A large-scale genomic study has revealed that the genetic factors influencing whether an individual develops a disease are largely distinct from those determining how severe the disease becomes. The findings, suggest that while genome-wide association studies (GWAS) have been successful in identifying genetic risk factors for disease susceptibility, their ability to illuminate genetic drivers of disease progression remains limited.
In this study, researchers systematically examined the overlap between genetic effects on disease susceptibility and a common measure of progression across nine major diseases. By conducting the largest within-patient GWAS of disease-specific mortality to date, the team uncovered several key insights.
First, genetic variants known to increase susceptibility to disease generally showed little or no effect on disease-specific mortality. The study also found that GWASs of disease-specific mortality identified far fewer significant loci than susceptibility GWASs of comparable size. This suggests that detecting genetic influences on progression may require larger sample sizes or more refined clinical phenotypes.
Polygenic scores (PGSs) for disease susceptibility also performed poorly in predicting disease-specific mortality. According to the authors, this indicates that current PGSs are better suited for identifying individuals at higher risk of developing a disease rather than those likely to experience more severe outcomes. Interestingly, polygenic scores for traits measured in the general population showed stronger predictive value for survival across multiple diseases than disease-specific genetic scores.
The limited overlap between genetic effects on susceptibility and progression may stem from several factors. Disease progression is often shaped by environmental and clinical influences that can overshadow subtle genetic effects. Additionally, mortality may be an imperfect proxy for biological mechanisms driving progression, and the heterogeneity among patients further complicates genetic analyses.