• Magnus Ingelman-Sundberg, PhD; BSc.Med

CV

Magnus Ingelman-Sundberg, PhD; BSc.Med is Senior Professor of Molecular Toxicology and research group leader in Pharmacogenetics at the Department of Physiology and Pharmacology, Karolinska Institutet. He has more than 520 original papers and a h-factor of 101 (ISI/Clarivate) or 132 (Google Scholar). Assigned “Highly Cited Researcher” for 2014-2017 and 2021-2024  by Thomson & Reuters/Clarivate. He is ranked #18 most cited scientists of 21,811 in pharmaceutical science according to AD Scientific Index 2025. He was a member of The Nobel Assembly at Karolinska Institutet 2008-2018 and a member of Several Editorial Advisory Boards. He has received numerous Awards, most recently The RT Williams ISSX award, an honorary doctorship at SydDansk University and the Big silver Medal at Karolinska Institute. His research focuses on genetics, polymorphism, regulation, function and toxicology of the hepatic ADME system with aims at understanding interindividual differences in drug response. Furthermore he develops novel hepatic in vitro systems for studying liver function, liver diseases, siRNA action and metabolism and validation of hepatic drug targets.

ABSTRACT

Bridging the Gap: understanding missing heritability in pharmacogenomics

Missing heritability—where identified genetic variants explain only a fraction of observed heritability—poses a significant challenge. This gap limits the ability to fully account for inter-individual variability in drug response, hindering the development of precise therapeutic strategies. Missing heritability in pharmacogenomics stems from rare variants, structural variations, epigenetic modifications, and complex gene-environment interactions that are often overlooked in traditional genome-wide association studies (GWAS). These studies primarily focus on common single nucleotide polymorphisms (SNPs), which may not capture the full genetic architecture influencing drug metabolism and response. Addressing this issue requires advanced methodologies, such as whole-genome sequencing, multi-omics integration, and machine learning, to uncover rare variants, haplotype influence and non-genetic factors. Additionally, diverse population studies are essential to reduce bias and enhance generalizability. By tackling missing heritability, pharmacogenomics can improve predictive models, optimize drug dosing, minimize adverse reactions, and advance equitable precision medicine. Prioritizing this challenge will bridge the gap between genetic potential and clinical application, ensuring more effective and personalized therapeutic outcomes.