A novel computational strategy for DNA methylation imputation using mixture regression model (MRM)

• Published in: BMC bioinformatics Vol. 21; no. 1; pp. 552 - 17
• Main Authors: Yu, Fangtang, Xu, Chao, Deng, Hong-Wen, Shen, Hui
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 01.12.2020; BioMed Central; BMC
• Subjects: Annotations; Arrays; Binding sites; Bisulfite; Bone mineral density; Chromosome 17; Chromosomes; Computational biology; Computer applications; CpG islands; CpG Islands - genetics; Datasets; Deoxyribonucleic acid; Disorders; DNA; DNA Methylation; DNA sequencing; Empirical analysis; Epigenesis, Genetic; Epigenetic inheritance; Epigenetics; Epigenomic association studies; Gene regulation; Genetic research; Genetic transcription; Genome, Human - genetics; Genomes; Genomics - methods; Humans; Imputation; Methodology; Methods; Methylation; Mixture of regression models; Nucleotide sequencing; Pathogenesis; Radial basis function; Regions; Regression Analysis; Regression models; Statistical models; Stem cells; Support vector machines; Transcription; United States; Epigenomic association studies; Mixture of regression models; Imputation; Methylation
• DOI: 10.1186/s12859-020-03865-z
• ISSN: 1471-2105

DNA methylation is an important heritable epigenetic mark that plays a crucial role in transcriptional regulation and the pathogenesis of various human disorders. The commonly used DNA methylation measurement approaches, e.g., Illumina Infinium HumanMethylation-27 and -450 BeadChip arrays (27 K and 450 K arrays) and reduced representation bisulfite sequencing (RRBS), only cover a small proportion of the total CpG sites in the human genome, which considerably limited the scope of the DNA methylation analysis in those studies. We proposed a new computational strategy to impute the methylation value at the unmeasured CpG sites using the mixture of regression model (MRM) of radial basis functions, integrating information of neighboring CpGs and the similarities in local methylation patterns across subjects and across multiple genomic regions. Our method achieved a better imputation accuracy over a set of competing methods on both simulated and empirical data, particularly when the missing rate is high. By applying MRM to an RRBS dataset from subjects with low versus high bone mineral density (BMD), we recovered methylation values of ~ 300 K CpGs in the promoter regions of chromosome 17 and identified some novel differentially methylated CpGs that are significantly associated with BMD. Our method is well applicable to the numerous methylation studies. By expanding the coverage of the methylation dataset to unmeasured sites, it can significantly enhance the discovery of novel differential methylation signals and thus reveal the mechanisms underlying various human disorders/traits.