predCrossMeanTGVs: predCrossMeanTGVs

In wolfemd/predCrossVar: Prediction of family genomic variances and covariances

Description

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

Usage

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

wolfemd/predCrossVar documentation built on Dec. 21, 2020, 10:14 a.m.