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PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies

Author

Listed:
  • Molly A. Hall

    (University of Pennsylvania)

  • John Wallace

    (Geisinger Health System)

  • Anastasia Lucas

    (Geisinger Health System)

  • Dokyoon Kim

    (Geisinger Health System)

  • Anna O. Basile

    (The Pennsylvania State University)

  • Shefali S. Verma

    (Geisinger Health System
    The Pennsylvania State University)

  • Cathy A. McCarty

    (Essentia Institute of Rural Health)

  • Murray H. Brilliant

    (Marshfield Clinic Research Institute)

  • Peggy L. Peissig

    (Marshfield Clinic Research Institute)

  • Terrie E. Kitchner

    (Marshfield Clinic Research Institute)

  • Anurag Verma

    (Geisinger Health System
    The Pennsylvania State University)

  • Sarah A. Pendergrass

    (Geisinger Health System)

  • Scott M. Dudek

    (Geisinger Health System)

  • Jason H. Moore

    (University of Pennsylvania)

  • Marylyn D. Ritchie

    (Geisinger Health System
    The Pennsylvania State University)

Abstract

Genome-wide, imputed, sequence, and structural data are now available for exceedingly large sample sizes. The needs for data management, handling population structure and related samples, and performing associations have largely been met. However, the infrastructure to support analyses involving complexity beyond genome-wide association studies is not standardized or centralized. We provide the PLatform for the Analysis, Translation, and Organization of large-scale data (PLATO), a software tool equipped to handle multi-omic data for hundreds of thousands of samples to explore complexity using genetic interactions, environment-wide association studies and gene–environment interactions, phenome-wide association studies, as well as copy number and rare variant analyses. Using the data from the Marshfield Personalized Medicine Research Project, a site in the electronic Medical Records and Genomics Network, we apply each feature of PLATO to type 2 diabetes and demonstrate how PLATO can be used to uncover the complex etiology of common traits.

Suggested Citation

  • Molly A. Hall & John Wallace & Anastasia Lucas & Dokyoon Kim & Anna O. Basile & Shefali S. Verma & Cathy A. McCarty & Murray H. Brilliant & Peggy L. Peissig & Terrie E. Kitchner & Anurag Verma & Sarah, 2017. "PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00802-2
    DOI: 10.1038/s41467-017-00802-2
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    Cited by:

    1. Meghana Pagadala & Timothy J. Sears & Victoria H. Wu & Eva Pérez-Guijarro & Hyo Kim & Andrea Castro & James V. Talwar & Cristian Gonzalez-Colin & Steven Cao & Benjamin J. Schmiedel & Shervin Goudarzi , 2023. "Germline modifiers of the tumor immune microenvironment implicate drivers of cancer risk and immunotherapy response," Nature Communications, Nature, vol. 14(1), pages 1-22, December.
    2. Xinyuan Zhang & Anastasia M. Lucas & Yogasudha Veturi & Theodore G. Drivas & William P. Bone & Anurag Verma & Wendy K. Chung & David Crosslin & Joshua C. Denny & Scott Hebbring & Gail P. Jarvik & Ifti, 2022. "Large-scale genomic analyses reveal insights into pleiotropy across circulatory system diseases and nervous system disorders," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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