CONQUER

Gerard Bouland, Joline Beulens, Joey Nap, Arno van der Slik, Arnaud Zaldumbide, Leen ’t Hart and Roderick Slieker 26 oktober, 2021

• 0.1 Change log
• 0.2 Installation
  • 0.2.1 Install the depencies
  • 0.2.2 Install CONQUER
• 0.3 Overview
• 0.4 Note on precalculated argument
• 0.5 Note on pcutoff argument
• 0.6 Note on multianalyze
• 0.7 Citation
• 0.8 Example without multianalyze
• 0.9 Example with multianalyze
• 0.10 Figure examples
  • 0.10.1 Modules
  • 0.10.2 Enrichment
  • 0.10.3 Pathways shared by tissues
  • 0.10.4 LD
  • 0.10.5 Chromosomal interactions
  • 0.10.6 Chromatin states
  • 0.10.7 eQTLs
  • 0.10.8 Gene expression

0.1 Change log

v.1.1.3

• Added support for different P-value cut-offs in multi-analyze (see below)

v.1.1.2

• Allow users to use precalculated SNPs in GTEx instead of calculating them all.

• Added overlap tab of QTL to show overlap between types of QTLs

0.2 Installation

0.2.1 Install the depencies

depp <- c("BioCircos","cluster","ggplot2","enrichR","htmlwidgets",
          "rio","shiny","shinycssloaders","stringr","viridis",
          "DT","coloc","curl","dplyr","grDevices","jsonlite","plotly",
          "shinyjs","reshape2","shinythemes","stats","purrr","readr","UpSetR")

BioDepp <- c("IRanges","BiocGenerics","clusterProfiler","GenomicRanges")


# Check present packages
depp.new<-depp[!(depp%in%installed.packages())]
if (length(depp.new)) {
  install.packages(depp.new)
}
# Bioconductor
BioDepp.new<-BioDepp[!(BioDepp%in%installed.packages())]
if (length(BioDepp.new)) {
  if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
  BiocManager::install(BioDepp, type="source")
}
# load packages
sapply(depp, library, character.only = TRUE)
sapply(BioDepp, library, character.only = TRUE)

0.2.2 Install CONQUER

Install the the development version from GitLab:

install.packages("devtools")

# Install CoNQUER.db
install.packages("https://github.com/roderickslieker/CONQUER.db/releases/download/v0.1.2/conquer.db_0.1.2.tar.gz", type="source", repos=NULL)
devtools::install_github("roderickslieker/GTEx.Data")

#Install other two dependencies
devtools::install_github("roderickslieker/CONQUER.d3")
devtools::install_github("roderickslieker/CONQUER")

0.3 Overview

With the use of two functions, SNPs are summarised and visualised, namely: summarise() and visualise().

• The summarise() function is used to collect all data related to SNPs.
• The visualise() function initiates a RStudio Shiny-based dashboard that visualises all relevant plots.

Note: We use the LD data from the API of NIH. You will need to register on the site to obtain a token. Please see:

https://ldlink.nci.nih.gov/?tab=apiaccess

The token is send by email and can be provided as character string.

0.4 Note on precalculated argument

To also allow faster pre-process of SNPs, we also allow users to only take the precalculated eQTLs from GTEx. Pros: much faster to summarize. Cons: you will miss interesting QTLs from your results because GTEx only includes genes with TSSs 1Mb from the SNP. So, when precalculated is FALSE the GTEx API will be used to test the lead SNP against genes in cis and trans.

0.5 Note on pcutoff argument

Given that the numbers of individuals per tissue vary in GTEx one may be interested to analyse the data with a more liberal P-value to adjust for the strong correlation between the number of eQTLs and the number of individuals in that dataset. As such the package allows to analyze with three different settings:

• stringent : only allow eQTLs that match the pValue threshold as defined by GTEx for SNP

• liberal : P<0.001 and P-value at max 2x higher than GTEx’s threshold.

• veryliberal : All eQTLs that are nominally significant P<0.05.

0.6 Note on multianalyze

Multianalyze works best if the number of SNPs is large (for example >50 SNPs). Lower numbers may result in spurious results. Instead one should focus on the single SNPs.

0.7 Citation

CONQUER: an interactive toolbox to understand functional consequences of GWAS hits.

Gerard A Bouland, Joline W J Beulens, Joey Nap, Arno R van der Slik, Arnaud Zaldumbide, Leen M ’t Hart, Roderick C Slieker

NAR Genomics and Bioinformatics, Volume 2, Issue 4, December 2020, lqaa085, https://doi.org/10.1093/nargab/lqaa085

0.8 Example without multianalyze

DIR <- "somedirectory"

library(CONQUER)

summarize(variants = c("rs878521","rs10830963"),
          directory=DIR,
          precalculated = TRUE, 
          multiAnalyze=FALSE,
          token="sometoken",
          tissues=NULL)

0.9 Example with multianalyze

The available tissues can be viewed with the following command:

tissues <- conquer.db::gtexTissuesV8

The summary files from the example below can also be obtained from https://github.com/roderickslieker/CONQUER.test/tree/master/Test

library(CONQUER)
snps <- c("rs11642430","rs11820019","rs11842871","rs13426680","rs1377807","rs1783541",
"rs1801212","rs1801645","rs2268078","rs2581787","rs34855406","rs3802177",
"rs3810291","rs4148856","rs5213","rs6011155","rs601945","rs75423501",
"rs8010382","rs8046545")

CONQUER::summarize(variants = snps,
          directory=DIR,
          multiAnalyze=TRUE,
          precalculated = TRUE, 
          token=NULL,
          tissues=c("Pancreas","Muscle_Skeletal","Liver"))


visualize(directory = "somedirectory", SNPs = snps)

0.10 Figure examples

0.10.1 Modules

 

0.10.2 Enrichment

 

0.10.3 Pathways shared by tissues

 

0.10.4 LD

 

0.10.5 Chromosomal interactions

 

0.10.6 Chromatin states

 

0.10.7 eQTLs

 

0.10.8 Gene expression