DRAGON-Data: a platform and protocol for integrating genomic and phenotypic data across large psychiatric cohorts
Amy Lynham, Sarah Knott, Jack F. G. Underwood, Leon Hubbard, Sharifah Shameem Agha, Jonathan I. Bisson, Marianne B. M. van den Bree, Samuel J. R. A. Chawner, Nicholas Craddock, Michael O‘Donovan, Ian Jones, George Kirov, K. Langley, Joanna Martin, Frances Rice, Neil P. Roberts, Anita Thapar, Richard Anney, Michael J. Owen, Jérémy Hall, Antonio F. Pardiñas, James Walters
Abstract
BACKGROUND: Current psychiatric diagnoses, although heritable, have not been clearly mapped onto distinct underlying pathogenic processes. The same symptoms often occur in multiple disorders, and a substantial proportion of both genetic and environmental risk factors are shared across disorders. However, the relationship between shared symptoms and shared genetic liability is still poorly understood. AIMS: Well-characterised, cross-disorder samples are needed to investigate this matter, but few currently exist. Our aim is to develop procedures to purposely curate and aggregate genotypic and phenotypic data in psychiatric research. METHOD: As part of the Cardiff MRC Mental Health Data Pathfinder initiative, we have curated and harmonised phenotypic and genetic information from 15 studies to create a new data repository, DRAGON-Data. To date, DRAGON-Data includes over 45 000 individuals: adults and children with neurodevelopmental or psychiatric diagnoses, affected probands within collected families and individuals who carry a known neurodevelopmental risk copy number variant. RESULTS: We have processed the available phenotype information to derive core variables that can be reliably analysed across groups. In addition, all data-sets with genotype information have undergone rigorous quality control, imputation, copy number variant calling and polygenic score generation. CONCLUSIONS: DRAGON-Data combines genetic and non-genetic information, and is available as a resource for research across traditional psychiatric diagnostic categories. Algorithms and pipelines used for data harmonisation are currently publicly available for the scientific community, and an appropriate data-sharing protocol will be developed as part of ongoing projects (DATAMIND) in partnership with Health Data Research UK.