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To reproduce these results or to learn how to use the CAUSE Snakemake pipeline, follow the tutorial below
We have setup a Snakemake pipeline that will make it easy to run CAUSE (and a handful of other methods) on many pairs of traits. We will demonstrate the pipeline with an example that produces a subset of the results above.
We use Snakemake and a conda environment to run this analysis. If you don’t have Miniconda or Anaconda installed you can either install one of them (recomended) or just make sure that you have Python3, pandas and Snakemake installed. We have chosen to not include R in the conda environment so you will need to have R installed outside the environment and also have the cause and tidyverse R packages installed and running. If you were able to work through the package tutorial you should be in good shape. For some alternative MR methods you will need the MendelianRandomization R package and the MR-PRESSO R package which can be installed here.
First create the conda environment
conda create -n cause_large python=3.6 snakemakeNext create a working directory that you would like to analyze the data in. Change to that directory using cd in Mac or Linux. For example
mkdir gwas_pairs
cd gwas_pairsFinally, inside the working directory and using R, setup a CAUSE pipeline. The download_ld=TRUE argument causes the function to download correctly formatted ld data computed using 1k genomes CEU individuals. If you’ve already downloaded this or you want to use different LD data, use download_ld=FALSE.
cause::setup_cause_pipeline(download_ld=TRUE)Set up a pipeline analysis. From the directory you want to use, in R run
cause::setup_cause_pipeline()The setup_cause_pipeline() function provides you with everything you need to run an analysis except for data and a .csv file describing that data. The analysis is controlled by the config.yaml file which has four sections. You should edit the file to match your analysis desires.
The input section gives the location of a csv file that describes each set of GWAS summary statistics. More on this file later.
# Path to data spreadsheed
# csv format, columns include:
# name, raw_data_path, snp, A1, A2, beta_hat, se, p_value, sample_size, delimeter
input:
sum_stats: "gwas_pairs.csv"The analysis section gives instructions about what analysis to run.
# What analysis to do
# If all_pairs do all pairs of triats. Otherwise
# use traits in trait1 as M and traits in trait2 as Y
# methods should be a comma separated list with no spaces.
# Optional methods should be one of
# cause_*_*_*, mrpackage, lcv, mrpresso
analysis:
all_pairs: True
trait1: 1,2
trait2: 3,4,5
methods: cause_1_2_1,cause_1_10_1,cause_1_100_1,mrpackage,mrpresso,lcv
mr_pval: "5e-8"
cause_seed: 100If all_pairs is True then the pipeline will run the desired methods for all pairs of traits in the csv. Otherwise it will use the trait1 and trait2 fields to determine which pairs to run. Numbers in these fields refer to line numbers (beginnin at 1 after the header) in the csv. The methods line lists the methods you would like to run. Options are listed above in the comments. The numbers following cause designate the prior on \(q\). If the method is given as cause_a_b_c then a Beta(a,b) prior truncated at c will but used. The mrpackage option runs a set of six different methods using the MendelianRandomization R package. These are IVW and Egger regression both with random effects, the weighted median and the weighted mode with with values of phi equal to 1, 0.5, and 0.25. The mr_pval field gives the minimum p-value for variants used in methods besides CAUSE and LCV which use all variants. The cause_seed provides a random seed to CAUSE and ensures that results can be reproduced exactly.
The ld section tells the pipeline where to find correctly formatted LD data and what files are named.
#Path to directory containing LD data
ld:
dir: "ld/"
r2_file: "_AF0.05_0.1.RDS"
info_file: "_AF0.05_snpdata.RDS"The pipeline will expect to find files in the directory given in dir: with names {chr}_{r2_file} and {chr}_{info_file} where {r2_file} and {info_file} are the file endings given in those respective fields and {chr} is a chromosme (e.g. chr1, chr2) for chromosomes 1-22.
The out section tells the pipeline where to store output files.
out:
gwas_data_dir: "cause_standard_format/"
other_data_dir: "data/"
output_dir: "results/"The gwas_data_dir field is a directory to store formatted summary statistics. It can be helpful to store these in a centralized location if you are running multiple pipelines ot save on work and storage. The other_data_dir lists a directory to store other data files. These include some temporary files that are removed at the end of the pipeline and lists of SNPs pruned for LD that are saved. output_dir is a directory to store analysis results.
