השוואת שיטות

	ניתוח eQTL בסיוע למידת מכונה	מחקר השוואתי של כלל הגנום (GWAS)
תחום	ביואינפורמטיקה	ביואינפורמטיקה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	2015 (key ML-eQTL methods; foundational eQTL work: Jansen & Nap 2001)	2005–2007
הוגה השיטה	Gamazon et al. (PrediXcan), Zhou & Troyanskaya (DeepSEA); broader field ca. 2015-onward	Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007)
סוג	Statistical-computational genomics pipeline	Observational genomic association study
מקור מכונן	Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., ... & Im, H. K. (2015). A gene-based association method for mapping traits using reference transcriptome data. Nature Genetics, 47(9), 1091-1098.	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.
כינויים	ML-assisted eQTL analysis, ML eQTL mapping, deep learning eQTL, predictive eQTL modeling	GWAS, genome-wide association analysis, whole-genome association study, WGAS
קשורות	6	6
תקציר	Machine learning-assisted eQTL analysis integrates supervised learning models — ranging from elastic-net regression to deep neural networks — into the classical eQTL framework to predict and map genetic variants that regulate gene expression. By training predictive models on reference panels (e.g., GTEx), the approach enables imputation of gene expression in cohorts lacking RNA data, substantially increasing statistical power and enabling trans-tissue generalisation.	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.