Fast K-sample Ball Divergence Test for GWAS Data
Usage
bd.gwas.test(
x,
snp,
screening.method = c("permute", "spectrum"),
refine = TRUE,
num.permutations,
distance = FALSE,
alpha,
screening.result = NULL,
verbose = TRUE,
seed = 1,
num.threads = 0,
...
)
Arguments
- x
a numeric vector, matrix, data.frame, dist object.
- snp
a numeric matrix recording the values of single nucleotide polymorphism (SNP). Each column must be an integer vector.
- screening.method
if
screening.method = "spectrum"
, the spectrum method is applied to screening the candidate SNPs, or otherwise, the permutation method is applied. Default:screening.method = "permute"
.- refine
a logical value. If
refine = TRUE
, a \(p\)-values refining process is applied to the SNPs which passes the pre-screening process. Default:refine = TRUE
(At present,refine = FALSE
is not available).- num.permutations
the number of permutation replications. When
num.permutations = 0
, the function just returns the Ball Divergence statistic. Default:num.permutations = 100 * ncol(snp)
- distance
if
distance = TRUE
, the elements ofx
will be considered as a distance matrix. Default:distance = FALSE
.- alpha
the significance level. Default:
0.05 / ncol(snp)
.- screening.result
A object return by
bd.gwas.test
that preserving the pre-screening result. It works only if the pre-screening is available. Default:screening.result = NULL
.- verbose
Show computation status and estimated runtimes. Default:
verbose = FALSE
.- seed
the random seed. Default
seed = 1
.- num.threads
number of threads. If
num.threads = 0
, then all of available cores will be used. Defaultnum.threads = 0
.- ...
further arguments to be passed to or from methods.
Value
bd.gwas.test returns a list containing the following components:
statistic
ball divergence statistics vector.
permuted.statistic
a data.frame containing permuted ball divergence statistic for pre-screening SNPs. If
refine = FALSE
, it takes valueNULL
.eigenvalue
the eigenvalue of spectrum decomposition. If
refine = TRUE
, it takes valueNULL
.p.value
the p-values of ball divergence test.
refined.snp
the SNPs have been refined.
refined.p.value
the refined \(p\)-value of significant snp.
refined.permuted.statistic
a data.frame containing permuted ball divergence statistics for refining \(p\)-values.
screening.result
a list containing the result of screening.
References
Yue Hu, Haizhu Tan, Cai Li, and Heping Zhang. (2021). Identifying genetic risk variants associated with brain volumetric phenotypes via K-sample Ball Divergence method. Genetic Epidemiology, 1–11. https://doi.org/10.1002/gepi.22423
Examples
# \donttest{
library(Ball)
set.seed(1234)
num <- 200
snp_num <- 500
p <- 5
x <- matrix(rnorm(num * p), nrow = num)
snp <- sapply(1:snp_num, function(i) {
sample(0:2, size = num, replace = TRUE)
})
snp1 <- sapply(1:snp_num, function(i) {
sample(1:2, size = num, replace = TRUE)
})
snp <- cbind(snp, snp1)
res <- Ball::bd.gwas.test(x = x, snp = snp)
#> =========== Pre-screening SNPs ===========
#> [1] "None of SNP pass the pre-screening process!"
mean(res[["p.value"]] < 0.05)
#> [1] 0.039
mean(res[["p.value"]] < 0.005)
#> [1] 0.005
## only return the test statistics;
res <- Ball::bd.gwas.test(x = x, snp = snp, num.permutation = 0)
## save pre-screening process results:
x <- matrix(rnorm(num * p), nrow = num)
snp <- sapply(1:snp_num, function(i) {
sample(0:2, size = num, replace = TRUE, prob = c(1/2, 1/4, 1/4))
})
snp_screening <- Ball::bd.gwas.test(x = x, snp = snp,
alpha = 5*10^-4,
num.permutations = 19999)
#> =========== Pre-screening SNPs ===========
#> [1] "None of SNP pass the pre-screening process!"
mean(res[["p.value"]] < 0.05)
#> [1] 0
mean(res[["p.value"]] < 0.005)
#> [1] 0
mean(res[["p.value"]] < 0.0005)
#> [1] 0
## refine p-value according to the pre-screening process result:
res <- Ball::bd.gwas.test(x = x, snp = snp, alpha = 5*10^-4,
num.permutations = 19999,
screening.result = snp_screening[["screening.result"]])
# }