R: Mixed models for testing self and neighbor effects

nei_lmm {rNeighborGWAS}

R Documentation

Mixed models for testing self and neighbor effects

A function to provide coefficients and p-values of self and neighbor effects for each marker.

nei_lmm(
  geno,
  g_nei,
  pheno,
  addcovar = NULL,
  response = c("quantitative", "binary"),
  n_core = 1L,
  asym = FALSE
)

`geno`	An individual x marker matrix. Bialleles (i.e., A or a) must be converted into -1 or 1 digit.
`g_nei`	An output of `nei_coval()` object, namely an individual x marker matrix including neighbor genotypic identity.
`pheno`	A numeric vector including phenotypes for individuals
`addcovar`	An optional matrix including additional non-genetic covariates. It contains no. of individuals x no. of covariates.
`response`	An option to select if the phenotype is a `"quantitative"` trait subject to linear models, or a `"binary"` trait subject to logistic models.
`n_core`	No. of cores for a multi-core computation. This does not work for Windows OS. Default is a single-core computation.
`asym`	If TRUE, asymmetric neighbor effects are also tested and returned as "beta_sxn" and "p_sxn".

This function is a subset of neiGWAS(). nei_lmm() gives detailed results but requires more computational time.

A data.frame including coefficients and p-values of self and neighbor effects, without the chromosome numbers and marker position.

beta_self coefficient for self effects
beta_self coefficient for neighbor effects
p_self p-value for self effects by a likelihood ratio test between a null and standard GWAS model
p_nei p-value for neighbor effects by a likelihood ratio test between models with or without neighbor effects