Stephen Dorn

This study performs the first GWAS on a latent partner choice index using 206,617 individuals, identifying genetic correlates and solutions to reduce assortative mating biases in genetic studies.

This paper introduces a statistical framework for regularized ensemble polygenic risk prediction using GWAS summary statistics, showing improved performance across traits and populations.

This study introduces PENGUIN, a framework to control genetic confounding in epidemiologic studies, showing it effectively removes confounding in educational attainment associations.

This study identifies biases in proxy GWAS using parental Alzheimer's history, showing misleading genetic correlations and highlighting design issues in biobank cohorts.