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https://github.com/hdg204/Rdna-nexus
Functions to analyse UK Biobank genetic data directly from DNA Nexus' RStudio Workbench
https://github.com/hdg204/Rdna-nexus
biobank bioinformatics dna genetics nexus r risk scores snp uk
Last synced: 2 days ago
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Functions to analyse UK Biobank genetic data directly from DNA Nexus' RStudio Workbench
- Host: GitHub
- URL: https://github.com/hdg204/Rdna-nexus
- Owner: hdg204
- Created: 2023-01-26T12:26:36.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-08T14:54:09.000Z (3 months ago)
- Last Synced: 2024-08-08T17:23:14.121Z (3 months ago)
- Topics: biobank, bioinformatics, dna, genetics, nexus, r, risk, scores, snp, uk
- Language: R
- Homepage:
- Size: 69.3 KB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-uk-biobank - Rdna-nexus
README
# Rdna-nexus
## Introduction
This repository contains a few useful functions for analysing genetic data from the UK Biobank using the RStudio Workbench implementation on DNA Nexus. These functions were written for use on the University of Exeter's project, but as none of our curated files are used, this should run universally. There are currently three functions included:
* **extract_snp**, which is for extracting everbody's genotype for a particular SNP
* **extract_snp_bulk**, which is for extracting everbody's genotype for lots of SNPs
* **generate_grs**, which combines lots of SNPs into a Genetic Risk Score
For testing gene-phenotype interactions, I recommend using https://github.com/hdg204/UKBB to derive phenotypes from healthcare records.
SNPs on the X Chromosome should be inputted using Chromosome 23. All base pair positions should be in Genome Reference Consortium Human Build 37.
For any further information / questions / errors, please contact [email protected]
## Installation
To make all functions available, as well as an example file, use
```
library(devtools)
source_url("https://raw.githubusercontent.com/hdg204/Rdna-nexus/main/install.R")
```
This will source all R functions in the repository. The example file, Example_GRS, contains the SNP information of 269 SNPs obtained from the recent Prostate Cancer meta-analysis from Conti et. al. https://pubmed.ncbi.nlm.nih.gov/33398198/
## extract_snp
*Extract the data of one SNP from the imputed genotype file*
This function takes the chromosome and base pair position of one SNP and extracts the genotype data. The inputs are two numeric variables for base pair and position.
E.g. to extract the data for the SNP on chromosome 8, position 38644914, run:
`a=extract_snp(8,38644914)`
The output of this function is a list with two elements.
`a$snp_data` is a dataframe with two headings, one for the eid, and one for the genotype of everyone in Biobank
`a$variant_info` contains information about the SNP itself: a dataframe with headings chromosome, position, rsid, number_of_alleles, allele0, allele1
where the rsid is extracted from the bgen file.
An image of the genotype data cannot be provided as this contains individual-level genetic data.
## extract_snp_bulk
*Extract the data of many SNPs*
While many SNPs can be extracted using `extract_snp`, this is slow as it would need to read the bgen for each SNP. The most efficient way is to read the bgen for each chromosome, since these are split in the UKBB data. This function is built for extracting multiple SNPs from a file.
The file should be a tab separated file, with the first two columns being chr and bp, Ideally, these should be named chr and bp, but they will be renamed anyway if not. An example of this is given in https://github.com/hdg204/Rdna-nexus/blob/main/Example_GRS, where only the first two columns are used. It's fine if there are more columns, as long as the first two represent chromosome and base pair.
After installation, this function can be run using:
`a=extract_snp_bulk('Example_GRS')`
It can be a little slow but it prints to screen which chromosome it's on. The output is similar to extract_snp. It is a list of two elements, genotypes and variants. This time there are multiple columns for the genotypes, named 'chr:bp_A0_A1', and multiple rows for the variants, one for each SNP. The variants table looks like hte following:
## generate_grs
*Calculate a genetic risk score using SNP weights*
This function takes a weight file, which must have the columns chromosome, base pair, other, effect, and weight, and calculates a genetic risk score applying the formula $\sum \beta_{i}G_{i}$, where $\beta_i$ is the weight for snp $i$ and $G_i$ is the individuals's imputed genotype for SNP $i$. This can be run using
`a=generate_grs('Example_GRS')`.
Note that this is the same file used in the extract_snp_bulk function. This is intentional, as it allows users to check each any individual SNP in the GRS easily without generating a new file. The output is a data frame with two columns, the eid and the calculated risk score.
Side note: Although the term 'genetic risk score' is used throughout this repository, as it was designed for disease phenotypes, this function is equally applicable for generating genetic scores for continuous traits (which are technically NOT risk scores as they do not confer a risk - https://www.nature.com/articles/s41586-021-03243-6).
The ouptut is a list which contains:
* `a$grs` is a data frame with eid and the genetic risk score
* `a$missing` is a list of snps that were not extracted from the bgen file
* `a$snp_data` is the dosage file. This contains the output of `extract_snp_bulk('Example_GRS')` and can be used to check maf, HEW, etc
## For internal Exeter use
There is a script available at https://universityofexeteruk.sharepoint.com/:u:/r/sites/GeneticsofComplexTraitsTeams/Shared%20Documents/R,%20STATA%20and%20other%20coding%20tips/GRS_DNA_Nexus_EXAMPLE.R?csf=1&web=1&e=dk7xaY
This uses the scripts to extract phenotypes from the healthcare records available at https://github.com/hdg204/UKBB to derive a basic prostate cancer phenotype and test the genetic risk score, plotting the distribution within cases and controls, and the ROC AUC of an integrated model including age at baseline. The following figures should be produced.
## Acknowledgements
If any of the contained code is used in any academic article, please acknowledge the authors (Harry Green and Bethan Rimmer) include a link to this github repository in the acknowledgements section, and please make any other code used in your article open source.