Description Usage Arguments Value Author(s) References Examples

View source: R/linear.weight.test.R

Test for association between a set of SNPS/genes and continuous outcomes by including variant characteristic information and using weighted score statistics.

1 | ```
linear.weight.test(y, X, G, Z, maf, weight.beta = c(1, 25), method = "liu")
``` |

`y` |
a numeric vector of the continuous outcome variables. Missing values are not allowed. |

`X` |
a numeric matrix of covariates with rows for individuals and columns for covariates. |

`G` |
a numeric genotype matrix with rows for individuals and columns for SNPs. Each SNP should be coded as 0, 1, and 2 for AA, Aa, aa, where A is a major allele and a is a minor allele. Missing genotypes are not allowed. |

`Z` |
a numeric matrix of second level covariates for variant characteristics. Each row corresponds to a variant and each column corresponds to a variant characteristic. If there is no second level covariates, a vector of 1 should be used. |

`maf` |
a numeric vector of MAF (minor allele frequency) for each SNP. |

`weight.beta` |
a numeric vector of parameters of beta function which is the weight for score statistics. The default value is "c(1,25)". |

`method` |
a method to compute the p-value and the default value is "liu". Method "davies" represents an exact method that computes the p-value by inverting the characteristic function of the mixture chisq. Method "liu" represents an approximation method that matches the first 3 moments. |

`S.tau ` |
score statistic for the variant hetergenous effect. |

`S.pi ` |
score statistic for the variant mean effect. |

`p.value.S.tau` |
p-value for testing the variant hetergenous effect. |

`p.value.S.pi` |
p-value for testing the variant mean effect. |

`p.value.overall` |
overall p-value for testing the association between the set of SNPS/genes and outcomes. It combines p.value.S.pi and p.value.S.tau by using Fisher's procedure. |

Jianping Sun, Yingye Zheng, and Li Hsu.

Sun, J., Zheng, Y., and Hsu, L. (2013) A Unified Mixed-Effects Model for Rare-Variant Association in Sequencing Studies. Genet Epidemiol. 2013 Mar 9. doi: 10.1002/gepi.21717

H. Liu, Y. Tang, H.H. Zhang (2009) A new chi-square approximation to the distribution of non-negative definite quadratic forms in non-central normal variables, Computational Statistics and Data Analysis, 53, 853-856.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
data(MiST.data)
attach(MiST.data)
##########################################################################
# Test the association between a set of SNPs and continuous outcomes
# - without information about SNP characteristics. Z is a vector of 1's.
out <- linear.weight.test(y.con, X, G, Z, maf)
##########################################################################
# Test the association between a set of SNPs and continuous outcomes
# - including SNP characteristics
out <- linear.weight.test(y.con, X, G, Z.func, maf)
``` |

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