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SIMER Data Simulation for Life Science and Breeding Authors: 🧰 Relevant software tools for genetic analyses and genomic breeding Contents Installation Installation Data Preparation Genotype Genotypic map Pedigree Data Input Basic Optional Quick Start Quick Start for Population Simulation Quick Start for Genotype Simulation Quick Start for Phenotype Simulation Genotype Simulation Gallery of genotype simulation parameters Generate an external genotype matrix Generate a random genotype matrix 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 covariate and environmental random effect Generate phenotype by GxE model Generate phenotype controlled by varied QTN effect distribution 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 program design preparation Breeding program design evaluation Global Options Gallery of global parameters Counts of total population size Multi-thread simulation Multi-population simulation File output Generation-selective output Output Annotation data Genotype data Phenotype data Citation FAQ and Hints

README.md

SIMER

GitHub issues CRAN Version

Data Simulation for Life Science and Breeding

Authors:

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 👉here👈.

🧰 Relevant software tools for genetic analyses and genomic breeding

📫 HIBLUP: Versatile and easy-to-use GS toolbox. 🏔️ ISWINE: an omics knowledgebase for swine.
🚴‍♂️ KAML: Advanced GS method for complex traits. 📊 CMplot: A drawing tool for genetic analyses.
📮 rMVP: Efficient and easy-to-use GWAS tool. 🏊 hibayes: A Bayesian-based GWAS and GS tool.

Contents

Installation

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

Installation

  • The stable version:
install.packages("simer")
  • The latest version:
devtools::install_github("xiaolei-lab/SIMER")

After installed successfully, SIMER can be loaded by typing

> library(simer)

Typing ?simer could get the details of all parameters.

Data Preparation

Genotype

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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).

genotype.txt

2 1 0 1 0 … 0
1 2 0 1 0 … 0
1 1 2 1 0 … 0
1 1 0 2 1 … 0
0 0 0 0 2 … 0

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.

map.txt

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

Pedigree

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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.

userped.txt

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

Data Input

Basic

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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

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The mating process can be designed by user-designed pedigree.

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

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

Quick Start for Population Simulation

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A quick start for Population Simulation is shown below:

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

# Run Simer
SP <- simer(SP)

Quick Start for Genotype Simulation

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A quick start for Genotype Simulation is shown below:

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

# Run genotype simulation
SP <- genotype(SP)

Quick Start for Phenotype Simulation

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A quick start for Phenotype Simulation is shown below:

# 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 = list(tr1 = 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

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

Gallery of genotype simulation parameters

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genotype, main function of Genotype Simulation:

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.

annotation, main function of Annotation Simulation:

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.

Generate an external genotype matrix

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Users can use real genotype data with specific genetic structure for subsequent simulation.

# Create a genotype matrix
# pop.geno <- read.table("genotype.txt")
# pop.geno <- bigmemory::attach.big.matrix("genotype.geno.desc")
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)

Generate a random genotype matrix

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Users can also specify pop.marker and pop.ind to generate random genotype data.

# 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

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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 processes. Users can set recom.spot = FALSE to add only mutations to the 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

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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) Genetic Model with Varied QTN Effect Distributions (QTN effect distribution: Normal distribution, Geometric distribution, Gamma distribution, Beta distribution, and their combination)
(6) Linear Mixed Model (Fixed effect, Covariate, Environmental Random effect, Genetic Random effect, and Genetic-Environmental interaction effect)

Gallery of phenotype simulation parameters

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phenotype, main function of Phenotype Simulation:

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.

annotation, main function of Annotation Simulation:

Paramater Default Options Description
pop.map NULL data.frame the map data with annotation information.
qtn.model 'A' character the genetic model of QTN such as 'A + D'.
qtn.index 10 list the QTN index for each trait.
qtn.num 10 list the QTN number for (each group in) each trait.
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.

Generate phenotype by A model

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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:
If users want to output files, please see File output.

# 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 = list(tr1 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
# SP <- param.geno(SP = SP, pop.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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 the multiple-trait simulation, SIMER can build accurate Additive genetic correlation between multiple traits. Additive multiple-trait simulation is displayed as follows:
If users want to output files, please see File output.

# 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 = list(tr1 = 10, tr2 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
# SP <- param.geno(SP = SP, pop.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype

# 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 by AD model

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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:
If users want to output files, please see File output.

