| Title: | Genotype-by-Environment Interaction in Polygenic Score Models |
|---|---|
| Description: | A novel PRS model is introduced to enhance the prediction accuracy by utilising GxE effects. This package performs Genome Wide Association Studies (GWAS) and Genome Wide Environment Interaction Studies (GWEIS) using a discovery dataset. The package has the ability to obtain polygenic risk scores (PRSs) for a target sample. Finally it predicts the risk values of each individual in the target sample. Users have the choice of using existing models (Li et al., 2015) <doi:10.1093/annonc/mdu565>, (Pandis et al., 2013) <doi:10.1093/ejo/cjt054>, (Peyrot et al., 2018) <doi:10.1016/j.biopsych.2017.09.009> and (Song et al., 2022) <doi:10.1038/s41467-022-32407-9>, as well as newly proposed models for genomic risk prediction (refer to the URL for more details). |
| Authors: | Dovini Jayasinghe [aut, cre, cph], Hong Lee [aut, cph], Moksedul Momin [aut, cph] |
| Maintainer: | Dovini Jayasinghe <[email protected]> |
| License: | GPL (>=3) |
| Version: | 1.2 |
| Built: | 2025-02-20 05:14:23 UTC |
| Source: | https://github.com/dovinij/gxeprs |
Covariate data file of the discovery dataset when the outcome is binary. This contains covariate information of the individuals in the discovery dataset following confounders.
Bcov_discoveryBcov_discovery
A dataframe with 800 rows and 18 columns
Family ID
Individual ID
Standardized covariate
Square of the standardized covariate
Confounder 1
Confounder 2
Confounder 3
Confounder 4
Confounder 5
Confounder 6
Confounder 7
Confounder 8
Confounder 9
Confounder 10
Confounder 11
Confounder 12
Confounder 13
Confounder 14
Covariate data file of the target dataset when the outcome is binary. This contains covariate information of the individuals in the target dataset following confounders.
Bcov_targetBcov_target
A dataframe with 200 rows and 18 columns
Family ID
Individual ID
Standardized covariate
Square of the standardized covariate
Confounder 1
Confounder 2
Confounder 3
Confounder 4
Confounder 5
Confounder 6
Confounder 7
Confounder 8
Confounder 9
Confounder 10
Confounder 11
Confounder 12
Confounder 13
Confounder 14
Phenotype data file of the discovery dataset when the outcome is binary. This contains phenotype information of the individuals in the discovery dataset.
Bphe_discoveryBphe_discovery
A dataframe with 800 rows and 3 columns
Family ID
Individual ID
Phenotype (1=controls, 2=cases)
Phenotype data file of the target dataset when the outcome is binary. This contains phenotype information of the individuals in the target dataset.
Bphe_targetBphe_target
A dataframe with 200 rows and 3 columns
Family ID
Individual ID
Phenotype (0=controls, 1=cases)
PLINK .bim file
DummyData.bimDummyData.bim
This follows PLINK general format
Chromosome ID
SNP ID
Position of centimorgans
Base-pair coordinate
Minor Allele
Reference Allele
PLINK .fam file
DummyData.famDummyData.fam
This follows PLINK general format
Family ID
Individual ID
Father's ID
Mother's ID
Sex
Phenotype value
PLINK .map file
DummyData.mapDummyData.map
This follows PLINK general format
PLINK .ped file
DummyData.pedDummyData.ped
This follows PLINK general format
GWAS_binary function This function performs GWAS using plink2 and outputs the GWAS summary statistics with additive SNP effects. Users may save the output in a user-specified file (see example).
