A study published in Military Medical Research presents a promising precision medicine approach to evaluate treatment response to antipsychotic drug treatment for patients with schizophrenia.
]. The first step involved constructing machine learning models for each pathway, where CpG sites were mapped. The second step entailed constructing a collective model of the pathway models. The final output was a quantified score representing the likelihood of participants having SCZ. For each step, 1000 bootstrapping iterations were conducted.Genotyping and DNA methylation data were obtained from peripheral whole blood samples of participants.
Differential methylation analysis was conducted to identify risk-related differentially methylated regions between cases and controls as well as response-related DMRs between the RES and non-RES groups. meQTL analysis was used to find genetic-epigenetic interactions and locate allele-specific methylated genes from risk-DMRs and RES-DMRs, which were validated in the mQTL Database .
section, including methylation quantitative trait loci analysis, Bayesian colocalization analysis, epigenome-wide association analysis, epigenome-wide differential methylation analysis, and prediction of promoter-anchored chromatin interaction.A machine-learning approach was utilized to regress the methylation level from the genotype of meQTL as a proxy DNA methylation model by the R package caret .
We used the clinical information , PRS, GRS, and proxy methylation level in four combination patterns to develop the prediction model for treatment response with the algorithms including quantile random forest, random forest, and support vector machines with radial basis function kernel by R package caret. The preprocessing and optimization of the hyperparameters of the models were the same as those of proxyDNAm.
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