SIMER

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

(★ 45)

Data Simulation for Life Science and Breeding

SIMER 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# SIMER 
[![GitHub issues](https://img.shields.io/github/issues/xiaolei-lab/SIMER?color=green)](https://github.com/xiaolei-lab/SIMER/issues/new) [![CRAN Version](https://www.r-pkg.org/badges/version/simer?color=yellow)](https://CRAN.R-project.org/package=simer) [![](https://img.shields.io/badge/GitHub-1.0.0-blueviolet.svg)](https://github.com/xiaolei-lab/SIMER) ![](http://cranlogs.r-pkg.org/badges/grand-total/simer?color=red) [![](https://cranlogs.r-pkg.org/badges/last-month/simer)](https://CRAN.R-project.org/package=simer) <img alt="Hits" src="https://hits.sh/github.com/xiaolei-lab/SIMER.svg?view=today-total&label=Today%2FTotal%20Visitors&extraCount=12000"/>

## 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, Xiang Zhou, Xinyun Li, and Shuhong Zhao.  
***Citation:*** Yin D., Zhang X, Yin L, Zhang H, Tang Z, Xu J, Yuan X, Zhou X, Li X, Zhao S, Liu X. [SIMER: an accurate and intelligent tool for simulating customizable population data across species in complex scenarios](https://doi.org/10.1186/s40537-025-01102-z), ***Journal of Big Data*** , 2025, 12 (49), doi: [https://doi.org/10.1186/s40537-025-01102-z](https://doi.org/10.1186/s40537-025-01102-z).

***If you have any bug reports or questions, please feed back :point_right:[here](https://github.com/xiaolei-lab/SIMER/issues/new):point_left:.***

### :toolbox: Relevant software tools for genetic analyses and genomic breeding

<table>
 <tr>
	<td><g-emoji class="g-emoji" alias="mailbox" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f4eb.png">📫</g-emoji> <a href="https://www.hiblup.com/" rel="nofollow">HIBLUP</a>: Versatile and easy-to-use GS toolbox.</td>
	<td><g-emoji class="g-emoji" alias="mountain_snow" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f3d4.png">🏔️</g-emoji> <a href="https://ianimal.pro/" rel="nofollow">IAnimal</a>: an omics knowledgebase for animals.</td>
 </tr>
 <tr>
 <td><g-emoji class="g-emoji" alias="biking_man" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f6b4-2642.png">🚴&zwj;♂️</g-emoji> <a href="https://github.com/YinLiLin/KAML">KAML</a>: Advanced GS method for complex traits.</td>
 <td><g-emoji class="g-emoji" alias="bar_chart" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f4ca.png">📊</g-emoji> <a href="https://github.com/YinLiLin/CMplot">CMplot</a>: A drawing tool for genetic analyses.</td>
 </tr>
 <tr>
	<td><g-emoji class="g-emoji" alias="postbox" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f4ee.png">📮</g-emoji> <a href="https://github.com/xiaolei-lab/rMVP">rMVP</a>: Efficient and easy-to-use GWAS tool.</td>
 <td><g-emoji class="g-emoji" alias="swimmer" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f3ca.png">🏊</g-emoji> <a href="https://github.com/YinLiLin/hibayes">hibayes</a>: A Bayesian-based GWAS and GS tool.</td>
 </tr>
</table>