GWAS_binary(plink_path, b_file, Bphe_discovery, Bcov_discovery, thread = 20)GWAS_binary(plink_path, b_file, Bphe_discovery, Bcov_discovery, thread = 20)
plink_path |
Path to the PLINK executable application |
b_file |
Prefix of the binary files, where all .fam, .bed and .bim files have a common prefix |
Bphe_discovery |
Name (with file extension) of the phenotype file containing family ID, individual ID and phenotype of the discovery dataset as columns, without heading |
Bcov_discovery |
Name (with file extension) of the covariate file containing family ID, individual ID, standardized covariate, square of standardized covariate, and/or confounders of the discovery dataset as columns, without heading |
thread |
Number of threads used |
This function will perform GWAS and output
B_out.trd.sum |
GWAS summary statistics with additive SNP effects |
## Not run: x <- GWAS_binary(plink_path, DummyData, Bphe_discovery, Bcov_discovery, thread = 20) sink("B_out.trd.sum") #to create a file in the working directory write.table(x[c("ID", "A1", "BETA")], sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output head(x) #to obtain the head of GWAS summary statistics of additive SNP effects x$CHROM #to extract the chromosome number x$POS #to extract the base pair position x$ID #to extract the SNP ID x$REF #to extract the reference allele x$ALT #to extract the alternate allele x$A1 #to extract the minor allele x$OBS_CT #to extract the number of allele observations x$BETA #to extract the SNP effects x$SE #to extract the standard errors of the SNP effects x$Z_STAT #to extract the test statistics x$P #to extract the p values ## End(Not run)## Not run: x <- GWAS_binary(plink_path, DummyData, Bphe_discovery, Bcov_discovery, thread = 20) sink("B_out.trd.sum") #to create a file in the working directory write.table(x[c("ID", "A1", "BETA")], sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output head(x) #to obtain the head of GWAS summary statistics of additive SNP effects x$CHROM #to extract the chromosome number x$POS #to extract the base pair position x$ID #to extract the SNP ID x$REF #to extract the reference allele x$ALT #to extract the alternate allele x$A1 #to extract the minor allele x$OBS_CT #to extract the number of allele observations x$BETA #to extract the SNP effects x$SE #to extract the standard errors of the SNP effects x$Z_STAT #to extract the test statistics x$P #to extract the p values ## End(Not run)
GWAS_quantitative function This function performs GWAS using plink2 and outputs the GWAS summary statistics with additive SNP effects. Users may save the output in a user-specified file (see example).
GWAS_quantitative( plink_path, b_file, Qphe_discovery, Qcov_discovery, thread = 20 )GWAS_quantitative( plink_path, b_file, Qphe_discovery, Qcov_discovery, thread = 20 )
plink_path |
Path to the PLINK executable application |
b_file |
Prefix of the binary files, where all .fam, .bed and .bim files have a common prefix |
Qphe_discovery |
Name (with file extension) of the phenotype file containing family ID, individual ID and phenotype of the discovery dataset as columns, without heading |
Qcov_discovery |
Name (with file extension) of the covariate file containing family ID, individual ID, standardized covariate, square of standardized covariate, and/or confounders of the discovery dataset as columns, without heading |
thread |
Number of threads used |
This function will perform GWAS and output
Q_out.trd.sum |
GWAS summary statistics with additive SNP effects |
## Not run: x <- GWAS_quantitative(plink_path, DummyData, Qphe_discovery, Qcov_discovery, thread = 20) sink("Q_out.trd.sum") #to create a file in the working directory write.table(x[c("ID", "A1", "BETA")], sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output head(x) #to obtain the head of GWAS summary statistics of additive SNP effects x$CHROM #to extract the chromosome number x$POS #to extract the base pair position x$ID #to extract the SNP ID x$REF #to extract the reference allele x$ALT #to extract the alternate allele x$A1 #to extract the minor allele x$OBS_CT #to extract the number of allele observations x$BETA #to extract the SNP effects x$SE #to extract the standard errors of the SNP effects x$T_STAT #to extract the test statistics x$P #to extract the p values ## End(Not run)## Not run: x <- GWAS_quantitative(plink_path, DummyData, Qphe_discovery, Qcov_discovery, thread = 20) sink("Q_out.trd.sum") #to create a file in the working directory write.table(x[c("ID", "A1", "BETA")], sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output head(x) #to obtain the head of GWAS summary statistics of additive SNP effects x$CHROM #to extract the chromosome number x$POS #to extract the base pair position x$ID #to extract the SNP ID x$REF #to extract the reference allele x$ALT #to extract the alternate allele x$A1 #to extract the minor allele x$OBS_CT #to extract the number of allele observations x$BETA #to extract the SNP effects x$SE #to extract the standard errors of the SNP effects x$T_STAT #to extract the test statistics x$P #to extract the p values ## End(Not run)
GWEIS_binary function This function performs GWEIS using plink2 and outputs the GWEIS summary statistics with additive SNP effects and interaction SNP effects. Users may save the outputs in separate user-specified files (see examples).
