Package: eGST
Title: Leveraging eQTLs to Identify Individual-Level Tissue of Interest
        for 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
        manifested through multiple tissues of interest at different
        time points in the development. As an example, the genetic
        predisposition for obesity could be manifested through
        inherited variants that control metabolism through regulation
        of genes expressed in the brain and/or through the control of
        fat storage in the adipose tissue by dysregulation of genes
        expressed in adipose tissue. We present a method eGST
        (eQTL-based genetic subtyper) that integrates tissue-specific
        eQTLs with GWAS data for a complex trait to probabilistically
        assign a tissue of interest to the phenotype of each individual
        in the study. eGST estimates the posterior probability that an
        individual's phenotype can be assigned to a tissue based on
        individual-level genotype data of tissue-specific eQTLs and
        marginal phenotype data in a genome-wide association study
        (GWAS) cohort. Under a Bayesian framework of mixture model,
        eGST employs a maximum a posteriori (MAP)
        expectation-maximization (EM) algorithm to estimate the
        tissue-specific posterior probability across individuals.
        Methodology is available from: A Majumdar, C Giambartolomei, N
        Cai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019)
        <doi:10.1101/674226>.
Depends: R (>= 3.2.0)
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)
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
Imports: purrr, mvtnorm, MASS, utils, stats, matrixStats
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: 2026-06-11 11:02:16 UTC; root
Author: Arunabha Majumdar [aut, cre],
  Tanushree Haldar [aut],
  Bogdan Pasaniuc [aut]
Maintainer: Arunabha Majumdar <statgen.arunabha@gmail.com>