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Multi-ancestry GWAS meta-analyses of lung cancer reveal susceptibility loci and elucidate smoking-independent genetic risk

Author

Listed:
  • Bryan R. Gorman

    (VA Boston Healthcare System
    Booz Allen Hamilton)

  • Sun-Gou Ji

    (VA Boston Healthcare System
    BridgeBio Pharma)

  • Michael Francis

    (VA Boston Healthcare System
    Booz Allen Hamilton)

  • Anoop K. Sendamarai

    (VA Boston Healthcare System
    University of Wisconsin)

  • Yunling Shi

    (VA Boston Healthcare System)

  • Poornima Devineni

    (VA Boston Healthcare System)

  • Uma Saxena

    (VA Boston Healthcare System)

  • Elizabeth Partan

    (VA Boston Healthcare System)

  • Andrea K. DeVito

    (VA Boston Healthcare System
    Booz Allen Hamilton)

  • Jinyoung Byun

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Younghun Han

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Xiangjun Xiao

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Don D. Sin

    (St Paul’s Hospital)

  • Wim Timens

    (University of Groningen
    University Medical Centre Groningen, University of Groningen)

  • Jennifer Moser

    (Department of Veterans Affairs)

  • Sumitra Muralidhar

    (Department of Veterans Affairs)

  • Rachel Ramoni

    (Department of Veterans Affairs)

  • Rayjean J. Hung

    (University of Toronto)

  • James D. McKay

    (World Health Organization)

  • Yohan Bossé

    (Laval University)

  • Ryan Sun

    (University of Texas MD Anderson Cancer Center)

  • Christopher I. Amos

    (Baylor College of Medicine
    Baylor College of Medicine
    Baylor College of Medicine)

  • Saiju Pyarajan

    (VA Boston Healthcare System
    Brigham and Women’s Hospital, Harvard Medical School)

Abstract

Lung cancer remains the leading cause of cancer mortality, despite declining smoking rates. Previous lung cancer GWAS have identified numerous loci, but separating the genetic risks of lung cancer and smoking behavioral susceptibility remains challenging. Here, we perform multi-ancestry GWAS meta-analyses of lung cancer using the Million Veteran Program cohort (approximately 95% male cases) and a previous study of European-ancestry individuals, jointly comprising 42,102 cases and 181,270 controls, followed by replication in an independent cohort of 19,404 cases and 17,378 controls. We then carry out conditional meta-analyses on cigarettes per day and identify two novel, replicated loci, including the 19p13.11 pleiotropic cancer locus in squamous cell lung carcinoma. Overall, we report twelve novel risk loci for overall lung cancer, lung adenocarcinoma, and squamous cell lung carcinoma, nine of which are externally replicated. Finally, we perform PheWAS on polygenic risk scores for lung cancer, with and without conditioning on smoking. The unconditioned lung cancer polygenic risk score is associated with smoking status in controls, illustrating a reduced predictive utility in non-smokers. Additionally, our polygenic risk score demonstrates smoking-independent pleiotropy of lung cancer risk across neoplasms and metabolic traits.

Suggested Citation

  • Bryan R. Gorman & Sun-Gou Ji & Michael Francis & Anoop K. Sendamarai & Yunling Shi & Poornima Devineni & Uma Saxena & Elizabeth Partan & Andrea K. DeVito & Jinyoung Byun & Younghun Han & Xiangjun Xiao, 2024. "Multi-ancestry GWAS meta-analyses of lung cancer reveal susceptibility loci and elucidate smoking-independent genetic risk," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52129-4
    DOI: 10.1038/s41467-024-52129-4
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