Multi-trait prediction of one additive _and_ one dominance genetic variance (or covariance) among full-siblings.

User specifies a trait variance (or trait-trait covariance) to predict for a specific pair of parents. Predicts the additive genetic variance (or covariance) among full-siblings of that cross.

predOneCrossVarAD(
  Trait1,
  Trait2,
  sireID,
  damID,
  haploMat,
  recombFreqMat,
  predType,
  postMeanAddEffects,
  postMeanDomEffects,
  postVarCovarOfAddEffects = NULL,
  postVarCovarOfDomEffects = NULL,
  ...
)

Arguments

Trait1	string, label for Trait1. When Trait1==Trait2 computes the genomic variance of the trait, when Trait1!=Trait2 computes the genomic covariance between traits.
Trait2	string, label for Trait2. When Trait1==Trait2 computes the genomic variance of the trait, when Trait1!=Trait2 computes the genomic covariance between traits.
sireID	string, Sire genotype ID. Needs to correspond to renames in haploMat
damID	string, Dam genotype ID. Needs to correspond to renames in haploMat
haploMat	matrix of phased haplotypes, 2 rows per sample, cols = loci, 0,1, rownames assumed to contain GIDs with a suffix, separated by "_" to distinguish haplotypes
recombFreqMat	a square symmetric matrix with values = (1-2*c1), where c1=matrix of expected recomb. frequencies. The choice to do 1-2c1 outside the function was made for computation efficiency; every operation on a big matrix takes time.
predType	string, "VPM" or "PMV". Choose option "VPM" if you have REML marker effect estimates (or posterior-means from MCMC) one set of marker effect estimates per trait. Variance of posterior means is faster but the alternative predType=="PMV" is expected to be less biassed. PMV requires user to supply a (probably LARGE) variance-covariance matrix of effects estimates.
postMeanAddEffects	list of named vectors (or column matrices) with the additive marker effects (can posterior-mean effects from MCMC _or_ from REML, if setting predType="PMV".
postMeanDomEffects	list of named vectors (or column matrices) with the dominance marker effects (can posterior-mean effects from MCMC _or_ from REML, if setting predType="PMV".
postVarCovarOfAddEffects	Only if setting predType="PMV". Matrix of dimension N SNP x N SNP. ADDITIVE Posterior Sample Variance-Covariance Matrix of Marker Effects Estimates.
postVarCovarOfDomEffects	Only if setting predType="PMV". Matrix of dimension N SNP x N SNP. DOMINANCE Posterior Sample Variance-Covariance Matrix of Marker Effects Estimates.
...

Value

tibble with predicted additive and dominance variance for one cross, one variance parameter