| Type: | Package |
| Title: | Estimation of Pleiotropic Heritability from Genome-Wide Association Studies (GWAS) Summary Statistics |
| Version: | 0.1.0 |
| Description: | Provides tools to compute unbiased pleiotropic heritability estimates of complex diseases from genome-wide association studies (GWAS) summary statistics. We estimate pleiotropic heritability from GWAS summary statistics by estimating the proportion of variance explained from an estimated genetic correlation matrix (Bulik-Sullivan et al. 2015 <doi:10.1038/ng.3406>) and employing a Monte-Carlo bias correction procedure to account for sampling noise in genetic correlation estimates. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Depends: | R (≥ 2.10) |
| RoxygenNote: | 7.3.2 |
| Imports: | data.table, dplyr, stats, rlang, mvtnorm, fs, arrow, checkmate, cli, gdata, glue, purrr, tibble, vroom |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| Language: | en-US |
| NeedsCompilation: | no |
| Packaged: | 2025-10-26 14:46:50 UTC; yujiezhao |
| Author: | Yujie Zhao [aut, cre] |
| Maintainer: | Yujie Zhao <yujiezhao@hsph.harvard.edu> |
| Repository: | CRAN |
| Date/Publication: | 2025-10-28 09:50:02 UTC |
Compute a vector of pleioh2g for all diseases before correction This function computes pleioh2g for all diseases before correction in one go.
Description
Compute a vector of pleioh2g for all diseases before correction This function computes pleioh2g for all diseases before correction in one go.
Usage
Cal_cor_pleiotropic_h2(rg_mat, h2g_T)
Arguments
rg_mat |
genetic correlation matrix. |
h2g_T |
heritability vector for all diseases. |
Value
pleioh2g vector
Examples
data(Results_full_rg_15D)
data(h2_vector_15D)
Cal_cor_pleiotropic_h2(Results_full_rg_15D,h2_vector_15D)
Compute single pleioh2g for target disease after correction with referred disease index in the rg matrix and corrected ratio
Description
This function computes pleioh2g for the target disease after correction.
Usage
Cal_cor_pleiotropic_h2_corrected_single(
rg_mat,
h2g_T_single,
corrected_weight_updated,
plei_h2_idx
)
Arguments
rg_mat |
genetic correlation matrix. |
h2g_T_single |
heritability for target diseases. |
corrected_weight_updated |
the ratio for correction |
plei_h2_idx |
index of the target disease in the rg_mat. |
Value
pleioh2g value for the target disease after correction
Examples
data(Results_full_rg_15D)
data(h2_vector_15D)
plei_h2_idx<-1
h2g_T_single <- h2_vector_15D[plei_h2_idx]
corrected_weight_updated <- 0.78
Cal_cor_pleiotropic_h2_corrected_single(Results_full_rg_15D,h2g_T_single,
corrected_weight_updated,plei_h2_idx)
Compute single pleioh2g for target disease before correction with referred disease index in the rg matrix
Description
This function computes pleioh2g for the target disease before correction.
Usage
Cal_cor_pleiotropic_h2_single(rg_mat, h2g_T_single, plei_h2_idx)
Arguments
rg_mat |
genetic correlation matrix. |
h2g_T_single |
heritability for target diseases. |
plei_h2_idx |
index of the target disease in the rg_mat. |
Value
pleioh2g value for the target disease before correction
Examples
data(Results_full_rg_15D)
data(h2_vector_15D)
plei_h2_idx<-1
h2g_T_single<-h2_vector_15D[plei_h2_idx]
Cal_cor_pleiotropic_h2_single(Results_full_rg_15D,h2g_T_single,plei_h2_idx)
Compute inversed elements for the target disease in bias correction procedure with referred disease index in the rg matrix
Description
This function inversed elements for the target disease in bias correction procedure.
Usage
Cal_cor_test_single(rg_mat, plei_h2_idx)
Arguments
rg_mat |
genetic correlation matrix. |
plei_h2_idx |
index of the target disease in the rg_mat. |
Value
inverse element value for the target disease used for bias correction
Examples
data(Results_full_rg_15D)
plei_h2_idx<-1
Cal_cor_test_single(Results_full_rg_15D,plei_h2_idx)
Compute rg + h2g
Description
This function is used to compute rg + h2g using LDSC.
