Imputation strategy for population DNA methylation sequencing data

Motivation: DNA methylation plays a crucial role in gene regulation and epigenetic inheritance, making it essential information for studies of genotype-environment interactions. As whole genome sequencing for DNA methylation analysis is still expensive at the population level, studies focus on regions of interest. However, data matrices may contain missing data for some individuals, which can hamper analyses. Here, several methods for imputing missing data were evaluated for targeted DNA methylation population sequencing data in two species. Results: We compare seven different imputation methods on targeted methylation sequencing data obtained from 200 and 189 individuals from natural poplar and oak populations, respectively. We evaluate their accuracy and performance to determine the most suitable approach for this type of data. Our results provide a reference for the selection of imputation strategies in targeted sequencing studies, improving the reliability of methylation analyses and broadening the applicability of this type of data in epigenomic research.