ukbpheno

ukbpheno_concept

Description

ukbpheno is an R package for efficiently munging the files provided by UK Biobank to generate data tables of with unified format for further analysis such as making dichotomous phenotypes for UKbio and a composite time-to-event variable combining record level data (HESIN/GP/cancer registry) and main dataset (self reports i.e. nurse interview / touchscreen). Aim of the package is to define binary phenotype data for different types of longitudinal data analysis (e.g. GWAS analysis, cox regressions, baseline tables) in a standardized and reproducible manner. The package can also be used for data exploration with efficient subsetting of the main dataset and visualization functions.

Please check out the wiki for short tutorials on downloading the data as well as usage of the package.

Installation

devtools::install_github("niekverw/ukbpheno")

Basic Usage

library(data.table)
library(ukbpheno)

# the directory with datafiles
pheno_dir <-"mydata/ukb99999/"

# main dataset 
fukbtab <- paste(pheno_dir,"ukb99999.tab",sep="")

# meta data file
fhtml <- paste(pheno_dir,"ukb99999.html",sep="")

# hospital inpatient data
fhesin <- paste(pheno_dir,"hesin.txt",sep="")
fhesin_diag <- paste(pheno_dir,"hesin_diag.txt",sep="")
fhesin_oper <- paste(pheno_dir,"hesin_oper.txt",sep="")

# GP data
fgp_clinical <- paste(pheno_dir,"gp_clinical.txt",sep="")
fgp_scripts <- paste(pheno_dir,"gp_scripts.txt",sep="")

# harmonize the data without any definition
lst.harmonized.data<-harmonize_ukb_data(f.ukbtab = fukbtab,f.html = fhtml,f.gp_clinical = fgp_clinical,f.gp_scripts = fgp_scripts,f.hesin = fhesin,f.hesin_diag = fhesin_diag,f.hesin_oper=fhesin_oper,allow_missing_fields = TRUE)

Ascertainment of health outcomes

Health outcomes are ascertained using data from linkage with national registries (e.g. primary /secondary care) or self report. Full definitions are described in https://bit.ly/3KrMsYD

Coronary artery disease (doi: 10.1161/CIRCRESAHA.117.312086)
- Ischemic heart diseases diagnosis codes
- Myocardial infarction diagnosis codes
- Coronary Artery Bypass Graft operation codes
- Percutaneous Coronary Intervention operation codes
Heart failure due to ischemia vs no heart failure after ischemia
- heart failure among participants with coronary artery disease
- exclude any participant with cardiomyopathy diagnosis from controls

# definition table included in the package 
fdefinitions <- system.file("extdata", "definitions_cardiometabolic_traits.tsv", package="ukbpheno")
# data setting file included in the package
fdata_setting <- system.file("extdata", "data.settings.tsv", package="ukbpheno")
dfData.settings <-fread(fdata_setting)
# process the definition table based on data setting
dfDefinitions_processed_expanded<-read_defnition_table(fdefinitions,fdata_setting,dir.code.map=system.file("extdata", package="ukbpheno"))
# harmonize data
lst.harmonized.data<-harmonize_ukb_data(f.ukbtab = fukbtab,f.html = fhtml,dfDefinitions=dfDefinitions_processed_expanded,f.gp_clinical = fgp_clinical,f.gp_scripts = fgp_scripts,f.hesin = fhesin,f.hesin_diag = fhesin_diag,f.hesin_oper=fhesin_oper,allow_missing_fields = TRUE)

# to identify cases/controls status for CAD  
trait<-"Cad"
df_reference_dt_v0<-lst.harmonized.data$dfukb[,c("identifier","f.53.0.0")]
# read withdrawal list, individuals to be removed from the analysis
f_particip_withdraw<-paste(pheno_dir,"w12345_20210809.csv",sep="")
df_withdrawal<-fread(f_particip_withdraw)
df_reference_dt_v0<-df_reference_dt_v0[! identifier  %in% df_withdrawal$V1]
lst.Cad.case_control <- get_cases_controls(definitions=dfDefinitions_processed_expanded %>% filter(TRAIT==trait), lst.harmonized.data$lst.data,dfData.settings, df_reference_date=df_reference_dt_v0)
# summary of diagnosis per participant including case/control status before/after the reference date (baseline visit) as well as the corresponding time-to-event information
View(lst.Cad.case_control$df.casecontrol)

# HF in CAD
trait<-"HfInCad"

# the reference date is the date of diagnosis of CAD
lst.HfInCad.case_control <- get_cases_controls(definitions=dfDefinitions_processed_expanded %>% filter(TRAIT==trait), lst.harmonized.data$lst.data,dfData.settings, vct.identifiers=df_reference_dt_v0$identifier)

dotplot4readme

Figure above: Relative contribution of different data sources to selected cardiovascular diseases

Code lookup with shiny app

Required:

the code maps (Excel workbook) provided by UK Biobank Showcase Resource 592.
R library "optparse"

cd ../ukbpheno/inst/util
# show input options 
Rscript shiny.lookup_codes.R --help
# to start the app
Rscript shiny.lookup_codes.R --fcoding_xls path_to_download/all_lkps_maps_v3.xlsx

Citation

If you use ukbpheno, please cite Yeung, M. W., van der Harst, P., & Verweij, N. (2022). ukbpheno v1.0: An R package for phenotyping health-related outcomes in the UK Biobank. STAR Protocols, 3(3), 101471.

niekverw / ukbpheno

readme