README

Design and Maintenance: Dong Yin, Xuanning Zhang, Lilin Yin ,Haohao Zhang, and Xiaolei Liu.
Contributors: Zhenshuang Tang, Jingya Xu, Xiaohui Yuan, Xinyun Li, and Shuhong Zhao.

If you have any bug reports or questions, please feed back :point_right:here:point_left:.

Installation

WE STRONGLY RECOMMEND TO INSTALL SIMER ON Microsoft R Open (https://mran.microsoft.com/download/).

Installation

devtools::install_github("xiaolei-lab/SIMER")

Data Preparation

Genotype

Genotype data should be Numeric format (m rows and n columns, m is the number of SNPs, n is the number of individuals). Other genotype data such as PLINK Binary format (details see http://zzz.bwh.harvard.edu/plink/data.shtml#bed), VCF or Hapmap can be converted to Numeric format using MVP.Data function in the rMVP (https://github.com/xiaolei-lab/rMVP).

Genotypic map

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Genotypic Map is necessary in SIMER. The first column is SNP name, the second column is Chromosome ID, the third column is physical position, the fourth column is REF, and the fifth column is ALT. It will be used to generate annotation data, genotype data, and phenotype data.

Pedigree

SNP	Chrom	BP	REF	ALT
1_10673082	1	10673082	T	C
1_10723065	1	10723065	A	G
1_11407894	1	11407894	A	G
1_11426075	1	11426075	T	C
1_13996200	1	13996200	T	C
1_14638936	1	14638936	T	C

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SIMER supports user designed pedigree to control mating process. User designed pedigree is useful only in userped reproduction. The first column is sample id, the second column is paternal id, and the third column is maternal id. Please make sure that paternal id and maternal id can match to genotype data.

Data Input

Basic

Index	Sire	Dam
41	1	11
42	1	11
43	1	11
44	1	11
45	2	12
46	2	12

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At least users should prepare two datasets: genotypic map and genotype data.

genotype data, Numeric format (m rows and n columns, m is the number of SNPs, n is the number of individuals)
genotypic map, SNP map information, the first column is SNP name, the second column is Chromosome ID, the third column is physical position, the fourth column is REF, and the fifth column is ALT.

pop.geno <- read.table("genotype.txt")
pop.map <- read.table("map.txt" , head = TRUE)

Optional

pedigree, pedigree information, the first column is sample id, the second column is paternal id, and the third column is maternal id. Note that the individuals in the pedigree do not need to be sorted by the date of birth, and the missing value can be replaced by NA or 0.

userped <- read.table("userped.txt", header = TRUE)

Quick Start

All simulation processes can be devided into 2 steps: 1) generate simulation parameters; 2) run simulation process.

Quick Start for Complete Simulation

# Generate all simulation parameters
SP <- param.simer(out = "simer")

# Run Simer
SP <- simer(SP)

Quick Start for Annotation Simulation

# Real genotypic map
# pop.map <- read.table("Real_Genotypic_map.txt", header = TRUE)
# Simulated genotypic map
pop.map <- generate.map(pop.marker = 1e4)

# Generate annotation simulation parameters
SP <- param.annot(pop.map = pop.map, qtn.num = 10)

# Run annotation simulation
SP <- annotation(SP)

Quick Start for Genotype Simulation

# Generate genotype simulation parameters
SP <- param.geno(pop.marker = 1e4, pop.ind = 1e2)

# Run genotype simulation
SP <- genotype(SP)

Quick Start for Phenotype Simulation

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Genotype Simulation

Genotype data in SIMER will be generated randomly or from outside genotype matrix. Chromosome crossovers and base mutations depend on block information and recombination information of Annotation data.

Gallery of genotype simulation parameters

Generate genotype matrix of base population

Paramater	Default	Options	Description
pop.geno	NULL	big.matrix or matrix	the genotype data.
incols	1	1 or 2	‘1’: one-column genotype represents an individual; ‘2’: two-column genotype represents an individual.
pop.marker	1e4	num	the number of markers.
pop.ind	1e2	num	the number of individuals in the base population.
prob	NULL	num vector	the genotype code probability.
rate.mut	1e-8	num	the mutation rate of the genotype data.

Paramater	Default	Options	Description
recom.spot	FALSE	TRUE or FALSE	whether to generate recombination events.
range.hot	4:6	num vector	the recombination times range in the hot spot.
range.cold	1:5	num vector	the recombination times range in the cold spot.

### 01 Use Genotype Data from Outside ###
# Create a genotype matrix
pop.geno <- matrix(0, nrow = 1e4, ncol = 1e2)

# Generate genotype simulation parameters
SP <- param.geno(pop.geno = pop.geno)

# Run genotype simulation
SP <- genotype(SP)

### 02 Create Genotype Data Randomly ###
# Generate genotype simulation parameters
SP <- param.geno(pop.marker = 1e4, pop.ind = 1e2)

# Run genotype simulation
SP <- genotype(SP)

Add chromosome crossovers and mutations to genotype matrix

With annotation data, chromosome crossovers and mutations can be added to genotype matrix.

# Generate annotation simulation parameters
# If recom.spot = TRUE, chromsome crossovers will be added to genotype matrix
SP <- param.annot(recom.spot = TRUE)
# Generate genotype simulation parameters
# Base mutation rate is 1e8
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2, rate.mut = 1e-8)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)

Note that recombination only exists in meiosis. Therefore, some reproduction methods such as clone do not have recombination process. Users can set recom.spot = FALSE to add only mutations to genotype matrix.

# Generate annotation simulation parameters
# If recom.spot = FALSE, chromsome crossovers will not be added to genotype matrix
SP <- param.annot(recom.spot = FALSE)
# Generate genotype simulation parameters
# Base mutation rate is 1e8
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2, rate.mut = 1e-8)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)

Phenotype Simulation

Phenotype data in SIMER will be generated according to different models, including:
(1) Single-Trait Model
(2) Multiple-Trait Model
(3) Repeated Record Model
(4) Genetic Effect Model (Additive effect, Dominant effect, and Genetic-Genetic interaction effect)
(5) Effect Distribution Model (QTN effect distribution: ****Normal distribution, Geometric distribution, Gamma*** distribution, and Beta distribution)
(6) Linear Mixed Model (Fixed effect, Environmental Random effect, Genetic Random effect, and Genetic-Environmental effect)

Gallery of phenotype simulation parameters

Generate phenotype by A model

Paramater	Default	Options	Description
pop	NULL	data.frame	the population information containing environmental factors and other effects.
pop.ind	100	num	the number of individuals in the base population.
pop.rep	1	num	the repeated times of repeated records.
pop.rep.bal	TRUE	TRUE or FALSE	whether repeated records are balanced.
pop.env	NULL	list	a list of environmental factors setting.
phe.model	list(tr1 = “T1 = A + E”)	list	a list of genetic model of phenotype such as “T1 = A + E”.
phe.h2A	list(tr1 = 0.3)	list	a list of additive heritability.
phe.h2D	list(tr1 = 0.1)	list	a list of dominant heritability.
phe.h2GxG	NULL	list	a list of GxG interaction heritability.
phe.h2GxE	NULL	list	a list of GxE interaction heritability.
phe.h2PE	NULL	list	a list of permanent environmental heritability.
phe.var	NULL	list	a list of phenotype variance.
phe.corA	NULL	matrix	the additive genetic correlation matrix.
phe.corD	NULL	matrix	the dominant genetic correlation matrix.
phe.corGxG	NULL	list	a list of the GxG genetic correlation matrix.
phe.corPE	NULL	matrix	the permanent environmental correlation matrix.
phe.corE	NULL	matrix	the residual correlation matrix.