Usage
Cal_rg_h2g_alltraits(
phenotype,
munged_sumstats,
ld_path,
wld_path,
sample_prev = NULL,
population_prev = NULL
)
Arguments
phenotype |
Vector of the phenotype name |
munged_sumstats |
All LDSC-munged GWAS .stat.gz |
ld_path |
Path to directory containing ld score files. |
wld_path |
Path to directory containing weight files. |
sample_prev |
Vector of sample prevalence, in the same order of input GWAS summary statistics. |
population_prev |
Vector of population prevalence, in the same order of input GWAS summary statistics. |
Value
A named list containing LDSC-based heritability and genetic correlation estimates across all input phenotypes. The list includes the following elements:
-
h2: Matrix of SNP-heritability estimates on the observed scale (rows = 1, columns = input phenotypes). -
h2Z: Matrix of corresponding heritability Z-scores. -
liah2: Matrix of heritability estimates on the liability scale. -
rg: Symmetric matrix of pairwise genetic correlations between traits. -
rgz: Matrix of Z-scores for the genetic correlation estimates. -
gcov: Symmetric matrix of genetic covariances between traits.
Each element corresponds to one LDSC-derived summary statistic, with trait names used as both row and column names.
genomic-block jackknife and compute rg + h2g
Description
This function performs genomic-block jackknife and computes rg + h2g.
Usage
Cal_rg_h2g_jk_alltraits(
n_block = 200,
hmp3,
phenotype,
munged_sumstats,
ld_path,
wld_path,
sample_prev = NULL,
population_prev = NULL
)
Arguments
n_block |
number of jackknife blocks. |
hmp3 |
Directory for hapmap 3 snplist. |
phenotype |
Vector of the phenotype name |
munged_sumstats |
All LDSC-munged GWAS .stat.gz |
ld_path |
Path to directory containing ld score files. |
wld_path |
Path to directory containing weight files. |
sample_prev |
Vector of sample prevalence, in the same order of input GWAS summary statistics. |
population_prev |
Vector of population prevalence, in the same order of input GWAS summary statistics. |
Value
A named list containing block jackknife estimates of SNP-heritability and genetic correlation across all input phenotypes. The list includes the following elements:
-
h2array: A matrix of per-block SNP-heritability estimates on the observed scale. Rows correspond to jackknife blocks, and columns correspond to input phenotypes. -
liah2array: A matrix of per-block SNP-heritability estimates on the liability scale, with the same row and column structure ash2array. -
rgarray: A three-dimensional array of pairwise genetic correlation estimates. The first two dimensions represent phenotype pairs (rows and columns), and the third dimension indexes the jackknife blocks. -
gcovarray: A three-dimensional array of pairwise genetic covariance estimates, aligned in structure withrgarray.
Each element provides per-block estimates that can be used to compute standard errors or confidence intervals via the block jackknife method.
Prune disease selection
Description
Prune disease selection
Usage
Prune_disease_selection_DTrgzscore(
Target_disease,
trait_name,
Rg_mat,
Rg_mat_z,
rg_threshold
)
Arguments
Target_disease |
trait_name of target disease |
trait_name |
trait_name of pre-prune rg_matrix |
Rg_mat |
pre-prune rg_matrix |
Rg_mat_z |
pre-prune rg z matrix |
rg_threshold |
rg_threshold |
Value
Rg_mat_leave
Examples
trait_name<-c("401.1","244.5","318","735.3","411.4",
"427.2","454.1","278.1","250.2","550.1","530.11",
"296.22","519.8","562.1","763")
data("Results_full_rg_15D")
data("Rg_mat_z_15D")
Target_disease<-'401.1'
rg_threshold<-sqrt(0.3)
Rg_prune<-Prune_disease_selection_DTrgzscore(Target_disease, trait_name,
Results_full_rg_15D,Rg_mat_z_15D,rg_threshold)
Genetic correlation matrix for 15 diseases
Description
Example genetic correlation matrix used in the vignette and examples.
Usage
Results_full_rg_15D
Format
A numeric matrix.
Source
Internal simulation
Jackknife array of genetic correlations (15 diseases)
Description
Jackknife array of genetic correlations (15 diseases)
Usage
Results_full_rg_array_15D
Format
A 3-dim array.
Source
Internal simulation
Genetic correlation Z matrix for 15 diseases
Description
Example genetic correlation Z matrix used in the vignette and examples.
