For GSA of SNP data, the following two-step procedure is implemented (see Biernacka et al[1] for more details on the method). Step 1: Principal components analysis for SNPs within a gene is completed with the components needed to explain 80 percent of the variation retained. Using these components, a gene-level association test is completed to determine the association of the gene with the phenotype. Step 2: The gene-level p values for genes within a given gene set are combined using the Gamma Method, a variation of Fisher's Method, to determine the association of the gene set with the phenotype. The GSA function for SNP data allow quantitative, binary and time-to-event phenotypes (i.e., linear models, logistic models, Cox proportional hazard models).

1 2 3 |

`formula` |
formula for model, include phenotype and covars. SNPs will be added by function |

`data` |
All data including matrix of genetic markers, each marker represented by the dosage of some allele, could also be CNV, treated as continuous and covariates |

`snpprefix` |
prefix for SNP variable, defaults to "snp" |

`gene` |
vector disignating the gene each marker belongs to, must be in same order as SNPs |

`PCpctVar` |
numeric indicating the percent of variation (in percent) in the genetic markers that is to be explained by PCs |

`gammaShape` |
numeric indicating the gamma shape parameter to be used for p-value summarization |

`STT` |
numeric indicating soft truncation threshold to be used, will calculate gamma parameter (must be <= 0.4) |

`pheno.type` |
type of phenotype, case-control results in logistic regression, quantitative results in OLS, and survival results in cox model |

`perm` |
boolean indicating whether permutation p-value are to be used for the gamma summary method |

`n.perm` |
numeric indicating number of permutations to be used |

`seed` |
numeric to set RNG for reproducability |

This functions returns a list.

`gamma.pvalue` |
Gamma P value |

`perm.pvalue` |
Gamma permutation p value, if specified. Else NA |

`gene.info` |
Info for each gene |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
###Case Control (logistic) example
data(testdata)
data(gene_example)
PCgamma(pheno~strata(study)+age,
data=testdata,gene=gene_example,pheno.type="case.control",
STT = 0.2, gammaShape = NULL,
perm=FALSE, n.perm = 10, seed = 12212012)
##Here is a survival example
set.seed(1234)
time_example <- rnorm(150, m=50, sd=10)
event_example <- rbinom(150, 1, 0.3)
testdata <- cbind(testdata,time_example,event_example)
PCgamma(Surv(time_example,event_example)~strata(study)+age,
data=testdata,gene=gene_example,pheno.type="survival",
STT = 0.2, gammaShape = NULL,
perm=FALSE, n.perm = 10, seed = 12212012)
``` |

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