# 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 = list(tr1 = 10), qtn.model = "A + D") # Additive effect and Dominant effect
# Generate genotype simulation parameters
# SP <- param.geno(SP = SP, pop.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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:
If users want to output files, please see File output.

# 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 = list(tr1 = 10, tr2 = 10), qtn.model = "A + D") # Additive effect and Dominant effect
# Generate genotype simulation parameters
# SP <- param.geno(SP = SP, pop.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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

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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:
If users want to output files, please see File output.

# 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 = list(tr1 = 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.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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 the 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:
If users want to output files, please see File output.

# 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 = list(tr1 = 10, tr2 = 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.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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

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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:
If users want to output files, please see File output.

# 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 = list(tr1 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
# SP <- param.geno(SP = SP, pop.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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 the multiple-trait simulation, SIMER can build accurate Permanent Environmental correlation between multiple traits. Repeated Record in multiple-trait simulation is displayed as follows:
If users want to output files, please see File output.

# 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 = list(tr1 = 10, tr2 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
# SP <- param.geno(SP = SP, pop.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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

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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:
If users want to output files, please see File output.

# 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 = list(tr1 = 10),
  qtn.model = "A",
  qtn.dist = list(tr1 = "norm"),
  qtn.sd = list(tr1 = 1)
)
# Generate genotype simulation parameters
# SP <- param.geno(SP = SP, pop.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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:
If users want to output files, please see File output.

# 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 = list(tr1 = 10, tr2 = 10),
  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.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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

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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:
If users want to output files, please see File output.

# 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 = list(tr1 = 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.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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:
If users want to output files, please see File output.

# 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 = list(tr1 = 10, tr2 = 10),
  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.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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

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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:
If users want to output files, please see File output.

# 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 = list(tr1 = 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.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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:
If users want to output files, please see File output.

# 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 = list(tr1 = 10, tr2 = 10),
  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.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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

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Beta distribution is a density function of conjugate prior distribution as Bernoulli distribution and Binomial distribution. Phenotype controlled by QTNs subject to the Beta distribution in single-trait simulation is displayed as follows:
If users want to output files, please see File output.

# 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 = list(tr1 = 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.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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:
If users want to output files, please see File output.

# 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 = list(tr1 = 10, tr2 = 10),
  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.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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 covariate and environmental random effect

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SIMER supports adding Fixed effects, Covariates, and Environmental Random effects to phenotype. Users should prepare a list of environmental factors setting. Fixed effects, Covariates , and Environmental Random effects are determined by effect, slope, and ratio respectively. Phenotype with Fixed effect, Covariate, and Environmental Random effect in single-trait simulation is displayed as follows:
If users want to output files, please see File output.

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

# Prepare environmental factor list
pop.env <- list(
  F1 = list( # fixed effect 1
    level = c("1", "2"),
    effect = list(tr1 = c(50, 30))
  ), 
  F2 = list( # fixed effect 2
    level = c("d1", "d2", "d3"),
    effect = list(tr1 = c(10, 20, 30))
  ),
  C1 = list( # covariate 1
    level = c(70, 80, 90),
    slope = list(tr1 = 1.5)
  ),
  R1 = list( # random effect 1
    level = c("l1", "l2", "l3"),
    ratio = list(tr1 = 0.1)
  )
)

# Generate genotype simulation parameters
SP <- param.annot(pop.map = pop.map, qtn.num = list(tr1 = 10), qtn.model = "A")
# Generate annotation simulation parameters
# SP <- param.geno(SP = SP, pop.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP, 
  pop.ind = 100,
  pop.env = pop.env,
  phe.model = list(tr1 = "T1 = A + F1 + F2 + C1 + R1 + E"), # "T1" (Trait 1) consists of Additive effect, F1, F2, C1, 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, Covariate, and Environmental Random effect in multiple-trait simulation is displayed as follows:
If users want to output files, please see File output.

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

# Prepare environmental factor list
pop.env <- list(
  F1 = list( # fixed effect 1
    level = c("1", "2"),
    effect = list(tr1 = c(50, 30), tr2 = c(50, 30))
  ), 
  F2 = list( # fixed effect 2
    level = c("d1", "d2", "d3"),
    effect = list(tr1 = c(10, 20, 30), tr2 = c(10, 20, 30))
  ),
  C1 = list( # covariate 1
    level = c(70, 80, 90),
    slope = list(tr1 = 1.5, tr2 = 1.5)
  ),
  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(pop.map = pop.map, qtn.num = list(tr1 = 10, tr2 = 10), qtn.model = "A")
# Generate annotation simulation parameters
# SP <- param.geno(SP = SP, pop.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP, 
  pop.ind = 100,
  pop.env = pop.env,
  phe.model = list(
    tr1 = "T1 = A + F1 + F2 + C1 + R1 + E", # "T1" (Trait 1) consists of Additive effect, F1, F2, C1, R1, and Residual effect
    tr2 = "T2 = A + F1 + F2 + C1 + R1 + E"  # "T2" (Trait 1) consists of Additive effect, F1, F2, C1, 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

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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:
If users want to output files, please see File output.