GWEIS_binary(plink_path, b_file, Bphe_discovery, Bcov_discovery, thread = 20)GWEIS_binary(plink_path, b_file, Bphe_discovery, Bcov_discovery, thread = 20)
plink_path |
Path to the PLINK executable application |
b_file |
Prefix of the binary files, where all .fam, .bed and .bim files have a common prefix |
Bphe_discovery |
Phenotype file containing family ID, individual ID and phenotype of the discovery dataset as columns, without heading |
Bcov_discovery |
Covariate file containing family ID, individual ID, standardized covariate, square of standardized covariate, and/or confounders of the discovery dataset as columns, without heading |
thread |
Number of threads used |
This function will perform GWEIS and output
B_out.sum |
GWEIS summary statistics with additive and interaction SNP effects |
## Not run: x <- GWEIS_binary(plink_path, DummyData, Bphe_discovery, Bcov_discovery, thread = 20) sink("B_out.add.sum") #to create a file in the working directory write.table(x[c("ID", "A1", "ADD_BETA")], sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output sink("B_out.gxe.sum") #to create a file in the working directory write.table(x[c("ID", "A1", "INTERACTION_BETA")], sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output head(x) #to extract the head of all columns in GWEIS summary #statistics of additive and interaction SNP effects x$CHROM #to extract the chromosome number x$POS #to extract the base pair position x$ID #to extract the SNP ID x$REF #to extract the reference allele x$ALT #to extract the alternate allele x$A1 #to extract the minor allele x$OBS_CT #to extract the number of allele observations x$ADD_BETA #to extract the additive SNP effects x$ADD_SE #to extract the standard errors of the #additive SNP effects x$ADD_Z_STAT #to extract the test statistics of additive #SNP effects x$ADD_P #to extract the p values of additive SNP effects x$INTERACTION_BETA #to extract the interaction SNP effects x$INTERACTION_SE #to extract the standard errors of the #interaction SNP effects x$INTERACTION_Z_STAT #to extract the test statistics of #interaction SNP effects x$INTERACTION_P #to extract the p values of interaction #SNP effects ## End(Not run)## Not run: x <- GWEIS_binary(plink_path, DummyData, Bphe_discovery, Bcov_discovery, thread = 20) sink("B_out.add.sum") #to create a file in the working directory write.table(x[c("ID", "A1", "ADD_BETA")], sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output sink("B_out.gxe.sum") #to create a file in the working directory write.table(x[c("ID", "A1", "INTERACTION_BETA")], sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output head(x) #to extract the head of all columns in GWEIS summary #statistics of additive and interaction SNP effects x$CHROM #to extract the chromosome number x$POS #to extract the base pair position x$ID #to extract the SNP ID x$REF #to extract the reference allele x$ALT #to extract the alternate allele x$A1 #to extract the minor allele x$OBS_CT #to extract the number of allele observations x$ADD_BETA #to extract the additive SNP effects x$ADD_SE #to extract the standard errors of the #additive SNP effects x$ADD_Z_STAT #to extract the test statistics of additive #SNP effects x$ADD_P #to extract the p values of additive SNP effects x$INTERACTION_BETA #to extract the interaction SNP effects x$INTERACTION_SE #to extract the standard errors of the #interaction SNP effects x$INTERACTION_Z_STAT #to extract the test statistics of #interaction SNP effects x$INTERACTION_P #to extract the p values of interaction #SNP effects ## End(Not run)
GWEIS_quantitative function This function performs GWEIS using plink2 and outputs the GWEIS summary statistics with additive SNP effects and interaction SNP effects separately. It is recommended to save the outputs in separate user-specified files (see examples).
GWEIS_quantitative( plink_path, b_file, Qphe_discovery, Qcov_discovery, thread = 20 )GWEIS_quantitative( plink_path, b_file, Qphe_discovery, Qcov_discovery, thread = 20 )
plink_path |
Path to the PLINK executable application |
b_file |
Prefix of the binary files, where all .fam, .bed and .bim files have a common prefix |
Qphe_discovery |
Phenotype file containing family ID, individual ID and phenotype of the discovery dataset as columns, without heading |
Qcov_discovery |
Covariate file containing family ID, individual ID, standardized covariate, square of standardized covariate, and/or confounders of the discovery dataset as columns, without heading |
thread |
Number of threads used |
This function will perform GWEIS and output
Q_out.sum |
GWEIS summary statistics with additive and interaction SNP effects |
