View source: R/functions_for_RGWAS.R

score.calc.int.MC | R Documentation |

Calculate -log10(p) of each SNP by the Wald test for the model inluding interaction term.

```
score.calc.int.MC(
M.now,
ZETA.now,
y,
X.now,
package.MM = "gaston",
interaction.with.SNPs.now,
test.method.interaction = "simultaneous",
include.SNP.effect = TRUE,
Hinv,
n.core = 2,
parallel.method = "mclapply",
P3D = TRUE,
eigen.G = NULL,
optimizer = "nlminb",
min.MAF = 0.02,
count = TRUE
)
```

`M.now` |
A |

`ZETA.now` |
A list of variance (relationship) matrix (K; |

`y` |
A |

`X.now` |
A |

`package.MM` |
The package name to be used when solving mixed-effects model. We only offer the following three packages:
"RAINBOWR", "MM4LMM" and "gaston". Default package is 'gaston'.
See more details at |

`interaction.with.SNPs.now` |
A |

`test.method.interaction` |
Method for how to test SNPs and the interactions between SNPs and the genetic background. We offer three methods as follows: "simultaneous": All effects (including SNP efects) are tested simultanously. "snpSeparate": SNP effects are tested as one effect, and the other interaction effects are simulateneously. "oneByOne": All efects are tested separately, one by one. |

`include.SNP.effect` |
Whether or not including SNP effects into the tested effects. |

`Hinv` |
The inverse of |

`n.core` |
Setting n.core > 1 will enable parallel execution on a machine with multiple cores. This argument is not valid when 'parallel.method = "furrr"'. |

`parallel.method` |
Method for parallel computation. We offer three methods, "mclapply", "furrr", and "foreach". When 'parallel.method = "mclapply"', we utilize When 'parallel.method = "furrr"', we utilize When 'parallel.method = "foreach"', we utilize We recommend that you use the option 'parallel.method = "mclapply"', but for Windows users, this parallelization method is not supported. So, if you are Windows user, we recommend that you use the option 'parallel.method = "foreach"'. |

`P3D` |
When P3D = TRUE, variance components are estimated by REML only once, without any markers in the model. When P3D = FALSE, variance components are estimated by REML for each marker separately. |

`eigen.G` |
A list with - $values
Eigen values - $vectors
Eigen vectors
The result of the eigen decompsition of |

`optimizer` |
The function used in the optimization process. We offer "optim", "optimx", and "nlminb" functions. This argument is only valid when ‘package.MM = ’RAINBOWR''. |

`min.MAF` |
Specifies the minimum minor allele frequency (MAF). If a marker has a MAF less than min.MAF, it is assigned a zero score. |

`count` |
When count is TRUE, you can know how far RGWAS has ended with percent display. |

-log10(p) for each marker

Kennedy, B.W., Quinton, M. and van Arendonk, J.A. (1992) Estimation of effects of single genes on quantitative traits. J Anim Sci. 70(7): 2000-2012.

Kang, H.M. et al. (2008) Efficient Control of Population Structure in Model Organism Association Mapping. Genetics. 178(3): 1709-1723.

Kang, H.M. et al. (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 42(4): 348-354.

Zhang, Z. et al. (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet. 42(4): 355-360.

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