Emil M. Pedersen

LT-FGRS estimates genetic liabilities from large-scale family data, unifying methods into an R package for efficient population-scale analysis.

The paper shows genotype- and phenotype-based genetic instruments explain largely independent components of liability to psychiatric disorders, supported by theoretical analysis and simulations.

This study identifies genetic variants associated with income and shows they correlate with health outcomes and educational attainment.

ADuLT is a time-to-event GWAS method that outperforms Cox regression in strongly ascertained studies, finding more genome-wide significant associations in psychiatric disorder analyses.

The paper introduces PA-FGRS, a method to estimate genetic liability from family records, and shows it improves MDD classification and GWAS power when combined with genotypes.

This paper presents a publicly available privacy-preserving benchmark for polygenic prediction using summary-level data, enabling accurate model performance estimation without access to individual-level genetic data.

This paper introduces LT-FH++, a method that improves GWAS power by incorporating age-of-onset and family history, showing increased significant associations in psychiatric disorders and mortality data.