Henrik Hjalgrim

This study evaluates polygenic risk scores (PRS) for colorectal cancer (CRC) screening, showing they stratify risk independently of ancestry and sex but offer limited additional predictive value when combined with FIT.

This paper introduces DoBSeq, a low-cost framework for detecting rare variants in population-scale genetic screening, achieving 95% variant detection and recovering all clinically relevant pathogenic variants in ACMG genes.

DoBSeqWF is a pipeline for analyzing pooled sequencing data to detect rare genetic variants, improving sensitivity with machine learning filters.

This study identifies 51 genome-wide significant loci associated with dosage changes in 42 drug pair phenotypes, revealing distinct genetic patterns in polypharmacy.

The paper introduces a deep learning framework for polygenic risk score prediction, showing improved performance over traditional methods for various diseases.

The paper analyzes whole genome sequences of 150,119 UK Biobank individuals, identifying variants and associations with traits, and provides a haplotype reference panel for imputation.

This study identifies seven new genetic loci associated with febrile seizures, linking them to fever response and neuronal excitability genes, and suggests potential clinical applications of polygenic risk scores.

This study uses machine learning on Danish health registers to predict five-year risk of T2D comorbidities, showing modest improvements over reference models.