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, 20, 1026-1036

Associations Between Polygenic Risk Scores for Four Psychiatric Illnesses and Brain Structure Using Multivariate Pattern Recognition

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Associations Between Polygenic Risk Scores for Four Psychiatric Illnesses and Brain Structure Using Multivariate Pattern Recognition

Siri Ranlund et al. Neuroimage Clin.

Abstract

Psychiatric 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 autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.

Keywords: ADHD; Autism; Bipolar disorder; MRI; Major depression; Schizophrenia.

Figures

Fig. 1
Fig. 1
Results for the autism polygenic risk score (PRS). A) Correlations between actual autism PRS and the PRS predicted from the grey matter volume maps. pFDR = False Discover Rate corrected p-values. B) Weight maps of contribution of voxels across the whole brain for the predicted autism PRS shown in A (in the whole sample, N = 213).
Fig. 2
Fig. 2
Results for the schizophrenia polygenic risk score (PRS). A) Correlations between actual schizophrenia PRS and the PRS predicted from grey matter volume maps. pFDR = False Discover Rate corrected p-values. B) Weight maps of contribution of voxels across the whole brain for the predicted schizophrenia PRS shown in A (in the whole sample, N = 213).

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