Speaker
Description
Genome-wide association studies (GWAS) for biomarkers and molecular phenotypes can lead to clinically relevant discoveries. Numerous lines of evidence from both model organisms and human studies suggest that genetic associations can be highly heterogeneous, dynamic and context dependent. Despite twenty years of GWAS, most studies are based on statistical models that fail to account for such heterogeneity. In this talk I will present alternative approaches based on quantile regression (QR) models that naturally extend linear regression models to the analysis of the entire conditional distribution of a phenotype of interest. I will introduce novel, computationally efficient tools that enable scalable genetic discovery across large genomic datasets.
Furthermore, I will discuss how QR can be applied to quantify uncertainty in polygenic score (PGS) predictions. QR shifts the focus from predicting the conditional phenotypic mean in classical PGS to predicting the conditional phenotypic quantiles. When combined with conformal prediction, this framework offers a natural way to construct prediction intervals with correct coverage.
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