score.calc.epistasis.score {RAINBOWR}

Calculate -log10(p) of epistatic effects with score test

Description

Calculate -log10(p) of epistatic effects with score test

Usage

score.calc.epistasis.score(
  M.now,
  y,
  X.now,
  ZETA.now,
  Gu,
  Ge,
  P0,
  map,
  haplotype = TRUE,
  num.hap = NULL,
  window.size.half = 5,
  window.slide = 1,
  chi0.mixture = 0.5,
  gene.set = NULL,
  dominance.eff = TRUE,
  skip.self.int = FALSE,
  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.

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.

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"!

Gu

A n \times n matrix. You should assign ZKZ', where K is covariance (relationship) matrix and Z is its design matrix.

Ge

A n \times n matrix. You should assign identity matrix I (diag(n)).

P0

A n \times n matrix. The Moore-Penrose generalized inverse of SV0S, where S = X(X'X)^{-1}X' and V0 = \sigma^2_u Gu + \sigma^2_e Ge. \sigma^2_u and \sigma^2_e are estimators of the null model.

map

Data frame of map information where the first column is the marker names, the second and third column is the chromosome amd map position, and the forth column is -log10(p) for each marker.

haplotype

If the number of lines of your data is large (maybe > 100), you should set haplotype = TRUE. When haplotype = TRUE, haplotype-based kernel will be used for calculating -log10(p). (So the dimension of this gram matrix will be smaller.) The result won't be changed, but the time for the calculation will be shorter.

num.hap

When haplotype = TRUE, you can set the number of haplotypes which you expect. Then similar arrays are considered as the same haplotype, and then make kernel(K.SNP) whose dimension is num.hap x num.hap. When num.hap = NULL (default), num.hap will be set as the maximum number which reflects the difference between lines.

window.size.half

This argument decides how many SNPs (around the SNP you want to test) are used to calculated K.SNP. More precisely, the number of SNPs will be 2 * window.size.half + 1.

window.slide

This argument determines how often you test markers. If window.slide = 1, every marker will be tested. If you want to perform SNP set by bins, please set window.slide = 2 * window.size.half + 1.

chi0.mixture

RAINBOWR assumes the test statistic l1' F l1 is considered to follow a x chisq(df = 0) + (1 - a) x chisq(df = r). where l1 is the first derivative of the log-likelihood and F is the Fisher information. And r is the degree of freedom. The argument chi0.mixture is a (0 <= a < 1), and default is 0.5.

gene.set

If you have information of gene, you can use it to perform kernel-based GWAS. You should assign your gene information to gene.set in the form of a "data.frame" (whose dimension is (the number of gene) x 2). In the first column, you should assign the gene name. And in the second column, you should assign the names of each marker, which correspond to the marker names of "geno" argument.

dominance.eff

If this argument is TRUE, dominance effect is included in the model, and additive x dominance and dominance x dominance are also tested as epistatic effects. When you use inbred lines, please set this argument FALSE.

skip.self.int

As default, the function also tests the self-interactions among the same SNP-sets. If you want to avoid this, please set 'skip.self.int = TRUE'.

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) of epistatic effects for each SNP-set

References

Listgarten, J. et al. (2013) A powerful and efficient set test for genetic markers that handles confounders. Bioinformatics. 29(12): 1526-1533.

Lippert, C. et al. (2014) Greater power and computational efficiency for kernel-based association testing of sets of genetic variants. Bioinformatics. 30(22): 3206-3214.

Jiang, Y. and Reif, J.C. (2015) Modeling epistasis in genomic selection. Genetics. 201(2): 759-768.