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Written by: Steve Burgess

Published: 15 October 2018

Recently the PhenoScanner webtool has been updated by James Staley and colleagues (in particular Mihir Kamat). A number of factors were improved in the update, including more genetic associations, and the ability to search associations by gene and by risk factor. Another important update is that PhenoScanner can now be called directly from R. Here we present some code demonstrating how to call PhenoScanner from R, and how to integrate output from the PhenoScanner package into the Mendelian randomization package, and perform Mendelian randomization using a single line of R code.

Please note that this code is currently inefficient, as the PhenoScanner code currently queries the whole database, rather than specifically querying just the desired variables. Watch this space for developments! However, this does mean that the code may run slowly, particularly if many people across the world are trying to access the resource simultaneously.

The first step is to install the PhenoScanner package (and the MendelianRandomization package if you haven't done this previously):

install.packages("devtools")
library(devtools)
install_github("phenoscanner/phenoscanner")
library(phenoscanner)

install.packages("MendelianRandomization")
library(MendelianRandomization)

The code below takes as inputs a list of rsids, the name of the exposure (risk factor), the PubMed ID of the study that published the association estimates for the exposure, and the ancestry group for the association estimates (eg "Mixed", "European", or "African") - this triple of name/PubMed ID/ancestry is required to uniquely specify a dataset for genetic associations - and the name, PubMed ID and ancestry group for genetic associations with the outcome. It creates an MRInput object, which can then be used as an input for the functions in the MendelianRandomization package.

pheno_input <- function(snps, exposure, pmidE, ancestryE, outcome, pmidO, ancestryO) {

dataTable <- phenoscanner(snpquery = snps, pvalue = 1)$results

snp.list.exposure = unique(dataTable[which(dataTable$trait == exposure & dataTable$pmid == pmidE & dataTable$ancestry == ancestryE & !is.na(dataTable$beta) & !is.na(dataTable$se)),1])
snp.list.outcome = unique(dataTable[which(dataTable$trait == outcome & dataTable$pmid == pmidO & dataTable$ancestry == ancestryO & !is.na(dataTable$beta) & !is.na(dataTable$se)),1])
snp.list = intersect(snp.list.exposure, snp.list.outcome)
if (length(snp.list) == 0) { cat("No variants found with beta-coefficients and standard errors for given risk factor and outcome combination. Please check spelling and PMIDs.\n"); return() }

row.exp = NULL; row.out = NULL
for (j in 1:length(snp.list)) {
row.exp[j] = which(dataTable$trait == exposure & dataTable$pmid == pmidE & dataTable$ancestry == ancestryE & !is.na(dataTable$beta) & !is.na(dataTable$se) & dataTable$snp == snp.list[j])[1]
row.out[j] = which(dataTable$trait == outcome & dataTable$pmid == pmidO & dataTable$ancestry == ancestryO & !is.na(dataTable$beta) & !is.na(dataTable$se) & dataTable$snp == snp.list[j])[1]
}

Bx. <- dataTable[row.exp, c("snp", "beta", "se")]
By. <- dataTable[row.out, c("snp", "beta", "se")]

dataSet <- merge(Bx., By., "snp")

return(mr_input(exposure = exposure, outcome = outcome, snps = as.character(dataSet[,1]),
bx=as.numeric(dataSet[,2]), bxse=as.numeric(dataSet[,3]), by=as.numeric(dataSet[,4]), byse=as.numeric(dataSet[,5]),
correlation = matrix()))
}

pheno_obj = pheno_input(snps=c("rs12916", "rs2479409", "rs217434", "rs1367117", "rs4299376", "rs629301", "rs4420638", "rs6511720"),
exposure = "Low density lipoprotein", pmidE = "24097068", ancestryE = "European",
outcome = "Coronary artery disease", pmidO = "26343387", ancestryO = "Mixed")

This code can then be combined with any of the functions from the MendelianRandomization package to perform a Mendelian randomization analysis. Here, we use the mr_ivw function to perform a Mendelian randomization analysis in a single line of R code:

mr_obj = mr_ivw(pheno_input(snps=c("rs12916", "rs2479409", "rs217434", "rs1367117", "rs4299376", "rs629301", "rs4420638", "rs6511720"),
exposure = "Low density lipoprotein", pmidE = "24097068", ancestryE = "European",
outcome = "Coronary artery disease", pmidO = "26343387", ancestryO = "Mixed"))

This analysis shows that LDL-cholesterol is a causal risk factor for coronary artery disease, with an estimate of 0.498 corresponding to an odds ratio of exp(0.498) = 1.65 per 1 unit (here, one standard deviation) increase in LDL-cholesterol.

Comments are welcome!

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Written by: Steve Burgess

Published: 29 September 2017

The MendelianRandomization R package was recently updated to version v0.2.2. There were some fixes in terms of p-values and confidence intervals (for example, previously p-values were based on a t-distribution regardless of the choice specified by the user). The package can be downloaded from https://cran.r-project.org/web/packages/MendelianRandomization/index.html. A paper introducing the package can be found at https://www.ncbi.nlm.nih.gov/pubmed/28398548.

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Written by: Steve Burgess

Published: 08 January 2016

MR Catalogue (http://mrcatalogue.medschl.cam.ac.uk/), a web-based tool for performing genetic look-ups in publicly-available data, was launched today. The tool takes genetic variants (either rsid or chromosome and position) as inputs, and outputs the associations of the variants (batch query is needed for multiple variants) with up to 200 different variables, including disease outcomes and continuous phenotypes. There is an option for a proxy search, so that if your specified variant is not available, the association of a correlated variant will be given. The default display is the top 10 associations (ranked by p-value); a download is needed to access all associations. Genetic associations (including proxies) have been orientated across datasets, so that the signs of all association estimates are consistent.

This tool enables Mendelian randomization to be performed quickly and easily using summarized data - beta-coefficients and standard errors - see http://www.ncbi.nlm.nih.gov/pubmed/24114802, http://www.ncbi.nlm.nih.gov/pubmed/25773750, or http://spark.rstudio.com/sb452/summarized/ for details on how to perform a basic Mendelian randomization analysis using summarized data, or http://www.ncbi.nlm.nih.gov/pubmed/26050253 (MR-Egger) https://www.academia.edu/15479132/Consistent (median-based method) for robust methods using summarized data.

Outside of Mendelian randomization, this is a useful tool for calculating proxies, or checking the association of variants with a wide range of variables (phenome scan). Even if you switch off the catalogue and the proxy search, it is a quick tool for converting chromosome/position (hg19) to rsid and vice versa, or for getting major/minor alleles. Hopefully will be a widely used tool!

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Written by: Steve Burgess

Published: 08 January 2016

I recently made software code for implementing Mendelian randomization analyses available as a GitHub-editable document. The motivation for this is that much of this code is scattered in the appendices of various manuscripts, and is hard to find, hard to edit (when you want to update), and the notation isn't always consistent.

Code can be found here, and the associated project is here. Please read through and get in touch if you spot any typos, or else edit yourself! The document currently contains mostly R code with some Stata code, but other software code would be welcome. This is a new initiative: feedback is very welcome.