README.md

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

predCrossVar

The predCrossVar package

contains a complete set of functions for the prediction of additive and dominance genetic variances and co-variances among full-siblings based on parents. predCrossVar enables the prediction of genetic variance on multi-trait selection indices. Built for diploid organisms with phased, chromosome- or linkage-group ordered biallelic marker data, and a centimorgan-scale genetic map.

Installation

You can install predCrossVar My GitHub with:

devtools::install_github("wolfemd/predCrossVar", ref = 'master') 

HIGHLIGHTS

• Allows for parents to be of arbitrary heterozygosity/homozygosity (outbred or inbred)
• Predicts the additive and dominance genetic variances in the $F_1$
• Predicts genetic variances and co-variances. Enables prediction of genetic variance on an multi-trait selection index.
• Handles simple (one trait, one cross) predictions, but built for complex (multi-trait, many crosses) scenarios.
• Single estimate of marker effects from REML or MCMC (posterior mean effects --> predicts "variance of posterior means") supported
• Posterior Mean Variance (PMV) also supported: the estimator of Lehermeier et al. 2017b which computes the predicted variance across a sample of marker effects, e.g. the thinned MCMC samples, usually stored on disk. For the multi-trait case, a multivariate Bayesian model is required as only a marker effects for each trait must be computed on the same Gibbs chain.

Core functions

The package helps automate prediction for both simple (one trait, one cross) and complex (multi-trait, many crosses) scenarios.

| Single-trait Functions | REML or $MCMC^{VPM}$ | |------------------------|------------------------------------------------------------| | predCrossVarA() | Predicts one cross variance | | runCrossVarPredsA() | Wraps around predCrossVarA() to predict multiple crosses |

Equivalent functions for an additive-plus-dominance models are: predCrossVarAD() --> runCrossVarPredsAD() No function as of now to do dominance separately.

Note that these functions were developed early, and the multi-trait functions below should supersede these in function.

| Multi-trait Functions | REML, $MCMC^{VPM}$, $MCMC^{PMV}$ | |-----------------------|----------------------------------| |predOneCrossVarA() | Predicts one cross, one variance (or covariance), additive-only model | | predCrossVarsA() | wraps around predOneCrossVarA(). Predicts multiple crosses (potentially in parallel/multicore), one variance (or covariance), additive-only model | | runMtCrossVarPredsA() | wraps around predCrossVarsA(). Predicts the variances and covariances in the multi-trait case for a set of crosses, additive-only model. |

Equivalent functions for an additive-plus-dominance models are: predOneCrossVarAD() --> predCrossVarsAD() --> runMtCrossVarPredsAD(). No function as of now to do only dominance.

There are also functions for the much less computationally challenging prediction of cross means (predCrossMeanBVsOneTrait() --> predCrossMeanBVs()) for convenience.

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