### Contents

- [Installation](#installation)
- [Data Preparation](#data-preparation)
 - [Genotype](#genotype)
 - [Genetic map](#genetic-map)
 - [Pedigree](#pedigree)<img src="https://raw.githubusercontent.com/xiaolei-lab/SIMER/master/inst/extdata/00simer_logo/simer_logo.png" height="250" align="right" />
- [Data Input](#data-input)
 - [Basic](#basic)
 - [Optional](#optional)
- [Quick Start](#quick-start)
 - [Quick Start for Population Simulation](#quick-start-for-population-simulation)
 - [Quick Start for Genotype Simulation](#quick-start-for-genotype-simulation)
 - [Quick Start for Phenotype Simulation](#quick-start-for-phenotype-simulation)
- [Genotype Simulation](#genotype-simulation)
 - [Gallery of genotype simulation parameters](#gallery-of-genotype-simulation-parameters)
 - [Generate an external or species-specific or random genetic map](#generate-an-external-or-species-specific-or-random-genetic-map)
 - [Generate an external or species-specific or random genotype matrix](#generate-an-external-or-species-specific-or-random-genotype-matrix)
 - [Generate a genotype matrix with complete linkage disequilibrium](#generate-a-genotype-matrix-with-complete-linkage-disequilibrium)
 - [Add chromosome crossovers and mutations to genotype matrix](#add-chromosome-crossovers-and-mutations-to-genotype-matrix)
- [Phenotype Simulation](#phenotype-simulation)
 - [Gallery of phenotype simulation parameters](#gallery-of-phenotype-simulation-parameters)
 - [Generate phenotype using an external or species-specific or random genotype matrix](#generate-phenotype-using-an-external-or-species-specific-or-random-genotype-matrix)
 - [Generate continuous phenotype](#generate-continuous-phenotype)
 - [Generate case-control phenotype](#generate-case-control-phenotype)
 - [Generate categorical phenotype](#generate-categorical-phenotype)
 - [Generate phenotype using A model](#generate-phenotype-using-A-model)
 - [Generate phenotype using AD model](#generate-phenotype-using-AD-model)
 - [Generate phenotype using GxG model](#generate-phenotype-using-GxG-model)
 - [Generate phenotype using Repeated Record model](#generate-phenotype-using-repeated-record-model)
 - [Generate phenotype controlled by QTNs subject to Normal distribution](#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-geometric-distribution)
 - [Generate phenotype controlled by QTNs subject to Gamma distribution](#generate-phenotype-controlled-by-QTNs-subject-to-gamma-distribution)
 - [Generate phenotype controlled by QTNs subject to Beta distribution](#generate-phenotype-controlled-by-QTNs-subject-to-beta-distribution)
 - [Generate phenotype with fixed effect and covariate and environmental random effect](#generate-phenotype-with-fixed-effect-and-covariate-and-environmental-random-effect)
 - [Generate phenotype using GxE model](#generate-phenotype-using-GxE-model)
 - [Generate phenotype using ExE model](#generate-phenotype-using-ExE-model)
 - [Generate phenotype controlled by varied QTN effect distribution](#generate-phenotype-controlled-by-varied-QTN-effect-distribution)
- [Population Simulation of Multiple-Generation with Genotype and Phenotype](#population-simulation-of-multiple-generation-with-genotype-and-phenotype)
 - [Gallery of population simulation parameters](#gallery-of-population-simulation-parameters)
 - [Individual selection for a single trait](#individual-selection-for-a-single-trait)
 - [Family selection for a single trait](#family-selection-for-a-single-trait)
 - [Within-family selection for a single trait](#within-family-selection-for-a-single-trait)
 - [Combined selection for a single trait](#combined-selection-for-a-single-trait)
 - [Tandem selection for multiple traits](#tandem-selection-for-multiple-traits)
 - [Independent culling selection for multiple traits](#independent-culling-selection-for-multiple-traits)
 - [Index selection for multiple traits](#index-selection-for-multiple-traits)
 - [Clone for plants](#clone-for-plants)
 - [Doubled haploid for plants](#doubled-haploid-for-plants)
 - [Self-pollination for plants and micro-organisms](#self-pollination-for-plants-and-micro-organisms)
 - [Random mating for plants and animals](#random-mating-for-plants-and-animals)
 - [Random mating excluding self-pollination for animals](#random-mating-excluding-self-pollination-for-animals)
 - [Assortative mating for plants and animals](#assortative-mating-for-plants-and-animals)
 - [Disassortative mating for plants and animals](#disassortative-mating-for-plants-and-animals)
 - [Two way cross for animals](#two-way-cross-for-animals)
 - [Three way cross for animals](#three-way-cross-for-animals)
 - [Four way cross for animals](#four-way-cross-for-animals)
 - [Back cross for animals](#back-cross-for-animal)
 - [User-designed pedigree mating for plants and animals](#user-designed-pedigree-mating-for-plants-and-animals)
 - [AN EASY WAY TO GENERATE A POPULATION](#an-easy-way-to-generate-a-population)
- [Breeding Program Design](#breeding-program-design)
 - [Gallery of breeding program design parameters](#gallery-of-breeding-program-design-parameters)
 - [Definition of a breeding program design](#definition-of-a-breeding-program-design)
 - [Data quality control](#data-quality-control)
 - [Model optimization](#model-optimization)
 - [Construction of selection index](#construction-of-selection-index)
- [Global Options](#global-options)
 - [Gallery of global parameters](#gallery-of-global-parameters)
 - [Counts of total population size](#counts-of-total-population-size)
 - [Multi-thread computation](#multi-thread-computation)
 - [Multi-population simulation](#multi-population-simulation)
 - [File output](#file-output)
 - [Generation-selective output](#generation-selective-output)
- [Output](#output)
 - [Annotation data](#annotation-data)
 - [Genotype data](#genotype-data)
 - [Phenotype data](#phenotype-data)
- [Citation](#citation)
- [FAQ and Hints](#faq-and-hints)

---

# Installation
**[back to top](#contents)**

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

## Installation

* The stable version: 
```R
install.packages("simer")
```

* The latest version: 
```R
devtools::install_github("xiaolei-lab/SIMER")
```

After installed successfully, **```SIMER```** can be loaded by typing
```r
> library(simer)
```
Typing ```?simer``` could get the details of all parameters.

---

# Data Preparation

## Genotype
**[back to top](#contents)**

***Genotype data*** should be ***Numeric*** format (either m * n or n * m is acceptable, ***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`

## Genetic map
**[back to top](#contents)**  
A ***genetic map*** is necessary in **```SIMER```**. The first column is the ***SNP name***, the second column is the ***Chromosome ID***, the third column is ***physical position***, the fourth column is ***REF***, and the fifth column is ***ALT***.  This 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
**[back to top](#contents)**  
**```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
**[back to top](#contents)**  
At least users should prepare two datasets: ***genetic map*** and ***genotype data***.

***genetic 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***.  
***genotype data***, ***Numeric*** format (either m * n or n * m is acceptable, ***m*** is the number of SNPs, ***n*** is the number of individuals)

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

**```SIMER```** also supports genotype data in bigmemory format.

```r
pop.map <- read.table("map.geno.map", head = TRUE)
pop.geno <- bigmemory::attach.big.matrix("genotype.geno.desc")
```

## Optional
**[back to top](#contents)**  
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.

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

---

# Quick Start
**[back to top](#contents)**

All simulation processes can be divided into two steps: ***1) generation of simulation parameters***; ***2) run simulation process***.

## Quick Start for Population Simulation
**[back to top](#contents)**

A quick start for ***Population Simulation*** is shown below:

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

# Run Simer
SP <- simer(SP)
```

## Quick Start for Genotype Simulation
**[back to top](#contents)**

A quick start for ***Genotype Simulation*** is shown below:

```r
# Generate annotation simulation parameters
SP <- param.annot(species = "pig")
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2)

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

## Quick Start for Phenotype Simulation
**[back to top](#contents)**

A quick start for ***Phenotype Simulation*** is shown below:

```r
# Generate annotation simulation parameters
SP <- param.annot(species = "pig")
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, phe.h2A = list(tr1 = 0.3))

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

---

# Genotype Simulation
**[back to top](#contents)**

***Genotype data*** in **```SIMER```** is generated randomly or through an external genotype matrix. Chromosome crossovers and base mutations depend on block information and recombination information of ***Annotation data***.

## Gallery of genotype simulation parameters
**[back to top](#contents)**

```genotype```, main function of ***Genotype Simulation***:

```annotation```, main function of ***Annotation Simulation***:

## Generate an external or species-specific or random genetic map
**[back to top](#contents)**

Users can generate a genetic map by inputting an external genetic map.

