Designed and Maintained by Dong Yin, Xuanning Zhang, Lilin Yin, Haohao Zhang, and Xiaolei Liu.
Contributors: Zhenshuang Tang, Jingya Xu, Xinyun Li, Mengjin Zhu, Xiaohui Yuan, and Shuhong Zhao
Questions, suggestions, and bug reports are welcome and appreciated: xiaoleiliu@mail.hzau.edu.cn
- Installation
- Data Preparation
- Data Input
- Quick Start
- Genotype Simulation
- Phenotype Simulation
- Gallery of phenotype simulation input parameters
- Generate base population information
- Generate phenotype of single trait by A model
- Generate phenotype of single trait by AD model
- Generate phenotype of single trait by ADI model
- Generate phenotype of multiple traits
- Add fixed effects and random effects to phenotype
- Different QTN effect distributions
- Different selection criteria
- Multiple groups QTN effects
- Selection
- Gallery of selection input parameters
- Individual selection on single trait
- Family selection on single trait
- Within family selection on single trait
- Combined selection on single trait
- Tandem selection on multiple traits
- Independent culling selection on multiple traits
- Index selection on multiple traits
- Reproduction
- Comparison on Breeding Plans
- Global Options
- Output
- Citation
- FAQ and Hints
WE STRONGLY RECOMMEND TO INSTALL SIMER ON Microsoft R Open(https://mran.microsoft.com/download/)
# the the development version from GitHub:
# install.packages("devtools")
devtools::install_github("xiaolei-lab/SIMER")After installed successfully, SIMER can be loaded by typing
> library(simer)Typing ?simer could get the details of all parameters.
Genotype data should be Numeric (m rows and (2 * n) columns, m is the number of SNPs, n is the number of individuals) format. If you have genotype data in PLINK Binary format (details see http://zzz.bwh.harvard.edu/plink/data.shtml#bed), VCF or Hapmap, please convert them using "MVP.Data" function in the rMVP(https://github.com/xiaolei-lab/rMVP).
genotype.txt
| 1 | 1 | 0 | 1 | 0 | … | 0 |
| 1 | 1 | 0 | 1 | 0 | … | 0 |
| 1 | 1 | 0 | 1 | 0 | … | 0 |
| 1 | 1 | 0 | 1 | 1 | … | 0 |
| 0 | 0 | 0 | 0 | 0 | … | 0 |
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Map file is necessary in SIMER. It will generate genotype matrix according to number of markers in input map file.
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 |
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SIMER supports user designed pedigree to control mating process. User designed pedigree is useful only in "userped" reproduction. Pedigree should at least start with generation 2. 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 be found in the last generation.
userped.txt
| Index | Sire | Dam |
|---|---|---|
| 41 | 1 | 11 |
| 42 | 1 | 11 |
| 43 | 1 | 11 |
| 44 | 1 | 11 |
| 45 | 2 | 12 |
| 46 | 2 | 12 |
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At least you should prepare two datasets: genotypic map and genotype.
genotype, genotype data in Numeric format (m * (2 * n), m rows and n columns, m is the number of SNPs, n is the number of individuals) genotypic map, SNP map information, the first column is SNP name, the second column is Chromosome ID, the third column is physical position, the fourth column is REF, and the fifth column is ALT
rawgeno <- read.table("genotype.txt")
input.map <- read.table("map.txt" , head = TRUE)back to top
If you want to control mating process 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 data file do not need to be sorted by the date of birth, and the missing value can be replaced by NA or 0.
userped <- read.table("userped.txt", header = TRUE)After obtaining genotypic map data and genotype data, we can start our simulation.
num.gen, number of generations in simulation
replication, replication index of simulation
verbose, whether to print detail
mrk.dense, whether markers are dense, it is TRUE when sequencing data
out, prefix of output file name
outpath, path of output files
out.format, format of output, "numeric" or "plink"
seed.geno, random seed of a simulation process
seed.map, random seed of map file
out.geno.gen, indice of generations of output genotype
out.pheno.gen, indice of generations of output phenotype
rawgeno1, extrinsic genotype matrix1
rawgeno2, extrinsic genotype matrix2
rawgeno3, extrinsic genotype matrix3
rawgeno4, extrinsic genotype matrix4
num.ind, population size of base population
prob, weight of "0" and "1" in genotype matrix, the sum of elements in vector equal to 1
input.map, map that should be input, the marker number should be consistent in both map file and genotype data
len.block, length of every blocks
range.hot, range of exchages in hot spot block
range.cold, range of exchages in cold spot block
rate.mut, mutation rate between 1e-8 and 1e-6
cal.model, phenotype model with "A", "AD", "ADI"
FR, list of fixed effects, random effects, and their combination
h2.tr1, heritability vector of a single trait, every element are corresponding to a, d, aXa, aXd, dXa, dXd respectively
num.qtn.tr1, integer or integer vector, the number of QTN in a single trait
sd.tr1, standard deviation of different effects, the last 5 vector elements are corresponding to d, aXa, aXd, dXa, dXd respectively and the rest elements are corresponding to a
dist.qtn.tr1, distribution of QTN's effects with options: "normal", "geometry" and "gamma", vector elements are corresponding to a, d, aXa, aXd, dXa, dXd respectively