Paramater	Default	Options	Description
pop.map	NULL	data.frame	the map data with annotation information.
qtn.num	10	num, vector or matrix	integer: the QTN number of single trait; vector: the multiple group QTN number of single trait; matrix: the QTN number of multiple traits.
qtn.model	‘A’	character	the genetic model of QTN such as ‘A + D’.
qtn.dist	list(tr1 = ‘norm’)	list	the QTN distribution containing ‘norm’, ‘geom’, ‘gamma’ or ‘beta’.
qtn.sd	list(tr1 = 1)	list	the standard deviations for normal distribution.
qtn.prob	NULL	list	the probability of success for geometric distribution.
qtn.shape	NULL	list	the shape parameter for gamma distribution.
qtn.scale	NULL	list	the scale parameter for gamma distribution.
qtn.shape1	NULL	list	the shape1 parameter for beta distribution.
qtn.shape2	NULL	list	the shape2 parameter for beta distribution.
qtn.ncp	NULL	list	the ncp parameter for beta distribution.
qtn.spot	NULL	list	the QTN distribution probability in each block.
len.block	5e7	num	the block length.
maf	NULL	num	the maf threshold, markers less than this threshold will be exclude.

In A model, SIMER only considers Additive effect as genetic effect. Users should prepare Additive QTN effect in the Annotation data for generating Additive Individual effect. Additive single-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10, qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(tr1 = "T1 = A + E"), # "T1" (Trait 1) consists of Additive effect and Residual effect
  # phe.var = list(tr1 = 100),
  phe.h2A = list(tr1 = 0.3)
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

In multiple-trait simulation, SIMER can build accurate Additive genetic correlation between multiple traits. Additive multiple-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = matrix(c(6, 4, 4, 6), 2, 2), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(
    tr1 = "T1 = A + E", # "T1" (Trait 1) consists of Additive effect and Residual effect
    tr2 = "T2 = A + E"  # "T2" (Trait 2) consists of Additive effect and Residual effect
  ),
  # phe.var = list(tr1 = 100, tr2 = 100),
  phe.h2A = list(tr1 = 0.3, tr2 = 0.3),
  phe.corA = matrix(c(1, 0.5, 0.5, 1), 2, 2) # Additive genetic correlation
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

In single-trait simulation, the trait can be controlled by multiple-group QTNs. An example of single-trait controlled by two-group QTNs is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = c(2, 8), qtn.model = "A") # Group1: 2 QTNs; Group 2: 8 QTNs
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(tr1 = "T1 = A + E"), # "T1" (Trait 1) consists of Additive effect and Residual effect
  # phe.var = list(tr1 = 100),
  phe.h2A = list(tr1 = 0.3)
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Generate phenotype by AD model

In AD model, SIMER considers Additive effect and Dominant effect as genetic effect. Users should prepare Additive QTN effect and Dominant QTN effect in the Annotation data for generating Additive Individual effect and Dominant Individual effect. Additive and Dominant single-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10, qtn.model = "A + D") # Additive effect and Dominant effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(tr1 = "T1 = A + D + E"), # "T1" (Trait 1) consists of Additive effect, Dominant effect, and Residual effect
  # phe.var = list(tr1 = 100),
  phe.h2A = list(tr1 = 0.3),
  phe.h2D = list(tr1 = 0.1)
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

In multiple-trait simulation, SIMER can build accurate Additive genetic correlation and accurate Dominant genetic correlation between multiple traits. Additive and Dominant multiple-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = matrix(c(6, 4, 4, 6), 2, 2), qtn.model = "A + D") # Additive effect and Dominant effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(
    tr1 = "T1 = A + D + E", # "T1" (Trait 1) consists of Additive effect, Dominant effect, and Residual effect
    tr2 = "T2 = A + D + E"  # "T2" (Trait 2) consists of Additive effect, Dominant effect, and Residual effect
  ),
  # phe.var = list(tr1 = 100, tr2 = 100),
  phe.h2A = list(tr1 = 0.3, tr2 = 0.3),
  phe.h2D = list(tr1 = 0.1, tr2 = 0.1),
  phe.corA = matrix(c(1, 0.5, 0.5, 1), 2, 2), # Additive genetic correlation
  phe.corD = matrix(c(1, 0.5, 0.5, 1), 2, 2)  # Dominant genetic correlation
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Generate phenotype by GxG model

In GxG model, SIMER considers Genetic-Genetic effect as genetic effect. Users should prepare Genetic-Genetic QTN effect in the Annotation data for generating Genetic-Genetic Individual effect. An example of Additive-Dominant interaction in single-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10, qtn.model = "A + D + A:D") # Additive effect, Dominant effect, and Additive-Dominant interaction effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(tr1 = "T1 = A + D + A:D + E"), # "T1" (Trait 1) consists of Additive effect, Dominant effect, Additive-Dominant interaction effect, and Residual effect
  # phe.var = list(tr1 = 100),
  phe.h2A = list(tr1 = 0.3),
  phe.h2D = list(tr1 = 0.1),
  phe.h2GxG = list(tr1 = list("A:D" = 0.1))
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

In multiple-trait simulation, SIMER can build accurate Genetic-Genetic interaction correlation between multiple traits. An example of Additive-Dominant interaction in multiple-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = matrix(c(6, 4, 4, 6), 2, 2), qtn.model = "A + D + A:D") # Additive effect, Dominant effect, and Additive-Dominant interaction effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(
    tr1 = "T1 = A + D + A:D + E", # "T1" (Trait 1) consists of Additive effect, Dominant effect, Additive-Dominant interaction effect, and Residual effect
    tr2 = "T2 = A + D + A:D + E"  # "T2" (Trait 2) consists of Additive effect, Dominant effect, Additive-Dominant interaction effect, and Residual effect
  ),
  # phe.var = list(tr1 = 100, tr2 = 100),
  phe.h2A = list(tr1 = 0.3, tr2 = 0.3),
  phe.h2D = list(tr1 = 0.1, tr2 = 0.1),
  phe.h2GxG = list(tr1 = list("A:D" = 0.1), tr2 = list("A:D" = 0.1)),
  phe.corA = matrix(c(1, 0.5, 0.5, 1), 2, 2),                 # Additive genetic correlation
  phe.corD = matrix(c(1, 0.5, 0.5, 1), 2, 2),                 # Dominant genetic correlation
  phe.corGxG = list("A:D" = matrix(c(1, 0.5, 0.5, 1), 2, 2))  # Additive-Dominant interaction genetic correlation
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Generate phenotype by Repeated Record model

