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

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 A. Pendergrass, Scott M. Dudek, Jason H. Moore and Marylyn D. Ritchie ()
Additional contact information
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
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
Sarah A. Pendergrass: Geisinger Health System
Scott M. Dudek: Geisinger Health System
Jason H. Moore: University of Pennsylvania
Marylyn D. Ritchie: Geisinger Health System

Nature Communications, 2017, vol. 8, issue 1, 1-10

Abstract: 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.

Date: 2017
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DOI: 10.1038/s41467-017-00802-2

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