## Not run: x <- GWEIS_quantitative (plink_path, DummyData, Qphe_discovery, Qcov_discovery, thread = 20) sink("Q_out.add.sum") #to create a file in the working directory write.table(x[c("ID", "A1", "ADD_BETA")], sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output sink("Q_out.gxe.sum") #to create a file in the working directory write.table(x[c("ID", "A1", "INTERACTION_BETA")], sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output head(x) #to extract the head of all columns in GWEIS summary #statistics of additive and interaction SNP effects x$CHROM #to extract the chromosome number x$POS #to extract the base pair position x$ID #to extract the SNP ID x$REF #to extract the reference allele x$ALT #to extract the alternate allele x$A1 #to extract the minor allele x$OBS_CT #to extract the number of allele observations x$ADD_BETA #to extract the additive SNP effects x$ADD_SE #to extract the standard errors of the #additive SNP effects x$ADD_T_STAT #to extract the test statistics of additive #SNP effects x$ADD_P #to extract the p values of additive SNP effects x$INTERACTION_BETA #to extract the interaction SNP effects x$INTERACTION_SE #to extract the standard errors of the #interaction SNP effects x$INTERACTION_T_STAT #to extract the test statistics of #interaction SNP effects x$INTERACTION_P #to extract the p values of interaction #SNP effects ## End(Not run)## Not run: x <- GWEIS_quantitative (plink_path, DummyData, Qphe_discovery, Qcov_discovery, thread = 20) sink("Q_out.add.sum") #to create a file in the working directory write.table(x[c("ID", "A1", "ADD_BETA")], sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output sink("Q_out.gxe.sum") #to create a file in the working directory write.table(x[c("ID", "A1", "INTERACTION_BETA")], sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output head(x) #to extract the head of all columns in GWEIS summary #statistics of additive and interaction SNP effects x$CHROM #to extract the chromosome number x$POS #to extract the base pair position x$ID #to extract the SNP ID x$REF #to extract the reference allele x$ALT #to extract the alternate allele x$A1 #to extract the minor allele x$OBS_CT #to extract the number of allele observations x$ADD_BETA #to extract the additive SNP effects x$ADD_SE #to extract the standard errors of the #additive SNP effects x$ADD_T_STAT #to extract the test statistics of additive #SNP effects x$ADD_P #to extract the p values of additive SNP effects x$INTERACTION_BETA #to extract the interaction SNP effects x$INTERACTION_SE #to extract the standard errors of the #interaction SNP effects x$INTERACTION_T_STAT #to extract the test statistics of #interaction SNP effects x$INTERACTION_P #to extract the p values of interaction #SNP effects ## End(Not run)
PRS_binary function This function uses plink2 and outputs Polygenic Risk Scores (PRSs) of all the individuals, using pre-generated GWAS and/or GWEIS summary statistics. Note that the input used in this function can be generated by using GWAS_binary and/or GWEIS_binary functions. Users may save the output in a user-specified file (see examples).
PRS_binary(plink_path, b_file, summary_input)PRS_binary(plink_path, b_file, summary_input)
plink_path |
Path to the PLINK executable application |
b_file |
Prefix of the binary files, where all .fam, .bed and .bim files have a common prefix |
summary_input |
Pre-generated GWAS and/or GWEIS summary statistics |
This function will output
prs.sscore |
PRSs for each individual |
## Not run: a <- GWAS_binary(plink_path, DummyData, Bphe_discovery, Bcov_discovery) trd <- a[c("ID", "A1", "BETA")] b <- GWEIS_binary(plink_path, DummyData, Bphe_discovery, Bcov_discovery) add <- b[c("ID", "A1", "ADD_BETA")] gxe <- b[c("ID", "A1", "INTERACTION_BETA")] x <- PRS_binary(plink_path, DummyData, summary_input = trd) sink("B_trd.sscore") #to create a file in the working directory write.table(x, sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output head(x) #to read the head of all columns in the output x$FID #to extract the family ID's of full dataset x$IID #to extract the individual ID's of full dataset x$PRS #to extract the polygenic risk scores of full dataset y <- PRS_binary(plink_path, DummyData, summary_input = add) sink("B_add.sscore") #to create a file in the working directory write.table(y, sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output z <- PRS_binary(plink_path, DummyData, summary_input = gxe) sink("B_gxe.sscore") #to create a file in the working directory write.table(z, sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output ## End(Not run)## Not run: a <- GWAS_binary(plink_path, DummyData, Bphe_discovery, Bcov_discovery) trd <- a[c("ID", "A1", "BETA")] b <- GWEIS_binary(plink_path, DummyData, Bphe_discovery, Bcov_discovery) add <- b[c("ID", "A1", "ADD_BETA")] gxe <- b[c("ID", "A1", "INTERACTION_BETA")] x <- PRS_binary(plink_path, DummyData, summary_input = trd) sink("B_trd.sscore") #to create a file in the working directory write.table(x, sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output head(x) #to read the head of all columns in the output x$FID #to extract the family ID's of full dataset x$IID #to extract the individual ID's of full dataset x$PRS #to extract the polygenic risk scores of full dataset y <- PRS_binary(plink_path, DummyData, summary_input = add) sink("B_add.sscore") #to create a file in the working directory write.table(y, sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output z <- PRS_binary(plink_path, DummyData, summary_input = gxe) sink("B_gxe.sscore") #to create a file in the working directory write.table(z, sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output ## End(Not run)
PRS_quantitative function This function uses plink2 and outputs Polygenic Risk Scores (PRSs) of all the individuals, using pre-generated GWAS and/or GWEIS summary statistics. Note that the input used in this function can be generated by using GWAS_quantitative and/or GWEIS_quantitative functions. Users may save the output in a user-specified file (see examples).
