eGST-package {eGST}R Documentation

eGST (eQTL-based Genetic Sub-Typer): 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 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 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.



It estimates the posterior probability that the genetic susceptibility of the phenotype of an individual in the study is mediated through eQTLs specific to a tissue of interest. The phenotype across individuals can be classified into tissues under consideration based on the estimated tissue-specific posterior probability across individuals.


Maintainer: Arunabha Majumdar



Majumdar A, Giambartolomei C, Cai N, Freund MK, Haldar T, J Flint, Pasaniuc B (2019) Leveraging eQTLs to identify tissue-specific genetic subtype of complex trait. bioRxiv.

See Also

Useful links:

[Package eGST version 1.0.0 Index]