eff.unit.tr1, unit effect of geometric distribution of a single trait, vector elements are corresponding to a, d, aXa, aXd, dXa, dXd respectively
shape.tr1, shape of gamma distribution of a single trait, vector elements are corresponding to a, d, aXa, aXd, dXa, dXd respectively
scale.tr1, scale of gamma distribution of a single trait, vector elements are corresponding to a, d, aXa, aXd, dXa, dXd respectively
multrait, whether applying pair traits with overlapping, TRUE represents applying, FALSE represents not
num.qtn.trn, QTN distribution matrix, diagnal elements are total QTN number of the trait, non-diagnal are QTN number of overlop qtn
eff.sd, a matrix with the standard deviation of QTN effects
gnt.cov, genetic covariance matrix among all traits
env.cov, environment covariance matrix among all traits
qtn.spot, QTN probability in every blocks
maf, Minor Allele Frequency, markers selection range is from maf to 0.5
sel.crit, selection criteria with options: "TGV", "TBV", "pEBVs", "gEBVs", "ssEBVs", "pheno"
sel.on, whether to add selection
mtd.reprod, different reproduction methods with options: "clone", "dh", "selfpol", "singcro", "tricro", "doubcro", "backcro","randmate", "randexself" and "userped"
userped, user-designed pedigree to control mating process
num.prog, litter size of dams
ratio, ratio of males in all individuals
prog.tri, litter size of the first single cross process in trible cross process
prog.doub, litter size of the first two single cross process in double cross process
prog.back, a vector with litter size in every generation of back-cross
ps, fraction selected in selection
decr, whether to sort by decreasing
sel.multi, selection method of multiple traits with options: "tdm", "indcul" and "index"
index.wt, economic weights of selection index method, its length should equals to the number of traits
index.tdm, index represents which trait is being selected
goal.perc, percentage of goal more than mean of scores of individuals
pass.perc, percentage of expected excellent individuals
sel.sing, selection method of single trait with options: "ind", "fam", "infam" and "comb"
# simdata: rawgeno input.map userped
data(simdata)
simer.list <-
simer(num.gen = 5,
verbose = TRUE,
outpath = NULL,
input.map = input.map,
rawgeno1 = rawgeno, # use your own genotype matrix
# num.ind = 100,
mtd.reprod = "randmate",
num.prog = 2,
ratio = 0.5)If you want to make replicated simulation
rep <- 2
for (i in 1:rep) {
simer.list <-
simer(num.gen = 3,
replication = i, # set index of replication
verbose = TRUE,
outpath = NULL,
input.map = input.map,
rawgeno1 = rawgeno, # use your own genotype matrix
# num.ind = 100,
mtd.reprod = "randmate",
num.prog = 2,
ratio = 0.5)
}SIMER can simulate genotype matrix with random elements and receive real genotype data.
# receive real genotype data
geno <- genotype(rawgeno = your_genotype)
# use num.marker and num.ind to generate a new genotype matrix
geno <- genotype(num.marker = 1e5, num.ind = 1e3)Phenotype simulation needs marker effects, population information, and genotype matrix.
# generate marker effects
# 1 column of genotype represents an individual
effs <- cal.effs(pop.geno = geno, num.qtn.tr1 = 500, sd.tr1 = 0.01, incols = 1)
# 2 columns of genotype represent an individual
# effs <- cal.effs(pop.geno = geno, num.qtn.tr1 = 500, sd.tr1 = 0.01, incols = 2)
# generate population information
pop = getpop(nind = ncol(geno))
# generate phenotype
pheno <- phenotype(effs = effs, pop = pop, pop.geno = geno, h2.tr1 = 0.8)
# generate marker information
map <- generate.map(num.marker = 5e5, num.chr = 18, len.chr = 1.5e8)
Genotype data in SIMER will be generated randomly or from outside genotype matrix. Chromosome crossovers and base mutations depend on block information and recombination information of map.
genotype(), main function of genotype simulation:
rawgeno, extrinsic genotype matrix
geno, genotype matrix need dealing with
num.marker, number of markers
num.ind, population size of base population
prob, weight of "0" and "1" in genotype matrix, the sum of elements in vector equal to 1
blk.rg, it represents the started and ended position blocks
recom.spot, whether to consider recombination in every blocks
range.hot, range of exchages in hot spot block
range.cold, range of exchages in cold spot block
rate.mut, mutation rate between 1e-8 and 1e-6
verbose, whether to print details
check.map(), add block id and combination information to genotypic map:
input.map, map that should be input, the marker number should be consistent in both map file and genotype data
num.marker, number of markers
len.block, length of every blocks
cal.blk(), get start position and end position of blocks:
pos.map, map with block information and recombination information
input.geno(), input sub-genotype matrix to total genotype matrix:
bigmtr, total genotype matrix
mtr, genotype matrix should be inputting
ed, index of the last column in each process
mrk.dense, whether markers are dense, it is TRUE when sequencing data
simer(), main function:
rawgeno1, extrinsic genotype matrix1
rawgeno2, extrinsic genotype matrix2
rawgeno3, extrinsic genotype matrix3
rawgeno4, extrinsic genotype matrix4
There are two different ways to generate genotype matrix of base population.
