手法を比較

	バリアントコーリング	エピゲノムワイド関連解析（EWAS）
分野	バイオインフォマティクス	バイオインフォマティクス
系統	Process / pipeline	Process / pipeline
提唱年	2009–2010 (modern high-throughput era)	2008–2011 (term and framework established c. 2011)
提唱者	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)	Rakyan, Down, Balding & Beck (conceptual framework); Illumina arrays enabled large-scale application
種類	Computational genomics pipeline	Population-scale epigenomic association study
原典	McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., ... & DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303.	Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.
別名	SNP calling, genotyping from sequencing, mutation detection, variant detection	EWAS, methylome-wide association study, epigenetic association study, DNA methylation association study
関連	6	5
概要	Variant calling is the computational process of identifying positions in a sequenced genome that differ from a reference sequence — including single nucleotide polymorphisms (SNPs), small insertions and deletions (indels), and structural variants. It transforms aligned sequencing reads into an interpretable catalogue of genetic differences, forming the foundation for population genetics, disease-gene discovery, and clinical genomics applications.	An epigenome-wide association study (EWAS) is a hypothesis-free, genome-scale method that systematically tests whether epigenetic marks — predominantly CpG-site DNA methylation — differ between individuals with and without a trait, disease, or exposure. By scanning hundreds of thousands of genomic positions simultaneously, EWAS identifies loci where the epigenome is reproducibly associated with a phenotype, offering a layer of biological regulation that classical GWAS does not capture.