Using dbgap2x, R package to explore, download and decrypt phenotypic and genomic data from dbGaP

You can test this software:

• Using the dockerized version on your local device by running

docker run -p 80:8888 -v /var/run/docker.sock:/var/run/docker.sock  gversmee/dbgap2x

and then open your web browser at http://localhost, and use the password dbgap2x

• Using your local R by installing the package with

install.packages("devtools")
devtools::install_github("gversmee/dbgap2x")

For using the package with a fresh R installation, make sure your system has the following libraries: libcurl4-openssl-dev libssl-dev libxml2-dev. Example for a debian system:

sudo apt-get update
sudo apt-get install libcurl4-openssl-dev libssl-dev libxml2-dev -y

Introduction

Load the package

#devtools::install_github("gversmee/dbgap2x", force = TRUE)
library(dbgap2x)

Get the list of the function for this new package

lsf.str("package:dbgap2x")

browse.dbgap : function (phs, no.browser = FALSE)  
browse.study : function (phs, no.browser = FALSE)  
consent.groups : function (phs)  
datatables.dict : function (phs)  
dbgap.data_dict : function (xml, dest)  
dbgap.decrypt : function (files, key = FALSE)  
dbgap.download : function (krt, key = FALSE)  
is.parent : function (phs)  
n.pop : function (phs, consentgroups = TRUE, gender = TRUE)  
n.tables : function (phs)  
n.variables : function (...)  
parent.study : function (phs)  
phs.version : function (phs)  
search.dbgap : function (term, no.browser = FALSE)  
study.name : function (phs)  
sub.study : function (phs)  
variables.dict : function (phs)  

Search for dbGaP studies

Let's try to explore the "Jackson Heart Study" cohort that exists on dbGaP.

We created the function "browse.dbgap", which helps you to find the studies related to the term that you search for.

search.dbgap("Jackson")

https://www.ncbi.nlm.nih.gov/gap/?term=Jackson%5BStudy+Name%5D 
                          

<table> <thead><tr><th scope=col>Study ID</th><th scope=col>Study Name</th><th scope=col>Release Date</th><th scope=col>Nb Participants</th><th scope=col>Study Design</th><th scope=col>Project</th><th scope=col>Diseases</th><th scope=col>Ancestor ID</th><th scope=col>Ancestor Name</th><th scope=col>Molecular Data Type</th><th scope=col>Tumor Type</th><th scope=col>UID</th></tr></thead> <tbody> <tr><td>phs001356.v1.p2 </td><td>Exome Chip Genotyping: The Jackson Heart Study </td><td> 2019-05-10 </td><td>2788 </td><td>Prospective Longitudinal Cohort </td><td>National Heart, Lung, Blood Institute </td><td>Cardiovascular Diseases, Hypertension, Diabetes Mellitus</td><td>phs000286.v6.p2 </td><td>The Jackson Heart Study (JHS) </td><td>SNP Genotypes (Array) </td><td>germline </td><td>1692088 </td></tr> <tr><td>phs001098.v2.p2 </td><td>T2D-GENES Multi-Ethnic Exome Sequencing Study: Jackson Heart Study</td><td> 2019-05-10 </td><td>1029 </td><td>Case-Control </td><td>NHLBI GO-ESP </td><td>Diabetes Mellitus, Type 2 </td><td>phs000286.v6.p2 </td><td>The Jackson Heart Study (JHS) </td><td>SNP/CNV Genotypes (NGS), WXS </td><td>germline </td><td>1597258 </td></tr> <tr><td>phs000499.v4.p2 </td><td>NHLBI Jackson Heart Study Candidate Gene Association Resource (CARe)</td><td> 2019-05-10 </td><td>3352 </td><td>Prospective Longitudinal Cohort </td><td>NHLBI CARe </td><td>Longitudinal Studies </td><td>phs000286.v6.p2 </td><td>The Jackson Heart Study (JHS) </td><td>SNP Genotypes (Array) </td><td>germline, unspecified </td><td>1597257 </td></tr> <tr><td>phs000498.v4.p2 </td><td>Jackson Heart Study Allelic Spectrum Project</td><td> 2019-05-10 </td><td>1983 </td><td>Prospective Longitudinal Cohort </td><td>National Heart, Lung, Blood Institute </td><td>Cardiovascular Diseases </td><td>phs000286.v6.p2 </td><td>The Jackson Heart Study (JHS) </td><td>SNP Genotypes (NGS), WXS </td><td>germline </td><td>1597256 </td></tr> <tr><td>phs000286.v6.p2 </td><td>The Jackson Heart Study (JHS) </td><td> 2019-05-10 </td><td>3889 </td><td>Prospective Longitudinal Cohort </td><td>National Heart, Lung, Blood Institute, NHLBI GO-ESP, NHLBI CARe </td><td>Cardiovascular Diseases, Coronary Artery Disease, Diabetes Mellitus, Type 2, Obesity, Hypertension, Kidney Failure, Chronic, Stroke, Heart Failure, Peripheral Vascular Diseases, Arrhythmias, Cardiac</td><td> </td><td> </td><td> </td><td>germline, unspecified </td><td>1597254 </td></tr> <tr><td>phs000964.v3.p1 </td><td>NHLBI TOPMed: The Jackson Heart Study </td><td> 2018-05-18 </td><td>3596 </td><td>Prospective Longitudinal Cohort </td><td>National Human Genome Research Institute </td><td>Cardiovascular Diseases, Hypertension, Diabetes Mellitus</td><td> </td><td> </td><td>SNP/CNV Genotypes (NGS), WGS </td><td>germline </td><td>1768620 </td></tr> </tbody> </table>

dbGaP returns the list of the studies related to your term. As you see, there are 6 studies associated with the "Jackson Heart Study" (JHS). One of these study is the main one a.k.a the "parent study", whereas the other ones are substudies. In this case, phs000286.v5.p1 is the parent study. Firslty, we can use the phs.version() function in order to be sure that this is the latest version of the study. We can abbreviate the phs name by giving just the digit, or we can use the full dbGaP id.

phs.version("286")

'phs000286