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RR-BLUP with Dominance Model 2

Source: R/GS.R
RRBLUP_D2.Rd

Fits an RR-BLUP model for genomic predictions that includes dominance effects. This implementation is meant for situations where RRBLUP_D is too slow. Note that RRBLUP_D2 is only faster in certain situations. Most users should use RRBLUP_D.

Usage

RRBLUP_D2(
  pop,
  traits = 1,
  use = "pheno",
  snpChip = 1,
  useQtl = FALSE,
  maxIter = 10,
  Va = NULL,
  Vd = NULL,
  Ve = NULL,
  useEM = TRUE,
  tol = 1e-06,
  simParam = NULL,
  ...
)

Arguments

pop

a Pop-class to serve as the training population

traits

an integer indicating the trait to model, a trait name, or a function of the traits returning a single value.

use

train model using phenotypes "pheno", genetic values "gv", estimated breeding values "ebv", breeding values "bv", or randomly "rand"

snpChip

an integer indicating which SNP chip genotype to use

useQtl

should QTL genotypes be used instead of a SNP chip. If TRUE, snpChip specifies which trait's QTL to use, and thus these QTL may not match the QTL underlying the phenotype supplied in traits.

maxIter

maximum number of iterations. Only used when number of traits is greater than 1.

Va

marker effect variance for additive effects. If value is NULL, a reasonable starting point is chosen automatically.

Vd

marker effect variance for dominance effects. If value is NULL, a reasonable starting point is chosen automatically.

Ve

error variance. If value is NULL, a reasonable starting point is chosen automatically.

useEM

use EM to solve variance components. If false, the initial values are considered true.

tol

tolerance for EM algorithm convergence

simParam

an object of SimParam

...

additional arguments if using a function for traits

Examples

#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=20)

#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitAD(10, meanDD=0.5)
SP$setVarE(h2=0.5)
SP$addSnpChip(10)

#Create population
pop = newPop(founderPop, simParam=SP)

#Run GS model and set EBV
ans = RRBLUP_D2(pop, simParam=SP)
pop = setEBV(pop, ans, simParam=SP)

#Evaluate accuracy
cor(gv(pop), ebv(pop))
#>        est_GV_Trait1
#> Trait1     0.5624375