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

# Prepare environmental factor list
pop.env <- list(
  F1 = list( # fixed effect 1
    level = c("1", "2"),
    effect = list(tr1 = c(50, 30))
  ), 
  F2 = list( # fixed effect 2
    level = c("d1", "d2", "d3"),
    effect = list(tr1 = c(10, 20, 30))
  ),
  C1 = list( # covariate 1
    level = c(70, 80, 90),
    slope = list(tr1 = 1.5)
  ),
  R1 = list( # random effect 1
    level = c("l1", "l2", "l3"),
    ratio = list(tr1 = 0.1)
  )
)

# Generate annotation simulation parameters
SP <- param.annot(pop.map = pop.map, qtn.num = list(tr1 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
# SP <- param.geno(SP = SP, pop.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  pop.env = pop.env,
  phe.model = list(
    tr1 = "T1 = A + F1 + F2 + C1 + R1 + A:F1 + E" # "T1" (Trait 1) consists of Additive effect, F1, F2, C1, 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:
If users want to output files, please see File output.

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

# Prepare environmental factor list
pop.env <- list(
  F1 = list( # fixed effect 1
    level = c("1", "2"),
    effect = list(tr1 = c(50, 30), tr2 = c(50, 30))
  ), 
  F2 = list( # fixed effect 2
    level = c("d1", "d2", "d3"),
    effect = list(tr1 = c(10, 20, 30), tr2 = c(10, 20, 30))
  ),
  C1 = list( # covariate 1
    level = c(70, 80, 90),
    slope = list(tr1 = 1.5, tr2 = 1.5)
  ),
  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(pop.map = pop.map, qtn.num = list(tr1 = 10, tr2 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
# SP <- param.geno(SP = SP, pop.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP,
  pop.ind = 100,
  pop.env = pop.env,
  phe.model = list(
    tr1 = "T1 = A + F1 + F2 + C1 + R1 + A:F1 + E", # "T1" (Trait 1) consists of Additive effect, F1, F2, C1, R1, Additive-F1 interaction effect, and Residual effect
    tr2 = "T2 = A + F1 + F2 + C1 + R1 + A:F1 + E"  # "T2" (Trait 2) consists of Additive effect, F1, F2, C1, 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)

Generate phenotype controlled by varied QTN effect distribution

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In the single-trait simulation, the trait can be controlled by varied QTN effect distribution. An example of the single-trait controlled by two-group QTNs is displayed as follows:
If users want to output files, please see File output.

# 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 = list(tr1 = c(2, 8)), # Group1: 2 QTNs; Group 2: 8 QTNs
  qtn.dist = list(tr1 = c("norm", "norm")),
  qtn.model = "A"
)
# Generate genotype simulation parameters
# SP <- param.geno(SP = SP, pop.geno = pop.geno)           # external genotype
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2) # random genotype
# 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)

Population Simulation of Multiple-Generation with Genotype and Phenotype

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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

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selects, main function of Selection:

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.

reproduces, main function of Reproduction:

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 on single trait

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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 be used for traits with high heritability.
If users want to output files, please see File output.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 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

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Family selection is a selection method by family based on the average of the family. This selection method is used for traits with low heritability.
If users want to output files, please see File output.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 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

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Within-family selection is a selection 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.
If users want to output files, please see File output.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 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

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Combined selection is a selecting method according to weighed combination of the deviation of individual phenotype and family mean value.
If users want to output files, please see File output.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 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

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Tandem selection is a method for sequentially selecting a plurality of target traits one by one. The index of the selected trait is index.tdm and this parameter should not be controlled by Users.
If users want to output files, please see File output.

# Generate genotype simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 10, tr2 = 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,
  # 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

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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.
If users want to output files, please see File output.

# Generate genotype simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 10, tr2 = 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,
  # 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

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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.
If users want to output files, please see File output.

