Ball (version 1.3.13)

bd.gwas.test: Fast K-sample Ball Divergence Test for GWAS Data

Description

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,
  ...
)

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 value NULL.

eigenvalue

the eigenvalue of spectrum decomposition. If refine = TRUE, it takes value NULL.

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.

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 of x 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. Default num.threads = 0.

...

further arguments to be passed to or from methods.

Author

Jin Zhu

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

See Also

bd, bd.test

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)
mean(res[["p.value"]] < 0.05)
mean(res[["p.value"]] < 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)
mean(res[["p.value"]] < 0.05)
mean(res[["p.value"]] < 0.005)
mean(res[["p.value"]] < 0.0005)
## 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"]])
# }