statgenGWAS

See https://biometris.github.io/statgenGWAS for a full description

https://github.com/biometris/statgengwas

Science Score: 26.0%

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Low similarity (16.1%) to scientific vocabulary

Keywords

cran genetics gwas r-package

Last synced: 6 months ago · JSON representation

Repository

See https://biometris.github.io/statgenGWAS for a full description

Basic Info

Host: GitHub
Owner: Biometris
Language: R
Default Branch: main
Homepage: https://biometris.github.io/statgenGWAS/
Size: 25.4 MB

Statistics

Stars: 15
Watchers: 3
Forks: 6
Open Issues: 5
Releases: 0

Topics

cran genetics gwas r-package

Created about 6 years ago · Last pushed 8 months ago

Metadata Files

Readme Changelog

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "75%"
)
```

# statgenGWAS

[![](https://www.r-pkg.org/badges/version/statgenGWAS)](https://www.r-pkg.org/pkg/statgenGWAS)
[![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/statgenGWAS)](https://www.r-pkg.org/pkg/statgenGWAS)
[![R-CMD-check](https://github.com/Biometris/statgenGWAS/workflows/R-CMD-check/badge.svg)](https://github.com/Biometris/statgenGWAS/actions?workflow=R-CMD-check)
[![codecov](https://codecov.io/gh/Biometris/statgenGWAS/branch/master/graph/badge.svg)](https://app.codecov.io/gh/Biometris/statgenGWAS)

**statgenGWAS** is an R package for fast single trait Genome Wide Association Studies (GWAS).

statgenGWAS has extensive options for summarizing and visualizing results. The example below shows a selection of what is possible. For a full overview of all options it is best to read the [**vignette**](https://biometris.github.io/statgenGWAS/articles/GWAS.html).

## Installation

* Install from CRAN:

```{r, eval = FALSE}
install.packages("statgenGWAS")
```

* Install latest development version from GitHub (requires [remotes](https://github.com/r-lib/remotes) package):

```{r, eval = FALSE}
remotes::install_github("Biometris/statgenGWAS", ref = "develop", dependencies = TRUE)
```

## Examples

Example using the data from the European Union project DROPS. The same data is used in the vignette and described there in detail.

First get the data in the form required by the package, converting it to an object of class **g**(enomic)**Data**.
```{r createGData}
library(statgenGWAS)
## Read data.
data("dropsMarkers")
data("dropsMap")
data("dropsPheno")

## Add genotypes as row names of dropsMarkers and drop Ind column.
rownames(dropsMarkers) <- dropsMarkers[["Ind"]]
dropsMarkers <- dropsMarkers[colnames(dropsMarkers) != "Ind"]

## Add genotypes as row names of dropsMap.
rownames(dropsMap) <- dropsMap[["SNP.names"]]
## Rename Chomosome and Position columns.
colnames(dropsMap)[match(c("Chromosome", "Position"), colnames(dropsMap))] <- c("chr", "pos")

## Rename Variety_ID in phenotypic data to genotype.
colnames(dropsPheno)[colnames(dropsPheno) == "Variety_ID"] <- "genotype"
## Select relevant columns and convert data to a list.
dropsPhenoList <- split(x = dropsPheno[c("genotype", "grain.yield",
                                         "grain.number", "seed.size",
                                         "anthesis", "silking", "plant.height",
                                         "tassel.height", "ear.height")], 
                        f = dropsPheno[["Experiment"]])

## Create a gData object all data.
gDataDrops <- createGData(geno = dropsMarkers, map = dropsMap, pheno = dropsPhenoList)
```

```{r removeDupMarkers}
## Remove duplicate SNPs from gDataDrops.
gDataDropsDedup <- codeMarkers(gDataDrops, impute = FALSE, verbose = TRUE) 
```

