAMP) method, allows investigating genetic associations with multiple traits in a joint copula model and is evaluated for genetic association analyses of rare genetic variants with quantitative traits. The second approach, called the causal inference using estimating equations (CIEE) method, allows estimating and testing direct genetic effects in directed acyclic graphs, and is evaluated for association analyses of common genetic variants with quantitative and time-to-event traits. The results of extensive simulation studies show that both approaches yield unbiased and efficient parameter estimators and can improve the power of association tests in comparison to existing approaches, which yield invalid inference in many scenarios. For the second goal of this thesis, to identify novel genetic and transcriptomic markers associated with cardiometabolic traits, C-JAMP and CIEE are applied in two large-scale studies including genome- and transcriptome-wide data. In the analyses, several novel candidate markers and genes are identified, which highlights the merit of developing, evaluating, and implementing novel statistical approaches. R packages are available for both methods and enable their application in future studies.