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Multivariate analyses incorporating multiple phenotypes are available, but they emphasize linear combinations of phenotypes. We present MTClass, a machine learning framework that attempts to classify an individual\u2019s genotype based on a vector of multiphenotype expression levels of a given gene. We conduct simulation studies and multiple case studies using real and imputed data, and we demonstrate that MTClass detects more functionally relevant variants and genes compared to existing single-tissue approaches as well as multi-phenotype association tests. Our results suggest that the importance of expression regulation at the MHC region may have been underestimated, and they provide fresh biological insights into genetic variants that have pleiotropic effects, influencing gene expression in a complex manner.<\/jats:p>","DOI":"10.1093\/bib\/bbag238","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T11:48:17Z","timestamp":1777376897000},"source":"Crossref","is-referenced-by-count":0,"title":["A novel machine learning-based algorithm for eQTL identification reveals complex pleiotropic effects in the MHC region"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3178-8614","authenticated-orcid":false,"given":"Ronnie Y","family":"Li","sequence":"first","affiliation":[{"name":"Neuroscience Graduate Program, Emory University , 1462 Clifton Road NE, Suite 314, Atlanta, GA 30322 ,","place":["United 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