Usage
Rg_mat_z_15D
Format
A numeric matrix.
Source
Internal simulation
Generate samples based on sampling covariance matrix and rg matrix for target disease
Description
This function is used to generate samples based on sampling covariance matrix and rg matrix for target disease
Usage
generate_proposal_sample_changea_cor(
Results_full_rg,
Results_full_rg_array,
plei_h2_idx,
ratio_a
)
Arguments
Results_full_rg |
genetic correlation matrix. |
Results_full_rg_array |
genetic correlation jackknife-block array. |
plei_h2_idx |
index of the target disease in the rg_mat. |
ratio_a |
corrected ratio. |
Value
noisy_inversed_element for bias correction
Examples
data(Results_full_rg_15D)
data(Results_full_rg_array_15D)
plei_h2_idx<-1
ratio_a <- 0.75
generate_proposal_sample_changea_cor(Results_full_rg_15D,
Results_full_rg_array_15D, plei_h2_idx, ratio_a)
Convert Heritability to Liability Scale
Description
'h2_liability()' converts heritability estimates from the observed to liability scale.
Usage
h2_liability(h2, sample_prev, population_prev)
Arguments
h2 |
(numeric) Estimate of observed-scale heritability |
sample_prev |
(numeric) Proportion of cases in the current sample |
population_prev |
(numeric) Population prevalence of trait |
Value
(numeric) Liability-scale heritability
Examples
h2_liability(0.28, 0.1, 0.05)
h2 vector for 15 diseases
Description
Example h2 vector used in the vignette and examples.
Usage
h2_vector_15D
Format
A numeric matrix.
Source
Internal simulation
h2 jk matrix for 15 diseases
Description
Example h2 jk matrix used in the vignette and examples.
Usage
h2_vector_mat_15D
Format
A numeric matrix.
Source
Internal simulation
Estimate heritability - refer to ldscr R package (https://github.com/mglev1n/ldscr)
Description
'ldsc_h2()' uses ldscore regression to estimate the heritability of a trait from GWAS summary statistics and reference LD information.
Usage
ldsc_h2(
munged_sumstats,
sample_prev = NA,
population_prev = NA,
ld,
wld,
n_blocks = 200,
chisq_max = NA,
chr_filter = seq(1, 22, 1)
)
Arguments
munged_sumstats |
Either a dataframe, or a path to a file containing munged summary statistics. Must contain at least columns named 'SNP' (rsid), 'A1' (effect allele), 'A2' (non-effect allele), 'N' (total sample size) and 'Z' (Z-score) |
sample_prev |
(numeric) For binary traits, this should be the prevalence of cases in the current sample, used for conversion from observed heritability to liability-scale heritability. The default is 'NA', which is appropriate for quantitative traits or estimating heritability on the observed scale. |
population_prev |
(numeric) For binary traits, this should be the population prevalence of the trait, used for conversion from observed heritability to liability-scale heritability. The default is 'NA', which is appropriate for quantitative traits or estimating heritability on the observed scale. |
ld |
(character) Path to directory containing ld score files, ending in '*.l2.ldscore.gz'. |
wld |
(character) Path to directory containing weight files. |
n_blocks |
(numeric) Number of blocks used to produce block jackknife standard errors. Default is '200' |
chisq_max |
(numeric) Maximum value of Z^2 for SNPs to be included in LD-score regression. Default is to set 'chisq_max' to the maximum of 80 and N*0.001. |
chr_filter |
(numeric vector) Chromosomes to include in analysis. Separating even/odd chromosomes may be useful for exploratory/confirmatory factor analysis. |
Value
A [tibble][tibble::tibble-package] containing heritability information. If 'sample_prev' and 'population_prev' were provided, the heritability estimate will also be returned on the liability scale.
Estimate cross-trait genetic correlations (Robust Version) - refer to ldscr R package (https://github.com/mglev1n/ldscr)
Description
'ldsc_rg()' uses ldscore regression to estimate the pairwise genetic correlations between traits. The function relies on named lists of traits, sample prevalences, and population prevalences. The name of each trait should be consistent across each argument.
