Multi-trait, multi-cross 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. Wrapper around `predOneCrossVarAD()` for predicting multiple families. Input a data.frame of crosses to predict.

predCrossVarsAD(
  Trait1,
  Trait2,
  CrossesToPredict,
  predType,
  haploMat,
  recombFreqMat,
  postMeanAddEffects,
  postMeanDomEffects,
  AddEffectList = NULL,
  DomEffectList = NULL,
  ncores,
  ...
)

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.
CrossesToPredict	data.frame or tibble, col/colnames: sireID, damID. sireID and damID must both be in the haploMat.
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.
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.
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="VPM".
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="VPM".
AddEffectList	Only if setting predType="PMV". List of ADDITIVE effect matrices. One matrix per trait. Each element of the list is named with a string identifying the trait and the colnames of each matrix are labelled with snpIDs. If users effects are from REML or posterior-means MCMC, matrices will be of dimension 1 x N SNP. If users chose predType="PMV", each matrix will be dimension N thinned-MCMC sample x N SNP.
DomEffectList	Only if setting predType="PMV". List of DOMINANCE effect matrices. One matrix per trait. Each element of the list is named with a string identifying the trait and the colnames of each matrix are labelled with snpIDs. If users effects are from REML or posterior-means MCMC, matrices will be of dimension 1 x N SNP. If users chose predType="PMV", each matrix will be dimension N thinned-MCMC sample x N SNP.
ncores	If ncores set > 1 parallelizes across families, but beware it is memory intensive and options(future.globals.maxSize=___) may need to be adjusted.
...

Value

list with two elements, "predictedfamvars" contains a tibble with all predictions for all requested families, "totcomputetime" gives the time taken to compute one var. parameter across all families, at the given ncores.