In Repeated Record model, SIMER adds PE (Permanent Environmental) effect to the phenotype. The number of repeated records can be set by pop.rep. In the meantime, pop.rep.bal can be used to determine whether repeated records are balanced. Repeated Record in single-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10, qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  pop.rep = 2,                          # The number of repeated records is 2
  pop.rep.bal = TRUE,                   # Repeated records are balanced
  phe.model = list(tr1 = "T1 = A + E"), # "T1" (Trait 1) consists of Additive effect and Residual effect
  # phe.var = list(tr1 = 100),
  phe.h2A = list(tr1 = 0.3)
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

In multiple-trait simulation, SIMER can build accurate Permanent Environmental correlation between multiple traits. Repeated Record in multiple-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = matrix(c(6, 4, 4, 6), 2, 2), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  pop.rep = 2,          # The number of repeated records is 2
  pop.rep.bal = TRUE,   # Repeated records are balanced
  phe.model = list(
    tr1 = "T1 = A + E", # "T1" (Trait 1) consists of Additive effect and Residual effect
    tr2 = "T2 = A + E"  # "T2" (Trait 2) consists of Additive effect and Residual effect
  ),
  # phe.var = list(tr1 = 100, tr2 = 100),
  phe.h2A = list(tr1 = 0.3, tr2 = 0.3),
  phe.corA = matrix(c(1, 0.5, 0.5, 1), 2, 2), # Additive genetic correlation
  phe.corPE = matrix(c(1, 0.5, 0.5, 1), 2, 2) # Permanent Environmental correlation
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Generate phenotype controlled by QTNs subject to Normal distribution

Normal distribution is the most common QTN effect distribution. Phenotype controlled by QTNs subject to Normal distribution in single-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(
  qtn.num = 10,
  qtn.model = "A",
  qtn.dist = list(tr1 = "norm"),
  qtn.sd = list(tr1 = 1)
)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(tr1 = "T1 = A + E"), # "T1" (Trait 1) consists of Additive effect and Residual effect
  # phe.var = list(tr1 = 100),
  phe.h2A = list(tr1 = 0.3)
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Phenotype controlled by QTNs subject to Normal distribution in multiple-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(
  qtn.num = matrix(c(6, 4, 4, 6), 2, 2),
  qtn.model = "A",
  qtn.dist = list(tr1 = "norm", tr2 = "norm"),
  qtn.sd = list(tr1 = 1, tr2 = 1)
)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(
    tr1 = "T1 = A + E", # "T1" (Trait 1) consists of Additive effect and Residual effect
    tr2 = "T2 = A + E"  # "T2" (Trait 2) consists of Additive effect and Residual effect
  ),
  # phe.var = list(tr1 = 100, tr2 = 100),
  phe.h2A = list(tr1 = 0.3, tr2 = 0.3),
  phe.corA = matrix(c(1, 0.5, 0.5, 1), 2, 2) # Additive genetic correlation
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Generate phenotype controlled by QTNs subject to Geometric distribution

Geometric distribution is the probability of success for the first time obtained only after K trials among the N Bernoulli trials. Geometric distribution can be used as a QTN effect distribution. Phenotype controlled by QTNs subject to Geometric distribution in single-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(
  qtn.num = 10,
  qtn.model = "A",
  qtn.dist = list(tr1 = "geom"),
  qtn.prob = list(tr1 = 0.5)
)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(tr1 = "T1 = A + E"), # "T1" (Trait 1) consists of Additive effect and Residual effect
  # phe.var = list(tr1 = 100),
  phe.h2A = list(tr1 = 0.3)
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Phenotype controlled by QTNs subject to Geometric distribution in multiple-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(
  qtn.num = matrix(c(6, 4, 4, 6), 2, 2),
  qtn.model = "A",
  qtn.dist = list(tr1 = "geom", tr2 = "geom"),
  qtn.prob = list(tr1 = 0.5, tr2 = 0.5)
)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(
    tr1 = "T1 = A + E", # "T1" (Trait 1) consists of Additive effect and Residual effect
    tr2 = "T2 = A + E"  # "T2" (Trait 2) consists of Additive effect and Residual effect
  ),
  # phe.var = list(tr1 = 100, tr2 = 100),
  phe.h2A = list(tr1 = 0.3, tr2 = 0.3),
  phe.corA = matrix(c(1, 0.5, 0.5, 1), 2, 2) # Additive genetic correlation
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Generate phenotype controlled by QTNs subject to Gamma distribution

Gamma distribution is the sum of N independent exponential random variables. Note that Exponential distribution is a special form of Gamma distribution when qtn.shape = 1 and qtn.scale = 1. Phenotype controlled by QTNs subject to Gamma distribution in single-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(
  qtn.num = 10,
  qtn.model = "A",
  qtn.dist = list(tr1 = "gamma"),
  qtn.shape = list(tr1 = 1),
  qtn.scale = list(tr1 = 1)
)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(tr1 = "T1 = A + E"), # "T1" (Trait 1) consists of Additive effect and Residual effect
  # phe.var = list(tr1 = 100),
  phe.h2A = list(tr1 = 0.3)
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Phenotype controlled by QTNs subject to Gamma distribution in multiple-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(
  qtn.num = matrix(c(6, 4, 4, 6), 2, 2),
  qtn.model = "A",
  qtn.dist = list(tr1 = "gamma", tr2 = "gamma"),
  qtn.shape = list(tr1 = 1, tr2 = 1),
  qtn.scale = list(tr1 = 1, tr2 = 1)
)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(
    tr1 = "T1 = A + E", # "T1" (Trait 1) consists of Additive effect and Residual effect
    tr2 = "T2 = A + E"  # "T2" (Trait 2) consists of Additive effect and Residual effect
  ),
  # phe.var = list(tr1 = 100, tr2 = 100),
  phe.h2A = list(tr1 = 0.3, tr2 = 0.3),
  phe.corA = matrix(c(1, 0.5, 0.5, 1), 2, 2) # Additive genetic correlation
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Generate phenotype controlled by QTNs subject to Beta distribution

Beta distribution is a density function of conjugate prior distribution as Bernoulli distribution and Binomial distribution. Phenotype controlled by QTNs subject to Beta distribution in single-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(
  qtn.num = 10,
  qtn.model = "A",
  qtn.dist = list(tr1 = "beta"),
  qtn.shape1 = list(tr1 = 1),
  qtn.shape2 = list(tr1 = 1),
  qtn.ncp = list(tr1 = 0)
)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(tr1 = "T1 = A + E"), # "T1" (Trait 1) consists of Additive effect and Residual effect
  # phe.var = list(tr1 = 100),
  phe.h2A = list(tr1 = 0.3)
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Phenotype controlled by QTNs subject to Beta distribution in multiple-trait simulation is displayed as follows:

