{ "_id": "6a1039ceacfb0bcc41c9b1d0", "Package": "eGST", "Title": "Leveraging eQTLs to Identify Individual-Level Tissue of Interest\nfor a Complex Trait", "Version": "1.0.0", "Date": "2019-06-30", "Authors@R": "c(person(\"Arunabha\", \"Majumdar\", email = \"statgen.arunabha@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Tanushree\", \"Haldar\", email = \"tanushree.haldar@gmail.com\", role = \"aut\"), person(\"Bogdan\", \"Pasaniuc\", email = \"pasaniuc@ucla.edu\", role = \"aut\"))", "Description": "Genetic predisposition for complex traits is often\nmanifested through multiple tissues of interest at different\ntime points in the development. As an example, the genetic\npredisposition for obesity could be manifested through\ninherited variants that control metabolism through regulation\nof genes expressed in the brain and/or through the control of\nfat storage in the adipose tissue by dysregulation of genes\nexpressed in adipose tissue. We present a method eGST\n(eQTL-based genetic subtyper) that integrates tissue-specific\neQTLs with GWAS data for a complex trait to probabilistically\nassign a tissue of interest to the phenotype of each individual\nin the study. eGST estimates the posterior probability that an\nindividual's phenotype can be assigned to a tissue based on\nindividual-level genotype data of tissue-specific eQTLs and\nmarginal phenotype data in a genome-wide association study\n(GWAS) cohort. Under a Bayesian framework of mixture model,\neGST employs a maximum a posteriori (MAP)\nexpectation-maximization (EM) algorithm to estimate the\ntissue-specific posterior probability across individuals.\nMethodology is available from: A Majumdar, C Giambartolomei, N\nCai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019)\n.", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "URL": "https://github.com/ArunabhaCodes/eGST", "BugReports": "https://github.com/ArunabhaCodes/eGST/issues", "RoxygenNote": "6.1.1", "Roxygen": "list(markdown = TRUE)", "VignetteBuilder": "knitr", "Repository": "https://arunabhacodes.r-universe.dev", "Date/Publication": "2019-07-01 06:43:24 UTC", "RemoteUrl": "https://github.com/arunabhacodes/egst", "RemoteRef": "HEAD", "RemoteSha": "c5e81a84b75839554da4f8c1738e1e26a15fa10a", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-12 05:23:36 UTC", "User": "root" }, "Author": "Arunabha Majumdar [aut, cre],\nTanushree Haldar [aut],\nBogdan Pasaniuc [aut]", "Maintainer": "Arunabha Majumdar ", "MD5sum": "baf156177d7008f66770afad740c7960", "_user": "arunabhacodes", "_type": "src", "_file": "eGST_1.0.0.tar.gz", "_fileid": "d04b09a018407ae994f2b9dace67f4d074f3e854db7b89d3b27e3b84f851ebd7", "_filesize": 262055, "_sha256": "d04b09a018407ae994f2b9dace67f4d074f3e854db7b89d3b27e3b84f851ebd7", "_created": "2026-05-12T05:23:36.000Z", "_published": "2026-05-22T11:11:10.059Z", "_distro": "noble", "_jobs": [ { "job": 77367962164, "time": 127, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6936317846" }, { "job": 77367962165, "time": 124, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "6936317319" }, { "job": 77367962246, "time": 152, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "6936318284" }, { "job": 77367962115, "time": 128, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "6936313528" }, { "job": 77367961412, "time": 147, 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