Description Usage Arguments Details Value Author(s) Examples

Test for the association between a genetic variant and a non-normal distributed quantitative trait based on the nonparametric risk under a specific genetic model.

1 | ```
npt(y, g, varphi)
``` |

`y` |
a numeric vector of the observed quantitative trait
values in which the |

`g` |
a numeric vector of the observed genotype values ( |

`varphi` |
a numeric value which represents the genetic
model. It should be |

For a non-normal distributed quantitative trait, three genetic models (recessive, additive and dominant) used commonly are defined in terms of the nonparametric risk (NR). The recessive, additive, and dominant models can be classified based on the nonparametric risks. More specifically, the recessive, additive, and dominant models refer to NR20 > NR10 = 1/2, NR12 = NR10>1/2, and NR10 = NR20 > 1/2, respectively, where NR10 and NR20 are the nonparametric risks of the groups with the genotypes 1 and 2 relative to the group with the genotype 0, respectively, and NR12 is the nonparametric risk of the group with the genotype 2 relative to the group with the genotype 1.

`varphi`

can be `0`

, `0.5`

, or `1`

for the recessive, additive, or
dominant model, respectively. When `varphi`

is `0`

, the test is
constructed under the recessive model by pooling together the
subjects with the genotypes 0 and 1. Similarly, when `varphi`

is `1`

, the test is constructed under the dominant model by pooling
together the subjects with the genotypes 1 and 2. When
`varphi`

is `0.5`

, the test is based on the weighted sum of
NR10 and NR12.

A list with class "`htest`

" containing the following components:

`statistic` | |||

the observed value of the test statistic. | |||

`p.value` | |||

the p-value for the test. | |||

`alternative` | |||

a character string describing the alternative hypothesis. | |||

`method` | |||

a character string indicating the type of test performed. | |||

`data.name` | |||

a character string giving the names of the data. |

Lin Wang, Wei Zhang, and Qizhai Li.

1 2 3 |

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