# Generate genotype simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 10, tr2 = 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,
  # 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

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Clone is a sexual reproduction method that 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.
If users want to output files, please see File output.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 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

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Double haploid is a reproduction method for breeding workers to obtain haploid plants. It induced double the number of chromosomes and restore the number of chromosomes in normal plants. Sex of offspring will be 0 in dh.
If users want to output files, please see File output.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 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

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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.
If users want to output files, please see File output.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 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

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In random mating, any female or male individual has 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.
If users want to output files, please see File output.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 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

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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.
If users want to output files, please see File output.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 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

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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.
If users want to output files, please see File output.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 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

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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.
If users want to output files, please see File output.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 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

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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.
If users want to output files, please see File output.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 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

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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.
If users want to output files, please see File output.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 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

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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.
If users want to output files, please see File output.

# Generate annotation simulation parameters
SP <- param.annot(qtn.num = list(tr1 = 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

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The above methods are to generate population step by step, which are easy to understand. Actually, SIMER can directly generate a population in a MORE CONVENIENT way.
If users want to output files, please see File output.

# Generate all simulation parameters
SP <- param.simer(qtn.num = list(tr1 = 10), pop.marker = 1e4, pop.ind = 1e2, sel.single = "comb", reprod.way = "randmate")

# Run Simer
SP <- simer(SP)

Breeding Program Design

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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 by simulation.

Gallery of breeding program design parameters

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simer.Data.Json, main function of Breeding Program Design:

Paramater Default Options Description
jsonFile NULL character the path of JSON file.
out 'simer.qc' character the prefix of output files.
dataQC TRUE TRUE or FALSE whether to make data quality control.
buildModel TRUE TRUR or FALSE whether to build EBV model.
buildIndex TRUE TRUR or FALSE whether to build Selection Index.
ncpus 10 num the number of threads used, if NULL, (logical core number - 1) is automatically used.
verbose TRUE TRUE or FALSE whether to print detail.

Breeding program design preparation

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Breeding program design should be stored on a JSON file.

plan1.json

genotype: the path of genotype data
pedigree: the filename of pedigree data
selection_index: the economic weight of phenotype for each trait
breeding_value_index: the economic weight of breeding value for each trait
auto_optimization: optimizing EBV estimated model and selection index automatically
quality_control_plan: the quality control plan for genotype, pedigree, and phenotype

genotype_quality_control: the quality control plan for genotype

filter: the 'filter' (individual) condition for genotyped individual
filter_geno: the genotype missing rate filter
filter_mind the sample missing rate filter
filter_maf the Minor Allele Frequency filter
filter_hwe the Hardy-Weinberg Equilibrium filter

pedigree_quality_control: the quality control plan for pedigree

standard_ID: whether ID is standard 15-digit ID
candidate_sire_file: the filename of candidate sire
candidate_dam_file: the filename of candidate dam
exclude_threshold: if the number of base error is more than this threshold, this individual will be excluded
assign_threshold: if the number of base error is less than this threshold, this parent will be assigned to this individual

phenotype_quality_control: the quality control plan for phenotype

job_name: the name of phenotype quality control job
sample_info: the filename of phenotype data
repeated_records: whether phenotype data contains repeated records
multi_trait: whether phenotype data contains multiple traits
filter: the 'filter' (individual) condition for phenotyped individual
select: the 'select' (trait) condition for phenotyped individual
arrange: the 'arrange' (order) condition for phenotyped individual
job_traits: the trait need quality control and its definition and range

analysis_plan: the genetic evaluation plan

job_name: the name of phenotype quality control job
sample_info: the filename of phenotype data
repeated_records: whether phenotype data contains repeated records
multi_trait: whether phenotype data contains multiple traits
random_ratio: the least random effect ratio to phenotype variance
job_traits: the trait need analysis and its covariate, fixed effect, and random effect