# Generate annotation simulation parameters
SP <- param.annot(
  qtn.num = matrix(c(6, 4, 4, 6), 2, 2),
  qtn.model = "A",
  qtn.dist = list(tr1 = "beta", tr2 = "beta"),
  qtn.shape1 = list(tr1 = 1, tr2 = 1),
  qtn.shape2 = list(tr1 = 1, tr2 = 1),
  qtn.ncp = list(tr1 = 0, tr2 = 0)
)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  phe.model = list(
    tr1 = "T1 = A + E", # "T1" (Trait 1) consists of Additive effect and Residual effect
    tr2 = "T2 = A + E"  # "T2" (Trait 2) consists of Additive effect and Residual effect
  ),
  # phe.var = list(tr1 = 100, tr2 = 100),
  phe.h2A = list(tr1 = 0.3, tr2 = 0.3),
  phe.corA = matrix(c(1, 0.5, 0.5, 1), 2, 2) # Additive genetic correlation
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Generate phenotype with fixed effect and environmental random effect

SIMER supports add Fixed effects and Environmental Random effects to phenotype. Users should prepare a list of environmental factors setting. The effect value of Fixed effect is determined by eff and the variance ratio to phenotype variance (similar to heritability) of Environmental Random is set by ratio. Phenotype with Fixed effect and Environmental Random effect in single-trait simulation is displayed as follows:

# Prepare environmental factor list
pop.env <- list(
  F1 = list( # fixed effect 1
    level = c("1", "2"),
    eff = list(tr1 = c(50, 30))
  ), 
  F2 = list( # fixed effect 2
    level = c("d1", "d2", "d3"),
    eff = list(tr1 = c(10, 20, 30))
  ),
  R1 = list( # random effect 1
    level = c("l1", "l2", "l3"),
    ratio = list(tr1 = 0.1)
  )
)

# Generate genotype simulation parameters
SP <- param.annot(qtn.num = 10, qtn.model = "A")
# Generate annotation simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP, 
  pop.ind = 100,
  pop.env = pop.env,
  phe.model = list(tr1 = "T1 = A + F1 + F2 + R1 + E"), # "T1" (Trait 1) consists of Additive effect, F1, F2, R1, and Residual effect
  # phe.var = list(tr1 = 100),
  phe.h2A = list(tr1 = 0.3)
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Phenotype with Fixed effect and Environmental Random effect in multiple-trait simulation is displayed as follows:

# Prepare environmental factor list
pop.env <- list(
  F1 = list( # fixed effect 1
    level = c("1", "2"),
    eff = list(tr1 = c(50, 30), tr2 = c(50, 30))
  ), 
  F2 = list( # fixed effect 2
    level = c("d1", "d2", "d3"),
    eff = list(tr1 = c(10, 20, 30), tr2 = c(10, 20, 30))
  ),
  R1 = list( # random effect 1
    level = c("l1", "l2", "l3"),
    ratio = list(tr1 = 0.1, tr2 = 0.1)
  )
)

# Generate genotype simulation parameters
SP <- param.annot(qtn.num = matrix(c(6, 4, 4, 6), 2, 2), qtn.model = "A")
# Generate annotation simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP, 
  pop.ind = 100,
  pop.env = pop.env,
  phe.model = list(
    tr1 = "T1 = A + F1 + F2 + R1 + E", # "T1" (Trait 1) consists of Additive effect, F1, F2, R1, and Residual effect
    tr2 = "T2 = A + F1 + F2 + R1 + E"  # "T2" (Trait 1) consists of Additive effect, F1, F2, R1, and Residual effect
  ),
  # phe.var = list(tr1 = 100, tr2 = 100),
  phe.h2A = list(tr1 = 0.3, tr2 = 0.3),
  phe.corA = matrix(c(1, 0.5, 0.5, 1), 2, 2) # Additive genetic correlation
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Generate phenotype by GxE model

In GxE model, SIMER adds Genetic-Environmental interaction effect to phenotype. Users should prepare Genetic QTN effect in the Annotation data and environmental factor by pop.env for generating Genetic-Environmental Individual effect. An example of Genetic-Environmental interaction in single-trait simulation is displayed as follows:

# Prepare environmental factor list
pop.env <- list(
  F1 = list( # fixed effect 1
    level = c("1", "2"),
    eff = list(tr1 = c(50, 30))
  ), 
  F2 = list( # fixed effect 2
    level = c("d1", "d2", "d3"),
    eff = list(tr1 = c(10, 20, 30))
  ),
  R1 = list( # random effect 1
    level = c("l1", "l2", "l3"),
    ratio = list(tr1 = 0.1)
  )
)

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10, qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  pop.env = pop.env,
  phe.model = list(
    tr1 = "T1 = A + F1 + F2 + R1 + A:F1 + E" # "T1" (Trait 1) consists of Additive effect, F1, F2, R1, Additive-F1 interaction effect, and Residual effect
  ),
  # phe.var = list(tr1 = 100),
  phe.h2A = list(tr1 = 0.3),
  phe.h2GxE = list(tr1 = list("A:F1" = 0.1))
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

An example of Genetic-Environmental interaction in multiple-trait simulation is displayed as follows:

# Prepare environmental factor list
pop.env <- list(
  F1 = list( # fixed effect 1
    level = c("1", "2"),
    eff = list(tr1 = c(50, 30), tr2 = c(50, 30))
  ), 
  F2 = list( # fixed effect 2
    level = c("d1", "d2", "d3"),
    eff = list(tr1 = c(10, 20, 30), tr2 = c(10, 20, 30))
  ),
  R1 = list( # random effect 1
    level = c("l1", "l2", "l3"),
    ratio = list(tr1 = 0.1, tr2 = 0.1)
  )
)

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = matrix(c(6, 4, 4, 6), 2, 2), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  pop.env = pop.env,
  phe.model = list(
    tr1 = "T1 = A + F1 + F2 + R1 + A:F1 + E", # "T1" (Trait 1) consists of Additive effect, F1, F2, R1, Additive-F1 interaction effect, and Residual effect
    tr2 = "T2 = A + F1 + F2 + R1 + A:F1 + E"  # "T2" (Trait 2) consists of Additive effect, F1, F2, R1, Additive-F1 interaction effect, and Residual effect
  ),
  # phe.var = list(tr1 = 100, tr2 = 100),
  phe.h2A = list(tr1 = 0.3, tr2 = 0.3),
  phe.corA = matrix(c(1, 0.5, 0.5, 1), 2, 2) # Additive genetic correlation
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)

Population Simulation of Multiple-Generation with Genotype and Phenotype

SIMER imitates the reproductive process of organisms to generate Multiple-Generation population. The genotype data and phenotype data of the population are screened by single-trait selection or multiple-trait selection, and then amplified by species-specific reproduction.

Gallery of population simulation parameters

Individual selection on single trait

Paramater	Default	Options	Description
pop.sel	NULL	list	the selected males and females.
ps	c(0.8, 0.8)	num vector	if ps <= 1, fraction selected in selection of males and females; if ps > 1, ps is number of selected males and females.
decr	TRUE	TRUE or FALSE	whether the sort order is decreasing.
sel.crit	‘pheno’	character	the selection criteria, it can be ‘TBV’, ‘TGV’, and ‘pheno’.
sel.single	‘comb’	character	the single-trait selection method, it can be ‘ind’, ‘fam’, ‘infam’, and ‘comb’.
sel.multi	‘index’	character	the multiple-trait selection method, it can be ‘index’, ‘indcul’, and ‘tmd’.
index.wt	c(0.5, 0.5)	num vector	the weight of each trait for multiple-trait selection.
index.tdm	1	num	the index of tandem selection for multiple-trait selection.
goal.perc	0.1	num	the percentage of goal more than the mean of scores of individuals.
pass.perc	0.9	num	the percentage of expected excellent individuals.

Paramater	Default	Options	Description
pop.gen	2	num	the generations of simulated population.
reprod.way	‘randmate’	character	reproduction method, it consists of ‘clone’, ‘dh’, ‘selfpol’, ‘randmate’, ‘randexself’, ‘2waycro’, ‘3waycro’, ‘4waycro’, ‘backcro’, and ‘userped’.
sex.rate	0.5	num	the male rate in the population.
prog	2	num	the progeny number of an individual.

Individual selection is a selecting method according to the phenotype of individual traits, also known as mixed selection or collective selection. This selection method is simple and easy to used for traits with high heritability.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.single = "ind")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Run selection
SP <- selects(SP)

Family selection on single trait

Family selection is a selecting method by family based on the average of the family. This selection method is used for traits with low heritability.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.single = "fam")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Run selection
SP <- selects(SP)

Within family selection on single trait

Within-family selection is a selecting method according to the deviation of individual phenotype and family mean value in each family. This selection method is used for traits with low heritability and small family.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.single = "infam")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Run selection
SP <- selects(SP)

Combined selection on single trait

Combined selection is a selecting method according to weighed combination of the deviation of individual phenotype and family mean value.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.single = "comb")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Run selection
SP <- selects(SP)

Tandem selection on multiple traits

Tandem selection is a method for sequentially selecting a plurality of target traits one by one. The index of selected trait is index.tdm and this parameter should not controlled by Users.

# Generate genotype simulation parameters
SP <- param.annot(qtn.num = matrix(c(6, 4, 4, 6), 2, 2), qtn.model = "A")
# Generate annotation simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP, 
  pop.ind = 100,
  # phe.var = list(tr1 = 100, tr2 = 100),
  phe.model = list(
    tr1 = "T1 = A + E",
    tr2 = "T2 = A + E"
  )
)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.multi = "tdm")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Run selection
SP <- selects(SP)

Independent culling selection on multiple traits

After setting a minimum selection criterion for each target trait. Independent culling selection will eliminate this individual when the candidate’s performance on any trait is lower than the corresponding criteria.

# Generate genotype simulation parameters
SP <- param.annot(qtn.num = matrix(c(6, 4, 4, 6), 2, 2), qtn.model = "A")
# Generate annotation simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP, 
  pop.ind = 100,
  # phe.var = list(tr1 = 100, tr2 = 100),
  phe.model = list(
    tr1 = "T1 = A + E",
    tr2 = "T2 = A + E"
  )
)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.multi = "indcul")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Run selection
SP <- selects(SP)

Index selection on multiple traits

Index selection is a comprehensive selection that will consider several traits based on their respective heritabilities, phenotypic variances, economic weights, corresponding genetic correlations, and phenotypes. Then calculate the index value of each trait, and eliminate or select it according to its level. Users can set the weight of each trait by index.wt.

# Generate genotype simulation parameters
SP <- param.annot(qtn.num = matrix(c(6, 4, 4, 6), 2, 2), qtn.model = "A")
# Generate annotation simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP, 
  pop.ind = 100,
  # phe.var = list(tr1 = 100, tr2 = 100),
  phe.model = list(
    tr1 = "T1 = A + E",
    tr2 = "T2 = A + E"
  )
)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.multi = "index")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Run selection
SP <- selects(SP)

Clone for plant

Clone is an sexual reproduction method which does not involve germ cells, and does not require a process of fertilization, directly forming a new individual’s reproductive mode from a part of the mother. Sex of offspring will be 0 in clone.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.single = "comb")
# Generate reproduction parameters
SP <- param.reprod(SP = SP, reprod.way = "clone")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Run selection
SP <- selects(SP)
# Run reproduction
SP <- reproduces(SP)

Double haploid for plant

Double haploid is a reproduction method for breeding workers to obtain haploid plants. It induced to double the number of chromosomes and restore the number of chromosomes in normal plants. Sex of offspring will be 0 in dh.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.single = "comb")
# Generate reproduction parameters
SP <- param.reprod(SP = SP, reprod.way = "dh")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Run selection
SP <- selects(SP)
# Run reproduction
SP <- reproduces(SP)

Self pollination for plant and micro-organism

Self pollination refers to the combination of male and female gametes from the same individual or between individuals from the same clonal breeding line. Sex of offspring will be 0 in selfpol.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.single = "comb")
# Generate reproduction parameters
SP <- param.reprod(SP = SP, reprod.way = "selfpol")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Run selection
SP <- selects(SP)
# Run reproduction
SP <- reproduces(SP)

Random mating for plant and animal

In random mating, any female or male individual have the same probability to mate with any opposite sex in a sexually reproducing organism. Sex of offspring in random mating is controlled by sex.ratio in randmate.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.single = "comb")
# Generate reproduction parameters
SP <- param.reprod(SP = SP, reprod.way = "randmate")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Run selection
SP <- selects(SP)
# Run reproduction
SP <- reproduces(SP)

Random mating excluding self pollination for animal

In random mating excluding self pollination, an individual cannot mate to itself. Sex of offspring in random mating is controlled by sex.ratio in randexself.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.single = "comb")
# Generate reproduction parameters
SP <- param.reprod(SP = SP, reprod.way = "randexself")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Run selection
SP <- selects(SP)
# Run reproduction
SP <- reproduces(SP)

Two-way cross for animal

Two-way cross method needs to use sex to distinguish two different breeds, in which the first breed is sire and the second breed is dam.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.single = "comb")
# Generate reproduction parameters
SP <- param.reprod(SP = SP, reprod.way = "2waycro")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Two different breeds are cut by sex
SP$pheno$pop$gen1$sex <- rep(c(1, 2), c(50, 50))
# Run selection
SP <- selects(SP)
# Run reproduction
SP <- reproduces(SP)

Three-way cross for animal

Three-way cross method needs to use sex to distinguish three different breeds, in which the first breed is sire and the second breed is dam in the first two-way cross, the third breed is termimal sire.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.single = "comb")
# Generate reproduction parameters
SP <- param.reprod(SP = SP, reprod.way = "3waycro")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Three different breeds are cut by sex
SP$pheno$pop$gen1$sex <- rep(c(1, 2, 1), c(30, 30, 40))
# Run selection
SP <- selects(SP)
# Run reproduction
SP <- reproduces(SP)

Four-way cross for animal

Four-way cross method needs to use sex to distinguish four different breeds, in which the first breed is sire and the second breed is dam in the first two-way cross, the third breed is sire and the fourth breed is dam in the second two-way cross.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.single = "comb")
# Generate reproduction parameters
SP <- param.reprod(SP = SP, reprod.way = "4waycro")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Three different breeds are cut by sex
SP$pheno$pop$gen1$sex <- rep(c(1, 2, 1, 2), c(25, 25, 25, 25))
# Run selection
SP <- selects(SP)
# Run reproduction
SP <- reproduces(SP)

Back cross for animal

Back cross method needs to use sex to distinguish two different breeds, in which the first breed is always sire in each generation and the second breed is dam in the first two-way cross.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)
# Generate selection parameters
SP <- param.sel(SP = SP, sel.single = "comb")
# Generate reproduction parameters
SP <- param.reprod(SP = SP, reprod.way = "backcro")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Two different breeds are cut by sex
SP$pheno$pop$gen1$sex <- rep(c(1, 2), c(50, 50))
# Run selection
SP <- selects(SP)
# Run reproduction
SP <- reproduces(SP)

User designed pedigree mating for plant and animal

User designed pedigree mating needs a specific user designed pedigree to control mating process. The first column is sample id, the second column is paternal id, and the third column is maternal id. Please make sure that paternal id and maternal id can match to genotype data.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = 10)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, pop.ind = 100)
# Generate reproduction parameters
SP <- param.reprod(SP = SP, reprod.way = "userped")

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Run reproduction
SP <- reproduces(SP)

AN EASY WAY TO GENERATE A POPULATION

The above methods are to generate population step by step, which are easy to understand. Actually, SIMER can directly generate a population by a MORE CONVENIENT way.

# Generate all simulation parameters
SP <- param.simer(qtn.num = 10, pop.marker = 1e4, pop.ind = 1e2, sel.single = "comb", reprod.way = "randmate")

# Run Simer
SP <- simer(SP)

Breeding Program Design

After generating a population, further work can be done. Breeders wish to evaluate their Breeding Program Design. To save a lot of money and time, SIMER can assist breeders to evaluate their Breeding Program Design.

Gallery of breeding program design parameters

read.selgeno(), function to make comparison on breeding plans:
pop, total population information
selPath, the path of breeding plans
outpath, path of output files

Breeding plan preparation

Breeding plans should be stored on different files respectively. Filenames must begin with breeding_plan, such like breeding_plan01.txt. For now, SIMER supports different breeding plans on generation, family_index, within_family_index, and sex.

> # get path of breeding plan
> selPath <- system.file("extdata", "01breeding_plan", package = "simer")

> # show filename format
> dir(selPath)
[1] "breeding_plan01.txt" "breeding_plan02.txt" "breeding_plan03.txt" "ReadMe.txt" 

> # show file contents format
> fn1 <- file.path(selPath, "breeding_plan01.txt")
> bp1 <- read.delim(fn1, header = TRUE)

> # ###NOTE###
> # 1. any generation can be choosed within num.gen.
> # 2. any sex(1 represents sir 2 represents dam) can be choosed.
> # 3. any family index can be choosed in all family. 1:5 means 
> #    that the first 5 family will be chosen.
> # 4. any infamily index can be choosed in every family.
> # 5. numbers should be separated by comma(",").
> # 6. ":" can choose serial number, "1:3,7" represents "1 2 3 7".
> # 7. "all" will choose all of options in a category.
> bp1
  generation family_index within_family_index sex
1          1        1:3,7                 all   1
2          2        1:3,7                 all   1
3          3        1:3,7                 all   1

Breeding plan comparison

# random-mating
simer.list <-
    simer(num.gen = 10,
          verbose = verbose, 
          outpath = outpath,
          input.map = input.map,
          rawgeno1 = rawgeno, # use your own genotype matrix
          # num.ind = 100,
          mtd.reprod = "randmate",
          num.prog = 2,
          ratio = 0.5)

pop <- simer.list$pop
out.pop <- read.selgeno(pop = pop, selPath = selPath, outpath = outpath)

# make comparison on breeding plans
plan1 <- out.pop$plan1
plan2 <- out.pop$plan2
plan3 <- out.pop$plan3
summary(plan1)
summary(plan2)
summary(plan3)

Global Options

In this part, calculation of population size and different ourput methods will be introduced.

Gallery of global input parameters

simer(), main function:
num.gen, number of generations in simulation
replication, replication index of simulation
verbose, whether to print detail
out, prefix of output file name
outpath, path of output files
out.format, format of output, “numeric” or “plink”
seed.sim, random seed of a simulation process
seed.map, random seed of map file
out.geno.gen, indice of generations of output genotype
out.pheno.gen, indice of generations of output phenotype

Counts of total population size

The following is the method of obtaining population size of every generation. Every elements in cound.ind are population size in this generation respectively.

# parameters that controls population size of every generations 
nind <- 40
num.gen <- 10
ratio <- 0.5
sel.on <- TRUE # whether selection exsits
ps <- 0.8
ps <- ifelse(sel.on, ps, 1)
num.prog <- 2
mtd.reprod <- "randmate"

# populations of the first generation
basepop <- getpop(nind = nind, from = 1, ratio = ratio)
pop2 <- getpop(nind = nind, from = 41, ratio = ratio)
pop3 <- getpop(nind = nind, from = 81, ratio = ratio)
pop4 <- getpop(nind = nind, from = 121, ratio = ratio)

# calculate number of individuals in every generation
count.ind <- rep(nind, num.gen)
if (mtd.reprod == "clone" || mtd.reprod == "dh" || mtd.reprod == "selfpol") {
  if (num.gen > 1) {
    for(i in 2:num.gen) {
      count.ind[i] <- round(count.ind[i-1] * (1-ratio) * ps) * num.prog
    }
  }
  
} else if (mtd.reprod == "randmate" || mtd.reprod == "randexself") {
  if (num.gen > 1) {
    for(i in 2:num.gen) {
      count.ind[i] <- round(count.ind[i-1] * (1-ratio) * ps) * num.prog
    }
  }

} else if (mtd.reprod == "singcro") {
  sing.ind <- round(nrow(pop2) * ps) * num.prog
  count.ind <- c(nrow(basepop), nrow(pop2), sing.ind)

} else if (mtd.reprod == "tricro") {
  dam21.ind <- round(nrow(pop2) * ps) * prog.tri
  tri.ind <- round(dam21.ind * (1-ratio) * ps) * num.prog
  count.ind <- c(nrow(basepop), nrow(pop2), nrow(pop3), dam21.ind, tri.ind)

} else if (mtd.reprod == "doubcro") {
  sir11.ind <- round(nrow(pop2) * ps) * prog.doub
  dam22.ind <- round(nrow(pop4) * ps) * prog.doub
  doub.ind <- round(dam22.ind * (1-ratio) * ps) * num.prog
  count.ind <- c(nrow(basepop), nrow(pop2), nrow(pop3), nrow(pop4), sir11.ind, dam22.ind, doub.ind)

} else if (mtd.reprod == "backcro") {
  count.ind[1] <- nrow(basepop) + nrow(pop2)
  if (num.gen > 1) {
    count.ind[2] <- round(nrow(pop2) * ps) * num.prog
    for(i in 3:num.gen) {
      count.ind[i] <- round(count.ind[i-1] * (1-ratio) * ps) * num.prog
    }
  }
}

Simulation of multiple populations

Simulation of multiple populations can be realized by “for” in R and replication. Random seed of simulation is random in every replication and random seed of map is fixed. In every replication, you can set your own random seed of simulation and random seed of map by seed.geno| and seed.map respectively.

