25gmds180 10.3205/25gmds180 urn:nbn:de:0183-25gmds1802 Meeting Abstract Kemda Ngueda Kemda Ngueda Christelle C

Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany

author Palm Palm Julia J

Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany

author Remo Remo Flavia F

Institut für Medizinische Statistik, Informatik und Datenwissenschaften (IMSID), Universitätsklinikum Jena, Jena, Germany

author Scherag Scherag André A

Institut für Medizinische Statistik, Informatik und Datenwissenschaften, Universitätsklinikum Jena, Jena, Germany

author Leistritz Leistritz Lutz L

Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany

author German Medical Science GMS Publishing House

Düsseldorf

610 imputation missing values DNA-methylation 20251103 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0631 180 Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie 70. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS) PS 12: Machine learning and AI applications Jena 20250907 20250911 Abstr. 107 TextIntroduction: Personalized medicine puts the patient's uniqueness at different omic layers at its center in order to select tailored disease preventions, diagnoses or treatments . DNA methylation is a key epigenetic omic layer, particularly valued because of its reversible nature . However, in practice, methylation datasets usually contain a considerable proportion of missing values . Missing values mainly arise during data collection and represent a ubiquitous problem for downstream data analysis. To impute missing DNA methylation values, several approaches have been proposed from both statistics computer sciences. They all have in common that they are in principle applicable to both DNA methylation microarray and sequencing data, but that they require information from at least two samples.We propose a time and cost-effective imputation method for replacing missing DNA-methylation values in a single patient methylome, i.e. a method that relies on the personalized medicine idea.Methods: Based on the observation that CpGs closer to each other are more likely to be methylated in a similar way, the method replaces a missing value by an available value of its nearest neighbouring CpG. Thereby, the distance between two CpGs refers to the smallest linear distance along the DNA sequence, measured in base pairs, between two CpG sites on the same chromosome and strand. We compared the new methods with two exemplary methods (impute.knn, methyLImp) using simulations.Results: The proposed method applied to a single methylome yielded an average root mean square error (RMSE) RMSE = 0.27 in β-value units (95%-CI: [0.26, 0.28]) based on publically available 450K BeadChip data set of 3,402 individuals (https://download.cncb.ac.cn/ewas/datahub/download/blood_methylation_v1.zip) with β-value ranging between 0 and 1. It is possible to consider the affiliation of CpGs to CpG islands when imputing missing methylation values. This improves the imputation accuracy. In addition, the imputation accuracy depends on the density of CpG sites on DNA-methylation microarrays and is higher the denser CpG sites are.Conclusions: The proposed method efficiently imputes missing values from a single methylome with minimal computational cost and memory requirements, making it a valuable addition to the imputation toolbox for single-subject applications. Its imputation accuracy is inferior to approaches exploiting multiple methylation samples. Here, an important aspect is the low density of the current chips compare to the richness of the whole methylome. Looking forward, improved accuracy can be expected the denser the chips or as we move to whole-methylome sequencing.The authors declare that they have no competing interests.The authors declare that an ethics committee vote is not required. Rasool M Malik A Naseer MI Manan A Ansari S Begum I The role of epigenetics in personalized medicine: challenges and opportunities 2015 BMC Med Genomics S5 Rasool M, Malik A, Naseer MI, Manan A, Ansari S, Begum I, et al. The role of epigenetics in personalized medicine: challenges and opportunities. BMC Med Genomics. 2015;8 Suppl 1(Suppl 1):S5. Gupta MK Peng H Li Y Xu CJ The role of DNA methylation in personalized medicine for immune-related diseases 2023 Pharmacol Ther 108508 Gupta MK, Peng H, Li Y, Xu CJ. The role of DNA methylation in personalized medicine for immune-related diseases. Pharmacol Ther. 2023;250:108508. Di Lena P Sala C Prodi A Nardini C Missing value estimation methods for DNA methylation data 2019 Bioinformatics 3786-93 Di Lena P, Sala C, Prodi A, Nardini C. Missing value estimation methods for DNA methylation data. Bioinformatics. 2019;35(19):3786-93. 0 0 0 0