snp_MAX3: MAX3 statistic
In bigsnpr: Analysis of Massive SNP Arrays

snp_MAX3

R Documentation

MAX3 statistic

Description

Compute the MAX3 statistic, which tests for three genetic models (additive, recessive and dominant).

Usage

snp_MAX3(Gna, y01.train, ind.train = rows_along(Gna), val = c(0, 0.5, 1))

Arguments

`Gna`	A FBM.code256 (typically `⁠<bigSNP>$genotypes⁠`). You can have missing values in these data.
`y01.train`	Vector of responses, corresponding to `ind.train`. Must be only 0s and 1s.
`ind.train`	An optional vector of the row indices that are used, for the training part. If not specified, all rows are used. Don't use negative indices.
`val`	Computing `\smash{\displaystyle\max_{x \in val}}~Z_{CATT}^2(x)`. Default is `c(0, 0.5, 1)` and corresponds to the MAX3 statistic. Only `c(0, 1)` corresponds to MAX2. And only `0.5` corresponds to the Armitage trend test. Finally, `seq(0, 1, length.out = L)` corresponds to MAXL.

Details

P-values associated with returned scores are in fact the minimum of the p-values of each test separately. Thus, they are biased downward.

Value

An object of classes mhtest and data.frame returning one score by SNP. See methods(class = "mhtest").

References

Zheng, G., Yang, Y., Zhu, X., & Elston, R. (2012). Robust Procedures. Analysis Of Genetic Association Studies, 151-206. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-1-4614-2245-7_6")}.

Examples

set.seed(1)

# constructing a fake genotype big.matrix
N <- 50; M <- 1200
fake <- snp_fake(N, M)
G <- fake$genotypes
G[] <- sample(as.raw(0:3), size = length(G), replace = TRUE)
G[1:8, 1:10]

# Specify case/control phenotypes
fake$fam$affection <- rep(1:2, each = N / 2)

# Get MAX3 statistics
y01 <- fake$fam$affection - 1
str(test <- snp_MAX3(fake$genotypes, y01.train = y01))
# p-values are not well calibrated
snp_qq(test)
# genomic control is not of much help
snp_qq(snp_gc(test))

# Armitage trend test (well calibrated because only one test)
test2 <- snp_MAX3(fake$genotypes, y01.train = y01, val = 0.5)
snp_qq(test2)

bigsnpr documentation built on Sept. 30, 2024, 9:18 a.m.