{
    "genotype": ["/home/yindong/R/x86_64-pc-linux-gnu-library/4.0/simer/extdata/02plinkb"],
    "pedigree": ["/home/yindong/R/x86_64-pc-linux-gnu-library/4.0/simer/extdata/05others/pedigree.txt"],
    "selection_index": [],
    "breeding_value_index": "0.2 * T1 + 0.8 * T2",
    "auto_optimization": true,
    "quality_control_plan": {
        "genotype_quality_control":{
            "filter": ["F1 == 'Male'"],
            "filter_geno": 0.1,
            "filter_mind": 0.1,
            "filter_maf": 0.05,
            "filter_hwe": 0.001
        },
        "pedigree_quality_control":{
            "standard_ID": false,
            "candidate_sire_file": [],
            "candidate_dam_file": [],
            "exclude_threshold": 0.01, 
            "assign_threshold": 0.005
        },
        "phenotype_quality_control":[
            {
                "job_name": "Data Quality Control Demo",
                "sample_info": "/home/yindong/R/x86_64-pc-linux-gnu-library/4.0/simer/extdata/05others/phenotype.txt",
                "repeated_records": false,
                "multi_trait": true,
                "filter": ["F1 == 'Male'"],
                "job_traits": [
                    {
                        "traits": "T1",
                        "definition": "T1",
                        "range": []
                    },
                    {
                        "traits": "T2",
                        "definition": "T2",
                        "range": []
                    }
                ]
            }
        ]
    },
    "analysis_plan":[
        {
            "job_name": "EBV Model Demo",
            "sample_info": "/home/yindong/R/x86_64-pc-linux-gnu-library/4.0/simer/extdata/05others/phenotype.txt",
            "repeated_records": false,
            "multi_trait": true,
            "random_ratio": 0.05,
            "job_traits": [
                {
                    "traits": "T1",
                    "covariates": [],
                    "fixed_effects": ["F1", "F2"],
                    "random_effects": ["R1"]
                },
                {
                    "traits": "T2",
                    "covariates": [],
                    "fixed_effects": ["F1", "F2"],
                    "random_effects": ["R1"]
                }
            ]
        }
    ]
}

Breeding program design evaluation

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In Breeding program design evaluation, SIMER will complete the following three tasks:
(1) Data quality control for genotype, pedigree, and phenotype
(2) Model optimization (the most suitable covariate, fixed effect, and random effect)
(3) Selection Index construction and Genetic Progress calculation

# Get JSON file
jsonFile <- system.file("extdata", "04breeding_plan", "plan1.json", package = "simer")

# It needs 'plink' and 'hiblup' software
jsonList <- simer.Data.Json(jsonFile = jsonFile)

Global Options

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Users can use global parameters to control the population properties , the number of threads used for simulation, and the output of simulation data.

Gallery of global parameters

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simer, main function of simulation:

Paramater Default Options Description
replication 1 num the replication times of simulation.
seed.sim random num simulation random seed.
out 'simer' character the prefix of output files.
outpath NULL character the path of output files, Simer writes files only if outpath is not 'NULL'.
out.format 'numeric' 'numeric' or 'plink' 'numeric' or 'plink', the data format of output files.
pop.gen 2 num the generations of simulated population.
out.geno.gen 1:2 num vector the output generations of genotype data.
out.pheno.gen 1:2 num vector the output generations of phenotype data.
useAllGeno FALSE TRUE or FALSE whether to use all genotype data to simulate phenotype.
ncpus 0 num the number of threads used, if NULL, (logical core number - 1) is automatically used.
verbose TRUE TRUE or FALSE whether to print detail.

Counts of total population size

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Users can calculate the number of individuals per generation by IndPerGen directly.

pop <- generate.pop(pop.ind = 100)
count.ind <- IndPerGen(pop = pop, pop.gen = 2, ps = c(0.8, 0.8), reprod.way = "randmate", sex.rate = 0.5, prog = 2)

Multi-thread simulation

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SIMER is able to run on multiple threads. Users can easily change the number of threads used for simulation by following:

# Generate all simulation parameters
SP <- param.simer(out = "simer", ncpus = 2)

# Run Simer
SP <- simer(SP)

Multi-population simulation

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Simulation of multiple populations can be realized by for in R software.

# Replication times
rep <- 2

# Result list
SPs <- rep(list(NULL), rep)

for (i in 1:rep) {
  # Generate all simulation parameters
  SP <- param.simer(replication = i, sim.seed = i, out = "simer")

  # Run Simer
  SPs[[i]] <- simer(SP)
}

File output

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SIMER won't output files by default. A series of files with the prefix out will output when specifying outpath.

### 01 Numeric Format ###
# Generate all simulation parameters
SP <- param.simer(
  # SP = SP, # uncomment it when users already have a 'SP'
  out = "simer",
  outpath = getwd(),
  out.format = "numeric"
 )

# Run Simer
SP <- simer(SP)

### 02 PLINK Binary Format ###
# Generate all simulation parameters
SP <- param.simer(
  # SP = SP, # uncomment it when users already have a 'SP'
  out = "simer",
  outpath = getwd(),
  out.format = "plink"
)

# Run Simer
SP <- simer(SP)

Generation-selective output

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Output of genotype and phenotype can be generation-selective by out.geno.gen and out.pheno.gen.