Usage
ldsc_rg(
munged_sumstats,
sample_prev = NA,
population_prev = NA,
ld,
wld,
n_blocks = 200,
chisq_max = NA,
chr_filter = seq(1, 22, 1)
)
Arguments
munged_sumstats |
(list) A named list of dataframes, or paths to files containing munged summary statistics. Each set of munged summary statistics contain at least columns named 'SNP' (rsid), 'A1' (effect allele), 'A2' (non-effect allele), 'N' (total sample size) and 'Z' (Z-score) |
sample_prev |
(list) A named list containing the prevalence of cases in the current sample, used for conversion from observed heritability to liability-scale heritability. The default is 'NA', which is appropriate for quantitative traits or estimating heritability on the observed scale. |
population_prev |
(list) A named list containing the population prevalence of the trait, used for conversion from observed heritability to liability-scale heritability. The default is 'NA', which is appropriate for quantitative traits or estimating heritability on the observed scale. |
ld |
(character) Path to directory containing ld score files, ending in '*.l2.ldscore.gz'. |
wld |
(character) Path to directory containing weight files. |
n_blocks |
(numeric) Number of blocks used to produce block jackknife standard errors. Default is '200' |
chisq_max |
(numeric) Maximum value of Z^2 for SNPs to be included in LD-score regression. Default is to set 'chisq_max' to the maximum of 80 and N*0.001. |
chr_filter |
(numeric vector) Chromosomes to include in analysis. Separating even/odd chromosomes may be useful for exploratory/confirmatory factor analysis. |
Details
This function estimates the pairwise genetic correlations between an arbitrary number of traits. The function also estimates heritability for each individual trait. There is a [ggplot2::autoplot()] method for visualizing a heatmap of the results.
This version handles cases where traits have non-positive heritability estimates more gracefully by returning NA values for correlations involving such traits.
Value
A list of class 'ldscr_list' containing heritablilty and genetic correlation information - 'h2' = [tibble][tibble::tibble-package] containing heritability information for each trait. If 'sample_prev' and 'population_prev' were provided, the heritability estimates will also be returned on the liability scale. - 'rg' = [tibble][tibble::tibble-package] containing pairwise genetic correlations information. - 'raw' = A list of correlation/covariance matrices
Internal Function to make weights - refer to ldscr R package (https://github.com/mglev1n/ldscr)
Description
'make_weights()' Internal Function to make weights
Usage
make_weights(chi1, L2, wLD, N, M.tot)
Arguments
chi1 |
chi-square |
L2 |
ld score |
wLD |
wld score |
N |
sample size |
M.tot |
Number of SNPs |
Value
A numeric vector of initial LDSC weights for each SNP
Merging summary statistics with LD-score files - refer to ldscr R package (https://github.com/mglev1n/ldscr)
Description
'merge_sumstats()' Merging summary statistics with LD-score files
Usage
merge_sumstats(sumstats_df, w, x, chr_filter)
Arguments
sumstats_df |
dataframe of sumstat |
w |
wld score |
x |
ld score |
chr_filter |
(numeric vector) Chromosomes to include in analysis. Separating even/odd chromosomes may be useful for exploratory/confirmatory factor analysis. |
Value
A tibble (data frame) containing the merged summary statistics and LD-score
Internal function to perform LDSC heritability/covariance analysis - refer to ldscr R package (https://github.com/mglev1n/ldscr)
Description
'perform_analysis()' Internal function to perform LDSC heritability/covariance analysis
Usage
perform_analysis(n.blocks, n.snps, weighted.LD, weighted.chi, N.bar, m)
Arguments
n.blocks |
Number of blocks |
n.snps |
Number of SNPs |
weighted.LD |
wld score |
weighted.chi |
chi-square |
N.bar |
Average N after merging |
m |
Number of SNPs from LD data |
Value
A list containing the results of the LDSC heritability/covariance analysis with the following elements:
-
reg.tot: Estimated total heritability or covariance (regression coefficient scaled bym). -
tot.se: Standard error of the total heritability/covariance estimate, computed using a block jackknife. -
intercept: LDSC regression intercept. -
intercept.se: Standard error of the intercept, estimated via block jackknife. -
pseudo.values: Vector of pseudo-values from the block jackknife procedure, one per block. -
N.bar: Average sample size across SNPs after merging.
