Disease areas:
  • clinical signs and symptoms
  • nutrition and metabolism
Last updated:
Author(s):
Zhiyu Yang, Fanny-Dhelia Pajuste, Kristina Zguro, Yipeng Cheng, Danielle E. Kurant, Andrea Eoli, Julian Wanner, Bradley Jermy, Joel Rämö, Stavroula Kanoni, David A. van Heel, Caroline Hayward, Riccardo E. Marioni, Daniel L. McCartney, Alessandra Renieri, Simone Furini, Reedik Mägi, Alexander Gusev, Petros Drineas, Peristera Paschou, Henrike Heyne, Samuli Ripatti, Nina Mars, Andrea Ganna
Publish date:
30 September 2025
Journal:
Nature Genetics
PubMed ID:
41028524

Abstract

Understanding disease progression is of high biological and clinical interest. Unlike disease susceptibility, whose genetic basis has been abundantly studied, less is known about the genetics of disease progression and its overlap with disease susceptibility. Considering nine common diseases (ncases ranging from 11,980 to 124,682) across seven biobanks, we systematically compared genetic architectures of susceptibility and progression, defined as disease-specific mortality. We identified only one locus substantially associated with disease-specific mortality and showed that, at a similar sample size, more genome-wide significant loci can be identified in a genome-wide association study of disease susceptibility. Variants substantially affecting disease susceptibility were weakly or not associated with disease-specific mortality. Moreover, susceptibility polygenic scores (PGSs) were weak predictors of disease-specific mortality, while a PGS for general lifespan was substantially associated with disease-specific mortality for seven of nine diseases. We explored alternative definitions of disease progression and found that genetic signals for macrovascular complications in type 2 diabetes overlap with similar phenotypes in the general population; however, these effects are attenuated. Overall, our findings indicate limited similarity in genetic effects between disease susceptibility and disease-specific mortality, suggesting that larger sample sizes, different measures of progression, or the integration of related phenotypes from the general population is needed to identify the genetic underpinnings of disease progression.

Related projects

Data science based methods (including artificial intelligence) are expected to have a transformative impact on our ability to understand disease development and trajectories and provide…

Institution:
University of Helsinki, Finland

All projects