AUTHOR=Fong Wei Jing , Tan Hong Ming , Garg Rishabh , Teh Ai Ling , Pan Hong , Gupta Varsha , Krishna Bernadus , Chen Zou Hui , Purwanto Natania Yovela , Yap Fabian , Tan Kok Hian , Chan Kok Yen Jerry , Chan Shiao-Yng , Goh Nicole , Rane Nikita , Tan Ethel Siew Ee , Jiang Yuheng , Han Mei , Meaney Michael , Wang Dennis , Keppo Jussi , Tan Geoffrey Chern-Yee TITLE=Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation JOURNAL=Frontiers in Neuroinformatics VOLUME=17 YEAR=2024 URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2023.1244336 DOI=10.3389/fninf.2023.1244336 ISSN=1662-5196 ABSTRACT=Introduction

Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort.

Methods

Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models’ performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites.

Results

Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model.

Discussion

The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing.