sampDomEff

Samples dominance deviation effects from a normal distribution
and uses previously sampled additive effects to form dominance
effects

internal

The successor to the 'AlphaSim' software for breeding program
simulation [Faux et al. (2016) <doi:10.3835/plantgenome2016.02.0013>].
Used for stochastic simulations of breeding programs to the level of DNA
sequence for every individual. Contained is a wide range of functions for
modeling common tasks in a breeding program, such as selection and crossing.
These functions allow for constructing simulations of highly complex plant and
animal breeding programs via scripting in the R software environment. Such
simulations can be used to evaluate overall breeding program performance and
conduct research into breeding program design, such as implementation of
genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS')
for fast simulation of biallelic sequences according to a population
demographic history [Chen et al. (2009) <doi:10.1101/gr.083634.108>].

Chris Gaynor

AlphaSimR

Breeding Program Simulations

Gregor Gorjanc

John Hickey

Daniel Money

David Wilson

Thiago Oliveira

Audrey Martin

Philip Greenspoon

Ros Craddock

sampDomEff function

<dl><dt>qtlLoci</dt>
<dd>total number of loci</dd>
<dt>nTraits</dt>
<dd>number of traits</dd>
<dt>addEff</dt>
<dd>previously sampled additive effects</dd>
<dt>corDD</dt>
<dd>correlation between dominance degrees</dd>
<dt>meanDD</dt>
<dd>mean value of dominance degrees</dd>
<dt>varDD</dt>
<dd>variance of dominance degrees</dd></dl>

Arguments

Sample dominance effects — sampDomEff

<dl>

<dt>qtlLoci</dt>
<dd>total number of loci</dd>


<dt>nTraits</dt>
<dd>number of traits</dd>


<dt>addEff</dt>
<dd>previously sampled additive effects</dd>


<dt>corDD</dt>
<dd>correlation between dominance degrees</dd>


<dt>meanDD</dt>
<dd>mean value of dominance degrees</dd>


<dt>varDD</dt>
<dd>variance of dominance degrees</dd>

</dl>

Sample dominance effects

sampDomEff: Sample dominance effects

Description

Usage

Value

Arguments