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Predictive modeling of schizophrenia from genomic data: Comparison of polygenic risk score with kernel support vector machines approach

Vivian-Griffiths, Timothy, Baker, Emily, Schmidt, Karl M., Bracher-Smith, Matthew, Walters, James, Artemiou, Andreas, Holmans, Peter, O'Donovan, Michael C., Owen, Michael J., Pocklington, Andrew and Escott-Price, Valentina 2019. Predictive modeling of schizophrenia from genomic data: Comparison of polygenic risk score with kernel support vector machines approach. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 180 (1) , pp. 80-85. 10.1002/ajmg.b.32705

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Abstract

A major controversy in psychiatric genetics is whether nonadditive genetic interaction effects contribute to the risk of highly polygenic disorders. We applied a support vector machines (SVMs) approach, which is capable of building linear and nonlinear models using kernel methods, to classify cases from controls in a large schizophrenia case–control sample of 11,853 subjects (5,554 cases and 6,299 controls) and compared its prediction accuracy with the polygenic risk score (PRS) approach. We also investigated whether SVMs are a suitable approach to detecting nonlinear genetic effects, that is, interactions. We found that PRS provided more accurate case/control classification than either linear or nonlinear SVMs, and give a tentative explanation why PRS outperforms both multivariate regression and linear kernel SVMs. In addition, we observe that nonlinear kernel SVMs showed higher classification accuracy than linear SVMs when a large number of SNPs are entered into the model. We conclude that SVMs are a potential tool for assessing the presence of interactions, prior to searching for them explicitly.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Medicine
Advanced Research Computing @ Cardiff (ARCCA)
MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Publisher: Wiley
ISSN: 1552-4841
Date of First Compliant Deposit: 18 December 2018
Date of Acceptance: 9 November 2018
Last Modified: 23 Aug 2019 10:31
URI: http://orca.cf.ac.uk/id/eprint/117751

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