predCrossMeanTGVs

Source: R/predCrossVar.R

From an additive+dominance model, fit to multiple traits, predict the total genetic merit of each cross for each trait. For each family , for a single trait, given parental allelic dosages and (posterior mean) marker effects. G = sum( 𝑎(𝑝 − 𝑞 − 𝑦) + 𝑑[2𝑝𝑞 + 𝑦(𝑝 − 𝑞)] ) a and d being the additive and dominance effects p and q being the allele frequencies of one parent y is the difference of freq. between the two parents

predCrossMeanTGVs(
  CrossesToPredict,
  postMeanAddEffects,
  postMeanDomEffects,
  doseMat
)

Arguments

CrossesToPredict

data.frame or tibble, col/colnames: sireID, damID. sireID and damID must both be in the haploMat.
postMeanAddEffects

list of named vectors (or column matrices) with the posterior mean ADDITIVE marker effects.
postMeanDomEffects

list of named vectors (or column matrices) with the posterior mean DOMINANCE marker effects.
doseMat

dosage matrix. Assumes SNPs in M coded 0, 1, 2 (requires rounding dosages to integers). M is Nind x Mrow, numeric matrix, with row/colnames to indicate SNP/ind ID

Value

tibble with predicted mean GV for each trait in each family