เปรียบเทียบวิธี

	การศึกษาความสัมพันธ์ทั่วทั้งจีโนม (GWAS)	การวิเคราะห์ความแปรผันของจำนวนสำเนา
สาขาวิชา	ชีวสารสนเทศศาสตร์	ชีวสารสนเทศศาสตร์
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2005–2007	1998–2006
ผู้ริเริ่ม	Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007)	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)
ประเภท	Observational genomic association study	Genomic structural variant detection pipeline
แหล่งต้นตำรับ	Wellcome Trust Case Control Consortium. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447(7145), 661–678.	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.
ชื่อเรียกอื่น	GWAS, genome-wide association analysis, whole-genome association study, WGAS	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis
ที่เกี่ยวข้อง	6	6
สรุป	A genome-wide association study (GWAS) systematically tests hundreds of thousands to millions of single-nucleotide polymorphisms (SNPs) across the human genome for statistical association with a trait or disease. By comparing allele frequencies between cases and controls — or by regressing SNP genotypes on a quantitative phenotype — GWAS identifies genomic loci that harbor common genetic variants contributing to complex traits. Since its large-scale debut in 2007, GWAS has catalogued thousands of robust disease–variant associations across virtually every common human condition.	Copy number variation (CNV) analysis is a genomic pipeline for detecting regions where individuals carry fewer or more copies of a DNA segment than the reference genome. CNVs span kilobases to megabases and are a major class of structural variation implicated in cancer, neurodevelopmental disorders, and population diversity. The pipeline typically processes SNP array intensities or read-depth signals from whole-genome sequencing, applies segmentation algorithms, calls gain and loss events, and annotates them against gene and clinical databases.