Compute pleioh2g after bias correction for target disease
Description
This function is used to compute pleioh2g after bias correction for target disease
Usage
pleiotropyh2_cor_computing_single(
G,
phenotype,
h2_vector,
h2_vector_mat,
Results_full_rg,
Results_full_rg_array,
sample_rep
)
Arguments
G |
index of target disease. |
phenotype |
Vector of the phenotype name |
h2_vector |
h2g vector for all traits - aligned as the order in phenotype file |
h2_vector_mat |
h2g array from jackknife-block estimates for all traits - aligned as the order in phenotype file |
Results_full_rg |
genetic correlation matrix. - aligned as the order in phenotype file |
Results_full_rg_array |
genetic correlation jackknife-block array. - aligned as the order in phenotype file |
sample_rep |
sampling times in bias correction |
Value
A 'list' containing the following elements: - 'target_disease' (character): The value "401.1". - 'target_disease_h2_est' (numeric): target disease h2g. - 'target_disease_h2_se' (numeric): target disease h2g_se. - 'selected_auxD' (character): auxiliary diseases. - 'h2pleio_uncorr' (numeric): pre-correction pleiotropic heritability estimate. - 'h2pleio_uncorr_se' (numeric): pre-correction pleiotropic heritability jackknife s.e. estimate. - 'percentage_h2pleio_uncorr' (numeric): pre-correction percentage of pleiotropic heritability estimate. - 'percentage_h2pleio_uncorr_se' (numeric): pre-correction percentage of pleiotropic heritability jackknife s.e. estimate. - 'percentage_h2pleio_uncorr_jackknife' (numeric): vector of all pre-correction percentage of pleiotropic heritability jackknife estimates. - 'h2pleio_corr' (numeric): post-correction pleiotropic heritability estimate. - 'h2pleio_corr_se' (numeric): post-correction pleiotropic heritability jackknife s.e. estimate. - 'percentage_h2pleio_corr' (numeric): post-correction percentage of pleiotropic heritability estimate. - 'percentage_h2pleio_corr_se' (numeric): post-correction percentage of pleiotropic heritability jackknife s.e. estimate. - 'corrected_weight' (numeric): corrected weight in bias correction.
Examples
G <- 1
data(Results_full_rg_15D)
data(Results_full_rg_array_15D)
data(h2_vector_15D)
data(h2_vector_mat_15D)
phenotype<-c("401.1","244.5","318","735.3","411.4",
"427.2","454.1","278.1","250.2","550.1","530.11",
"296.22","519.8","562.1","763")
sample_rep<-20
post_corrrresults_prune<-pleiotropyh2_cor_computing_single(G,phenotype,h2_vector_15D,
h2_vector_mat_15D,Results_full_rg_15D,Results_full_rg_array_15D, sample_rep)
Compute pleioh2g after bias correction for target disease
Description
This function is used to compute pleioh2g after bias correction for target disease
Usage
pleiotropyh2_cor_computing_single_prune(
G,
phenotype,
h2_vector,
h2_vector_mat,
Results_full_rg,
Results_full_rg_array,
sample_rep
)
Arguments
G |
index of target disease. |
phenotype |
Vector of the phenotype name |
h2_vector |
h2g vector for all traits - aligned as the order in phenotype file |
h2_vector_mat |
h2g array from jackknife-block estimates for all traits - aligned as the order in phenotype file |
Results_full_rg |
genetic correlation matrix. - aligned as the order in phenotype file |
Results_full_rg_array |
genetic correlation jackknife-block array. - aligned as the order in phenotype file |
sample_rep |
sampling times in bias correction |
Value
A 'list' containing the following elements: - 'target_disease' (character): The value "401.1". - 'target_disease_h2_est' (numeric): target disease h2g. - 'target_disease_h2_se' (numeric): target disease h2g_se. - 'selected_auxD' (character): auxiliary diseases. - 'h2pleio_uncorr' (numeric): pre-correction pleiotropic heritability estimate. - 'h2pleio_uncorr_se' (numeric): pre-correction pleiotropic heritability jackknife s.e. estimate. - 'percentage_h2pleio_uncorr' (numeric): pre-correction percentage of pleiotropic heritability estimate. - 'percentage_h2pleio_uncorr_se' (numeric): pre-correction percentage of pleiotropic heritability jackknife s.e. estimate. - 'percentage_h2pleio_uncorr_jackknife' (numeric): vector of all pre-correction percentage of pleiotropic heritability jackknife estimates. - 'h2pleio_corr' (numeric): post-correction pleiotropic heritability estimate. - 'h2pleio_corr_se' (numeric): post-correction pleiotropic heritability jackknife s.e. estimate. - 'percentage_h2pleio_corr' (numeric): post-correction percentage of pleiotropic heritability estimate. - 'percentage_h2pleio_corr_se' (numeric): post-correction percentage of pleiotropic heritability jackknife s.e. estimate. - 'corrected_weight' (numeric): corrected weight in bias correction.