# random-mating 
rep <- 2
for (i in 1:rep) {
  simer.list <-
    simer(num.gen = 3,
          replication = i, # set index of replication
          verbose = verbose, 
          outpath = outpath,
          input.map = input.map,
          # rawgeno1 = rawgeno, # use your own genotype matrix
          num.ind = 100,
          mtd.reprod = "randmate",
          num.prog = 2,
          ratio = 0.5)
}

File output

SIMER won’t output files by default. A series of files with prefix out = simer output when specify a exsited out path by outpath. File output format can be “numeric” or “plink” by out.format.

# set a output path
outpath = getwd()

# "numeric" format
simer.list <-
  simer(num.gen = 3,
        verbose = verbose, 
        outpath = outpath,
        out.format = "numeric", 
        input.map = input.map,
        # rawgeno1 = rawgeno, # use your own genotype matrix
        num.ind = 100,
        mtd.reprod = "randmate",
        num.prog = 2,
        ratio = 0.5)

# "plink" format
simer.list <-
  simer(num.gen = 3,
        verbose = verbose, 
        outpath = outpath,
        out.format = "plink", 
        input.map = input.map,
        # rawgeno1 = rawgeno, # use your own genotype matrix
        num.ind = 100,
        mtd.reprod = "randmate",
        num.prog = 2,
        ratio = 0.5)

Generation selective output

Output of genotype and phenotype can be generation-selective by out.geno.gen and out.pheno.gen. For example, out.geno.gen = 3:5 and out.pheno.gen = 1:5 represent outputting genotype from generation3 to generation5 and outputting phenotype from generation1 to generation5.

# set output generations of genotype and phenotype
out.geno.gen <- 3:5
out.pheno.gen <- 1:5
simer.list <-
  simer(num.gen = 5,
        verbose = verbose, 
        outpath = outpath,
        out.geno.gen = out.geno.gen, 
        out.pheno.gen = out.pheno.gen, 
        input.map = input.map,
        # rawgeno1 = rawgeno, # use your own genotype matrix
        num.ind = 100,
        mtd.reprod = "randmate",
        num.prog = 2,
        ratio = 0.5)

Output

SIMER outputs data including population information, marker effects, trait information, genotype, genotypic id, genotypic map, and selection intensity. Note that several result files with prefix out = "simer" will be generated. Firstly, a result path will be generated. The number at the beginning represents the total individuals number and the ending character is the format of output (“num_Simer_Data_format”). Secondly, you will see a path named “replication1”. It is the first replication of simulation and you can get numerous different replications by setting parameter replication. In “replication1”, results are following:
simer.geno.id contains indice of genotyped individuals
simer.geno.bin and simer.geno.desc contain genotype matrix of all individuals
simer.map contains input map with block information and recombination information
simer.ped contains pedigree of individuals
simer.phe contains phenotype of individuals

Population information

Population information contains generation, individual indice, family indice, within-family indice, sire indice, dam indice, sex, and phenotpye.

> pop <- simer.list$pop
> head(pop)
  gen index fam infam sir dam sex     pheno
1   1     1   1     1   0   0   1 -1.043716
2   1     2   2     2   0   0   1  0.551076
3   1     3   3     3   0   0   1 -5.727581
4   1     4   4     4   0   0   1  5.825591
5   1     5   5     5   0   0   1  4.430751
6   1     6   6     6   0   0   1 15.059641

Marker effects

> effs <- simer.list$effs
> str(effs)
List of 2
 $ mrk1: int [1:18] 4006 4950 10416 18070 21899 23817 26038 28886 29437 30012 ...
 $ eff1:List of 1
  ..$ eff.a: num [1:18] -0.0144 -5.7868 -1.2436 1.2436 -3.4893 ...

Trait information

Trait information is displayed by generation in single-breed reproduction or by breed in multiple-breeds reproduction. In every generation (or breed), it contains trait information (variance components and heritability), effect information (phenotype decomposition), phenotype information (TBV, TGV, pEBVs, gEBVs, ssEBVs, phenotype), and others.