# Generate all simulation parameters
SP <- param.simer(out = "simer", outpath = getwd(), pop.gen = 2, out.geno.gen = 1:2, out.pheno.gen = 1:2)

# Run Simer
SP <- simer(SP)

Output

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SIMER outputs data including annotation data, genotype data, and phenotype data in the following two format.
Numeric format:
simer.geno.ind contains indice of genotyped individuals;
simer.geno.desc and simer.geno.bin 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.
PLINK Binary format:
simer.bim contains marker information of genotype data;
simer.bed contains genotype data in binary format;
simer.fam contains sample information of genotype data;
simer.ped contains pedigree of individuals;
simer.phe contains phenotype of individuals.

Annotation data

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Annotation data contains SNP name, Chromosome name, Base Position, ALT, REF, and the QTN genetic effect. Note that only markers selected as QTNs have values.

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

# Run Simer
SP <- simer(SP)

# Show annotation data
head(SP$map$pop.map)
  SNP Chrom     BP ALT REF QTN1_A
1  M1     1 130693   C   A     NA
2  M2     1 168793   G   A     NA
3  M3     1 286553   A   T     NA
4  M4     1 306913   C   G     NA
5  M5     1 350926   T   A     NA
6  M6     1 355889   A   C     NA

Genotype data

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Genotype data is stored in big.matrix format.

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

# Run Simer
SP <- simer(SP)

# Show genotype data
print(SP$geno$pop.geno)
$gen1
An object of class "big.matrix"
Slot "address":
<pointer: 0x00000000176f09e0>


$gen2
An object of class "big.matrix"
Slot "address":
<pointer: 0x00000000176ef940>

print(SP$geno$pop.geno$gen1[1:6, 1:6])
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    0    2    0    1    0    2
[2,]    1    1    1    1    0    0
[3,]    0    1    2    2    1    0
[4,]    2    0    1    1    1    0
[5,]    2    1    0    1    2    1
[6,]    1    2    1    1    1    2

Phenotype data

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Phenotype data contains sample ID, generation index, family index, within-family index, sire, dam, sex, phenotype, TBV, TGV, and other effects.

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

# Run Simer
SP <- simer(SP)

# Show phenotype data
head(SP$pheno$pop$gen1)
  index gen fam infam sir dam sex          T1     T1_TBV     T1_TGV   T1_A_eff    T1_E_eff
1     1   1   1     1   0   0   1  -0.4934935 -1.3507888 -1.3507888 -1.3507888   0.8572953
2     2   1   2     2   0   0   1   7.7710404 -1.6756353 -1.6756353 -1.6756353   9.4466757
3     3   1   3     3   0   0   1  -4.6567338 -2.2608387 -2.2608387 -2.2608387  -2.3958951
4     4   1   4     4   0   0   1  -5.9064589 -1.7394139 -1.7394139 -1.7394139  -4.1670450
5     5   1   5     5   0   0   1 -16.7438931 -2.8000846 -2.8000846 -2.8000846 -13.9438085
6     6   1   6     6   0   0   1   6.0043912  0.3413561  0.3413561  0.3413561   5.6630351

Citation

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For SIMER:
Hope it will be coming soon!

For ADI model:
Kao, Chenhung, et al. "Modeling Epistasis of Quantitative Trait Loci Using Cockerham's Model." Genetics 160.3 (2002): 1243-1261.

For build.cov:
B. D. Ripley "Stochastic Simulation." Wiley-Interscience (1987): Page 98.

FAQ and Hints

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🆘 Question1: Failing to install "devtools":

ERROR: configuration failed for package ‘git2r’

removing ‘/Users/acer/R/3.4/library/git2r’

ERROR: dependency ‘git2r’ is not available for package ‘devtools’

removing ‘/Users/acer/R/3.4/library/devtools’

😋 Answer: Please try following codes in terminal:

apt-get install libssl-dev/unstable

🆘 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?)

😋 Answer: Please try following codes and then try agian.

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

Questions, suggestions, and bug reports are welcome and appreciated. ➡️

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About

Data Simulation for Life Science and Breeding

Resources

Readme

License

Apache-2.0 license

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