Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/annahutch/corrcoverage

Repo for corrcoverage package
https://github.com/annahutch/corrcoverage

Last synced: 9 days ago
JSON representation

Repo for corrcoverage package

Awesome Lists containing this project

README 

        



# corrcoverage 



[![Coverage

status](https://codecov.io/gh/annahutch/corrcoverage/branch/master/graph/badge.svg)](https://codecov.io/github/annahutch/corrcoverage?branch=master)

[![Build

Status](https://travis-ci.org/annahutch/corrcoverage.svg?branch=master)](https://travis-ci.org/annahutch/corrcoverage)



Webpage: 



Manuscript: 



The `corrcoverage` R package uses a computationally efficient algorithm

to find accurate coverage estimates of the causal variant in credible

sets of prioritised variants from single causal variant genetic fine mapping ([Maller et

al., 2012](https://www.ncbi.nlm.nih.gov/pubmed/23104008),

[Wakefield, 2009](https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.20359).).



The package only requires GWAS summary statistics and can be used to:



  - Perform single causal variant Bayesian fine-mapping

  - Estimate the true genetic effect at the causal variant (see `est_mu`

    function)

  - Obtain an accurate coverage estimate that the causal variant 

    is contained within a credible set, the "corrected coverage estimate" ("adjusted coverage estimate" in manuscript)

    (see [Corrected Coverage vignette](https://annahutch.github.io/corrcoverage/articles/corrected-coverage.html))

  - Find a new corrected credible set ("adjusted coverage estimate" in manuscript) with the desired coverage of the

    true causal variant (see [Corrected Credible Set vignette](https://annahutch.github.io/corrcoverage/articles/New-Credible-Set.html))



We've strived to make our R package as easy to use as possible. 

Please see the flowchart below to decide which function is best to solve

your problem. The interactive version (click-to-functions) is [available

here](https://annahutch.github.io/PhD/package_flowchart.html).



![](https://annahutch.github.io/PhD/package_flowchart.svg)



-----



## Installation



We recommend that all users download the package straight from github, as this contains a more complete version of the package:



``` r

install.packages("devtools") # if not already installed

devtools::install_github("annahutch/corrcoverage")

```



Alternatively (and if using a solaris operating system) download straight from cran using:



```r

install.packages("corrcoverage")

```



-----



## Examples



For examples, please see the relevant vignettes.



1. The Corrected Coverage vignette

[here](https://annahutch.github.io/corrcoverage/articles/corrected-coverage.html)

should be read first. This shows readers how to use the `corrcoverage` R

package to get an accurate coverage estimate of the causal variant in a

credible set.



2. The Corrected Credible Set vignette

[here](https://annahutch.github.io/corrcoverage/articles/New-Credible-Set.html)

follows on from the Corrected Coverage vignette and shows readers how

the `corrcoverage` R package can be used to obtain a new credible set with

the desired coverage of the causal variant.



3. The Useful Info vignette

[here](https://annahutch.github.io/corrcoverage/articles/Useful-Info.html)

provides supplementary information about the usage of the package,

including information about other useful functions.



-----



In brief, the correction method involves simulating many credible sets

from the same system as the original and calculating what proportion of

these contain the true causal variant. Since the true causal variant is 

unknown, each variant is considered as causal in turn and the proportions 

are normalised by that variant’s posterior probability of causality.



-----



## Abstract



Genome Wide Association Studies (GWAS) have successfully identified thousands of loci associated with human diseases. Bayesian genetic fine-mapping studies aim to identify the specific causal variants within GWAS loci responsible for each association, reporting credible sets of plausible causal variants, which are interpreted as containing the causal variant with some "coverage probability". Here, we use simulations to demonstrate that the coverage probabilities are over-conservative in most fine-mapping situations. We show that this is because fine-mapping data sets are not randomly selected from amongst all causal variants, but from amongst causal variants with larger effect sizes. We present a method to re-estimate the coverage of credible sets using rapid simulations based on the observed, or estimated, SNP correlation structure, we call this the “adjusted coverage estimate”. This is extended to find “adjusted credible sets”, which are the smallest set of variants such that their adjusted coverage estimate meets the target coverage. We use our method to improve the resolution of a fine-mapping study of type 1 diabetes. We found that in 27 out of 39 associated genomic regions our method could reduce the number of potentially causal variants to consider for follow-up, and found that none of the 95\% or 99\% credible sets required the inclusion of more variants – a pattern matched in simulations of well powered GWAS. Crucially, our method requires only GWAS summary statistics and remains accurate when SNP correlations are estimated from a large reference panel. Using our method to improve the resolution of fine-mapping studies will enable more efficient expenditure of resources in the follow-up process of annotating the variants in the credible set to determine the implicated genes and pathways in human diseases. 



-----



## Contact



If you have any questions or queries please contact me at [email protected] and I will be happy to assist.