```r
# Real genotypic map
mapPath <- system.file("extdata", "06map", "pig_map.txt", package = "simer")
pop.map <- read.table(mapPath, header = TRUE)

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

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

Users can also use the inner real genetic map with ```species```, which can be "arabidopsis", "cattle", "chicken", "dog", "horse", "human", "maize", "mice", "pig", and "rice".

```r
# Generate annotation simulation parameters
SP <- param.annot(species = "pig")

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

Users can generate a random genetic map with ```pop.marker```, ```num.chr```, and ```len.chr```.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, num.chr = 18, len.chr = 1.5e8)

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

## Generate an external or species-specific or random genotype matrix
**[back to top](#contents)**

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

```r
# Create a genotype matrix
# pop.geno <- read.table("genotype.txt")
# pop.geno <- bigmemory::attach.big.matrix("genotype.geno.desc")
pop.geno <- matrix(c(0, 1, 2), nrow = 1e2, ncol = 1e4, byrow = TRUE)

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

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

Users can also generate genotype matrix with the inner real genetic map with ```species```, which can be "arabidopsis", "cattle", "chicken", "dog", "horse", "human", "maize", "mice", "pig", and "rice".

```r
# Generate annotation simulation parameters
SP <- param.annot(species = "pig")
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2)

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

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

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

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

## Generate a genotype matrix with complete linkage disequilibrium
**[back to top](#contents)**

Users can generate a genotype matrix with ***complete linkage disequilibrium*** by ```inrows = 2``` and ```cld = TRUE```.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2, inrows = 2, cld = TRUE)

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

## Add chromosome crossovers and mutations to genotype matrix
**[back to top](#contents)**

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

```r
# Generate annotation simulation parameters
# If recom.spot = TRUE, chromosome crossovers will be added to genotype matrix
SP <- param.annot(pop.marker = 1e4, recom.spot = TRUE)
# Generate genotype simulation parameters
# Base mutation rate of QTN and SNP are 1e-8
SP <- param.geno(SP = SP, pop.ind = 1e2, rate.mut = list(qtn = 1e-8, snp = 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.

```r
# Generate annotation simulation parameters
# If recom.spot = FALSE, chromosome crossovers will not be added to genotype matrix
SP <- param.annot(pop.marker = 1e4, recom.spot = FALSE)
# Generate genotype simulation parameters
# Base mutation rate of QTN and SNP are 1e8
SP <- param.geno(SP = SP, pop.ind = 1e2, rate.mut = list(qtn = 1e-8, snp = 1e-8))

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

---

# Phenotype Simulation
**[back to top](#contents)**

***Phenotype data*** in **```SIMER```** is generated according to different models, which include:  
**(1)** Single-Trait Model  
**(2)** Multiple-Trait Model  
**(3)** Repeated Record Model  
**(4)** Genetic Effect Model (***A***dditive effect, ***D***ominant effect, and ***G***enetic-***G***enetic interaction effect)  
**(5)** Genetic Model with Varied QTN Effect Distributions (QTN effect distribution: ***Norm***al distribution, ***Geom***etric distribution, ***Gamma*** distribution, ***Beta*** distribution, and their combination)  
**(6)** Linear Mixed Model (***F***ixed effect, ***C***ovariate, ***E***nvironmental ***R***andom effect, ***G***enetic ***R***andom effect, ***G***enetic-***E***nvironmental interaction effect, and ***E***nvironmental-***E***nvironmental interaction effect)

## Gallery of phenotype simulation parameters
**[back to top](#contents)**

```phenotype```, main function of ***Phenotype Simulation***:

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

```annotation```, main function of ***Annotation Simulation***:

<table text-algn="left">
<thead>
<tr>
<td>Paramater</td>
<td>Default</td>
<td>Options</td>
<td>Description</td>
</tr>
</thead>
<tbody>
<tr>
<td>pop.map</td>
<td>NULL</td>
<td>data.frame</td>
<td>the map data with annotation information.</td>
</tr>
<tr>
<td>qtn.model</td>
<td>"A"</td>
<td>character</td>
<td>the genetic model of QTN such as "A + D".</td>
</tr>
<tr>
<td>qtn.index</td>
<td>NULL</td>
<td>list</td>
<td>the QTN index for each trait.</td>
</tr>
<tr>
<td>qtn.num</td>
<td>list(tr1 = 10)</td>
<td>list</td>
<td>the QTN number for (each group in) each trait.</td>
</tr>
<tr>
<td>qtn.dist</td>
<td>list(tr1 = "norm")</td>
<td>list</td>
<td>the QTN distribution containing "norm", "geom", "gamma" or "beta".</td>
</tr>
<tr>
<td>qtn.var</td>
<td>list(tr1 = 0.01)</td>
<td>list</td>
<td>the variances for normal distribution.</td>
</tr>
<tr>
<td>qtn.prob</td>
<td>list(tr1 = 0.5)</td>
<td>list</td>
<td>the probability of success for geometric distribution.</td>
</tr>
<tr>
<td>qtn.shape</td>
<td>list(tr1 = 1)</td>
<td>list</td>
<td>the shape parameter for gamma distribution.</td>
</tr>
<tr>
<td>qtn.scale</td>
<td>list(tr1 = 1)</td>
<td>list</td>
<td>the scale parameter for gamma distribution.</td>
</tr>
<tr>
<td>qtn.shape1</td>
<td>list(tr1 = 1)</td>
<td>list</td>
<td>the shape1 parameter for beta distribution.</td>
</tr>
<tr>
<td>qtn.shape2</td>
<td>list(tr1 = 1)</td>
<td>list</td>
<td>the shape2 parameter for beta distribution.</td>
</tr>
<tr>
<td>qtn.ncp</td>
<td>list(tr1 = 0)</td>
<td>list</td>
<td>the ncp parameter for beta distribution.</td>
</tr>
<tr>
<td>qtn.spot</td>
<td>FALSE</td>
<td>TRUE or FALSE</td>
<td>the QTN distribution probability in each block.</td>
</tr>
<tr>
<td>len.block</td>
<td>5e7</td>
<td>num</td>
<td>the block length.</td>
</tr>
<tr>
<td>maf</td>
<td>NULL</td>
<td>num</td>
<td>the maf threshold, markers less than this threshold will be exclude.</td>
</tr>
</tbody>
</table>

## Generate phenotype using an external or species-specific or random genotype matrix
**[back to top](#contents)**

Users can use ***real genotype data*** with specific genetic structure to generate phenotype.

# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.geno = pop.geno)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, phe.h2A = list(tr1 = 0.3))

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

Users can also generate phenotype using species-specific genotype matrix.

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

Users can also generate phenotype using random genotype.

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

## Generate continuous phenotype
**[back to top](#contents)**

**```SIMER```** generates continuous phenotypes by default. Continuous phenotype simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 phe.type = list(tr1 = "continuous"),
 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)
```

