| Type: | Package |
| Title: | Protein Structure Guided Local Test |
| Version: | 1.4 |
| Date: | 2025-05-03 |
| Author: | Rachel Marceau West [aut], Shannon T. Holloway [aut, cre] |
| Maintainer: | Shannon T. Holloway <shannon.t.holloway@gmail.com> |
| Description: | Provides an implementation of a rare variant association test that utilizes protein tertiary structure to increase signal and to identify likely causal variants. Performs structure-guided collapsing, which leads to local tests that borrow information from neighboring variants on a protein and that provide association information on a variant-specific level. For details of the implemented method see West, R. M., Lu, W., Rotroff, D. M., Kuenemann, M., Chang, S-M., Wagner M. J., Buse, J. B., Motsinger-Reif, A., Fourches, D., and Tzeng, J-Y. (2019) <doi:10.1371/journal.pcbi.1006722>. |
| License: | GPL-2 |
| Depends: | methods, stats, rARPACK, Matrix, CompQuadForm |
| NeedsCompilation: | no |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.1 |
| Collate: | 'A_Kernel.R' 'A_BurdenKernel.R' 'A_LinearKernel.R' 'A_PolyKernel.R' 'B_PvMethod.R' 'B_PvMethod_Davies.R' 'B_PvMethod_Liu.R' 'C_BinomialTrait.R' 'C_GaussianTrait.R' 'D_NullResult.R' 'calcLocalKernel.R' 'distanceMatrix.R' 'mainCode.R' 'point.R' 'pvResamp.R' |
| Packaged: | 2025-05-04 13:24:27 UTC; 19194 |
| Repository: | CRAN |
| Date/Publication: | 2025-05-04 13:40:02 UTC |
Protein Structure Guided Local Test
Description
A rare variant association test that utilizes protein tertiary structure to increase signal and to identify likely causal variants. Performs structure-guided collapsing, which leads to local tests that borrow information from neighboring variants on a protein and that provide association information on a variant-specific level.
Usage
point(
yy,
X,
snp,
proteinCoord,
...,
trait = "binomial",
cValues = c(0, 0.1, 0.2, 0.3, 0.4, 0.5),
weighted = TRUE,
weight = NULL,
kernel = "linear",
d = NULL,
pvMethod = "davies",
nperturb = 1000,
verbose = TRUE
)
Arguments
yy |
numeric vector; phenotype values. |
X |
numeric matrix; non-genetic covariates. |
snp |
numeric matrix; genotype snp matrix (count of minor alleles). Matrix cannot contain missing values. |
proteinCoord |
numeric matrix; columns correspond to 3 dimensional coordinates (x,y,z) of each variant in the protein tertiary structure. |
... |
optional additional arguments for p-value methods CompQuadForm::davies and CompQuadForm::liu. |
trait |
character; type of phenotype data. Must be one of {'gaussian','binomial'} quantitative or case control data, respectively. |
cValues |
numeric vector; c values from which to choose the optimal neighborhood size for borrowing significant information. |
weighted |
logical; whether or not to weight the local kernel test using (non-distance based) weights. |
weight |
numeric vector (optional) If NULL and weighted is TRUE (1.0-MAF)^24. Ignored if weighted is FALSE. |
kernel |
character; type of local kernel to use; Must be one of {'burden', 'linear', 'polynomial'}. |
d |
numeric; If kernel = 'poly', d is the order of the polynomial kernel. |
pvMethod |
character; method of calculating the p-value of each single marker test for fixed c values. Must be one of {'davies', 'liu'}. |
nperturb |
numeric, number of perturbations/resamples (perturbed test statistics) to calculate p-value of minP statistic. |
verbose |
logical; generate progress screen prints. |
Value
Returns a matrix the rows of which correspond to individual markers.
Columns correspond to:
(1) minP statistic;
(2) local kernel test p-value;
(3) optimal scale value from input cValues;
(4) minor allele frequency; and
(5) single variant score test p-value.
Examples
# number of subjects
nsubj <- 1000
# number of markers
nm <- 5
# generate coordinates for proteins
protein <- cbind( stats::rnorm(n = nm, mean = 17.6, sd = 6.6),
stats::rnorm(n = nm, mean = 1.6, sd = 13.6),
stats::rnorm(n = nm, mean = 22.9, sd = 10.4) )
# generate snp matrix
snp <- matrix(data = rbinom(n = nsubj*nm, size = 1, p = 0.02),
nrow = nsubj, ncol = nm)
colnames(snp) = paste0("m",1:nm)
# generate binmoial response
MAF <- colMeans(x = snp)/2
causal <- numeric(nm)
causal[c(2,4)] <- 1.0
betaG <- 0.4*abs(log10(x = MAF))*causal
#no non-genetic covariates
X <- NULL
mu <- -0.05 + snp %*% betaG
pryy <- exp(mu)/(1+exp(mu))
yy <- sapply(X = pryy, FUN = stats::rbinom, n = 1, size = 1)
res <- point(yy = yy, X = X, snp = snp, proteinCoord = protein,
trait = 'binomial', cValues = c(0.1,0.2),
weighted = TRUE, weight = NULL, kernel = 'linear',
pvMethod = 'liu', nperturb = 100,
verbose = FALSE)