score.calc.int: Calculate -log10(p) for single-SNP GWAS with interaction

Description Usage Arguments Value References

View source: R/functions_for_RGWAS.R

Description

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

Usage

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score.calc.int(
  M.now,
  ZETA.now,
  y,
  X.now,
  package.MM = "gaston",
  interaction.with.SNPs.now,
  test.method.interaction = "simultaneous",
  include.SNP.effect = TRUE,
  Hinv,
  P3D = TRUE,
  eigen.G = NULL,
  optimizer = "nlminb",
  n.core = 1,
  min.MAF = 0.02,
  count = TRUE
)

Arguments

M.now

A n \times m genotype matrix where n is sample size and m is the number of markers.

ZETA.now

A list of variance (relationship) matrix (K; m \times m) and its design matrix (Z; n \times m) of random effects. You can use only one kernel matrix. For example, ZETA = list(A = list(Z = Z, K = K)) Please set names of list "Z" and "K"!

y

A n \times 1 vector. A vector of phenotypic values should be used. NA is allowed.

X.now

A n \times p matrix. You should assign mean vector (rep(1, n)) and covariates. NA is not allowed.

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 EM3.general.

interaction.with.SNPs.now

A m \times q matrix. Interaction between each SNP and this matrix will also be tested. For example, principal components of genomic relationship matrix can be used as this matrix to test the interaction between SNPs and the genetic background.

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 H = ZKZ' + λ I where λ = σ^2_e / σ^2_u.

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 G = ZKZ'. You can use "spectralG.cpp" function in RAINBOWR. If this argument is NULL, the eigen decomposition will be performed in this function. We recommend you assign the result of the eigen decomposition beforehand for time saving.

optimizer

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

n.core

Setting n.core > 1 will enable parallel execution on a machine with multiple cores.

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.

Value

-log10(p) for each marker

References

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.


RAINBOWR documentation built on Jan. 7, 2022, 5:15 p.m.