Supplementary codes and tables for BiRS

• Previous data published in UK Biobank were used for this work and this research has been conducted using UK Biobank Resource under project 79237. One can refer to https://biobank.ctsu.ox.ac.uk/crystal/index.cgi for accessing and enabling data download or detailed description of UKB data.
• PLINK2.0 was used to pre-treatment UKB data, see https://www.cog-genomics.org/plink/2.0/ for detailed manuls of using PLINK2.0.

• GWAS-BiRS.csv: GWAS results of BiRS
• GWAS-Scan.csv: GWAS results of Q-SCAN
• GWAS-KS.csv: GWAS results of KnockoffScreen
• GWAS-REGENIE.csv: GWAS results of regenie

1. Package BiRS contains the main functions of BiRS algorithm and DCF test. One can install the package BiRS through the file BiRS_0.1.0.tar.gz.

2. Directory Simulation_BiRS contains the main functions for the simulations. Specifically,

• The R script KSDet contains the code for applying KnockoffScreen method to normal setting in simulation.

• The R script Speed-Test contains the code for calculating computational times of BiRS-DCF, DCF-SCAN, Q-SCAN and KnockoffScreen.

• The R script Size-Test/Size-Test contains the contains the code for generating data and conducting simulation for size validation of BiRS-DCF, BiRS-CL and Q-SCAN in all settings.

• The R scripts Size-Test/Describe_Size contains the code for generating tables of the size validation in all settings.

• The R script M-dependence/Normal_ESBA contains the code for generating data and conducting simulation for ''M-dependence with equal covariance and non-decay signal'' setting using BiRS-DCF, BiRS-CL, Q-SCAN and KnockoffScreen.

• The R script M-dependence/Normal_NSBA contains the code for generating data and conducting simulation for ''M-dependence with unequal covariance and non-decay signal'' setting using BiRS-DCF, BiRS-CL, Q-SCAN and KnockoffScreen.

• The R script M-dependence/Normal_ESBA_Decay conntains the code for generating data and conducting simulation for ''M-dependence with equal covariance and decay signal'' setting using BiRS-DCF, BiRS-CL, Q-SCAN and KnockoffScreen.

• The R script M-dependence/Normal_NSBA_Decay contains the code for generating data and conducting simulation for ''M-dependence with unequal covariance and decay signal'' setting using BiRS-DCF, BiRS-CL, Q-SCAN and KnockoffScreen.

• The R scripts M-dependence/Describe and M-dependence/Describe_Decay contain the code for generating description information of the detection results under M-dependence covariance structure.

• The R scripts M-dependence/Organize and M-dependence/Organize_Decay contain the code for generating figures and tables of the detection results under M-dependence covariance structure.

• The R script Weak-dependence/Normal_ESBA contains the code for generating data and conducting simulation for ''Weak-dependence with equal covariance and non-decay signal'' setting using BiRS-DCF, BiRS-CL, Q-SCAN and KnockoffScreen.

• The R script Weak-dependence/Normal_NSBA contains the code for generating data and conducting simulation for ''Weak-dependence with unequal covariance and non-decay signal'' setting using BiRS-DCF, BiRS-CL, Q-SCAN and KnockoffScreen.

• The R script Weak-dependence/Normal_ESBA_Decay conntains the code for generating data and conducting simulation for ''Weak-dependence with equal covariance and decay signal'' setting using BiRS-DCF, BiRS-CL, Q-SCAN and KnockoffScreen.

• The R script Weak-dependence/Normal_NSBA_Decay contains the code for generating data and conducting simulation for ''Weak-dependence with unequal covariance and decay signal'' setting using BiRS-DCF, BiRS-CL, Q-SCAN and KnockoffScreen.

• The R scripts Weak-dependence/Describe and Weak-dependence/Describe_Decay contain the code for generating description information of the detection results under Weak-dependence covariance structure.

• The R scripts Weak-dependence/Organize and Weak-dependence/Organize_Decay contain the code for generating figures and tables of the detection results under Weak-dependence covariance structure.

• The R script KSDetGenetic contains the code for applying KnockoffScreen method to genetic setting in simulation.

• The R script Genetic_Setting/Genetic_Setting contains the code for generating data and conducting simulation for genetic setting using BiRS-DCF, BiRS-CL, Q-SCAN and KnockoffScreen.

