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

Area of Science:

• Neuroimaging
• Psychiatric Genetics
• Machine Learning

Background:

• Psychiatric illnesses have complex genetic underpinnings and are associated with brain alterations.
• Previous studies used univariate methods to link polygenic risk scores (PRS) to brain structure.
• PRS likely influence distributed and covarying brain-wide effects, necessitating multivariate approaches.

Purpose of the Study:

• To investigate associations between brain structure and PRS for ADHD, autism, bipolar disorder, and schizophrenia using multivariate machine learning.
• To explore if genetic liability for psychiatric disorders is reflected in brain morphology.
• To determine if these associations are present independent of disease manifestation.

Main Methods:

• Utilized T1-weighted MRI scans from 213 individuals (including depression patients, bipolar disorder patients, and healthy controls).
• Calculated five psychiatric PRS based on Psychiatric Genomics Consortium data.
• Applied voxel-based morphometry and multivariate relevance vector regression to analyze grey matter patterns and PRS.

Main Results:

• A significant multivariate pattern of grey matter predicted the PRS for autism (r=0.20, p<0.03).
• Mean Squared Error (MSE) for the schizophrenia PRS was significant (MSE=1.30×10⁻⁵, p<0.02), suggesting an association.
• These brain-structure associations were observed in individuals without the specific psychiatric disorders.

Conclusions:

• Polygenic liability for autism and schizophrenia is associated with widespread grey matter concentration changes.
• These findings support the hypothesis that genetic risk for these disorders influences brain structure.
• The observed associations are likely driven by genetic predisposition rather than active disease processes.