# use genotype matrix from outside
basepop1.geno <- genotype(rawgeno = rawgeno, verbose = verbose)
# use num.marker and num.ind to generate a new genotype matrix
# use prob to control the proportion of "0" in genotype matrix
nmrk <- nrow(input.map)
basepop2.geno <- genotype(num.marker = nmrk, num.ind = 100, prob = c(0.5, 0.5), verbose = verbose)Add block information and recombination information to map file. Calculate block ranges and recombination states of blocks.
nmrk <- nrow(basepop1.geno)
nind <- ncol(basepop1.geno) / 2
# set block information and recombination information
pos.map <- check.map(input.map = input.map, num.marker = nmrk, len.block = 5e7)
blk.rg <- cal.blk(pos.map)
recom.spot <- as.numeric(pos.map[blk.rg[, 1], 7])After getting block information and recombination information, you can add chromosome crossovers and mutations to genotype matrix.
basepop.geno.em <- # genotype matrix after cross and Mutation
genotype(geno = basepop1.geno,
blk.rg = blk.rg,
recom.spot = recom.spot,
range.hot = 4:6, # 4~6 recombinations in hot spots
range.cold = 1:5, # 1~5 recombinations in cold spots
rate.mut = 1e-8,
verbose = verbose)Note that recombination only exists in meiosis. Therefore, some reproduction methods such like "clone" do not have recombination process. You can set recom.spot = NULL to add only mutations to genotype matrix.
basepop.geno.em <- # genotype matrix after crosses and mutations
genotype(geno = basepop1.geno,
blk.rg = blk.rg,
recom.spot = NULL, # only mutations on genotype matrix
range.hot = 4:6,
range.cold = 1:5,
rate.mut = 1e-8, # mutation rate
verbose = verbose)Phenotype data in SIMER will be generated according to different phenotype models, QTN effect distributions and selection criteria. SIMER supports generating single traits or multiple traits. In the case of the single trait, you could set different models by cal.model as your need. But in the case of multiple traits, only the "A" model can be used. In the single trait or multiple traits, you can set different QTN amounts, effect variances, effect distributions for QTNs and heritability for traits. You can also set gene jungles and gene deserts by qtn.spot. For a low-quality genotype matrix, you can make quality control by maf. Additionally, in the case of the single trait, multiple groups of QTN effects are also under consideration. In multiple traits, you can set specific genetic correlations.
phenotype(), main function of phenotype simulation:
effs, a list with number of overlap markers, selected markers, effects of markers
FR, a list of fixed effects, random effects, and their combination
pop, population information of generation, family index, within-family index, index, sire, dam, sex
pop.geno, genotype matrix of population, two columns represent a individual
pos.map, marker information of population
h2.tr1, heritability vector of a single trait, every element are corresponding to a, d, aXa, aXd, dXa, dXd respectively
gnt.cov, genetic covariance matrix among all traits
h2.trn, heritability among all traits
sel.crit, selection criteria with options: "TGV", "TBV", "pEBVs", "gEBVs", "ssEBVs", "pheno"
pop.total, total population infarmation
sel.on, whether to add selection
inner.env, environment of main function of simer
verbose, whether to print details
cal.effs(), calculate for marker effects:
pop.geno, genotype matrix of population, two columns represent a individual
cal.model, phenotype model with "A", "AD", "ADI"
num.qtn.tr1, integer or integer vector, the number of QTN in a single trait
sd.tr1, standard deviation of different effects, the last 5 vector elements are corresponding to d, aXa, aXd, dXa, dXd respectively and the rest elements are corresponding to a
dist.qtn.tr1, distribution of QTN's effects with options: "normal", "geometry" and "gamma", vector elements are corresponding to a, d, aXa, aXd, dXa, dXd respectively
eff.unit.tr1, unit effect of geometric distribution of a single trait, vector elements are corresponding to a, d, aXa, aXd, dXa, dXd respectively
shape.tr1, shape of gamma distribution of a single trait, vector elements are corresponding to a, d, aXa, aXd, dXa, dXd respectively
scale.tr1, scale of gamma distribution of a single trait, vector elements are corresponding to a, d, aXa, aXd, dXa, dXd respectively
multrait, whether applying pair traits with overlapping, TRUE represents applying, FALSE represents not
num.qtn.trn, QTN distribution matrix, diagnal elements are total QTN number of the trait, non-diagnal are QTN number of overlop qtn
sd.trn, a matrix with the standard deviation of the QTN effects
qtn.spot, QTN probability in every blocks
maf, Minor Allele Frequency, markers selection range is from maf to 0.5
verbose, whether to print details
getpop(), generate population information:
nind, number of individuals in the population
from, initial index of the population
ratio, ratio of males in all individuals
set.pheno(), add phenotype to population information:
pop, population information of generation, family index, within-family index, index, sire, dam, sex
pop.pheno, phenotype information
sel.crit, selection criteria with options: "TGV", "TBV", "pEBVs", "gEBVs", "ssEBVs", "pheno"
Generate base population information according to population size and sex ratio.