• The R scripts Weak-dependence/Describe and Weak-dependence/Describe_Decay contain the code for generating description information of the detection results under Weak-dependence covariance structure.

• The R scripts Weak-dependence/Organize and Weak-dependence/Organize_Decay contain the code for generating figures and tables of the detection results under Weak-dependence covariance structure.

3. Directory Application_Code contains the main functions for quality control and performing GWAS on UK Biobank data. Specifically,

• The R scripts Pretreatment/CTtraits and Pretreatment/covariate generate samples and covariates we needed from the main dataset of UK Biobank.
• The R scripts Pretreatment/SampleQC and Pretreatment/VariantQC perform the sample and variant quality control after generating .vcf file for each chromosome using PLINK 2.0 (the quality control on HWE and MAF performed at the same time as the .vcf file be generated)
• The R script Pretreatment/SampleGLM generates data for Q-SCAN to perform GWAS.
• The R script Pretreatment/GenerateFinalVariant generates the information for the selected variants for performing GWAS.
• The R script Pretreatment/SizeSampleGenerator generates the samples for performing permutation test in each chromosome.
• The R scripts BiRS/BiRSGenomeSize, QSCAN/QScanGenomeSize and BiRS/BiCLGenomeSize perform permutation tests of BiRS-DCF, BiRS-CL and QSCAN.
• The R scripts BiRS/BiRSGenome, QSCAN/QScanGenome and KnockoffScreen/KSGWAS perform GWAS on C50 Malignant neoplasms of breast using UK Biobank data by BiRS-DCF, QSCAN and KnockoffScreen.
• The R script sh_file_generator generates .sh file for performing GWAS on C50 Malignant neoplasms of breast using UK Biobank data using regenie.
• The .sh files fit.sh and test.sh perform GWAS on C50 Malignant neoplasms of breast using UK Biobank data using regenie.
• The .csv files C50Breast_BiRS.csv, C50Breast_Scan, C50Breast_KSGWAS and C50Breast_Regenie are GWAS results of the four methods, which are organized for plotting signal regions.
• The .csv files C50Breast_chr11_gene.csv, C50Breast_chr12_gene.csv and C50Breast_chr19_gene.csv are genomic locations of certain genes in chromosomes 11, 12 and 19, respectively.
• The R script plot_gene plots identified signal regions of the four methods.

Overall, please first install the BiRS package through BiRS_0.1.0.tar.gz.

1. Please create folders Simulation_BiRS/M-dependence/Normal_ESBA/ and Simulation_BiRS/M-dependence/Normal_NSBA/ for saving the simulation results in normal setting with M-dependence covariance structure generated by Simulation_BiRS/M-dependence/Normal_ESBA.R and Simulation_BiRS/M-dependence/Normal_NSBA.R, respectively, (the code results are saved as .RData files). Then run Simulation_BiRS/M-dependence/Normal_ESBA.R and Simulation_BiRS/M-dependence/Normal_NSBA.R.
2. Run Simulation_BiRS/M-dependence/Describe.R for generating description information of the detection results under M-dependence covariance structure.
3. Create folder Simulation_BiRS/M-dependence/Figures/ for saving the figures of the detection results under M-dependence covariance structure then run Simulation_BiRS/M-dependence/Organize.R to generate them.

1. Please create folders Simulation_BiRS/Weak-dependence/Normal_ESBA/ and Simulation_BiRS/Weak-dependence/Normal_NSBA/ for saving the simulation results in normal setting with Weak-dependence covariance structure generated by Simulation_BiRS/Weak-dependence/Normal_ESBA.R and Simulation_BiRS/Weak-dependence/Normal_NSBA.R, respectively, (the code results are saved as .RData files). Then run Simulation_BiRS/Weak-dependence/Normal_ESBA.R and Simulation_BiRS/Weak-dependence/Normal_NSBA.R.
2. Run Simulation_BiRS/Weak-dependence/Describe.R for generating description information of the detection results under Weak-dependence covariance structure.
3. Create folder Simulation_BiRS/Weak-dependence/Figures/ for saving the figures of the detection results under Weak-dependence covariance structure then run Simulation_BiRS/Weak-dependence/Organize.R to generate them.

