Fast K-sample Ball Divergence Test for GWAS Data

Fast K-sample Ball Divergence Test for GWAS Data

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 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.

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.

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

Author

Jin Zhu

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"]])
# }