# base population for single trait
basepop1 <- getpop(nind = nind, from = 1, ratio = 0.1)
# base population for double traits
basepop2 <- getpop(nind = 100, from = nind + 1, ratio = 0.1)In "A" model, SIMER considers only Additive effect(a). Therefore, only the first elements of var.tr1, dist.qtn.tr1, eff.unit.tr1, shape.tr1 , and scale.tr1 are useful for "A" model. Add phenotypes of single trait to base population1 are displayed as follows:
####################
### single trait ###
# calculate for marker information
# Additive model
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "A",
num.qtn.tr1 = 18,
sd.tr1 = 2,
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
pop1.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop1.pheno$pop
pop1.pheno$pop <- NULLIn "AD" model, SIMER considers both Additive effect(a) and Dominance effect(d). Therefore, only the first two elements of var.tr1, dist.qtn.tr1, eff.unit.tr1, shape.tr1, and scale.tr1 are useful for "AD" model. Add phenotypes of single trait to base population1 are displayed as follows:
####################
### single trait ###
# calculate for marker information
# Additive by Dominance model
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "AD", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = c(0.4, 0.2), # (a, d)
dist.qtn.tr1 = c("normal", "normal"),
eff.unit.tr1 = c(0.5, 0.5),
shape.tr1 = c(1, 1),
scale.tr1 = c(1, 1),
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
pop.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = c(0.3, 0.1),
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLNote that real variance components ratio may not be consistent with expected heritability h2.tr1. You can set sel.on = FALSE and pass a inner.env to get a more accurate one. In addition, markers effects will be corrected in this case.
####################
####################
### single trait ###
# calculate for marker information
# Additive by Dominance model
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "AD", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = c(0.4, 0.2), # (a, d)
dist.qtn.tr1 = c("normal", "normal"),
eff.unit.tr1 = c(0.5, 0.5),
shape.tr1 = c(1, 1),
scale.tr1 = c(1, 1),
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
inner.env <- environment()
pop.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = c(0.3, 0.1),
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop1,
sel.on = FALSE,
inner.env = inner.env,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLIn "ADI" model, SIMER considers not only Additive effect(a) and Dominance effect(d) but also interactive effects including Additive by Additive effect(aXa), Additive by Dominance effect(aXd), Dominance by Additive effect(dXa) and Dominance by Dominance effect(dXd). Therefore, all six elements of var.tr1, dist.qtn.tr1, eff.unit.tr1, shape.tr1, and scale.tr1 are useful for "ADI" model. Meanwhile, QTN amount should be an even in "ADI" model. Add phenotypes of single trait to base population1 are displayed as follows:
####################
### single trait ###
# calculate for marker information
# Additive by Dominance by Iteratiion model
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "ADI", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = c(18),
sd.tr1 = c(2, 1, 0.5, 0.5, 0.5, 0.1),
dist.qtn.tr1 = rep("normal", times = 6),
eff.unit.tr1 = rep(0.5, 6), # (a, d, aXa, aXd, dXa, dXd)
shape.tr1 = rep(1, times = 6),
scale.tr1 =rep(1, times = 6),
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
pop.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = c(0.3, 0.1, 0.05, 0.05, 0.05, 0.01),
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLNote that real variance components ratio may not be consistent with expected herirability h2.tr1. You can set sel.on = FALSE and pass a inner.env to get a more accurate one. In addition, markers effects will be corrected in this case.
####################
### single trait ###
# calculate for marker information
# Additive by Dominance by Iteratiion model
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "ADI", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = c(18),
sd.tr1 = c(2, 1, 0.5, 0.5, 0.5, 0.1),
dist.qtn.tr1 = rep("normal", times = 6),
eff.unit.tr1 = rep(0.5, 6), # (a, d, aXa, aXd, dXa, dXd)
shape.tr1 = rep(1, times = 6),
scale.tr1 =rep(1, times = 6),
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
inner.env <- environment()
pop.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = c(0.3, 0.1, 0.05, 0.05, 0.05, 0.01),
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop1,
sel.on = FALSE,
inner.env = inner.env,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLIn multiple traits, only "A" model is applied and multrait should be TRUE. If you want to generate multiple traits with specific genetic correlation please assign genetic covariance matrix gnt.cov. But when sel.on is TRUE, these traits will have random genetic correlation. Add phenotype of multiple traits to base population2 are displayed as follows:
#######################
### multiple traits ###
# calculate for marker information
effs <-
cal.effs(pop.geno = basepop2.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = c(2, 6, 10),
sd.tr1 = c(0.4, 0.2, 0.02, 0.02, 0.02, 0.02, 0.02, 0.001),
dist.qtn.tr1 = rep("normal", 6),
eff.unit.tr1 = rep(0.5, 6),
shape.tr1 = rep(1, 6),
scale.tr1 = rep(1, 6),
multrait = TRUE, # multiple traits
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = matrix(c(1, 0, 0, 2), 2, 2),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
pop2.pheno <-
phenotype(effs = effs,
pop = basepop2,
pop.geno = basepop2.geno,
pos.map = NULL,
h2.tr1 = c(0.4, 0.2, 0.1, 0.1, 0.1, 0.05),
gnt.cov = matrix(c(14, 10, 10, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop2,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop2 <- pop2.pheno$pop
pop2.pheno$pop <- NULLNote that real genetic covariance matrix may not be consistent with expected genetic covariance matrix gnt.cov. You can set sel.on = FALSE and pass a inner.env to get a more accurate one. In addition, markers effects will be corrected in this case.