Examples
G <- 1
data(Results_full_rg_15D)
data(Results_full_rg_array_15D)
data(h2_vector_15D)
data(h2_vector_mat_15D)
phenotype<-c("401.1","244.5","318","735.3","411.4",
"427.2","454.1","278.1","250.2","550.1","530.11",
"296.22","519.8","562.1","763")
sample_rep<-10
post_corrrresults_prune<-pleiotropyh2_cor_computing_single_prune(G,phenotype,h2_vector_15D,
h2_vector_mat_15D,Results_full_rg_15D,Results_full_rg_array_15D, sample_rep)
Compute pleioh2g before bias correction for target disease
Description
This function is used to compute pleioh2g after bias correction for target disease
Usage
pleiotropyh2_nocor_computing_single(
G,
phenotype,
h2_vector,
h2_vector_mat,
Results_full_rg,
Results_full_rg_array
)
Arguments
G |
index of target disease. |
phenotype |
Vector of the phenotype name |
h2_vector |
h2g vector for all traits - aligned as the order in phenotype file |
h2_vector_mat |
h2g array from jackknife-block estimates for all traits - aligned as the order in phenotype file |
Results_full_rg |
genetic correlation matrix.- aligned as the order in phenotype file |
Results_full_rg_array |
genetic correlation jackknife-block array.- aligned as the order in phenotype file |
Value
A 'list' containing the following elements: - 'target_disease' (character): The value "401.1". - 'target_disease_h2_est' (numeric): target disease h2g. - 'target_disease_h2_se' (numeric): target disease h2g_se. - 'selected_auxD' (character): auxiliary diseases. - 'h2pleio_uncorr' (numeric): pre-correction pleiotropic heritability estimate. - 'h2pleio_uncorr_se' (numeric): pre-correction pleiotropic heritability jackknife s.e. estimate. - 'percentage_h2pleio_uncorr' (numeric): pre-correction percentage of pleiotropic heritability estimate. - 'percentage_h2pleio_uncorr_se' (numeric): pre-correction percentage of pleiotropic heritability jackknife s.e. estimate. - 'percentage_h2pleio_jackknife_uncorr' (numeric): vector of all pre-correction percentage of pleiotropic heritability jackknife estimates.
Examples
G <- 1
data(Results_full_rg_15D)
data(Results_full_rg_array_15D)
data(h2_vector_15D)
data(h2_vector_mat_15D)
phenotype<-c("401.1","244.5","318","735.3","411.4",
"427.2","454.1","278.1","250.2","550.1","530.11",
"296.22","519.8","562.1","763")
h2pleiobeforecorr<-pleiotropyh2_nocor_computing_single(G,phenotype,h2_vector_15D,
h2_vector_mat_15D,Results_full_rg_15D,Results_full_rg_array_15D)
Perform pruning in computing pleioh2g and correct bias
Description
Perform pruning in computing pleioh2g and correct bias
Usage
pruning_pleioh2g_wrapper(
G,
phenotype,
munged_sumstats,
ld_path,
wld_path,
sample_prev = NULL,
population_prev = NULL,
n_block = 200,
hmp3,
sample_rep
)
Arguments
G |
index of target disease. |
phenotype |
Vector of the phenotype name |
munged_sumstats |
All LDSC-munged GWAS .stat.gz |
ld_path |
Path to directory containing ld score files. |
wld_path |
Path to directory containing weight files. |
sample_prev |
Vector of sample prevalence, in the same order of input GWAS summary statistics. |
population_prev |
Vector of population prevalence, in the same order of input GWAS summary statistics. |
n_block |
number of jackknife blocks. |
hmp3 |
Directory for hapmap 3 snplist. |
sample_rep |
sampling times in bias correction |
Value
A 'list' containing the following elements: - 'target_disease' (character): The value "401.1". - 'target_disease_h2_est' (numeric): target disease h2g. - 'target_disease_h2_se' (numeric): target disease h2g_se. - 'selected_auxD' (character): auxiliary diseases. - 'h2pleio_uncorr' (numeric): pre-correction pleiotropic heritability estimate. - 'h2pleio_uncorr_se' (numeric): pre-correction pleiotropic heritability jackknife s.e. estimate. - 'percentage_h2pleio_uncorr' (numeric): pre-correction percentage of pleiotropic heritability estimate. - 'percentage_h2pleio_uncorr_se' (numeric): pre-correction percentage of pleiotropic heritability jackknife s.e. estimate. - 'percentage_h2pleio_uncorr_jackknife' (numeric): vector of all pre-correction percentage of pleiotropic heritability jackknife estimates. - 'h2pleio_corr' (numeric): post-correction pleiotropic heritability estimate. - 'h2pleio_corr_se' (numeric): post-correction pleiotropic heritability jackknife s.e. estimate. - 'percentage_h2pleio_corr' (numeric): post-correction percentage of pleiotropic heritability estimate. - 'percentage_h2pleio_corr_se' (numeric): post-correction percentage of pleiotropic heritability jackknife s.e. estimate. - 'corrected_weight' (numeric): corrected weight in bias correction.
