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README.Rmd
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README.Rmd
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---
title: "RHOGE"
author: "Nick Mancuso"
date: "`r format(Sys.Date())`"
output: github_document
---
RHOGE is an R package that estimates the genome-wide genetic correlation between two complex traits (diseases) as
a function of predicted gene expression effect on trait (\\rho_{ge}). Given output from two transcriptome-wide association studies,
RHOGE estimates the mediating effect of predicted gene expression and estimates the correlation of effect sizes across traits (diseases).
This approach is extended to a bi-directional regression that provides putative causal directions between traits with non-zero \\rho_{ge}.
This approach is described in:
> [Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits](https://doi.org/10.1016/j.ajhg.2017.01.031) \
> Nicholas Mancuso, Huwenbo Shi, Pagé Goddard, Gleb Kichaev, Alexander Gusev, Bogdan Pasaniuc.\
> American Journal of Human Genetics. 2017.
# Installation
Bioconductor + devtools is the most straightforward way to install RHOGE. To do this open an R terminal and enter
```{r eval=FALSE}
source("http://bioconductor.org/biocLite.R")
biocLite("devtools") # only if devtools not yet installed
biocLite("bogdanlab/RHOGE")
```
# Example
The following example computes \\rho_{ge} between BMI and triglycerides, as well as putative causal directions.
```{r warning=FALSE}
library(RHOGE)
# example BMI TWAS results from FUSION
data(bmi)
head(bmi)
# example triglyceride TWAS results from FUSION
data(tg)
head(tg)
# Estimate rho_ge genome-wide for BMI and Triglyerides and approximate sample sizes
ge_cor_res <- rhoge.gw(bmi, tg, 14000, 91000)
head(ge_cor_res)
# Perform bi-directional regression to estimate putative causal directions
bidir_res <- rhoge.bd(bmi, tg, 14000, 91000, p1 = 0.05 / nrow(bmi), p2 = 0.05 / nrow(tg))
head(bidir_res)
```
# Notes
Currently, only [FUSION](https://github.com/gusevlab/fusion_twas) style output is supported.
RHOGE comes installed with estimates of approximately independent LD blocks for European, Asian, and African ancestries. Performance
should improve if you have in-sample estimates of LD blocks. The only requirement is that regions are stored as a data.frame-like
object with 3 columns ('CHR', 'START', 'STOP'). For example,
```{r}
library(RHOGE)
data("grch37.eur.loci")
head(grch37.eur.loci)
```