predOneCrossVarAD: Multi-trait prediction of one additive _and_ one dominance...
In wolfemd/predCrossVar: Prediction of family genomic variances and covariances

Description Usage Arguments Value

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,
  ...
)

`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.
`...`