#######################
### multiple traits ###
# calculate for marker information
effs <-
cal.effs(pop.geno = basepop2.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = c(2, 6, 10),
sd.tr1 = c(0.4, 0.2, 0.02, 0.02, 0.02, 0.02, 0.02, 0.001),
dist.qtn.tr1 = rep("normal", 6),
eff.unit.tr1 = rep(0.5, 6),
shape.tr1 = rep(1, 6),
scale.tr1 = rep(1, 6),
multrait = TRUE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = matrix(c(1, 0, 0, 2), 2, 2),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
inner.env <- environment()
pop.pheno <-
phenotype(effs = effs,
pop = basepop2,
pop.geno = basepop2.geno,
pos.map = NULL,
h2.tr1 = c(0.4, 0.2, 0.1, 0.1, 0.1, 0.05),
gnt.cov = matrix(c(14, 10, 10, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop2,
sel.on = FALSE,
inner.env = inner.env,
verbose = verbose)
# get population with phenotype
basepop2 <- pop.pheno$pop
pop.pheno$pop <- NULLSIMER supports add fixed effects and random effects to phenotype. Users should prepare a list with fixed effects, random effects, and their combination first. In the list, the attribution name are fixed. "cmb.fix" contains different combinations of fixed factors in different traits. "fix" contains different fixed factors which have their levels and effects. "cmb.rand" contains different combinations of random factors. Unlike the fixed effect, the user needs to specify the proportion of the variance of the random effect variance by "ratio". Besides, users can also set related random effects for different traits by "cr". Users can use attribute names in population information, meaning that users can assign specific levels to different individuals. At the same time, if the attribute name is not in the population information, SIMER will add its level to the population information.
# specific levels to different individuals
a <- sample(c("a1", "a2", "a3"), nind, replace = TRUE)
b <- sample(c("b1", "b2", "b3"), nind, replace = TRUE)
basepop1$a <- a # load your fixed effects
basepop1$b <- b # load your random effects
# combination of fixed effects
cmb.fix <- list(tr1 = c("mu", "gen", "sex", "a"), # trait 1
tr2 = c("mu", "diet", "season")) # trait 2
# available fixed effects
fix <- list(
mu = list(level = "mu", eff = 2),
gen = list(level = "1", eff = 1), # inherent fixed effect in simer
sex = list(level = c("1", "2"), eff = c(5, 3)), # inherent fixed effect in simer
diet = list(level = c("d1", "d2", "d3"), eff = c(1, 2, 3)),
season = list(level = c("s1", "s2", "s3", "s4"), eff = c(1, 2, 3, 2)),
a = list(level = c("a1", "a2", "a3"), eff = c(1, 2, 3)))
# combination and ralation of random effects
# rn, random effect name
# ratio, phenotype variance proportion of the random effects
# cr, corelation of the random effects
tr1 <- list(rn = c("sir", "dam", "b"), ratio = c(0.03, 0.05, 0.03))
tr2 <- list(rn = c("PE", "litter"), ratio = c(0.01, 0.03),
cr = matrix(c(1, 0.5, 0.5, 1), 2, 2))
cmb.rand <- list(tr1 = tr1, tr2 = tr2)
# available random effects
rand <- list(
sir = list(mean = 0, sd = 1), # sir and dam are inherent random effect in simer
dam = list(mean = 0, sd = 1), # control mean and sd only
PE = list(level = c("p1", "p2", "p3"), eff = c(1, 2, 3)),
litter = list(level = c("l1", "l2"), eff = c(1, 2)),
b = list(level = c("b1", "b2", "b3"), eff = c(1, 2, 3)))
FR <- list(cmb.fix = cmb.fix, fix = fix, cmb.rand = cmb.rand, rand = rand)After preparation above, you can get phenotype with fixed effects and random effects but without additive effects by setting pop.geno = NULL.
####################
### single trait ###
# calculate for marker information
# Additive model
effs <-
cal.effs(pop.geno = NULL, # set pop.geno = NULL
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
pop.pheno <-
phenotype(effs = effs,
FR = FR, # input fixed effects and random effects
pop = basepop1,
pop.geno = NULL, # set pop.geno = NULL
pos.map = NULL,
h2.tr1 = 0.8<
10000
/span>,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLIf inputting pop.geno a genotype matrix, you will get phenotype with fixed effects, random effects, and genetic effects.
####################
### single trait ###
# calculate for marker information
# Additive model
effs <-
cal.effs(pop.geno = basepop1.geno, # input genotype matrix
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
pop.pheno <-
phenotype(effs = effs,
FR = FR, # input fixed effects and random effects
pop = basepop1,
pop.geno = basepop1.geno, # input genotype matrix
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLUsers can also add fixed effects and random effects to different traits respectively by multiple traits mode.
#######################
### multiple traits ###
# calculate for marker information
effs <-
cal.effs(pop.geno = basepop2.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = c(2, 6, 10),
sd.tr1 = c(0.4, 0.2, 0.02, 0.02, 0.02, 0.02, 0.02, 0.001),
dist.qtn.tr1 = rep("normal", 6),
eff.unit.tr1 = rep(0.5, 6),
shape.tr1 = rep(1, 6),
scale.tr1 = rep(1, 6),
multrait = TRUE, # multiple traits
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = matrix(c(1, 0, 0, 2), 2, 2),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
pop2.pheno <-
phenotype(effs = effs,
FR = FR,
pop = basepop2,
pop.geno = basepop2.geno,
pos.map = NULL,
h2.tr1 = c(0.4, 0.2, 0.1, 0.1, 0.1, 0.05),
gnt.cov = matrix(c(14, 10, 10, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop2,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop2 <- pop2.pheno$pop
pop2.pheno$pop <- NULLIn different model, you can further set different QTN effect distributions of trait1 by dist.qtn.tr1|*. The most common distribution is "normal" distribution. You can set different variances in "normal" distribution by dist.qtn.tr1.
####################
### single trait ###
# calculate for marker information
# Additive model
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
pop.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLQTN effect distribution can be "geometry" distribution. You can set effect unit of "geometry" by eff.unit.tr1.
