Population based linkage disequilibrium genome screens represent one of the most recent approaches for the lo- calization of genes responsible for complex diseases. One open problem in this context is represented by the definition of an appropriate significance threshold that takes into account the multiple comparison problem. We explore the con- ceptual and practical implications of the multiple testing procedure known as False Discovery Rate (FDR). We argue that controlling the FDR better represents the interest of researcher in this area than more traditional approaches. We then explore the applicability of the Benjamini-Hochberg (BH) FDR controlling procedure in the specific context of association mapping from case-control data. We analyze the nature of dependency between the test statistics with an- alytic work and simulations and we conclude that the BH rule effectively controls FDR in our context of interest. The dependency between test statistics translates into a decrease of power, which highlights the necessity of developing resampling based rules to control FDR.
Bipolar disorder (BD) is a highly heritable mood disorder with a complex genetic architecture. It is commonly treated prophylactically with the mood stabilizer lithium, although treatment responses vary widely across patients. Both how BD genetic variants confer risk and the molecular mechanisms underlying lithium’s therapeutic effects remain poorly understood. This dissertation begins with a review of recent findings from BD and lithium-response genetic studies and from BD and lithium treatment transcriptomic studies. This review will show that while presenting an opportunity to learn valuable information about underlying biology, gene expression studies investigating these phenotypes have had low sample sizes and inconsistent findings. Then, an original study attempting to fill this gap by exploring the whole blood transcriptome in a large BD case-control RNA sequencing sample is reported on. In this study, strong effects of lithium treatment and cell-type composition were revealed, pointing to potential therapeutic mechanisms of lithium, and underlining the importance of carefully correcting for these variables. To put these findings in the context of the current understanding of BD etiology and lithium treatment mechanisms, a comparison was made with findings previously reported highlighting a list of high-confidence lithium-associated genes. A gene-set analysis comparing genes with differential expression to genes implicated from major psychiatric genome-wide association studies revealed that the observed gene expression changes were unrelated to genetic risk. The findings herein contribute to the current understanding of the BD transcriptome in whole blood and provide evidence for the mechanistic actions of lithium treatment.
Biobanks linked to Electronic Health Records (EHRs) herald a new era of opportunities for etiological research of Severe Mental Illness (SMI). However, because EHRs are not primarily designed for research, translating these opportunities into actionable insights demands innovative frameworks and accurate phenotyping tools. This dissertation harnesses the potential of EHRs from psychiatric hospitals for in-depth studies of SMI. I set the stage by contextualizing the relevance of EHRs in psychiatric genetic research. Then, I describe the organizational makeup and data types within the EHR of the Clinica San Juan de Dios in Manizales — a regional psychiatric hospital in Colombia. The subsequent chapters explore transdiagnostic phenotypes by combining clinical notes and diagnostic codes, leading to the delineation of disease trajectories in SMI. Then, I explore the extraction and validation of psychiatric diagnoses through both rule-based and machine learning strategies. And finally, I conclude with the design and validation of a Clinical Natural Language Processing (cNLP) tool for extracting highly detailed psychiatric phenotypes from unstructured text. Three strengths of EHRs are emphasized throughout this work: the integration of multi-dimensional data, enabling a comprehensive perspective of patient phenotypes; the innovative application of cNLP for symptom extraction from clinical narratives in Spanish; and the capacity of EHRs to provide longitudinal insights into patients' course of illness. Taken together, this dissertation not only highlights the potential of EHRs but also navigates the intricacies of employing them for psychiatric genetic research.
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