Multiple-trait simulation of continuous phenotype is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10, tr2 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 phe.type = list(tr1 = "continuous", tr2 = "continuous"),
 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 case-control phenotype
**[back to top](#contents)**

**```SIMER```** generates case-control phenotypes by ```phe.type```. ```phe.type``` consists of the variable names and their percentages. Case-control phenotype simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 phe.type = list(tr1 = list(case = 0.01, control = 0.99)), # "T1" (Trait 1) consists of 1% case and 99% control
 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)
```

Multiple-trait simulation of case-control phenotype is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10, tr2 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 phe.type = list(
 tr1 = list(case = 0.01, control = 0.99), # "T1" (Trait 1) consists of 1% case and 99% control
 tr2 = list(case = 0.01, control = 0.99) # "T2" (Trait 2) consists of 1% case and 99% control
 ),
 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 categorical phenotype
**[back to top](#contents)**

**```SIMER```** generates categorical phenotypes by ```phe.type```. ```phe.type``` consists of the variable names and their percentages. Categorical phenotype simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 phe.type = list(tr1 = list(low = 0.3, medium = 0.4, high = 0.3)), # "T1" (Trait 1) consists of 30% low, 40% medium, and 30% high
 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)
```

Multiple-trait simulation of categorical phenotype is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10, tr2 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 phe.type = list(
 tr1 = list(low = 0.3, medium = 0.4, high = 0.3), # "T1" (Trait 1) consists of 30% low, 40% medium, and 30% high
 tr2 = list(low = 0.3, medium = 0.4, high = 0.3) # "T2" (Trait 2) consists of 30% low, 40% medium, and 30% high
 ),
 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 using A model
**[back to top](#contents)**

In an "***A***" model, **```SIMER```** only considers an ***A***dditive effect as a genetic effect. Users should prepare ***A***dditive ***QTN*** effect in the ***Annotation data*** to generate an ***A***dditive ***I***ndividual effect. An ***A***dditive single-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

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

In the multiple-trait simulation, **```SIMER```** builds ***accurate Additive genetic correlation*** among multiple traits. An ***A***dditive multiple-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10, tr2 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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 using AD model
**[back to top](#contents)**

In an "***AD***" model, **```SIMER```** considers ***A***dditive effect and ***D***ominant effect as genetic effect. Users should prepare ***A***dditive ***QTN*** effect and ***D***ominant ***QTN*** effect in the ***Annotation data*** to generate an ***A***dditive ***I***ndividual effect and ***D***ominant ***I***ndividual effect. ***A***dditive and ***D***ominant single-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10), qtn.model = "A + D") # Additive effect and Dominant effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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```** builds ***accurate Additive genetic correlation*** and ***accurate Dominant genetic correlation*** among multiple traits. An ***A***dditive and ***D***ominant multiple-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, 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.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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 using GxG model
**[back to top](#contents)**

In a "***GxG***" model, **```SIMER```** considers ***G***enetic-***G***enetic effect as a genetic effect. Users should prepare ***G***enetic-***G***enetic ***QTN*** effect in the ***Annotation data*** to generate ***G***enetic-***G***enetic ***I***ndividual effect. An example of ***A***dditive-***D***ominant interaction in single-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, 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.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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```** builds ***accurate Genetic-Genetic interaction correlation*** among multiple traits. An example of ***A***dditive-***D***ominant interaction in multiple-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, 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.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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 using Repeated Record model
**[back to top](#contents)**

In the ***Repeated Record*** model, **```SIMER```** adds a ***PE*** (***P***ermanent ***E***nvironmental) 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. The ***Repeated Record*** in a single-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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),
 phe.h2PE = list(tr1 = 0.1)
)
SP$global$useAllGeno <- TRUE

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

In the multiple-trait simulation, **```SIMER```** builds ***accurate Permanent Environmental correlation*** among multiple traits. ***Repeated Record*** in multiple-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10, tr2 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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.h2PE = list(tr1 = 0.1, tr2 = 0.1)
 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
)
SP$global$useAllGeno <- TRUE

# 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
**[back to top](#contents)**

***Norm***al distribution is the most common QTN effect distribution. Phenotype controlled by QTNs subject to ***Norm***al distribution in single-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(
 pop.marker = 1e4,
 qtn.num = list(tr1 = 10),
 qtn.model = "A",
 qtn.dist = list(tr1 = "norm"),
 qtn.var = list(tr1 = 0.01)
)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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 ***Norm***al distribution in multiple-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(
 pop.marker = 1e4,
 qtn.num = list(tr1 = 10, tr2 = 10),
 qtn.model = "A",
 qtn.dist = list(tr1 = "norm", tr2 = "norm"),
 qtn.var = list(tr1 = 0.01, tr2 = 0.01)
)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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
**[back to top](#contents)**

***Geom***etric distribution is the probability of success for the first time obtained only after K trials among the N Bernoulli trials. ***Geom***etric distribution can be used as a QTN effect distribution. Phenotype controlled by QTNs subject to ***Geom***etric distribution in single-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(
 pop.marker = 1e4,
 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.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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 ***Geom***etric distribution in multiple-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(
 pop.marker = 1e4,
 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.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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
**[back to top](#contents)**