####################
### single trait ###
# calculate for marker information
# Additive model
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # a
dist.qtn.tr1 = "geometry",
eff.unit.tr1 = 0.5,
shape.tr1 = 1, # effect unit of geomtry distribution
scale.tr1 = 1,
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
pop.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULL"Gamma" distribution is also a kind of QTN effect distribution. You can set shape and scale of "gamma" distribution by shape.tr1 and scale.tr1. Note that default options of "gamma" distribution shape.tr1 = 1 and scale.tr1 = 1 exactly lead to exponential distribution.
####################
### single trait ###
# calculate for marker information
# Additive model
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # a
dist.qtn.tr1 = "gamma",
eff.unit.tr1 = 0.5,
shape.tr1 = 1, # shape of gamma distribution
scale.tr1 = 1, # scale of gamma distribution
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
pop.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLIn addition, "pheno" is not just phenotype. It can also be "TBV", "TGV", "pEBVs'", "gEBVs", or "ssEBVs". "TBV" is True Breeding Value, represents only that part of genotypic value that can be transmitted from parent to offspring.
####################
### single trait ###
# Additive model
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
# selection criterion is "TBV"
pop.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "TBV",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLPhenotype of multiple traits can also be represented as "TBV".
#######################
### multiple traits ###
# Additive model
effs <-
cal.effs(pop.geno = basepop2.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = TRUE, # multiple traits
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
# selection criterion is "TBV"
pop.pheno <-
phenotype(effs = effs,
pop = basepop2,
pop.geno = basepop2.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "TBV",
pop.total = basepop2,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop2 <- pop.pheno$pop
pop.pheno$pop <- NULL"TGV" is True Genotypic Value, represents the sum of additive effect, dominance effect and epistatic effect.
####################
### single trait ###
# Additive model
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
# selection criterion is "TGV"
pop.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "TGV",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLPhenotype of multiple traits can also be represented as "TGV".
#######################
### multiple traits ###
# Additive model
effs <-
cal.effs(pop.geno = basepop2.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = TRUE, # multiple traits
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
# selection criterion is "TGV"
pop.pheno <-
phenotype(effs = effs,
pop = basepop2,
pop.geno = basepop2.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "TGV",
pop.total = basepop2,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop2 <- pop.pheno$pop
pop.pheno$pop <- NULL"pEBVs" is pedigree Estimated Breeding Values. It means that BLUP constructs kinship by pedigree. You can get "pEBVs" by "ABLUP" model in HIBLUP.
# call "hiblup" package
suppressMessages(library("hiblup"))
####################
### single trait ###
# Additive model
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
# selection criterion is "pEBVs"
# "pEBVs" needs hiblup package
pop.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pEBVs",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLPhenotype of multiple traits can also be represented as "pEBVs".
#######################
### multiple traits ###
# Additive model
effs <-
cal.effs(pop.geno = basepop2.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = TRUE, # multiple traits
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
# selection criterion is "pEBVs"
pop.pheno <-
phenotype(effs = effs,
pop = basepop2,
pop.geno = basepop2.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pEBVs",
pop.total = basepop2,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop2 <- pop.pheno$pop
pop.pheno$pop <- NULL"gEBVs" is genomic Estimated Breeding Values. It means that BLUP constructs kinship by genotype matrix. You can get "gEBVs" by "GBLUP" model in HIBLUP.
####################
### single trait ###
# Additive model
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
# selection criterion is "gEBVs"
# "gEBVs" needs hiblup package
pop.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "gEBVs",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLPhenotype of multiple traits can also be represented as "gEBVs".
#######################
### multiple traits ###
# Additive model
effs <-
cal.effs(pop.geno = basepop2.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = TRUE, # multiple traits
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
# selection criterion is "gEBVs"
pop.pheno <-
phenotype(effs = effs,
pop = basepop2,
pop.geno = basepop2.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "gEBVs",
pop.total = basepop2,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop2 <- pop.pheno$pop
pop.pheno$pop <- NULL"ssEBVs" is single-step genomic Estimated Breeding Values. It means that BLUP constructs kinship by pedigree and genotype matrix. You can get "ssEBVs" by "SSBLUP" model in HIBLUP.
####################
### single trait ###
# Additive model
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
# selection criterion is "ssEBVs"
# "ssEBVs" needs hiblup package
pop.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "ssEBVs",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLPhenotype of multiple traits can also be represented as "ssEBVs".
#######################
### multiple traits ###
# Additive model
effs <-
cal.effs(pop.geno = basepop2.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = TRUE, # multiple traits
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
# selection criterion is "ssEBVs"
pop.pheno <-
phenotype(effs = effs,
pop = basepop2,
pop.geno = basepop2.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "ssEBVs",
pop.total = basepop2,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop2 <- pop.pheno$pop
pop.pheno$pop <- NULLAt last, "pheno" is phenotype including additive effect (and dominance effect) (and epistatic effect) and residual effect.
####################
### single trait ###
# Additive model
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
# selection criterion is "pheno"
pop.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLPhenotype of multiple traits can also be represented as "pheno".
