Σύγκριση μεθόδων

	Φυλογενετική Ανάλυση	Μελέτη Συσχέτισης σε Επίπεδο Γονιδιώματος (GWAS)
Πεδίο	Βιοπληροφορική	Βιοπληροφορική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1960s-1981 (distance trees ~1967; ML framework formalised 1981)	2005–2007
Δημιουργός	Joseph Felsenstein (maximum likelihood framework); Walter Fitch and Emanuel Margoliash (distance methods)	Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007)
Τύπος	Computational inference method	Observational genomic association study
Θεμελιώδης πηγή	Felsenstein, J. (2004). Inferring Phylogenies. Sinauer Associates. ISBN: 978-0878931774	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.
Εναλλακτικές ονομασίες	molecular phylogenetics, phylogenetic inference, evolutionary tree reconstruction, phylogenomics	GWAS, genome-wide association analysis, whole-genome association study, WGAS
Συναφείς	5	6
Σύνοψη	Phylogenetic analysis reconstructs the evolutionary history of organisms, genes, or proteins by comparing molecular sequence data and estimating the branching tree that best explains observed similarities and differences. Rooted in the work of Felsenstein and colleagues from the 1960s onward, it is a cornerstone technique in evolutionary biology, microbiology, epidemiology, and comparative genomics, supporting tasks from tracing viral outbreak origins to classifying novel species.	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.