R/data.R

In qingyuanzhao/mr.raps: Two Sample Mendelian Randomization using Robust Adjusted Profile Score

#' Effect of Body Mass Index (BMI) on Systolic Blood Pressure (SBP)
#'
#' Summary data obtained by combining three genome-wide association studies:
#' \enumerate{
#' \item{BMI-FEM:}{ BMI in females by the Genetic Investigation of ANthropometric Traits (GIANT) consortium (sample size: 171977).}
#' \item{BMI-MAL:}{ BMI in males in the same study by the GIANT consortium (sam- ple size: 152893)}
#' \item{SBP-UKBB:}{ SBP using the United Kingdom BioBank (UKBB) data (sample size: 317754)}
#' }
#'
#' The BMI-FEM dataset is used for SNP selection (column \code{pval.selection}). The BMI-MAL dataset estimates the SNPs' effect on BMI and the SBP-UKBB dataset estimates the SNPs' on SBP.
#'
#' @docType data
#'
#' @usage data(bmi.sbp)
#'
#' @format A \code{data.frame} with 160 rows and 29 variables.
#'
#' @keywords datasets
#'
#'
"bmi.sbp"

#' "Effect" of Body Mass Index (BMI) on Body Mass Index (BMI)
#'
#' Summary data obtained by combining three genome-wide association studies:
#' \enumerate{
#' \item{BMI-GIANT:}{ BMI in the Genetic Investigation of ANthropometric Traits (GIANT) consortium (sample size: 339224).}
#' \item{BMI-UKBB-1:}{ BMI in a half of the United Kingdom BioBank (UKBB) data (sample size: 234070)}
#' \item{SBP-UKBB-2:}{ BMI in the other half of the UKBB data (sample size: 234070)}
#' }
#'
#' The BMI-GIANT dataset is used for SNP selection (column \code{pval.selection}). The BMI-UKBB-1 dataset estimates the SNPs' effects on BMI (columns \code{beta.exposure} and \code{se.exposure}) and the BMI-UKBB-2 dataset provides independent estimates of the same effects (columns \code{beta.outcome} and \code{se.outcome}).
#'
#' @docType data
#'
#' @usage data(bmi.bmi)
#'
#' @format A \code{data.frame} with 812 rows and 28 variables.
#'
#' @keywords datasets
#'
#'
"bmi.bmi"

#' Effect of blood lipids (LDL cholesterol, HDL cholesterol, Triglycerides) on Cardiovascular disease risk
#'
#' This dataset is created from four genome-wide association studies:
#' \enumerate{
#' \item A 2010 GWAS of blood lipids (Teslovich et al.\, 2010), named "teslovich_2010" in the dataset.
#' \item The MetaboChip (MC) data in a 2013 GWAS of blood lipids (Willer et al.\, 2013), named "mc_2013" in the dataset.
#' \item The CARDIoGRAMplusC4D meta-analysis of coronary artery disease (CARDIoGRAMplusC4D Consortium, 20135, named "cardiogramplusc4d_1000genome" in the dataset.
#' \item The UK BioBank GWAS of self reported heart attack (interim release by the Neale lab), named "ukbb_6150_round2" in the dataset.
#' }
#'
#' \code{lipid.cad} contains in total 24 sub-datasets, each is suitable for a Mendelian randomization study. To obtain a sub-dataset, you must decide on
#' \describe{
#' \item{lipid}{Which lipid trait to consider? Either \code{ldl}, \code{hdl}, or \code{tg}.}
#' \item{gwas.selection}{Which GWAS is used for selection? Either \code{teslovich_2010} or \code{mc_2013}.}
#' \item{gwas.exposure}{Which GWAS is used for exposure? Either \code{teslovich_2010} or \code{mc_2013} and must be different from \code{gwas.selection}.}
#' \item{gwas.outcome}{Which GWAS is used for outcome? Either \code{cardiogramplusc4d} or \code{ukbb_self_report_heart}.}
#' \item{restrict}{Should we use SNPs that are not associated with the other lipids? For example, if we are studying the effect of HDL cholesterol (so \code{lipid} is "hdl") and \code{restrict} is TRUE, then the SNPs are not associated with LDL cholesterol and triglycerides (p-value > 0.01 in the \code{gwas.selection} data).}
#' }
#'
#' @docType data
#'
#' @usage data(lipid.cad)
#'
#' @format A \code{data.frame} with 12026 rows and 24 variables.
#'
#' @keywords datasets
#'
#'
"lipid.cad"