***Gamma*** distribution is the sum of N independent exponential random variables. Note that ***Exp***onential 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](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(
 pop.marker = 1e4,
 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.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(
 pop.marker = 1e4,
 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.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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
**[back to top](#contents)**

***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](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(
 pop.marker = 1e4,
 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.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(
 pop.marker = 1e4,
 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.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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
**[back to top](#contents)**

**```SIMER```** supports adding ***F***ixed effects, ***C***ovariates, and ***E***nvironmental ***R***andom effects to a phenotype. Users should prepare a list of environmental factors setting. ***F***ixed effects, ***C***ovariates , and ***E***nvironmental ***R***andom effects are determined by ```effect```, ```slope```, and ```ratio``` respectively. A phenotype with ***F***ixed effect, ***C***ovariate, and ***E***nvironmental ***R***andom effect in single-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# 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.marker = 1e4, qtn.num = list(tr1 = 10), qtn.model = "A")
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP, 
 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)
```

A phenotype with ***F***ixed effect, ***C***ovariate, and ***E***nvironmental ***R***andom effect in multiple-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# 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.marker = 1e4, qtn.num = list(tr1 = 10, tr2 = 10), qtn.model = "A")
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP, 
 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 using GxE model
**[back to top](#contents)**

In a "***GxE***" model, **```SIMER```** adds a ***G***enetic-***E***nvironmental interaction effect to the phenotype. Users should prepare the ***G***enetic ***QTN*** effect in the ***Annotation data*** and environmental factor by ```pop.env``` to generate a ***G***enetic-***E***nvironmental ***I***ndividual effect. An example of a ***G***enetic-***E***nvironmental interaction in a single-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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 ***G***enetic-***E***nvironmental interaction in multiple-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10, tr2 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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.h2GxE = list(tr1 = list("A:F1" = 0.1), tr2 = list("A:F1" = 0.1)),
 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 using ExE model
**[back to top](#contents)**

In an "***ExE***" model, **```SIMER```** adds ***E***nvironmental-***E***nvironmental interaction effect to phenotype. Users should prepare environmental factor by ```pop.env``` for generating ***E***nvironmental-***E***nvironmental ***I***ndividual effect. An example of ***E***nvironmental-***E***nvironmental interaction in single-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 pop.env = pop.env,
 phe.model = list(
 tr1 = "T1 = A + F1 + F2 + C1 + R1 + F1:R1 + E" # "T1" (Trait 1) consists of Additive effect, F1, F2, C1, R1, F1-R1 interaction effect, and Residual effect
 ),
 # phe.var = list(tr1 = 100),
 phe.h2A = list(tr1 = 0.3),
 phe.h2GxE = list(tr1 = list("F1:R1" = 0.1))
)

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

An example of ***E***nvironmental-***E***nvironmental interaction in multiple-trait simulation is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10, tr2 = 10), qtn.model = "A") # Additive effect
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 pop.env = pop.env,
 phe.model = list(
 tr1 = "T1 = A + F1 + F2 + C1 + R1 + F1:R1 + E", # "T1" (Trait 1) consists of Additive effect, F1, F2, C1, R1, F1:R1 interaction effect, and Residual effect
 tr2 = "T2 = A + F1 + F2 + C1 + R1 + F1:R1 + E" # "T2" (Trait 2) consists of Additive effect, F1, F2, C1, R1, F1:R1 interaction effect, and Residual effect
 ),
 # phe.var = list(tr1 = 100, tr2 = 100),
 phe.h2A = list(tr1 = 0.3, tr2 = 0.3),
 phe.h2GxE = list(tr1 = list("F1:R1" = 0.1), tr2 = list("F1:R1" = 0.1)),
 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
**[back to top](#contents)**

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](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(
 pop.marker = 1e4, 
 qtn.num = list(tr1 = c(2, 8)), # Group1: 2 QTNs; Group 2: 8 QTNs
 qtn.dist = list(tr1 = c("norm", "norm")),
 qtn.var = list(tr1 = c(0.01, 0.01)), # Group1: genetic variance of QTNs = 0.01; Group2: genetic variance of QTNs = 0.01
 qtn.model = "A"
)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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)

```
An example of the multiple-trait controlled by two-group QTNs is displayed as follows:   
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(
 pop.marker = 1e4, 
 qtn.num = list(tr1 = c(2, 8), tr2 = c(2, 8)), # Group1: 2 QTNs; Group 2: 8 QTNs
 qtn.dist = list(tr1 = c("norm", "norm"), tr2 = c("norm", "norm")),
 qtn.var = list(tr1 = c(0.01, 0.01), tr2 = c(0.01, 0.01)), # Group1: genetic variance of QTNs = 0.01; Group2: genetic variance of QTNs = 0.01
 qtn.model = "A"
)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2) # random genotype
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP,
 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)
```

---

# Population Simulation of Multiple-Generation with Genotype and Phenotype
**[back to top](#contents)**

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

## Gallery of population simulation parameters
**[back to top](#contents)**