> trait <- simer.list$trait
> str(trait)
List of 5
 $ gen1:List of 4
  ..$ info.tr   :List of 3
  .. ..$ Vg: num 23.3
  .. ..$ Ve: num 63
  .. ..$ h2: num 0.27
  ..$ info.eff  :'data.frame':  40 obs. of  2 variables:
  .. ..$ ind.a  : num [1:40] -6.46 6.46 6.46 6.46 0 ...
  .. ..$ ind.env: num [1:40] 5.419 -5.912 -12.191 -0.638 4.431 ...
  ..$ info.pheno:'data.frame':  40 obs. of  3 variables:
  .. ..$ TBV  : num [1:40] -6.46 6.46 6.46 6.46 0 ...
  .. ..$ TGV  : num [1:40] -6.46 6.46 6.46 6.46 0 ...
  .. ..$ pheno: num [1:40] -1.044 0.551 -5.728 5.826 4.431 ...
  ..$ qtn.a     : num [1:18, 1:40] 1 1 1 1 1 1 1 1 1 1 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : NULL
  .. .. ..$ : chr [1:40] "V1" "V3" "V5" "V7" ...
 $ gen2:List of 4
  ..$ info.tr   :List of 3
  .. ..$ Vg: num 41.7
  .. ..$ Ve: num 113
  .. ..$ h2: num 0.27
  ..$ info.eff  :'data.frame':  32 obs. of  2 variables:
  .. ..$ ind.a  : num [1:32] 5.17 1.29 6.46 6.46 6.46 ...
  .. ..$ ind.env: num [1:32] 10.8 -8.61 -2.41 1.8 -5.04 ...
  ..$ info.pheno:'data.frame':  32 obs. of  3 variables:
  .. ..$ TBV  : num [1:32] 5.17 1.29 6.46 6.46 6.46 ...
  .. ..$ TGV  : num [1:32] 5.17 1.29 6.46 6.46 6.46 ...
  .. ..$ pheno: num [1:32] 15.97 -7.31 4.06 8.26 1.43 ...
  ..$ qtn.a     : num [1:18, 1:32] 0 -1 0 0 0 0 0 0 0 0 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : NULL
  .. .. ..$ : chr [1:32] "V17" "V18" "t1" "t2" ...
 $ gen3:List of 4
  ..$ info.tr   :List of 3
  .. ..$ Vg: num 17.5
  .. ..$ Ve: num 36.5
  .. ..$ h2: num 0.324
  ..$ info.eff  :'data.frame':  26 obs. of  2 variables:
  .. ..$ ind.a  : num [1:26] -1.525 -1.525 0 0.609 6.463 ...
  .. ..$ ind.env: num [1:26] 3.29 2.31 -6.94 5.97 15.28 ...
  ..$ info.pheno:'data.frame':  26 obs. of  3 variables:
  .. ..$ TBV  : num [1:26] -1.525 -1.525 0 0.609 6.463 ...
  .. ..$ TGV  : num [1:26] -1.525 -1.525 0 0.609 6.463 ...
  .. ..$ pheno: num [1:26] 1.77 0.781 -6.936 6.576 21.743 ...
  ..$ qtn.a     : num [1:18, 1:26] 0 0 0 0 0 0 0 0 0 0 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : NULL
  .. .. ..$ : chr [1:26] "t2" "t2" "t2" "t1" ...
 $ gen4:List of 4
  ..$ info.tr   :List of 3
  .. ..$ Vg: num 15.8
  .. ..$ Ve: num 32
  .. ..$ h2: num 0.33
  ..$ info.eff  :'data.frame':  20 obs. of  2 variables:
  .. ..$ ind.a  : num [1:20] -2.808 -0.242 9.085 -2.013 4.052 ...
  .. ..$ ind.env: num [1:20] 3.96 2.64 -2.9 -2.65 2.2 ...
  ..$ info.pheno:'data.frame':  20 obs. of  3 variables:
  .. ..$ TBV  : num [1:20] -2.808 -0.242 9.085 -2.013 4.052 ...
  .. ..$ TGV  : num [1:20] -2.808 -0.242 9.085 -2.013 4.052 ...
  .. ..$ pheno: num [1:20] 1.15 2.4 6.19 -4.66 6.26 ...
  ..$ qtn.a     : num [1:18, 1:20] 1 1 1 0 0 1 1 1 0 1 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : NULL
  .. .. ..$ : chr [1:20] "V18" "t2" "t1" "t1" ...
 $ gen5:List of 4
  ..$ info.tr   :List of 3
  .. ..$ Vg: num 12.1
  .. ..$ Ve: num 28.4
  .. ..$ h2: num 0.299
  ..$ info.eff  :'data.frame':  16 obs. of  2 variables:
  .. ..$ ind.a  : num [1:16] 3.79 4.377 0.551 0.551 7.68 ...
  .. ..$ ind.env: num [1:16] -1.22 -5.5 -7.99 8.79 3.1 ...
  ..$ info.pheno:'data.frame':  16 obs. of  3 variables:
  .. ..$ TBV  : num [1:16] 3.79 4.377 0.551 0.551 7.68 ...
  .. ..$ TGV  : num [1:16] 3.79 4.377 0.551 0.551 7.68 ...
  .. ..$ pheno: num [1:16] 2.57 -1.12 -7.44 9.34 10.78 ...
  ..$ qtn.a     : num [1:18, 1:16] -1 -1 -1 0 0 0 -1 -1 -1 -1 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : NULL
  .. .. ..$ : chr [1:16] "t2" "t1" "t2" "t2" ...

Genotype

In genotype matrix, each row represents a marker and each column represents a individual.

> geno <- simer.list$geno
> geno[1:6, 1:6]
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    1    1    1    0    0
[2,]    1    1    1    1    0    0
[3,]    1    1    1    1    0    0
[4,]    1    1    1    1    0    0
[5,]    1    1    1    1    0    0
[6,]    1    1    1    1    0    0

Genotypic id

> genoid <- simer.list$genoid
> head(genoid)
[1] 73 74 75 76 77 78

Genotypic map

In SNP map information, the first column is SNP name, the second column is Chromosome ID, the third column is phsical position, the fourth column is REF, the fifth column is ALT, the sixth column is block ID, and the seventh is recombination information (“1” represents making recombination and “0” represents not).

> map <- simer.list$map
> head(map)
     SNP          Chrom BP          REF ALT block recom
[1,] "1_10673082" "1"   " 10673082" "T" "C" "1"   "1"  
[2,] "1_10723065" "1"   " 10723065" "A" "G" "1"   "1"  
[3,] "1_11407894" "1"   " 11407894" "A" "G" "1"   "1"  
[4,] "1_11426075" "1"   " 11426075" "T" "C" "1"   "1"  
[5,] "1_13996200" "1"   " 13996200" "T" "C" "1"   "1"  
[6,] "1_14638936" "1"   " 14638936" "T" "C" "1"   "1"

Selection intensity

> si <- simer.list$si
> si
[1] 0.3499524

Citation

FAQ and Hints

:sos: Question2: When installing packages from Github with “devtools”, an error occurred:

Error in curl::curl_fetch_disk(url, x$path, handle = handle): Problem with the SSL CA cert (path? access rights?)

library(httr)
set_config(config(ssl_verifypeer = 0L))

Questions, suggestions, and bug reports are welcome and appreciated. :arrow_right:

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SIMER

Data Simulation for Life Science and Breeding

Authors:

Contents

Installation

Installation

Data Preparation

Genotype

Genotypic map

Pedigree

Data Input

Basic

Optional

Quick Start

Quick Start for Complete Simulation

Quick Start for Annotation Simulation

Quick Start for Genotype Simulation

Quick Start for Phenotype Simulation

Genotype Simulation

Gallery of genotype simulation parameters

Generate genotype matrix of base population

Add chromosome crossovers and mutations to genotype matrix

Phenotype Simulation

Gallery of phenotype simulation parameters

Generate phenotype by A model

Generate phenotype by AD model

Generate phenotype by GxG model

Generate phenotype by Repeated Record model

Generate phenotype controlled by QTNs subject to Normal distribution

Generate phenotype controlled by QTNs subject to Geometric distribution

Generate phenotype controlled by QTNs subject to Gamma distribution

Generate phenotype controlled by QTNs subject to Beta distribution

Generate phenotype with fixed effect and environmental random effect

Generate phenotype by GxE model

Population Simulation of Multiple-Generation with Genotype and Phenotype

Gallery of population simulation parameters

Individual selection on single trait

Family selection on single trait

Within family selection on single trait

Combined selection on single trait

Tandem selection on multiple traits

Independent culling selection on multiple traits

Index selection on multiple traits

Clone for plant

Double haploid for plant

Self pollination for plant and micro-organism

Random mating for plant and animal

Random mating excluding self pollination for animal

Two-way cross for animal

Three-way cross for animal

Four-way cross for animal

Back cross for animal

User designed pedigree mating for plant and animal

AN EASY WAY TO GENERATE A POPULATION

Breeding Program Design

Gallery of breeding program design parameters

Breeding plan preparation

Breeding plan comparison

Global Options

Gallery of global input parameters

Counts of total population size

Simulation of multiple populations

File output

Generation selective output

Output

Population information

Marker effects

Trait information

Genotype

Genotypic id

Genotypic map

Selection intensity

Citation

FAQ and Hints