PRS_quantitative(plink_path, b_file, summary_input)PRS_quantitative(plink_path, b_file, summary_input)
plink_path |
Path to the PLINK executable application |
b_file |
Prefix of the binary files, where all .fam, .bed and .bim files have a common prefix |
summary_input |
Pre-generated GWAS and/or GWEIS summary statistics |
This function will output
prs.sscore |
PRSs for each individual |
## Not run: a <- GWAS_quantitative(plink_path, DummyData, Qphe_discovery, Qcov_discovery) trd <- a[c("ID", "A1", "BETA")] b <- GWEIS_quantitative(plink_path, DummyData, Qphe_discovery, Qcov_discovery) add <- b[c("ID", "A1", "ADD_BETA")] gxe <- b[c("ID", "A1", "INTERACTION_BETA")] x <- PRS_quantitative(plink_path, DummyData, summary_input = trd) sink("Q_trd.sscore") #to create a file in the working directory write.table(x, sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output head(x) #to read the head of all columns in the output x$FID #to extract the family ID's of full dataset x$IID #to extract the individual ID's of full dataset x$PRS #to extract the polygenic risk scores of full dataset y <- PRS_quantitative(plink_path, DummyData, summary_input = add) sink("Q_add.sscore") #to create a file in the working directory write.table(y, sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output z <- PRS_quantitative(plink_path, DummyData, summary_input = gxe) sink("Q_gxe.sscore") #to create a file in the working directory write.table(z, sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output ## End(Not run)## Not run: a <- GWAS_quantitative(plink_path, DummyData, Qphe_discovery, Qcov_discovery) trd <- a[c("ID", "A1", "BETA")] b <- GWEIS_quantitative(plink_path, DummyData, Qphe_discovery, Qcov_discovery) add <- b[c("ID", "A1", "ADD_BETA")] gxe <- b[c("ID", "A1", "INTERACTION_BETA")] x <- PRS_quantitative(plink_path, DummyData, summary_input = trd) sink("Q_trd.sscore") #to create a file in the working directory write.table(x, sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output head(x) #to read the head of all columns in the output x$FID #to extract the family ID's of full dataset x$IID #to extract the individual ID's of full dataset x$PRS #to extract the polygenic risk scores of full dataset y <- PRS_quantitative(plink_path, DummyData, summary_input = add) sink("Q_add.sscore") #to create a file in the working directory write.table(y, sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output z <- PRS_quantitative(plink_path, DummyData, summary_input = gxe) sink("Q_gxe.sscore") #to create a file in the working directory write.table(z, sep = " ", row.names = FALSE, quote = FALSE) #to write the output sink() #to save the output ## End(Not run)
Covariate data file of the discovery dataset when the outcome is quantitative. This contains covariate information of the individuals in the discovery dataset following confounders.
Qcov_discoveryQcov_discovery
A dataframe with 800 rows and 18 columns
Family ID
Individual ID
Standardized covariate
Square of the standardized covariate
Confounder 1
Confounder 2
Confounder 3
Confounder 4
Confounder 5
Confounder 6
Confounder 7
Confounder 8
Confounder 9
Confounder 10
Confounder 11
Confounder 12
Confounder 13
Confounder 14
Covariate data file of the target dataset when the outcome is quantitative. This contains covariate information of the individuals in the target dataset following confounders.
Qcov_targetQcov_target
A dataframe with 200 rows and 18 columns
Family ID
Individual ID
Standardized covariate
Square of the standardized covariate
Confounder 1
Confounder 2
Confounder 3
Confounder 4
Confounder 5
Confounder 6
Confounder 7
Confounder 8
Confounder 9
Confounder 10
Confounder 11
Confounder 12
Confounder 13
Confounder 14
Phenotype data file of the discovery dataset when the outcome is quantitative. This contains phenotype information of the individuals in the discovery dataset.
Qphe_discoveryQphe_discovery
A dataframe with 800 rows and 3 columns
Family ID
Individual ID
Phenotype
Phenotype data file of the target dataset when the outcome is quantitative. This contains phenotype information of the individuals in the target dataset.
Qphe_targetQphe_target
A dataframe with 200 rows and 3 columns
Family ID
Individual ID
Phenotype
summary_permuted_binary function This function outputs the p value of permuted model in the target dataset, using pre-generated Polygenic Risk Scores (PRSs) of all the individuals. Note that the input used in this function can be generated by using PRS_quantitative function. It is recommended to run this function, if you choose to fit 'PRS_gxe x E' interaction component (i.e. novel proposed model, Model 5) when generating risk scores. If the 'PRS_gxe x E' term is significant in Model 5, and insignificant in Model 5* (permuted p value), consider that the 'PRS_gxe x E' interaction component is actually insignificant (always give priority to the p value obtained from the permuted model).