#######################
### multiple traits ###
# Additive model
effs <-
cal.effs(pop.geno = basepop2.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = 18,
sd.tr1 = 0.6, # standard deviation of normal distribution
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 0.5,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = TRUE, # multiple traits
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
# selection criterion is "pheno"
pop.pheno <-
phenotype(effs = effs,
pop = basepop2,
pop.geno = basepop2.geno,
pos.map = NULL,
h2.tr1 = 0.8,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop2,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop2 <- pop.pheno$pop
pop.pheno$pop <- NULLMultiple groups QTN effects can be realized by setting different elements of num.qtn.tr1, every elements represent amount of QTNs affacting a effect. For example, num.qtn.tr1 = c(2, 6, 10) means that the additive effect of the trait is the sum of three QTN group effects. The first group has 2 QTNs, the second group has 6 QTNs and the third group has 10 QTNs. Because of the three groups, the first three elements of sd.tr1 mean the variances of the three QTN groups.
#####################
### single traits ###
# calculate for marker information
effs <-
cal.effs(pop.geno = basepop1.geno,
cal.model = "A", # it can be"A", "AD" or "ADI"
num.qtn.tr1 = c(2, 6, 10),
sd.tr1 = c(0.4, 0.2, 0.02),
dist.qtn.tr1 = "normal",
eff.unit.tr1 = 1,
shape.tr1 = 1,
scale.tr1 = 1,
multrait = FALSE, # single trait
num.qtn.trn = matrix(c(18, 10, 10, 20), 2, 2),
sd.trn = diag(c(1, 0.5)),
qtn.spot = rep(0.1, 10),
maf = 0,
verbose = verbose)
# generate phenotype
# generate single trait or multiple traits according to effs
pop.pheno <-
phenotype(effs = effs,
pop = basepop1,
pop.geno = basepop1.geno,
pos.map = NULL,
h2.tr1 = 0.3,
gnt.cov = matrix(c(1, 2, 2, 15), 2, 2),
h2.trn = c(0.3, 0.5),
sel.crit = "pheno",
pop.total = basepop1,
sel.on = TRUE,
inner.env = NULL,
verbose = verbose)
# get population with phenotype
basepop1 <- pop.pheno$pop
pop.pheno$pop <- NULLYou can get ordered individuals indice according to phenotype in the populaton information. Fraction selected can be used to keep a certain amount of individuals. SIMER chooses automatically single trait selection or multiple traits selection according to number of columns of phenotype.
selects(), main function of selection:
pop, population information of generation, family index, within-family index, index, sire, dam, sex, phenotpye
decr, whether to sort by descreasing
sel.multi, selection method of multiple traits with options: "tdm", "indcul" and "index"
index.wt, economic weights of selection index method, its length should equals to the number of traits
index.tdm, index represents which trait is being selected
goal.perc, percentage of goal more than mean of scores of individuals
pass.perc, percentage of expected excellent individuals
sel.sing, selection method of single trait with options: "ind", "fam", "infam" and "comb"
pop.total, total population infarmation
pop.pheno, list of all phenotype information
verbose, whether to print detail
simer(), main function:
ps, fraction selected in selection
Individual selection is a method of selecting according to the phenotype of individual traits, also known as mixed selection or collective selection. This selection method is simple and easy to used for traits with high heritability.
# output index.tdm and ordered individuals indice
# single trait selection
# individual selection
ind.ordered <-
selects(pop = basepop1, # population with single trait
decr = TRUE, # sort individuals by descreasing
sel.sing = "ind",
pop.total = basepop1,
pop.pheno = pop1.pheno,
verbose = verbose)
index.tdm <- ind.ordered[1]
ind.ordered <- ind.ordered[-1]
ind.orderedFamily selection is a method of selecting by family based on the average of the family. This selection method is used for traits with low heritability.
# output index.tdm and ordered individuals indice
# single trait selection
# family selection
ind.ordered <-
selects(pop = basepop1, # population with single trait
decr = TRUE, # sort individuals by descreasing
sel.sing = "fam",
pop.total = basepop1,
pop.pheno = pop1.pheno,
verbose = verbose)
index.tdm <- ind.ordered[1]
ind.ordered <- ind.ordered[-1]
ind.orderedWithin-family selection is a method of selecting according to the deviation of individual phenotype and family mean value in each family. This selection method is used for traits with low heritability and small family.
# output index.tdm and ordered individuals indice
# single trait selection
# within-family selection
ind.ordered <-
selects(pop = basepop1, # population with single trait
decr = TRUE, # sort individuals by descreasing
sel.sing = "infam",
pop.total = basepop1,
pop.pheno = pop1.pheno,
verbose = verbose)
index.tdm <- ind.ordered[1]
ind.ordered <- ind.ordered[-1]
ind.orderedCombined selection is a method of selecting according to weighed combination of the deviation of individual phenotype and family mean value and family mean value.
# output index.tdm and ordered individuals indice
# single trait selection
# combined selection
ind.ordered <-
selects(pop = basepop1, # population with single trait
decr = TRUE, # sort individuals by descreasing
sel.sing = "comb",
pop.total = basepop1,
pop.pheno = pop1.pheno,
verbose = verbose)
index.tdm <- ind.ordered[1]
ind.ordered <- ind.ordered[-1]
ind.orderedTandem selection is a method for sequentially selecting a plurality of target traits one by one. The index of selected trait is index.tdm and this parameter should not controlled by User.
