Breeding Program Simulations
Additive-by-additive epistatic deviations
Add residual error to genetic values
Add segregating site to MapPop
AlphaSimR: Breeding Program Simulations
Convert a normal (Gaussian) trait to an ordered categorical (threshold...
Lose individuals at random
Breeding value
Calculate GCA
Calculate phenotypes
Combine MapPop chromosomes
Find individuals of desired sex
Convert traits to a vector of names
Dominance deviations
Create new population
Double the ploidy of individuals
Estimated breeding value
Edit genome
Edit genome - the top QTL
Fast RR-BLUP
Find LociMap superset
Find trait QTL index
Additive genic variance
Additive-by-additive genic variance
Dominance genic variance
Total genic variance
Sumarize genetic parameters
Identify candidate individuals
Determine families
Get genetic map
Retrieves marker names from genMap
Number of available threads
Get pedigree
Get QTL genetic map
Returns a vector response from a population
Get SNP genetic map
Genetic value
Hybrid crossing
Hybrid population
Import genetic map
Import haplotypes
Import inbred, diploid genotypes
Test if individuals of a population are female or male
Test if object is of a Population class
Loci metadata
Make designed crosses
Make designed crosses
Generates DH lines
Finds positions of loci by marker name
Raw population with genetic map
Mean estimated breeding values
Mean genetic values
Mean phenotypic values
Combine genomes of individuals
Merge list of populations
Multi-Population
Add Random Mutations
Raw population with genetic map and id
Creates an empty population
New MapPop
Create new Multi Population
Create new population
Number of individuals
Pedigree cross
Phenotype
Population
Population variance
Pull IBD haplotypes
Pull marker genotypes
Pull marker haplotypes
Pull QTL genotypes
Pull QTL haplotypes
Pull segregating site genotypes
Pull seg site haplotypes
Pull SNP genotypes
Pull SNP haplotypes
Quick founder haplotype simulation
Make random crosses
Make random crosses
Raw Population
Create individuals with reduced ploidy
Reset population
Sample normal deviates using a seed
RR-BLUP Model with Dominance
RR-BLUP with Dominance Model 2
RR-BLUP GCA Model
RR-BLUP GCA Model 2
RR-BLUP SCA Model
RR-BLUP SCA Model 2
RR-BLUP Model
RR-BLUP Model 2
RRBLUP Memory Usage
RR-BLUP Solution
Create founder haplotypes using MaCS
Alternative wrapper for MaCS
Sample additive effects
Sample dominance effects
Sample epistatic effects
Sample haplotypes from a MapPop
Select and randomly cross
Select families
Select individuals
Find loci on specific chromosomes
Select open pollinating plants
Select individuals within families
Self individuals
Selection index
Selection intensity
Set estimated breeding values (EBV)
Set marker haplotypes
Set phenotypes
Set GCA as phenotype
Set progeny test as phenotype
Simulation parameters
Calculate Smith-Hazel weights
Solve Multikernel Model
Solve Multivariate Model
Solve RR-BLUP with EM
Solve RR-BLUP with EM and 2 random effects
Solve RR-BLUP with EM and 3 random effects
Solve RR-BLUP
Solve Multikernel RR-BLUP
Solve Multivariate RR-BLUP
Solve Univariate Model
Additive trait
Sex specific additive trait
Sex specific additive and dominance trait
Additive and dominance trait
Additive, dominance, and epistatic trait
Additive, dominance, epistasis, and GxE trait
Additive, dominance and GxE trait
Additive and epistatic trait
Additive, epistasis and GxE trait
Additive and GxE trait
Linear transformation matrix
Usefulness criterion
Additive variance
Additive-by-additive epistatic variance
Dominance variance
Variance of estimated breeding values
Total genetic variance
Phenotypic variance
Writes a Pop-class as PLINK files
Write data records
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>].
Useful links