summary_permuted_binary( Bphe_target, Bcov_target, iterations = 1000, add_score, gxe_score )summary_permuted_binary( Bphe_target, Bcov_target, iterations = 1000, add_score, gxe_score )
Bphe_target |
Phenotype file containing family ID, individual ID and phenotype of the target dataset as columns, without heading |
Bcov_target |
Covariate file containing family ID, individual ID, standardized covariate, square of standardized covariate, and/or confounders of the target dataset as columns, without heading |
iterations |
Number of iterations used in permutation |
add_score |
PRSs generated using additive SNP effects of GWEIS summary statistics |
gxe_score |
PRSs generated using interaction SNP effects of GWEIS summary statistics |
This function will output
B_permuted_p |
the p value of the permuted model |
## Not run: a <- GWEIS_binary(plink_path, DummyData, Bphe_discovery, Bcov_discovery) add <- a[c("ID", "A1", "ADD_OR")] gxe <- a[c("ID", "A1", "INTERACTION_OR")] p <- PRS_binary(plink_path, DummyData, summary_input = add) q <- PRS_binary(plink_path, DummyData, summary_input = gxe) x <- summary_permuted_binary(Bphe_target, Bcov_target, iterations = 1000, add_score = p, gxe_score = q) x ## End(Not run)## Not run: a <- GWEIS_binary(plink_path, DummyData, Bphe_discovery, Bcov_discovery) add <- a[c("ID", "A1", "ADD_OR")] gxe <- a[c("ID", "A1", "INTERACTION_OR")] p <- PRS_binary(plink_path, DummyData, summary_input = add) q <- PRS_binary(plink_path, DummyData, summary_input = gxe) x <- summary_permuted_binary(Bphe_target, Bcov_target, iterations = 1000, add_score = p, gxe_score = q) x ## End(Not run)
summary_permuted_quantitative function This function outputs the p value of permuted model in the target dataset, using pre-generated Polygenic Risk Scores (PRSs) of all the individuals. Note that the input used in this function can be generated by using PRS_quantitative functions. It is recommended to run this function, if you choose to fit 'PRS_gxe x E' interaction component (i.e. novel proposed model, Model 4) when generating risk scores. If the 'PRS_gxe x E' term is significant in Model 4, and insignificant in Model 4* (permuted p value), consider that the 'PRS_gxe x E' interaction component is actually insignificant (always give priority to the p value obtained from the permuted model).
summary_permuted_quantitative( Qphe_target, Qcov_target, iterations = 1000, add_score, gxe_score )summary_permuted_quantitative( Qphe_target, Qcov_target, iterations = 1000, add_score, gxe_score )
Qphe_target |
Phenotype file containing family ID, individual ID and phenotype of the target dataset as columns, without heading |
Qcov_target |
Covariate file containing family ID, individual ID, standardized covariate, square of standardized covariate, and/or confounders of the target dataset as columns, without heading |
iterations |
Number of iterations used in permutation |
add_score |
PRSs generated using additive SNP effects of GWEIS summary statistics |
gxe_score |
PRSs generated using interaction SNP effects of GWEIS summary statistics |
This function will output
Q_permuted_p |
the p value of the permuted model |
## Not run: a <- GWEIS_quantitative(plink_path, DummyData, Qphe_discovery, Qcov_discovery) add <- a[c("ID", "A1", "ADD_BETA")] gxe <- a[c("ID", "A1", "INTERACTION_BETA")] p <- PRS_quantitative(plink_path, DummyData, summary_input = add) q <- PRS_quantitative(plink_path, DummyData, summary_input = gxe) x <- summary_permuted_quantitative(Qphe_target, Qcov_target, iterations = 1000, add_score = p, gxe_score = q) x ## End(Not run)## Not run: a <- GWEIS_quantitative(plink_path, DummyData, Qphe_discovery, Qcov_discovery) add <- a[c("ID", "A1", "ADD_BETA")] gxe <- a[c("ID", "A1", "INTERACTION_BETA")] p <- PRS_quantitative(plink_path, DummyData, summary_input = add) q <- PRS_quantitative(plink_path, DummyData, summary_input = gxe) x <- summary_permuted_quantitative(Qphe_target, Qcov_target, iterations = 1000, add_score = p, gxe_score = q) x ## End(Not run)
summary_regular_binary function This function outputs the summary of regular model and final risk score values of each individual in the target dataset using pre-generated Polygenic Risk Scores (PRSs) of all the individuals. Note that the input used in this function can be generated by using PRS_binary function.
