Disease Onset genes don’t Dictate Survival: Lifespan Genes Offer Better Prognostic Clues
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New research across seven global biobanks shows that the DNA driving disease onset does not determine survival; instead, lifespan-linked genes and cross-trait scores hold the real clues to prognosis.
Study: Limited overlap between genetic effects on disease susceptibility and disease survival. Image credit: Natalia Kirsanova/shutterstock.com
In a recent study published in Nature Genetics, researchers tested whether genetic determinants of disease risk also predict post-diagnosis survival across nine diseases, and compared susceptibility versus longevity polygenic scores (PGSs) for prognosis.
Background
Two neighbors can share the same diagnosis yet live for dramatically different lengths of time. Genetics that nudge a person toward a disease may not be the same genetics that shape what happens after the first clinic visit.For years, genome-wide association studies (GWASs) have mapped thousands of variants for who gets a disease, but far fewer for how fast it progresses or whether it proves fatal.
Clinicians and families care about the latter as it guides treatment intensity, follow-up, and planning.Emerging biobanks and electronic health records make survival analyses possible at scale, yet signals look sparse. More research is needed to understand which genetic factors truly predict prognosis.
About the study
Researchers pooled seven biobanks (primary analysis) and registry-linked cohorts to study nine high-mortality conditions: Alzheimer’s disease, breast cancer, colorectal cancer, coronary artery disease, type 2 diabetes mellitus, chronic kidney disease, heart failure, prostate cancer, and stroke. Disease definitions and causes of death were standardized using the International Classification of Diseases, Tenth Revision (ICD-10).The main endpoint was disease-specific mortality, with all-cause mortality in sensitivity analyses.
Within-patient GWASs of disease-specific mortality used cox proportional hazards models implemented in genome-wide Analysis of Time-to-Event (GATE) or Saddlepoint Approximation Cox (SPACox), adjusting for age at diagnosis, birth year, sex, principal components (PCs), and study covariates. Eligible patients required ≥3 months follow-up. Summary statistics passed quality control (imputation details (INFO) score > 0.7; minor allele count ≥ 20), were aligned to human genome build 38 (hg38) via LiftOver, meta-analyzed with fixed-effect models in meta-Analysis Helper (METAL), and assessed for heterogeneity with Cochran’s Q.
PGSs were constructed with Mega Polygenic Risk Score (megaprs) under Baseline Linkage Disequilibrium-Linkage Disequilibrium Adjusted Kinships (BLD-LDAK) assumptions; a general longevity PGS used the Linkage Disequilibrium Adjusted Kinships-Thin
Genetic Predisposition to Disease Doesn’t Predict Survival, Large Study Finds
A large-scale analysis of data from multiple biobanks reveals a surprisingly limited connection between genetic factors that increase the risk of developing a disease and those that determine survival after diagnosis. The study, published in Nature Genetics in February 2025, suggests that genetic susceptibility scores for diseases are poor predictors of how long patients will live, and that incorporating information about general longevity offers a more accurate prognosis.
Researchers analyzed genetic data from hundreds of thousands of individuals to investigate the overlap between genes associated with disease progress and those linked to survival following a diagnosis. They found that genetic variants that increase disease risk rarely have a significant impact on survival rates. Specifically, the study examined a range of diseases, finding consistent results across many conditions.
“Our findings indicate that the biological mechanisms driving disease susceptibility and those governing survival after diagnosis are largely distinct,” explains the study’s lead author, Dr. Zoltang Yang, and colleagues. “This means that knowing someone is genetically predisposed to a disease doesn’t necessarily tell us much about their likely outcome if they actually develop it.”
The researchers utilized a liability-threshold framework in their simulations, acknowledging potential biases when focusing on diagnosed cases. While bias correction was attempted, its impact was limited, notably given the relatively low heritability of disease progression and the significant variability in mortality rates.
Interestingly, the study found that genetic scores predicting general longevity were more effective at stratifying post-diagnosis mortality risk than disease-specific susceptibility scores. This suggests that factors influencing overall lifespan – possibly including cardiovascular health and othre age-related processes – play a more significant role in survival after a disease diagnosis. This aligns with research showing that lifespan and cardiovascular risk can provide valuable insights into survival prospects.
Implications for Clinical Practice & Future Research
These findings have crucial implications for clinical practice. The study cautions against relying on disease susceptibility genetic scores to counsel patients about their prognosis. Instead, clinicians may benefit from incorporating broader measures of health, such as longevity-informed genetic scores or assessments of cardiovascular risk, into risk discussions and treatment planning. This could also aid in identifying patients who might benefit most from clinical trial participation.
The authors emphasize the need for further research. They call for larger studies with more refined disease progression phenotypes and the integration of data on related traits that influence general health. Addressing disparities in healthcare access and treatment, which considerably impact outcomes, is also crucial.
“More power, refined progression phenotypes, and integration of related general-population traits are needed to reveal progression biology and actionable targets, especially where care access and treatments strongly shape outcomes,” the researchers conclude.
Source:
Yang, Z., Pajuste, F.-D., Zguro, K., Cheng, Y., Kurant, D. E., Eoli, A., Wanner, J., Jermy, B., Rämö, J., FinnGen, Kanoni, S.,van Heel,D. A., Genes & Health Research Team, Hayward, C., Marioni, R. E., McCartney, D. L., Renieri, A., Furini, S., INTERVENE consortium, Mägi, R., Gusev, A., Drineas, P.,Paschou,P., Heyne, H., Ripatti, S., Mars, N., & Ganna, A. (2025). Limited overlap between genetic effects on disease susceptibility and disease survival.Nat Genet.https://doi.org/10.1038/s41588-025-02342-8