Read ld from either internal or external file - refer to ldscr R package (https://github.com/mglev1n/ldscr)
Description
'read_ld()' Read ld from either internal or external file.
Usage
read_ld(ld)
Arguments
ld |
(character) Path to directory containing ld score files, ending in '*.l2.ldscore.gz'. Default is 'NA', which will utilize the built-in ld score files from Pan-UK Biobank for the ancestry specified in 'ancestry'. |
Value
A data frame (tibble) containing LD score information read from the specified directory. Each row corresponds to a SNP, and columns typically include:
-
CHR: Chromosome number. -
SNP: SNP identifier (rsID). -
BP: Base pair position. -
L2: LD score value. -
M: Number of SNPs used in the LD score computation.
Read M from either internal or external file - refer to ldscr R package (https://github.com/mglev1n/ldscr)
Description
'read_m()' Read M from either internal or external file
Usage
read_m(ld)
Arguments
ld |
(character) Path to directory containing ld score files, ending in '*.l2.ldscore.gz'. Default is 'NA', which will utilize the built-in ld score files from Pan-UK Biobank for the ancestry specified in 'ancestry'. |
Value
A data frame (tibble) containing SNP counts read from the specified M files.
Read summary statistics from either internal or external file - refer to ldscr R package (https://github.com/mglev1n/ldscr)
Description
'read_sumstats()' Read summary statistics from either internal or external file
Usage
read_sumstats(munged_sumstats, name)
Arguments
munged_sumstats |
Either a dataframe, or a path to a file containing munged summary statistics. Must contain at least columns named 'SNP' (rsid), 'A1' (effect allele), 'A2' (non-effect allele), 'N' (total sample size) and 'Z' (Z-score) |
name |
trait name |
Value
A data frame (tibble) containing GWAS summary statistics for the specified trait. The returned object will always contain at least the following columns:
-
SNP: SNP identifier (rsID). -
A1: Effect allele. -
A2: Non-effect allele. -
N: Total sample size for the SNP. -
Z: Z-score of SNP-trait association.
Read wld from either internal or external file - refer to ldscr R package (https://github.com/mglev1n/ldscr)
Description
'read_wld()' Read wld from either internal or external file
Usage
read_wld(wld)
Arguments
wld |
(character) Path to directory containing weight files. Default is 'NA', which will utilize the built-in weight files from Pan-UK Biobank for the ancestry specified in 'ancestry'. |
Value
A data frame (tibble) containing LD weight information read from the specified directory. Each row corresponds to a SNP, and columns typically include:
-
CHR: Chromosome number. -
SNP: SNP identifier (rsID). -
BP: Base pair position. -
wLD: Weight for LD regression.
Example munged dataframe - refer to ldscr R package (https://github.com/mglev1n/ldscr)
Description
Example munged dataframe - refer to ldscr R package (https://github.com/mglev1n/ldscr)
Usage
sumstats_munged_example_input(example, dataframe = TRUE)
Arguments
example |
(character) "401.1" which have been included as example traits. |
dataframe |
(logical) If 'TRUE' (default), return an example munged dataframe. If 'FALSE', return path to the file on disk. |
Value
either a [tibble][tibble::tibble-package] containing a munged dataframe, or a path to the file on disk.