summary_regular_binary( Bphe_target, Bcov_target, add_score = NULL, gxe_score = NULL, Model )summary_regular_binary( Bphe_target, Bcov_target, add_score = NULL, gxe_score = NULL, Model )
Bphe_target |
Phenotype file containing family ID, individual ID and phenotype of the target dataset as columns, without heading |
Bcov_target |
Covariate file containing family ID, individual ID, standardized covariate, square of standardized covariate, and/or confounders of the target dataset as columns, without heading |
add_score |
PRSs generated using additive SNP effects of GWAS/GWEIS summary statistics |
gxe_score |
PRSs generated using interaction SNP effects of GWEIS summary statistics |
Model |
Specify the model number (0: y = PRS_trd + E + confounders, 1: y = PRS_trd + E + PRS_trd x E + confounders, 2: y = PRS_add + E + PRS_add x E + confounders, 3: y = PRS_add + E + PRS_gxe x E + confounders, 4: y = PRS_add + E + PRS_gxe + PRS_gxe x E + confounders, 5: y = PRS_add + E + E^2 + PRS_gxe + PRS_gxe x E + confounders, where y is the outcome variable, E is the covariate of interest, PRS_trd and PRS_add are the polygenic risk scores computed using additive SNP effects of GWAS and GWEIS summary statistics respectively, and PRS_gxe is the polygenic risk scores computed using GxE interaction SNP effects of GWEIS summary statistics.) |
This function will output
Bsummary |
the summary of the fitted model |
Individual_risk_values |
the estimated risk values of individuals in the target sample |
## Not run: a <- GWAS_binary(plink_path, DummyData, Bphe_discovery, Bcov_discovery) trd <- a[c("ID", "A1", "OR")] b <- GWEIS_binary(plink_path, DummyData, Bphe_discovery, Bcov_discovery) add <- b[c("ID", "A1", "ADD_OR")] gxe <- b[c("ID", "A1", "INTERACTION_OR")] p <- PRS_binary(plink_path, DummyData, summary_input = trd) q <- PRS_binary(plink_path, DummyData, summary_input = add) r <- PRS_binary(plink_path, DummyData, summary_input = gxe) summary_regular_binary(Bphe_target, Bcov_target, add_score = p, Model = 0) summary_regular_binary(Bphe_target, Bcov_target, add_score = p, Model = 1) summary_regular_binary(Bphe_target, Bcov_target, add_score = q, Model = 2) summary_regular_binary(Bphe_target, Bcov_target, add_score = q, gxe_score = r, Model = 3) summary_regular_binary(Bphe_target, Bcov_target, add_score = q, gxe_score = r, Model = 4) x <- summary_regular_binary(Bphe_target, Bcov_target, add_score = q, gxe_score = r, Model = 5) sink("Bsummary.txt") #to create a file in the working directory print(x$summary) #to write the output sink() #to save the output sink("Individual_risk_values.txt") #to create a file in the working directory write.table(x$risk.values, sep = " ", row.names = FALSE, col.names = FALSE, quote = FALSE) #to write the output sink() #to save the output x$summary #to obtain the model summary output x$risk.values #to obtain the predicted risk values of target individuals ## End(Not run)## Not run: a <- GWAS_binary(plink_path, DummyData, Bphe_discovery, Bcov_discovery) trd <- a[c("ID", "A1", "OR")] b <- GWEIS_binary(plink_path, DummyData, Bphe_discovery, Bcov_discovery) add <- b[c("ID", "A1", "ADD_OR")] gxe <- b[c("ID", "A1", "INTERACTION_OR")] p <- PRS_binary(plink_path, DummyData, summary_input = trd) q <- PRS_binary(plink_path, DummyData, summary_input = add) r <- PRS_binary(plink_path, DummyData, summary_input = gxe) summary_regular_binary(Bphe_target, Bcov_target, add_score = p, Model = 0) summary_regular_binary(Bphe_target, Bcov_target, add_score = p, Model = 1) summary_regular_binary(Bphe_target, Bcov_target, add_score = q, Model = 2) summary_regular_binary(Bphe_target, Bcov_target, add_score = q, gxe_score = r, Model = 3) summary_regular_binary(Bphe_target, Bcov_target, add_score = q, gxe_score = r, Model = 4) x <- summary_regular_binary(Bphe_target, Bcov_target, add_score = q, gxe_score = r, Model = 5) sink("Bsummary.txt") #to create a file in the working directory print(x$summary) #to write the output sink() #to save the output sink("Individual_risk_values.txt") #to create a file in the working directory write.table(x$risk.values, sep = " ", row.names = FALSE, col.names = FALSE, quote = FALSE) #to write the output sink() #to save the output x$summary #to obtain the model summary output x$risk.values #to obtain the predicted risk values of target individuals ## End(Not run)
summary_regular_quantitative function This function outputs the summary of regular model and final risk score values of each individual in the target dataset using pre-generated Polygenic Risk Scores (PRSs) of all the individuals. Note that the input used in this function can be generated by using PRS_quantitative function.
