Epigenome-wide association studies (EWAS) are increasingly used to identify epigenetic variations associated with common human diseases. While genome-wide association studies (GWAS) have successfully identified genetic variants linked to diseases, a significant portion of the genetic basis remains unexplained. EWAS offer new opportunities to study epigenetic factors, such as DNA methylation (DNAm), which are influenced by both genetic and environmental factors. However, EWAS present unique challenges, including the need for careful study design, sample selection, and statistical analysis. Integration of EWAS with GWAS can help dissect complex genetic haplotypes for functional analysis. Epigenetic variation includes DNA methylation, histone modifications, and non-coding RNAs. DNAm is the most studied epigenetic mark, with variations at single CpG sites (methylation variable positions, MVPs) and larger regions (differentially methylated regions, DMRs) being of particular interest. DNAm plays a key role in gene regulation and disease, particularly in cancer. However, the epigenetic component in non-malignant diseases like diabetes and autoimmunity is only beginning to be explored. Epigenetic variation can be causal or a consequence of disease. It can be inherited, environmentally induced, or influenced by genotype. Methylation quantitative trait loci (methQTLs) are genetic variants that influence methylation states. These can affect allele-specific methylation and are important for understanding the functional role of disease-associated variations. EWAS study designs vary, with case-control studies, parent-offspring pairs, and longitudinal cohorts being common approaches. The choice of tissue is critical, as epigenetic variation is tissue-specific. Blood and other easily accessible tissues are often used, but alternative sources may be necessary for certain diseases. Statistical considerations for EWAS include sample size, power, and the need to account for confounding factors such as population structure and technical variability. The significance thresholds for EWAS are typically more stringent than for GWAS, with genome-wide significance levels around 10^-8 to 10^-7. Post-EWAS follow-up studies aim to validate findings and explore the functional role of epigenetic variations. Integration of EWAS with GWAS can help identify genetic and epigenetic mechanisms underlying disease. The future of EWAS depends on the development of standardized study designs, access to appropriate samples, and the integration of computational and experimental approaches. Overall, EWAS have the potential to provide new insights into the etiology of complex diseases and contribute to the development of novel diagnostics and therapeutics. The field is still evolving, and ongoing research is essential to refine study designs and improve the interpretation of epigenetic data.Epigenome-wide association studies (EWAS) are increasingly used to identify epigenetic variations associated with common human diseases. While genome-wide association studies (GWAS) have successfully identified genetic variants linked to diseases, a significant portion of the genetic basis remains unexplained. EWAS offer new opportunities to study epigenetic factors, such as DNA methylation (DNAm), which are influenced by both genetic and environmental factors. However, EWAS present unique challenges, including the need for careful study design, sample selection, and statistical analysis. Integration of EWAS with GWAS can help dissect complex genetic haplotypes for functional analysis. Epigenetic variation includes DNA methylation, histone modifications, and non-coding RNAs. DNAm is the most studied epigenetic mark, with variations at single CpG sites (methylation variable positions, MVPs) and larger regions (differentially methylated regions, DMRs) being of particular interest. DNAm plays a key role in gene regulation and disease, particularly in cancer. However, the epigenetic component in non-malignant diseases like diabetes and autoimmunity is only beginning to be explored. Epigenetic variation can be causal or a consequence of disease. It can be inherited, environmentally induced, or influenced by genotype. Methylation quantitative trait loci (methQTLs) are genetic variants that influence methylation states. These can affect allele-specific methylation and are important for understanding the functional role of disease-associated variations. EWAS study designs vary, with case-control studies, parent-offspring pairs, and longitudinal cohorts being common approaches. The choice of tissue is critical, as epigenetic variation is tissue-specific. Blood and other easily accessible tissues are often used, but alternative sources may be necessary for certain diseases. Statistical considerations for EWAS include sample size, power, and the need to account for confounding factors such as population structure and technical variability. The significance thresholds for EWAS are typically more stringent than for GWAS, with genome-wide significance levels around 10^-8 to 10^-7. Post-EWAS follow-up studies aim to validate findings and explore the functional role of epigenetic variations. Integration of EWAS with GWAS can help identify genetic and epigenetic mechanisms underlying disease. The future of EWAS depends on the development of standardized study designs, access to appropriate samples, and the integration of computational and experimental approaches. Overall, EWAS have the potential to provide new insights into the etiology of complex diseases and contribute to the development of novel diagnostics and therapeutics. The field is still evolving, and ongoing research is essential to refine study designs and improve the interpretation of epigenetic data.