The cluster.yaml file describes resources allocated for each kind of job. The default will work but if it requests more resources than are available on your cluster (e.g. memory) you may need to change it.
The Snakemake command for submitting the pipeline is in the run-snakemake.sh file. You will need to modify it to match your cluster.
Once you have downloaded summary statistics and created the csv file you are ready to run the pipeline. You can run with
nohup ./run-snakemake.sh & The nohup is optional but is nice because the pipeline can run for a long time. I generally prefer to run the pipeline from a compute node rather than the login node but this will depend on your setup.
The next step is to acquire some GWAS summary statistics and to describe them in a spreadsheet so that the pipeline is able to format them properly. The spreadsheet has the following mandatory column headers in any order:
name: a unique string naming the study
delimeter: Field delimeter. One of “tab”, “,”, “space”, or any symbol.
snp: Column name of SNP ID (generally rs number but anything that matches the other file). This will be the field that studies are joined on.
A1: Column name of effect allele
A2: Column name of other allele
beta_hat: Column name of effect estimate
se: Column name of standard error of effect estimate
p_value: Column name of \(p\)-value
sample_size: Column name of per-SNP sample size
The p_value and sample_size fields may contain NAs if some studies don’t have them. The rest are required. Most studies can be used exactly as downloaded but you may have to a little bit of formatting before you can use the pipeline. For example, some studies do not contain rs numbers or have atypical variant names.
To run an example, from inside of R run
cause::cause_download_example_gwas_data()This function will set up an example analysis of three traits LDL cholesterol (Willer et al 2013 PMID 24097068 ), coronary artery disease (van der Harst et al 2017 PMID 29212778), and asthma (Demenais et al 2018 PMID 29273806). The function will download summary statistics, and in these cases they are ready to use without any modifications. This isn’t always the case. For example, some studies do not have rs ids included or report odds ratio rather than log odds ratio (the coefficient estimate from logistic regression). In these cases, you will need to modify the data so they contain the five mandatory colums of snp name, effect allele, other allele, coefficient (effect) estimate, and standard error of effect estimate. The function also downloads a csv file called gwas_pairs.csv. Take a look at the csv if you are having trouble making your own. This file has some extra (non-required) columns that we find useful for keeping track of studies.
Now to run an analysis all you need to do is
Edit the run-snakemake.sh file so that the cluster command is compatible with your cluster setup.
Edit the config.yaml file. You may not need to make any changes but you may need to change the location of the LD files or change where you would like data stored. Leave all_pairs: True or, alternatively to only run LDL -> CAD and LDL -> Asthma change to
analysis:
all_pairs: False
trait1: 1
trait2: 2,3leave the other fields in the analysis section as they are.
Change the persmissions of run-snakemake.sh to make it an executable. Use chmod a+x run-snakemake.sh.
Run (on a compute node if you like)
source activate cause_large
nohup ./run-snakemake.sh & When the pipeline is done, in the results directory there will be a handful of files named results/df_{method}.RDS where {method} is one of the methods run above. Source these into R and take a look using
df <- readRDS("results/cause_1_10_1.RDS")
dfAdditionally, the resuts folder will contain files named results/{name1}__{name2}_{method}.RDS containing results for each method. If the method is CAUSE, this will be a CAUSE object that you can look at using utilities in the cause package. Try
library(cause)
res <- readRDS("results/glg_ldl__vanderHarst_cad_cause_1_10_1.RDS")
summary(res)
plot(res, type="posteriors")
plot(res, type="data")
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] workflowr_1.4.0.9000 Rcpp_1.0.2 digest_0.6.20
[4] rprojroot_1.3-2 backports_1.1.4 git2r_0.26.1
[7] magrittr_1.5 evaluate_0.14 stringi_1.4.3
[10] fs_1.3.1 whisker_0.4 rmarkdown_1.15
[13] tools_3.6.1 stringr_1.4.0 glue_1.3.1
[16] xfun_0.9 yaml_2.2.0 compiler_3.6.1
[19] htmltools_0.3.6 knitr_1.24