4.8 Article

Cross-cancer evaluation of polygenic risk scores for 16 cancer types in two large cohorts

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NATURE COMMUNICATIONS
卷 12, 期 1, 页码 -

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NATURE RESEARCH
DOI: 10.1038/s41467-021-21288-z

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  1. National Institutes of Health [K07CA188142, K24CA169004, R01CA088164, R01CA201358, R25CA112355, RC2AG036607, U01CA127298]
  2. UCSF Goldberg-Benioff Program in Cancer Translational Biology

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This study investigated polygenic risk score (PRS)-specific pleiotropy across 16 cancers in European ancestry individuals, finding positive and inverse cross-cancer associations. The study identified bidirectional relationships between certain cancer types and highlighted the potential for informing risk prediction and precision cancer prevention strategies.
Even distinct cancer types share biological hallmarks. Here, we investigate polygenic risk score (PRS)-specific pleiotropy across 16 cancers in European ancestry individuals from the Genetic Epidemiology Research on Adult Health and Aging cohort (16,012 cases, 50,552 controls) and UK Biobank (48,969 cases, 359,802 controls). Within cohorts, each PRS is evaluated in multivariable logistic regression models against all other cancer types. Results are then meta-analyzed across cohorts. Ten positive and one inverse cross-cancer associations are found after multiple testing correction. Two pairs show bidirectional associations; the melanoma PRS is positively associated with oral cavity/pharyngeal cancer and vice versa, whereas the lung cancer PRS is positively associated with oral cavity/pharyngeal cancer, and the oral cavity/pharyngeal cancer PRS is inversely associated with lung cancer. Overall, we validate known, and uncover previously unreported, patterns of pleiotropy that have the potential to inform investigations of risk prediction, shared etiology, and precision cancer prevention strategies. While genetic loci shared between cancer types have been identified, cross-cancer relationships for polygenic risk scores have not been well studied. Here, the authors have developed polygenic risk scores for 16 cancers in two large cohorts and identified positive and inverse cross-cancer associations.

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