summary_regular_quantitative( Qphe_target, Qcov_target, add_score = NULL, gxe_score = NULL, Model )summary_regular_quantitative( Qphe_target, Qcov_target, add_score = NULL, gxe_score = NULL, Model )
Qphe_target |
Phenotype file containing family ID, individual ID and phenotype of the target dataset as columns, without heading |
Qcov_target |
Covariate file containing family ID, individual ID, standardized covariate, square of standardized covariate, and/or confounders of the target dataset as columns, without heading |
add_score |
PRSs generated using additive SNP effects of GWAS/GWEIS summary statistics |
gxe_score |
PRSs generated using interaction SNP effects of GWEIS summary statistics |
Model |
Specify the model number (0: y = PRS_trd + E + confounders, 1: y = PRS_trd + E + PRS_trd x E + confounders, 2: y = PRS_add + E + PRS_add x E + confounders, 3: y = PRS_add + E + PRS_gxe x E + confounders, 4: y = PRS_add + E + PRS_gxe + PRS_gxe x E + confounders, where y is the outcome variable, E is the covariate of interest, PRS_trd and PRS_add are the polygenic risk scores computed using additive SNP effects of GWAS and GWEIS summary statistics respectively, and PRS_gxe is the polygenic risk scores computed using GxE interaction SNP effects of GWEIS summary statistics.) |
This function will output
Qsummary.txt |
the summary of the fitted model |
Individual_risk_values.txt |
the estimated risk values of individuals in the target sample |
## Not run: a <- GWAS_quantitative(plink_path, DummyData, Qphe_discovery, Qcov_discovery) trd <- a[c("ID", "A1", "BETA")] b <- GWEIS_quantitative(plink_path, DummyData, Qphe_discovery, Qcov_discovery) add <- b[c("ID", "A1", "ADD_BETA")] gxe <- b[c("ID", "A1", "INTERACTION_BETA")] p <- PRS_quantitative(plink_path, DummyData, summary_input = trd) q <- PRS_quantitative(plink_path, DummyData, summary_input = add) r <- PRS_quantitative(plink_path, DummyData, summary_input = gxe) summary_regular_quantitative(Qphe_target, Qcov_target, add_score = p, Model = 0) summary_regular_quantitative(Qphe_target, Qcov_target, add_score = p, Model = 1) summary_regular_quantitative(Qphe_target, Qcov_target, add_score = q, Model = 2) summary_regular_quantitative(Qphe_target, Qcov_target, add_score = q, gxe_score = r, Model = 3) x <- summary_regular_quantitative(Qphe_target, Qcov_target, add_score = q, gxe_score = r, Model = 4) sink("Qsummary.txt") #to create a file in the working directory print(x$summary) #to write the output sink() #to save the output sink("Individual_risk_values.txt") #to create a file in the working directory write.table(x$risk.values, sep = " ", row.names = FALSE, col.names = FALSE, quote = FALSE) #to write the output sink() #to save the output x$summary #to obtain the model summary output x$risk.values #to obtain the predicted risk values of target individuals ## End(Not run)## Not run: a <- GWAS_quantitative(plink_path, DummyData, Qphe_discovery, Qcov_discovery) trd <- a[c("ID", "A1", "BETA")] b <- GWEIS_quantitative(plink_path, DummyData, Qphe_discovery, Qcov_discovery) add <- b[c("ID", "A1", "ADD_BETA")] gxe <- b[c("ID", "A1", "INTERACTION_BETA")] p <- PRS_quantitative(plink_path, DummyData, summary_input = trd) q <- PRS_quantitative(plink_path, DummyData, summary_input = add) r <- PRS_quantitative(plink_path, DummyData, summary_input = gxe) summary_regular_quantitative(Qphe_target, Qcov_target, add_score = p, Model = 0) summary_regular_quantitative(Qphe_target, Qcov_target, add_score = p, Model = 1) summary_regular_quantitative(Qphe_target, Qcov_target, add_score = q, Model = 2) summary_regular_quantitative(Qphe_target, Qcov_target, add_score = q, gxe_score = r, Model = 3) x <- summary_regular_quantitative(Qphe_target, Qcov_target, add_score = q, gxe_score = r, Model = 4) sink("Qsummary.txt") #to create a file in the working directory print(x$summary) #to write the output sink() #to save the output sink("Individual_risk_values.txt") #to create a file in the working directory write.table(x$risk.values, sep = " ", row.names = FALSE, col.names = FALSE, quote = FALSE) #to write the output sink() #to save the output x$summary #to obtain the model summary output x$risk.values #to obtain the predicted risk values of target individuals ## End(Not run)