Genetic associations on major depression: curation and functional analysis
Major depression (MD) is the leading cause of impairment worldwide. The lack of understanding of its biological underpinnings hampers the development of better diagnostic tools and treatments. Thanks to the advances in genetic association studies, multiple genetic variants significantly associated w...
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| Formato: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Recursos: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/687887 |
| Acesso em linha: | http://hdl.handle.net/10803/687887 |
| Access Level: | acceso abierto |
| Palavra-chave: | Psychiatric disorders Major depression Functional genomics Genome-wide association studies Bioinformatics Trastorns psiquiàtrics Depressió major Genòmica funcional Estudis d’associació del genoma complet Bioinformàtica 616.89 |
| Resumo: | Major depression (MD) is the leading cause of impairment worldwide. The lack of understanding of its biological underpinnings hampers the development of better diagnostic tools and treatments. Thanks to the advances in genetic association studies, multiple genetic variants significantly associated with MD have been identified. In this thesis, we aim to leverage this knowledge to advance in the understanding of MD and unravel its molecular mechanisms. For that, we developed curation guidelines to evaluate available genetic association data on MD of diverse nature, and created an expert-curated database of genetic variants associated with MD. Then, we leveraged these data and functional genomic tools to unravel the role of these variants in disease pathogenesis and propose mechanistic hypotheses. In light of the plethora of tools available to perform such analyses, we conducted a benchmarking analysis to evaluate their performance and compare their outcomes; highlighting the need for guidelines for method selection and evaluation. Overall, this thesis contributes to filling the gap regarding the quality assessment of genetic studies on MD, and to advance in discovering the functional role of MD-associated variants by using in silico approaches. |
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