Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom.
School of Mathematics, Cardiff University, Cardiff, United Kingdom.
Am J Med Genet B Neuropsychiatr Genet. 2019 Jan;180(1):80-85. doi: 10.1002/ajmg.b.32705. Epub 2018 Dec 4.
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.
精神疾病遗传学中的一个主要争议是，非加性遗传相互作用是否会导致高度多基因疾病的风险增加。我们应用支持向量机（SVM）方法，该方法能够使用核方法构建线性和非线性模型，对来自 11853 名受试者（5554 例病例和 6299 例对照）的大精神分裂症病例对照样本中的病例进行分类，并将其预测准确性与多基因风险评分（PRS）方法进行比较。我们还研究了 SVM 是否是检测非线性遗传效应（即相互作用）的合适方法。我们发现，PRS 提供了比线性或非线性 SVM 更准确的病例/对照分类，并对为什么 PRS 优于多元回归和线性核 SVM 给出了一个初步解释。此外，我们观察到，当大量 SNP 输入到模型中时，非线性核 SVM 的分类准确性高于线性 SVM。我们的结论是，在明确搜索相互作用之前，SVM 是评估相互作用存在的潜在工具。