1. Run plink2 in command line to get the 100 loci from UK Biobank imputed data, example code:

plink --bfile /zhw/ukb/genotype/ukb_imp_merged_v5 --keep /zhw/projects/BiRS/brifile.100000.txt --extract /zhw/projects/BiRS/10000variant.id_british.txt --make-bed --out /zhw/projects/BiRS/ukb_british_100000sample_10000SNP

2. Run Simulation_BiRS/Genetic_Setting/get_correlation_100loci.R to get the Pearson correlation matrix for simulation.
3. Run Simulation_BiRS/Genetic_Setting/Genetic_Setting.R to generate the detection results under genetic setting.
4. Run Simulation_BiRS/Genetic_Setting/Describe_Genetic.R for generating description information of the detection results under genetic setting.
5. Create folder Simulation_BiRS/Genetic_Setting/Figures/ for saving the figures of the detection results under genetic setting then run Simulation_BiRS/Genetic_Setting/Organize_Genetic.R to generate them.

1. Run Simulation_BiRS/Size-Test/Size-Test.R to generate the empirical FWERs of different methods for different settings (saving as .RData file).
2. Run Simulation_BiRS/Size-Test/Describe_Size.R for generating table of the empirical FWERs of different methods for different settings, which is the Table 1 in the main paper.

1. Run Simulation_BiRS/Speed-test.R to generate the computational time for different methods.

1. Please make sure that you can access the ukbiobank main dataset contains the fields list in Application_Code/Pretreatment/CTtraits.R, put them in the suitable directory (one can create the directories in my codes or use its own directory then change the directories in my codes to its own). Then run Application_Code/Pretreatment/CTtraits.R to generate the .RData file containing phenotypes and covariates.
2. Run Application_Code/Pretreatment/covariate.R to generate covariates used for Q-SCAN, REGENIE and KnockoffScreen.
3. Please make sure that you can access the ukbiobank genomic field 22418 then generating .vcf file for each chromosome using PLINK 2.0 (the quality control on HWE and MAF performed at the same time as the .vcf file be generated), saving in your own directory or the directory I give in the code.
4. Run Application_Code/Pretreatment/SampleQC.R and Application_Code/Pretreatment/VariantQC.R perform further sample and variant quality control on the .vcf file generated prerviously.
5. Run Application_Code/Pretreatment/GenerateFinalVariant.R to generate the information for the selected variants after sample and variant QC for performing GWAS.
6. Run Application_Code/Pretreatment/SampleGLM.R to generate data for Q-SCAN to perform GWAS.
7. Run Application_Code/Pretreatment/SizeSampleGenerator.R to generate the samples for performing permutation test in each chromosome.

1. Run Application_Code/BiRS/BiCLGenomeSize.R and Application_Code/BiRS/BiRSGenomeSize.R to perform permutation test for calculating empirical FWERs in each chromosome for BiRS-DCF and BiRS-CL.
2. Run Application_Code/Q-SCAN/QScanGenomeSize.R to perform permutation test for calculating empirical FWER in each chromosome for Q-SCAN.

1. Run Application_Code/REGENIE/sh_file_generator.R to generate .sh files for perform indvidual GWAS in each chromosome.
2. Run Application_Code/REGENIE/fit.sh to fit the regression model for generating test statistics.
3. Run Application_Code/REGENIE/test.sh to calculate the p-values of each snp then select the significant snps contained in GWAS-REGENIE.csv.
4. Run Application_Code/KnockoffScreen/KSGWAS.R to perform region-wise GWAS on the genomic data using KnockoffScreen and generate the GWAS results contained in GWAS-KS.csv.
5. Run Application_Code/QSCAN/QScanGenome.R to perform region-wise GWAS on the genomic data using Q-SCAN and generate the GWAS results contained in GWAS-Scan.csv.
6. Run Application_Code/BiRS/BiRSGenome.R to perform region-wise GWAS on the genomic data using BiRS-DCF and generate the GWAS results contained in GWAS-BiRS.csv.

1. Please make sure that the .csv files C50Breast_BiRS.csv, C50Breast_Scan, C50Breast_KSGWAS, C50Breast_Regenie, C50Breast_chr11_gene.csv, C50Breast_chr12_gene.csv, C50Breast_chr19_gene.csv and Rscript plot_gene in the same directory.
2. Run plot_gene to visualize the detected signal regions and related genes.