```{r stg}
## Run single trait GWAS for traits 'grain.yield' and 'anthesis' for trial Mur13W.
GWASDrops <- runSingleTraitGwas(gData = gDataDropsDedup,
                                trials = "Mur13W",
                                traits = c("grain.yield", "anthesis"))
```

```{r qqStg} 
## QQ plot of GWAS Drops.
plot(GWASDrops, plotType = "qq", trait = "grain.yield")
```

```{r manhattanStg}
## Manhattan plot of GWAS Drops.
plot(GWASDrops, plotType = "manhattan", trait = "grain.yield")
```

```{r qtlStgNorm}
## Qtl plot of GWAS Drops.
## Set significance threshold to 4 and normalize effect estimates.
plot(GWASDrops, plotType = "qtl", yThr = 4, normalize = TRUE)
```

Owner

Name: Wageningen Universtiy & Research, Biometris
Login: Biometris
Kind: organization
Email: biometris@wur.nl
Location: Wageningen, The Netherlands

Website: https://wur.eu/biometris
Repositories: 8
Profile: https://github.com/Biometris

Biometris develops statistical and mathematical methods for the quantification of biological processes and processes in our living environment.

GitHub Events

Total

Issues event: 4
Watch event: 3
Issue comment event: 7
Push event: 39
Fork event: 3
Create event: 2

Last Year

Issues event: 4
Watch event: 3
Issue comment event: 7
Push event: 39
Fork event: 3
Create event: 2

Committers

Last synced: over 2 years ago

All Time

Total Commits: 344
Total Committers: 2
Avg Commits per committer: 172.0
Development Distribution Score (DDS): 0.003

Past Year

Commits: 7
Committers: 1
Avg Commits per committer: 7.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Rossum, Bart-Jan van	b**m@w**l	343
mille016	e**t@w**l	1

Committer Domains (Top 20 + Academic)

wur.nl: 2

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 17
Total pull requests: 0
Average time to close issues: 5 months
Average time to close pull requests: N/A
Total issue authors: 16
Total pull request authors: 0
Average comments per issue: 3.47
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 4
Pull requests: 0
Average time to close issues: 4 days
Average time to close pull requests: N/A
Issue authors: 4
Pull request authors: 0
Average comments per issue: 2.5
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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JiayiHelenZhou (1)
teunbrand (1)
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Packages

Total packages: 1
Total downloads:
- cran 806 last-month
Total docker downloads: 21,659

Total dependent packages: 3
Total dependent repositories: 2
Total versions: 12
Total maintainers: 1

cran.r-project.org: statgenGWAS

Genome Wide Association Studies

Homepage: https://biometris.github.io/statgenGWAS/index.html
Documentation: http://cran.r-project.org/web/packages/statgenGWAS/statgenGWAS.pdf
License: GPL-3
Latest release: 1.0.12
published 8 months ago

Versions: 12
Dependent Packages: 3
Dependent Repositories: 2
Downloads: 806 Last month
Docker Downloads: 21,659

Rankings

Docker downloads count: 0.6%

Forks count: 12.2%

Average: 13.6%

Dependent packages count: 13.7%

Stargazers count: 17.4%

Downloads: 18.8%

Dependent repos count: 19.2%

Maintainers (1)

bart-jan.vanrossum@wur.nl

Last synced: 7 months ago

Dependencies

DESCRIPTION cran

R >= 3.6 depends
Rcpp * imports
data.table * imports
ggplot2 >= 3.0.0 imports
sommer >= 3.7.3 imports
knitr * suggests
officer * suggests
rmarkdown * suggests
tinytest * suggests

.github/workflows/check.yaml actions

actions/checkout v3 composite
r-lib/actions/check-r-package v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/pkgdown.yaml actions

JamesIves/github-pages-deploy-action v4.4.1 composite
actions/checkout v3 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/test-coverage.yaml actions

actions/checkout v3 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

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statgenGWAS

Science Score: 26.0%

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README.Rmd

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cran.r-project.org: statgenGWAS

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