```selects```, main function of ***Selection***:

```reproduces```, main function of ***Reproduction***:

## Individual selection for a single trait
**[back to top](#contents)**

***Individual selection*** is a selection method based on the ***phenotype*** of individual traits, which is also known as mixed selection or collective selection. This selection method is simple and easy to use for traits with ***high heritability***.  
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, phe.h2A = list(tr1 = 0.3))
# Generate selection parameters
SP <- param.sel(SP = SP, sel.single = "ind")
SP$global$pop.gen <- 2

# 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 for a single trait
**[back to top](#contents)**

***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](#file-output)**.

# 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 for a single trait
**[back to top](#contents)**

***Within-family*** selection is a selection method based on the ***deviation of individual phenotype and family mean value in each family***. This selection method is used for traits with ***low heritability and small families***.  
If users want to output files, please see **[File output](#file-output)**.

# 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 for a single trait
**[back to top](#contents)**

***Combined selection*** is a selection method based on ***weighed combination of the deviation of individual phenotype and family mean value***.  
If users want to output files, please see **[File output](#file-output)**.

# 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 for multiple traits
**[back to top](#contents)**

***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](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4, qtn.num = list(tr1 = 10, tr2 = 10))
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
 SP = SP, 
 # 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")
SP$global$pop.gen <- 2

# 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 for multiple traits
**[back to top](#contents)**

Set a ***minimum selection criterion*** for each target trait. Then a ***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](#file-output)**.

# 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 for multiple traits
**[back to top](#contents)**

***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, **```SIMER```** calculates the ***index value of each trait***, eliminates it, or selects it according to its level. Users can set the weight of each trait at ```index.wt```.  
If users want to output files, please see **[File output](#file-output)**.

# 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 plants
**[back to top](#contents)**

***Clone*** is a sexual reproduction method that does not involve germ cells and does not require a process of fertilization, but directly forms a new individual's reproductive mode from a part of the mother. ***Sex*** of offspring will be ***0*** in the ```clone```.   
If users want to output files, please see **[File output](#file-output)**.

# 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)
```

## Doubled haploid for plants
**[back to top](#contents)**

***Doubled haploid*** is a reproduction method for breeding workers to obtain haploid plants. It induces a doubling of the number of chromosomes and restores 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](#file-output)**.

## Self-pollination for plants and micro-organisms
**[back to top](#contents)**

***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](#file-output)**.

## Random mating for plants and animals
**[back to top](#contents)**

In ***random mating***, any female or male individual has the same probability to mate with any member of 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](#file-output)**.

## Random mating excluding self-pollination for animals
**[back to top](#contents)**

In ***random mating excluding self-pollination***, an individual cannot mate with itself. ***Sex*** of offspring in random mating is controlled by ```sex.ratio``` in ```randexself```.   
If users want to output files, please see **[File output](#file-output)**.

## Assortative mating for plants and animals
**[back to top](#contents)**

In ***assortative mating***, mated pairs are of the same phenotype more often than would occur by chance. ***Sex*** of offspring in assortative mating is controlled by ```sex.ratio``` in ```assort```.   
If users want to output files, please see **[File output](#file-output)**.

## Disassortative mating for plants and animals
**[back to top](#contents)**

In ***disassortative mating***, mated pairs are of the same phenotype less often than would occur by chance. ***Sex*** of offspring in disassortative mating is controlled by ```sex.ratio``` in ```disassort```.   
If users want to output files, please see **[File output](#file-output)**.

## Two-way cross for animals
**[back to top](#contents)**

The ***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](#file-output)**.

# 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 animals
**[back to top](#contents)**

The ***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***, and the ***third breed*** is terminal ***sire***.  
If users want to output files, please see **[File output](#file-output)**.

# 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 animals
**[back to top](#contents)**

The ***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](#file-output)**.

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# Four 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 to top](#contents)**

The ***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](#file-output)**.

## User-designed pedigree mating for plants and animals
**[back to top](#contents)**

***User-designed pedigree mating*** needs a specific ***user-designed pedigree*** to control the 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 the genotype data.  
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate annotation simulation parameters
SP <- param.annot(pop.marker = 1e4)
# Generate genotype simulation parameters
SP <- param.geno(SP = SP, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(SP = SP, phe.h2A = list(tr1 = 0.3))
# 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
**[back to top](#contents)**

The above methods are to generate populations ***step by step***, which are ***easy to understand***. Actually, **```SIMER```** can generate a population directly in a ***MORE CONVENIENT*** way. ```param.simer``` will call ```param.annot```, ```param.geno```, ```param.pheno```, ```param.sel```, and ```param.reprod```, so users can directly pass all parameters into ```param.simer```, or pass the generated ```SP``` into ```param.simer```. ```simer``` will call ```annotation```,``` genotype```, ```phenotype```, ```selects```, and ```reproduces```, so users can directly use ```simer``` to replace the above sub functions.  
If users want to output files, please see **[File output](#file-output)**.

```r
# Generate all simulation parameters
SP <- param.simer(qtn.num = list(tr1 = 10), pop.marker = 1e4, pop.ind = 1e2, pop.gen = 2, sel.single = "ind", reprod.way = "randmate")

# Run Simer
SP <- simer(SP)
```

---

# Breeding Program Design
**[back to top](#contents)**

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

## Gallery of breeding program design parameters
**[back to top](#contents)**

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

## Definition of a breeding program design
**[back to top](#contents)**

***Breeding program design*** should be stored on a ***JSON*** file. 
> ***definition.json***  
>> ***genotype***: the absolute path or relative path to JSON file of genotype data  
>> ***pedigree***: the filename with absolute path or relative path to JSON file of pedigree data  
>> ***selection_index***: the economic weight of phenotype for each trait  
>> ***threads***: the threads number used in multiple threads computation  
>> ***genetic_progress***: the genetic progress of a breeding plan  
>> ***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 the phenotype quality control job  
>>>> ***sample_info***: the filename with absolute path or relative path to JSON file 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

>> ***breeding_plan***: the genetic evaluation plan  
>>> ***job_name***: the name of the genetic evaluation job  
>>> ***sample_info***: the filename with absolute path or relative path to JSON file of phenotype data   
>>> ***repeated_records***: whether phenotype data contains repeated records  
>>> ***multi_trait***: whether phenotype data contains multiple traits  
>>> ***vc_vars***: the filename of variance component data  
>>> ***vc_covars***: the filename of covariance component data  
>>> ***random_ratio***: the least random effect variance ratio to phenotype variance  
>>> ***job_traits***: the trait need analysis and its covariate, fixed effect, and random effect

