Description Usage Arguments Details Value Author(s) References Examples

View source: R/pleio.glm.sequential.R

Perform sequential tests of pleiotropy to determine the number of traits, and which traits, are associatd with a vector of genotypes.

1 | ```
pleio.glm.sequential(obj.pleio.glm.fit, pval.threshold)
``` |

`obj.pleio.glm.fit` |
result of pleio.glm.fit |

`pval.threshold` |
p-value for rejecting the null hypothesis of the specified number of coefficients constrained to be zero. |

Perform sequential tests of pleiotropy, starting at the usual multivarite null hypothesis that all coefficients = 0. If this test rejects because the p-value < pval.threshold, then allow one coefficient to be non-zero in order to test whether the remaining coefficients = 0. If the test of one non-zero coefficient rejects, then allow two non-zero coefficients, considering all possible combinations of two non-zero coefficients and test whether the remaining coefficients = 0. Continue this sequential testing until the p-value for a test is greater than the specific pval.threshold. The step at which the p-value > pval.threshold determines which traits are associated with the genotype. If there are m traits, the sequential testing stops either when p-value > pval.threshold, or when (m-1) traits are tested. If the p-value remains less than pval.threshold when testing (m-1) traits, this implies that all m traits are associated with the genotype.

A list containing:

`pval ` |
p-value of the final test from the sequential testing |

`count` |
the number of nonzero coefficients |

`index.nonzero.coef` |
index of column(s) of y that have non-zero coefficients. These indices indicate which traits are associated with the genotype, accounting for the correlations among the traits. |

Dan Schaid and Jason Sinnwell

Schaid DJ, Tong X, Larrabee B, Kennedy RB, Poland GA, Sinnwell JP. Statistical Methods for Testing Genetic Pleiotropy. Genetics. 2016 Oct;204(2):483-497.

Schaid DJ, Tong X, Batzler A, Sinnwell JP, Qing J, Biernacka JM. Multivariate Generalized Linear Model for Genetic Pleiotropy. Under review.

1 2 3 4 5 6 7 8 9 10 | ```
data(pleio.demo)
## test without covars
fams <- c("gaussian","binomial","ordinal")
obj <- pleio.glm.fit(y, geno, glm.family=fams)
stat <- pleio.glm.test(obj, count.nonzero.coef = 0)
stat$stat
stat$pval
pseq <- pleio.glm.sequential(obj, pval.threshold=.5)
pseq
``` |

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