genetrait: Generate pseudo phenotypic values
In RAINBOWR: Genome-Wide Association Study with SNP-Set Methods

genetrait

R Documentation

Generate pseudo phenotypic values

Description

This function generates pseudo phenotypic values according to the following formula.

y = X \beta + Z u + e

where effects of major genes are regarded as fixed effects \beta and polygenetic effects are regarded as random effects u. The variances of u and e are automatically determined by the heritability.

Usage

genetrait(
  x,
  sample.sets = NULL,
  candidate = NULL,
  pos = NULL,
  x.par = NULL,
  ZETA = NULL,
  x2 = NULL,
  num.qtn = 3,
  weight = c(2, 1, 1),
  qtn.effect = rep("A", num.qtn),
  prop = 1,
  polygene.weight = 1,
  polygene = TRUE,
  h2 = 0.6,
  h.correction = FALSE,
  seed = NULL,
  plot = TRUE,
  saveAt = NULL,
  subpop = NULL,
  return.all = FALSE,
  seed.env = TRUE
)

Arguments

`x`	A `n \times m` genotype matrix where `n` is sample size and `m` is the number of markers.
`sample.sets`	A n.sample x n.mark genotype matrix. Markers with fixed effects (QTNs) are chosen from sample.sets. If sample.sets = NULL, sample.sets = x.
`candidate`	If you want to fix QTN postitions, please set the number where SNPs to be fixed are located in your data (so not position). If candidate = NULL, QTNs were randomly sampled from sample.sets or x.
`pos`	A n.mark x 1 vector. Cumulative position (over chromosomes) of each marker.
`x.par`	If you don't want to match the sampling population and the genotype data to QTN effects, then use this argument as the latter.
`ZETA`	A list of covariance (relationship) matrix (K: `m \times m`) and its design matrix (Z: `n \times m`) of random effects. Please set names of list "Z" and "K"! You can use more than one kernel matrix. For example, ZETA = list(A = list(Z = Z.A, K = K.A), D = list(Z = Z.D, K = K.D)) Z.A, Z.D Design matrix (`n \times m`) for the random effects. So, in many cases, you can use the identity matrix. K.A, K.D Different kernels which express some relationships between lines. For example, K.A is additive relationship matrix for the covariance between lines, and K.D is dominance relationship matrix.
`x2`	A genotype matrix to calculate additive relationship matrix when Z.ETA = NULL. If Z.ETA = NULL & x2 = NULL, calcGRM(x) will be calculated as kernel matrix.
`num.qtn`	The number of QTNs
`weight`	The weights for each QTN by their standard deviations. Negative value is also allowed.
`qtn.effect`	Additive of dominance for each marker effect. This argument should be the same length as num.qtn.
`prop`	The proportion of effects of QTNs to polygenetic effects.
`polygene.weight`	If there are multiple kernels, this argument determines the weights of each kernel effect.
`polygene`	If polygene = FALSE, pseudo phenotypes with only QTN effects will be generated.
`h2`	The wide-sense heritability for generating phenotypes. 0 <= h2 < 1
`h.correction`	If TRUE, this function will generate phenotypes to match the genomic heritability and "h2".
`seed`	If seed is not NULL, some fixed phenotypic values will be generated according to set.seed(seed)
`plot`	If TRUE, boxplot for generated phenotypic values will be drawn.
`saveAt`	When drawing any plot, you can save plots in png format. In saveAt, you should substitute the name you want to save. When saveAt = NULL, the plot is not saved.
`subpop`	If there is subpopulation structure, you can draw boxpots divide by subpopulations. n.sample x n.subpop matrix. Please indicate the subpopulation information by (0, 1) for each element. (0 means that line doen't belong to that subpopulation, and 1 means that line belongs to that subpopulation)
`return.all`	If FALSE, only returns generated phenotypic values. If TRUE, this function will return other information such as positions of candidate QTNs.
`seed.env`	If TRUE, this function will generate different environment effects every time.

Value

trait: Generated phenotypic values
u: Generated genotyope values
e: Generated environmental effects
candidate: The numbers where QTNs are located in your data (so not position).
qtn.position: QTN positions
heritability: Genomic heritability for generated phenotypic values.