The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

eGST: Leveraging eQTLs to Identify Individual-Level Tissue of Interest for a Complex Trait

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>.

Version: 1.0.0
Depends: R (≥ 3.2.0)
Imports: purrr, mvtnorm, MASS, utils, stats, matrixStats
Suggests: knitr, rmarkdown, testthat
Published: 2019-07-02
DOI: 10.32614/CRAN.package.eGST
Author: Arunabha Majumdar [aut, cre], Tanushree Haldar [aut], Bogdan Pasaniuc [aut]
Maintainer: Arunabha Majumdar <statgen.arunabha at gmail.com>
BugReports: https://github.com/ArunabhaCodes/eGST/issues
License: GPL-3
URL: https://github.com/ArunabhaCodes/eGST
NeedsCompilation: no
Materials: README NEWS
CRAN checks: eGST results

Documentation:

Reference manual: eGST.pdf
Vignettes: eGST Tutorial

Downloads:

Package source: eGST_1.0.0.tar.gz
Windows binaries: r-devel: eGST_1.0.0.zip, r-release: eGST_1.0.0.zip, r-oldrel: eGST_1.0.0.zip
macOS binaries: r-release (arm64): eGST_1.0.0.tgz, r-oldrel (arm64): eGST_1.0.0.tgz, r-release (x86_64): eGST_1.0.0.tgz, r-oldrel (x86_64): eGST_1.0.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=eGST to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.