UMINP is the Univariate minP that tests on each single genetic variant (e.g. SNP) one-by-one and then takes the minimum of their p-values, Its null distribution is based on numerical integration with respect to a multivariate normal distribution.

1 | ```
UMINP(y, X, perm = 100)
``` |

`y` |
numeric vector with phenotype status: 0=controls, 1=cases. No missing data allowed |

`X` |
numeric matrix or data frame with genotype data coded as 0, 1, 2. Missing data is allowed |

`perm` |
positive integer indicating the number of permutations (100 by default) |

There is no imputation for the missing data. Missing values are simply ignored in the computations.

An object of class `"assoctest"`

, basically a list with the following elements:

`uminp.stat` |
uminp statistic |

`asym.pval` |
asymptotic p-value |

`perm.pval` |
permuted p-value |

`args` |
descriptive information with number of controls, cases, variants, and permutations |

`name` |
name of the statistic |

Gaston Sanchez

Pan W (2009) Asymptotic tests of association with multiple SNPs in linkage disequilibrium. *Genetic Epidemiology*, **33**: 497-507

Pan W, Han F, Shen X (2010) Test Selection with Application to Detecting Disease Association with Multiple SNPs. *Human Heredity*, **69**: 120-130

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
## Not run:
# number of cases
cases = 500
# number of controls
controls = 500
# total (cases + controls)
total = cases + controls
# phenotype vector
phenotype = c(rep(1, cases), rep(0, controls))
# genotype matrix with 10 variants (random data)
set.seed(123)
genotype = matrix(rbinom(total*10, 2, 0.05), nrow=total, ncol=10)
# apply UMINP with 500 permutations
myuminp = UMINP(phenotype, genotype, perm=500)
myuminp
## End(Not run)
``` |

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