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. ORCID: https://orcid.org/0000-0002-0227-3024, Bracher-Smith, Matthew, Walters, James ORCID: https://orcid.org/0000-0002-6980-4053, Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090, Holmans, Peter ORCID: https://orcid.org/0000-0003-0870-9412, O'Donovan, Michael C. ORCID: https://orcid.org/0000-0001-7073-2379, Owen, Michael J. ORCID: https://orcid.org/0000-0003-4798-0862, Pocklington, Andrew ORCID: https://orcid.org/0000-0002-2137-0452 and Escott-Price, Valentina ORCID: https://orcid.org/0000-0003-1784-5483 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:	18 Jan 2025 22:17
URI:	https://orca.cardiff.ac.uk/id/eprint/117751

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