## Data quality control
**[back to top](#contents)**

Data quality is an important factor affecting the accuracy of genetic assessment. Therefore, **```SIMER```** provides quality control functions for pedigree, genotype, and phenotype data. The design of the data quality control plan is saved in a JSON file:

```json
{
    "genotype": "../02plinkb",
    "pedigree": "../05others/pedigree.txt",
    "threads": 16,
    "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": 0.1, 
            "assign_threshold": 0.05
        },
        "phenotype_quality_control":[
            {
                "job_name": "Data_Quality_Control_Demo",
                "sample_info": "../05others/phenotype.txt",
                "repeated_records": false,
                "multi_trait": true,
                "filter": "F1 == 'Male'",
                "job_traits": [
                    {
                        "traits": "T1",
                        "definition": "T1",
                        "range": []
                    },
                    {
                        "traits": "T2",
                        "definition": "T2",
                        "range": []
                    }
                ]
            }
        ]
    }
}
```

Running the data quality control plan process only requires two steps, the first step is to read in the JSON file path, and the second step is to use ```simer.Data.Json``` to read in a JSON file. It should be noted that users need to set ```buildModel``` and ```buildIndex``` to FALSE to ensure that **```SIMER```** only runs data quality control processes.

```r
# 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, dataQC = TRUE, buildModel = FALSE, buildIndex = FALSE)

```

## Model optimization
**[back to top](#contents)**

In addition to data quality, a suitable breeding value evaluation model is another important factor that affects the accuracy of genetic evaluation. When ```auto_optization``` is true, **```SIMER```** will optimize the environmental factors in the breeding value evaluation model, namely covariates, fixed effects, and random effects. The covariates and fixed effects will be optimized using a multi-step regression method combined with BIC (Bayesian Information Criterion) (the lower the BIC value, the better the model fitting effect), while the random effects will be optimized using the variance component method, which only retains random effects with a variance to phenotype variance ratio greater than ```random_ratio``` (the larger the variance to phenotype ratio, the more important the random effect). When ```auto_optization``` is false, **```SIMER```** will directly use the covariates, fixed effects, and random effects provided by the user as the optimal combination of environmental factors. The design of the model optimization plan is saved in a JSON file:

```json
{
    "genotype": "../02plinkb",
    "pedigree": "../05others/pedigree.txt",
    "threads": 16,
    "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": 0.1, 
            "assign_threshold": 0.05
        },
        "phenotype_quality_control":[
            {
                "job_name": "Data_Quality_Control_Demo",
                "sample_info": "../05others/phenotype.txt",
                "repeated_records": false,
                "multi_trait": true,
                "filter": "F1 == 'Male'",
                "job_traits": [
                    {
                        "traits": "T1",
                        "definition": "T1",
                        "range": []
                    },
                    {
                        "traits": "T2",
                        "definition": "T2",
                        "range": []
                    }
                ]
            }
        ]
    },
    "breeding_plan":[
        {
            "job_name": "EBV_Model_Demo",
            "sample_info": "../05others/phenotype.txt",
            "repeated_records": false,
            "multi_trait": true,
            "vc_vars": [],
            "vc_covars": [],
            "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"]
                }
            ]
        }
    ]
}
```

Running the breeding value evaluation model planning process only requires two steps, the first step is to read in the JSON file path, and the second step is to use ```simer.Data.Json``` reads in a JSON file. It should be noted that users need to set ```buildIndex``` to FALSE to ensure that **```SIMER```** only runs the data quality control process and genetic evaluation process.

```r
# Get JSON file
jsonFile <- system.file("extdata", "04breeding_plan", "plan2.json", package = "simer")

# It needs "plink" and "hiblup" software
jsonList <- simer.Data.Json(jsonFile = jsonFile, dataQC = TRUE, buildModel = TRUE, buildIndex = FALSE)

```

## Construction of selection index
**[back to top](#contents)**

In multiple-trait breeding, constructing an appropriate multiple-trait selection index is a powerful means to improve the accuracy of overall genetic evaluation. **```SIMER```** adopts the general selection index method as the core algorithm for constructing multiple-trait selection indices. When ```auto_optization``` is set to true, users only need to provide the economic weight of the breeding value of each trait, ```breeding-value_index```, and **```SIMER```** will automatically calculate the appropriate multiple-trait selection index, ```selection_index```. In the case where ```auto_optization``` is false, **```SIMER```** will consider the ```selection_index``` provided by the user as the optimal multiple-trait selection index.  The design of the multiple-trait selection index plan is saved in a JSON file:

```json
{
    "genotype": "../02plinkb",
    "pedigree": "../05others/pedigree.txt",
    "selection_index": "100 - 0.2 * T1 + 0.8 * T2",
    "threads": 16,
    "genetic_progress": [],
    "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": 0.1, 
            "assign_threshold": 0.05
        },
        "phenotype_quality_control":[
            {
                "job_name": "Data_Quality_Control_Demo",
                "sample_info": "../05others/phenotype.txt",
                "repeated_records": false,
                "multi_trait": true,
                "filter": "F1 == 'Male'",
                "job_traits": [
                    {
                        "traits": "T1",
                        "definition": "T1",
                        "range": []
                    },
                    {
                        "traits": "T2",
                        "definition": "T2",
                        "range": []
                    }
                ]
            }
        ]
    },
    "breeding_plan":[
        {
            "job_name": "EBV_Model_Demo",
            "sample_info": "../05others/phenotype.txt",
            "repeated_records": false,
            "multi_trait": true,
            "vc_vars": [],
            "vc_covars": [],
            "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"]
                }
            ]
        }
    ]
}
```

Running the multiple-trait selection index plan process only requires two steps, the first step is to read in the JSON file path, and the second step is to use ```simer.Data.Json``` to read in a JSON file. It should be noted that constructing a multiple-trait selection index requires the use of genetic evaluation results, therefore ```dataQC```, ```buildModel```, and ```buildIndex``` all need to be set to TRUE.

```r
# Get JSON file
jsonFile <- system.file("extdata", "04breeding_plan", "plan3.json", package = "simer")

# It needs "plink" and "hiblup" software
jsonList <- simer.Data.Json(jsonFile = jsonFile, dataQC = TRUE, buildModel = TRUE, buildIndex = TRUE)

