Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition

Publication date: Available online 9 October 2018Source: NeuroImage: ClinicalAuthor(s): Siri Ranlund, Maria Joao Rosa, Simone de Jong, James H. Cole, Marinos Kyriakopoulos, Cynthia H.Y. Fu, Mitul A. Mehta, Danai DimaAbstractPsychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) – a measure of the overall genetic risk an individual carries for a disorder – to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for auti...
Source: NeuroImage: Clinical - Category: Radiology Source Type: research