Description Usage Arguments Details Value Author(s) References See Also Examples

SSUW s been proposed by Pan (2009) as a modified test based on the score-type U statistic (i.e. logistic regression). More specifically, SSUW is a test based on a weighted sum of the squares of the marginal score statistics. Its null distribution have a quadratic form and can be approximated by a chi-square distribution.

1 | ```
SSUW(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:

`ssuw.stat` |
ssuw 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 SSUW with 500 permutations
myssuw = SSUW(phenotype, genotype, perm=500)
myssuw
## End(Not run)
``` |

```
Loading required package: mvtnorm
SSUW: Weighted Sum of Squared Score
Info:
cases controls variants n.perms
500 500 10 500
ssuw.stat asym.pval perm.pval
6.153771 0.800934 0.794000
```

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