```

---

# Global Options
**[back to top](#contents)**

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
**[back to top](#contents)**

```simer```, main function of simulation:

<table text-algn="left">
<thead>
<tr>
<td>Paramater</td>
<td>Default</td>
<td>Options</td>
<td>Description</td>
</tr>
</thead>
<tbody>
<tr>
<td>replication</td>
<td>1</td>
<td>num</td>
<td>the replication times of simulation.</td>
</tr>
<tr>
<td>seed.sim</td>
<td>random</td>
<td>num</td>
<td>simulation random seed.</td>
</tr>
<tr>
<td>out</td>
<td>"simer"</td>
<td>character</td>
<td>the prefix of output files.</td>
</tr>
<tr>
<td>outpath</td>
<td>NULL</td>
<td>character</td>
<td>the path of output files, Simer writes files only if outpath is not "NULL".</td>
</tr>
<tr>
<td>out.format</td>
<td>"numeric"</td>
<td>"numeric" or "plink"</td>
<td>"numeric" or "plink", the data format of output files.</td>
</tr>
<tr>
<td>pop.gen</td>
<td>1</td>
<td>num</td>
<td>the generations of simulated population.</td>
</tr>
<tr>
<td>out.geno.gen</td>
<td>1</td>
<td>num vector</td>
<td>the output generations of genotype data.</td>
</tr>
<tr>
<td>out.pheno.gen</td>
<td>1</td>
<td>num vector</td>
<td>the output generations of phenotype data.</td>
</tr>
<tr>
<td>useAllGeno</td>
<td>FALSE</td>
<td>TRUE or FALSE</td>
<td>whether to use all genotype data to simulate phenotype.</td>
</tr>
<tr>
<td>missing.geno</td>
<td>NULL</td>
<td>num</td>
<td>the ratio of missing values in genotype data.</td>
</tr>
<tr>
<td>missing.phe</td>
<td>NULL</td>
<td>list</td>
<td>the ratio of missing values in phenotype data.</td>
</tr>
<tr>
<td>ncpus</td>
<td>0</td>
<td>num</td>
<td>the number of threads used, if it is 0, (logical core number - 1) is automatically used.</td>
</tr>
<tr>
<td>verbose</td>
<td>TRUE</td>
<td>TRUE or FALSE</td>
<td>whether to print detail.</td>
</tr>
</tbody>
</table>

## Counts of total population size
**[back to top](#contents)**

Users can calculate the ***number of individuals per generation*** using ```IndPerGen``` directly.

```r
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 computation
**[back to top](#contents)**

This section uses the ```param.simer``` and ```simer``` functions for demonstration. Therefore, please read the usage of ```param.simer``` and ```simer``` in **[AN EASY WAY TO GENERATE A POPULATION](#an-easy-way-to-generate-a-population)** before reading this section. **```SIMER```** runs on ***multiple threads***. Users can easily change the number of threads used for simulation by the following:

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

# Run Simer
SP <- simer(SP)
```

## Multi-population simulation
**[back to top](#contents)**

This section uses the ```param.simer``` and ```simer``` functions for demonstration. Therefore, please read the usage of ```param.simer``` and ```simer``` in **[AN EASY WAY TO GENERATE A POPULATION](#an-easy-way-to-generate-a-population)** before reading this section. Simulation of ***multiple populations*** can be realized by ```for``` by using **R** software.

```r
# 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, seed.sim = i, out = "simer")

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

```

## File output
**[back to top](#contents)**

This section uses the ```param.simer``` and ```simer``` functions for demonstration. Therefore, please read the usage of ```param.simer``` and ```simer``` in **[AN EASY WAY TO GENERATE A POPULATION](#an-easy-way-to-generate-a-population)** before reading this section. **```SIMER```** will not output files by default. A series of files with the prefix ```out``` will output when specifying ```outpath```.

```r
### 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)

### 03 Numeric Format with missing values in genotype and phenotype ###
# Generate all simulation parameters
SP <- param.simer(
 # SP = SP, # uncomment it when users already have a "SP"
 out = "simer",
 outpath = getwd(),
 out.format = "numeric",
 missing.geno = 0.01,
 missing.phe = list(tr1 = 0.5)
)

# Run Simer
SP <- simer(SP)

### 04 PLINK Binary Format with missing values in genotype and phenotype ###
# Generate all simulation parameters
SP <- param.simer(
 # SP = SP, # uncomment it when users already have a "SP"
 out = "simer",
 outpath = getwd(),
 out.format = "plink",
 missing.geno = 0.01,
 missing.phe = list(tr1 = 0.5)
)

# Run Simer
SP <- simer(SP)
```

## Generation-selective output
**[back to top](#contents)**

This section uses the ```param.simer``` and ```simer``` functions for demonstration. Therefore, please read the usage of ```param.simer``` and ```simer``` in **[AN EASY WAY TO GENERATE A POPULATION](#an-easy-way-to-generate-a-population)** before reading this section. Output of genotype and phenotype can be ***generation-selective*** using ```out.geno.gen``` and ```out.pheno.gen```.

```r
# 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
**[back to top](#contents)**

**```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.geno.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
**[back to top](#contents)**

***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. 
```r
# 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
**[back to top](#contents)**

***Genotype data*** is stored in ```big.matrix``` format.

```r
# 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
**[back to top](#contents)**

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

```r
# 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
**[back to top](#contents)**

```
For SIMER:
Yin, D., Zhang, X., Yin, L. et al. "SIMER: an accurate and intelligent tool for simulating customizable population data across species in complex scenarios." J Big Data 12, 49 (2025). https://doi.org/10.1186/s40537-025-01102-z

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
**[back to top](#contents)**

:sos: **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’***

:yum: **Answer:** Please try following codes in terminal:
```ssh
apt-get install libssl-dev/unstable
```

---

: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?)***
 
:yum: **Answer:** Please try following codes and then try agian.
```r
library(httr)
set_config(config(ssl_verifypeer = 0L))
```

---

**Questions, suggestions, and bug reports are welcome and appreciated.** [:arrow_right:](https://github.com/xiaolei-lab/SIMER/issues)
 
**[back to top](#contents)**

SIMER

SIMER 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

SIMER

SIMER 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README