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Limited overlap between genetic effects on disease susceptibility and disease survival

Yang, Z.; Pajuste, F.-D.; Zguro, K.; Cheng, Y.; Kurant, D. E.; Eoli, A.; Wanner, J.; Jermy, B.; FinnGen, ; Estonian Biobank research team, ; Kanoni, S.; Heel, D. A. v.; Genes & Health Research Team, ; Hayward, C.; Marioni, R. E.; McCartney, D. L.; Renieri, A.; Furini, S.; Genomics England Research Consortium, ; Magi, R.; Gusev, A.; Drineas, P.; Paschou, P.; Heyne, H.; Ripatti, S.; Mars, N.; Ganna, A.

2023-10-12 genetic and genomic medicine
10.1101/2023.10.10.23296544 medRxiv
Show abstract

Understanding disease progression is of a 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 ten common diseases (N cases ranging from 17,152 to 99,666) across seven biobanks, we systematically compared the genetic architecture of susceptibility and progression, defined as disease-specific mortality. We identified only one locus significantly associated with disease-specific mortality and show that, at a similar sample size, more genome-wide significant loci can be identified in a GWAS of disease susceptibility. Variants that were significantly affecting disease susceptibility were weakly or not associated with disease-specific mortality. Moreover, susceptibility polygenic scores (PGSs) were weak predictor of disease-specific mortality while a PGS for general lifespan was significantly associated with disease-specific mortality for five out of ten diseases. We used theoretical derivation and simulation to propose plausible explanations for our empirical observations and account for potential index-event bias. Overall, our findings point to little similarity in genetic effects between disease susceptibility and disease-specific mortality and suggest that either larger sample sizes or different measures of progression are needed to identify the genetic underpinning of disease progression.

Published in Nature Genetics (predicted rank #5) · training set

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