GWAS and the depths of variation
This thesis explores methodological, biological, and translational aspects of genome-wide association studies (GWAS). It begins with a historical overview of statistical genetics, followed b y a primer on the GWAS methodology. Subsequently, it presents post-GW AS approaches for biological interpretation, focusing on gene-mapping , functional annotation, and convergent pathway analyses. Empirical c hapters investigate Alzheimer’s disease genetics through the larges t multi-ancestry GWAS to date, address methodological biases in GWASs of polygenic score-derived phenotypes, and examine local genetic sex differences across quantitative traits. Finally, the thesis introduc es a Bayesian framework to transform polygenic scores into directly i nterpretable disorder probabilities, thereby enhancing their clinical utility. Together, these contributions advance our understanding of Alzheimer’s disease genetics, genetic sex differences, refine metho ds for robust inference, and highlight translational opportunities fo r polygenic score prediction.
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DESCRIPTION: This thesis explores methodological, biological, and tran slational aspects of genome-wide association studies (GWAS). It begin s with a historical overview of statistical genetics, followed by a p rimer on the GWAS methodology. Subsequently, it presents post-GWAS ap proaches for biological interpretation, focusing on gene-mapping, fun ctional annotation, and convergent pathway analyses. Empirical chapte rs investigate Alzheimer’s disease genetics through the largest mul ti-ancestry GWAS to date, address methodological biases in GWASs of p olygenic score-derived phenotypes, and examine local genetic sex diff erences across quantitative traits. Finally, the thesis introduces a Bayesian framework to transform polygenic scores into directly interp retable disorder probabilities, thereby enhancing their clinical util ity. Together, these contributions advance our understanding of Alzhe imer’s disease genetics, genetic sex differences, refine methods fo r robust inference, and highlight translational opportunities for pol ygenic score prediction. More information on the thesis GWAS and the depths of variation END:VEVENT END:VCALENDAR