#' Effect of C-Reactive Protein on Coronary Artery Disease (CAD)
#'
#' This dataset is created from three genome-wide association studies:
#' \enumerate{
#' \item Prins et al.\ (2017) GWAS of CRP.
#' \item Dehghan et al.\ (2011) GWAS of CRP
#' \item The CARDIoGRAMplusC4D GWAS of CAD.
#' }
#' To obtain this dataset, the Prins study is used for SNP selection (column \code{pval.selection}). The Dehghan dataset estimates the SNPs' effect on CRP and the CARDIoGRAMplusC4D dataset estimates the SNPs' on CAD.
#'
#' @docType data
#'
#' @usage data(crp.cad)
#'
#' @format A \code{data.frame} with 1575 rows and 30 variables.
#'
#' @keywords datasets
#'
#'
"crp.cad"

#' Effect of Coronary Artery Disease (CAD) on Body Mass Index (BMI) (To study reverse causality)
#'
#' This dataset is created from three genome-wide association studies:
#' \enumerate{
#' \item The C4D GWAS of CAD.
#' \item The CARDIoGRAM GWAS of CAD.
#' \item The UK BioBank GWAS of BMI (2nd round release by the Neale lab).
#' }
#' To obtain this dataset, the C4D study is used for SNP selection (column \code{pval.selection}). The CARDIoGRAM dataset estimates the SNPs' effect on CAD and the UK BioBank dataset estimates the SNPs' on BMI.
#'
#'
#' @docType data
#'
#' @usage data(cad.bmi)
#'
#' @format A \code{data.frame} with 1625 rows and 34 variables.
#'
#' @keywords datasets
#'
#'
"cad.bmi"

#' Effect of Body Mass Index (BMI) on Coronary Artery Disease (CAD)
#'
#' This dataset is created from three genome-wide association studies:
#' \enumerate{
#' \item A 2017 GWAS of BMI by Akiyama et al.
#' \item The UK BioBank GWAS of BMI (2nd round release by the Neale lab).
#' \item The CARDIoGRAMplusC4D with 1000 Genome Project imputation GWAS of CAD.
#' }
#' To obtain this dataset, the Akiyama study is used for SNP selection (column \code{pval.selection}). The UK BioBank dataset estimates the SNPs' effect on BMI and the CARDIoGRAMplusC4D estimates the SNPs' effect on CAD.
#'
#'
#' @docType data
#'
#' @usage data(bmi.cad)
#'
#' @format A \code{data.frame} with 1119 rows and 42 variables.
#'
#' @keywords datasets
#'
#'
"bmi.cad"

#' Effect of Body Mass Index (BMI) on Acute Ischemic Stroke (AIS)
#'
#' This dataset is created from three genome-wide association studies:
#' \enumerate{
#' \item A 2017 GWAS of BMI by Akiyama et al.
#' \item The UK BioBank GWAS of BMI (2nd round release by the Neale lab).
#' \item A 2018 GWAS of AIS by Malik et al.
#' }
#' To obtain this dataset, the Akiyama study is used for SNP selection (column \code{pval.selection}). The UK BioBank dataset estimates the SNPs' effect on BMI and the Malik dataset estimates the SNPs' effect on AIS.
#'
#'
#' @docType data
#'
#' @usage data(bmi.ais)
#'
#' @format A \code{data.frame} with 1880 rows and 29 variables.
#'
#' @keywords datasets
#'
#'
"bmi.ais"

#' Effect of Coronary Artery Disease (BMI) on Coronary Artery Disease (CAD)
#'
#' This dataset is created from three genome-wide association studies:
#' \enumerate{
#' \item The UK BioBank GWAS of heart attack (2nd round release by the Neale lab).
#' \item The C4D GWAS of CAD.
#' \item The CARDIoGRAM GWAS of CAD.
#' }
#' This dataset serves the purpose of a validation study. Since both the "exposure" and the "outcome" are CAD, the joint normal model of the GWAS summary statistics is expected to hold with no pleiotropy and "causal effect" equal to 1.
#'
#' @docType data
#'
#' @usage data(cad.cad)
#'
#' @format A \code{data.frame} with 1650 rows and 39 variables.
#'
#' @keywords datasets
#'
#'
"cad.cad"