Description Usage Arguments Details Value Note Author(s) References Examples

POM-aSPU test evaluates an associations between an ordinal response (e.g. SNP) and multiple phenotypes; provides a series of p-values of POM-SPU(pow) and POM-aSPU tests.

1 |

`Y` |
a vector of categorical indicators |

`Y.level` |
a vector indicating an order of categories either numeric or character e.g. Y.level = c(0, 1, 2) or Y.level = c("Well", "Mild", "Impaired"). |

`pheno` |
a numeric phenotype matrix with each row as a different individual and each column as a separate phenotype. |

`Z` |
a numeric covariate matrix with each row as a different individual and each column as a covariate to be adjusted. |

`pow` |
a vector of the power weight to be used at a trait level (default = c(1:8, Inf)). |

`n.perm` |
a numeric value of number of null statistics (default = 1000). |

Adaptive association tests for an ordinal variable (e.g. SNP) and multiple phenotypes using POM.

a vector of p-values from POM-SPU(pow) tests and POM-aSPU test.

We provide a permutation based POM-aSPU test where null statistics are generated from permutations.

Junghi Kim and Wei Pan

Kim J, and Pan W (2017). "Adaptive testing for multiple traits in a proportional odds model with applications to detect SNP-brain network associations." *Genetic Epidemiology*, 41(3), 259–277.

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# Example 1)
# -- simulating phenotypes
# -- n.subjects: number of subjects
# -- n.traits: number of phenotypes
set.seed(136)
n.subjects <- 100
n.traits <- 10
pheno <- matrix(rnorm(n.subjects*n.traits), n.subjects, n.traits)
# -- simulating genotype (Y) and covariates (Z)
Y <- sample(c(0,1,2), n.subjects, replace = TRUE)
Z <- matrix(rnorm(n.subjects*2, 2, 1), n.subjects, 2)
# -- Computing the p-value of POMaSPU test with the permutation based method
Pvl <- POMaSPU(Y = Y, Y.level = c(0,1,2), pheno = pheno, Z = Z, pow = c(1,2,4,Inf), n.perm = 1000)
# -- Each element of Pvl is a p value of POM-SPU(pow) in order
# -- The last element of Pvl is a p value of POM-aSPU test
Pvl
Pvl[length(Pvl)]
# > Pvl
# SPU.1 SPU.2 SPU.4 SPU.Inf aSPU
# 0.1800000 0.5010000 0.3220000 0.1720000 0.3506494
# > Pvl[length(Pvl)]
# aSPU
# 0.3506494
# Example 2)
# n.subjects <- 100
# n.traits <- 10
# Y <- sample(c("Well", "Mild", "Sick"), n.subjects, replace = TRUE)
# pheno <- matrix(rnorm(n.subjects*n.traits), n.subjects, n.traits)
# Z <- matrix(rnorm(n.subjects*2, 2, 1), n.subjects, 2)
# Pvl <- POMaSPU(Y = Y, Y.level = c("Sick", "Mild", "Well"), pheno = pheno, Z = Z,
# pow = c(1,2,4,Inf), n.perm = 1000)
``` |

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