This R code repository focuses on the heritability of DNA methylation in sticklebacks, a crucial area of research in epigenetics and evolutionary biology. DNA methylation, an epigenetic modification, can influence gene expression and phenotypic variation, and its heritable nature can contribute to rapid adaptation and transgenerational plasticity in response to environmental changes . These scripts provide the computational framework to investigate how DNA methylation patterns are passed across generations in Gasterosteus aculeatus. The scripts are written in R, a powerful statistical programming language. They are designed to analyze data from reduced-representation bisulfite sequencing (RRBS) or similar methods used to quantify DNA methylation across the genome . Technical capabilities include calculating heritability estimates for specific CpG sites or genomic regions, performing methylation quantitative trait loci (meQTL) analysis to identify genetic variants influencing methylation levels, and assessing the relative contributions of cis- and trans-regulatory changes to methylation variation . The analysis may involve statistical models to account for genetic relatedness and environmental factors, and could include principal component analysis of methylation profiles . This digital resource is highly relevant for researchers in epigenetics, evolutionary biology, and environmental genomics. It supports studies on the role of epigenetics in adaptation, phenotypic plasticity, and the interplay between genetic and epigenetic inheritance. Applications include understanding how environmental factors (e.g., temperature, salinity) can induce heritable epigenetic changes, and exploring the potential for epigenetic mechanisms to facilitate rapid evolutionary responses . The benefits include providing robust analytical tools for complex epigenetic datasets and contributing to a deeper understanding of the mechanisms driving phenotypic diversity and adaptation. The code is compatible with standard R installations and is designed to work with bisulfite sequencing data from stickleback populations.