# output index.tdm and ordered individuals indice
# multiple traits selection
# tandem selection
ind.ordered <-
selects(pop = basepop2, # population with multiple traits
decr = TRUE,
sel.multi = "tdm",
index.wt = c(0.5, 0.5),
index.tdm = 1,
goal.perc = 0.1,
pass.perc = 0.9,
pop.total = basepop2,
pop.pheno = pop2.pheno,
verbose = verbose)
index.tdm <- ind.ordered[1]
ind.ordered <- ind.ordered[-1]
ind.orderedAfter setting a minimum selection criterion for each target trait. Independent culling selection will eliminate this individual when the candidate's performance on any trait is lower than the corresponding criteria.
# output index.tdm and ordered individuals indice
# multiple traits selection
# Independent culling selection
ind.ordered <-
selects(pop = basepop2, # population with multiple traits
decr = TRUE,
sel.multi = "indcul",
index.wt = c(0.5, 0.5),
index.tdm = 1,
goal.perc = 0.1,
pass.perc = 0.9,
pop.total = basepop2,
pop.pheno = pop2.pheno,
verbose = verbose)
index.tdm <- ind.ordered[1]
ind.ordered <- ind.ordered[-1]
ind.orderedIndex selection is a comprehensive selection that will consider several traits based on their respective heritabilities, phenotypic variances, economic weights, and corresponding genetic correlations and phenotypes. Then calculate the index value of each body, and eliminate or select it according to its level. You can set the weight of Index selection by index.wt.
# output index.tdm and ordered individuals indice
# multiple traits selection
# index selection
ind.ordered <-
selects(pop = basepop2, # population with multiple traits
decr = TRUE,
sel.multi = "index",
index.wt = c(0.5, 0.5), # trait1 and trait2
index.tdm = 1,
goal.perc = 0.1,
pass.perc = 0.9,
pop.total = basepop2,
pop.pheno = pop2.pheno,
verbose = verbose)
index.tdm <- ind.ordered[1]
ind.ordered <- ind.ordered[-1]
ind.orderedDifferent kind of reproduction methods need different preparation. Reproduction methods in mtd.reprod can be devided into single-breeds and multi-breeds methods. In every reproduction methods, you can set litter size num.prog and sex ratio ratio in freedom.
simer(), main function:
mtd.reprod, different reproduction methods with options: "clone", "dh", "selfpol", "singcro", "tricro", "doubcro", "backcro","randmate", "randexself" and "userped"
userped, user-designed pedigree to control mating process
num.prog, litter size of dams
ratio, ratio of males in all individuals
prog.tri, litter size of the first single cross process in trible cross process
prog.doub, litter size of the first two single cross process in double cross process
prog.back, a vector with litter size in every generation of back-cross
Asexual reproduction does not involve germ cells, and does not require a process of fertilization, directly forming a new individual's reproductive mode from a part of the mother. Sex of offspring will be "0" in clone. Single-breed methods needs only one genotype matrix, you can generate a random genotype matrix by setting num.prog or use your own genotype matrix by setting rawgeno1 = your_own_matrix.
# clone
simer.list <-
simer(num.gen = 3,
verbose = verbose,
outpath = outpath,
input.map = input.map,
# rawgeno1 = rawgeno, # use your own genotype matrix
num.ind = 50,
mtd.reprod = "clone",
num.prog = 2,
ratio = 0.5)Breeding workers often use another culture in vitro to obtain haploid plants, and then artificially induced to double the number of chromosomes and restore the number of chromosomes in normal plants. This method is named double-haploid reproduction. Sex of offspring will be "0" in double-haploid. Single-breed methods needs only one genotype matrix, you can generate a random genotype matrix by setting num.prog or use your own genotype matrix by setting rawgeno1 = your_own_matrix.
# double-haploid
simer.list <-
simer(num.gen = 4,
verbose = verbose,
outpath = outpath,
input.map = input.map,
# rawgeno1 = rawgeno, # use your own genotype matrix
num.ind = 50,
mtd.reprod = "dh",
num.prog = 2,
ratio = 0.5)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 self-pollination. Single-breed methods needs only one genotype matrix, you can generate a random genotype matrix by setting num.prog or use your own genotype matrix by setting rawgeno1 = your_own_matrix.
# self-pollination
simer.list <-
simer(num.gen = 5,
verbose = verbose,
outpath = outpath,
input.map = input.map,
# rawgeno1 = rawgeno, # use your own genotype matrix
num.ind = 50,
mtd.reprod = "selfpol",
num.prog = 2,
ratio = 0.5)In random mating, any female or male individual have the same probability to mate with any opposite sex in a sexually reproducing organism. Sex of offspring in random mating is up to sex of parents. Single-breed methods needs only one genotype matrix, you can generate a random genotype matrix by setting num.prog or use your own genotype matrix by setting rawgeno1 = your_own_matrix.
# random-mating
simer.list <-
simer(num.gen = 4,
verbose = verbose,
outpath = outpath,
input.map = input.map,
# rawgeno1 = rawgeno, # use your own genotype matrix
num.ind = 100,
mtd.reprod = "randmate",
num.prog = 2,
ratio = 0.5)In random mating without self-pollination, a individual cannot mate to itself. Sex of offspring in random mating without self-pollination is up to sex of parents. Single-breed methods needs only one genotype matrix, you can generate a random genotype matrix by setting num.prog or use your own genotype matrix by setting rawgeno1 = your_own_matrix.
# random-mating without self-pollination
simer.list <-
simer(num.gen = 3,
verbose = verbose,
outpath = outpath,
input.map = input.map,
# rawgeno1 = rawgeno, # use your own genotype matrix
num.ind = 100,
mtd.reprod = "randexself",
num.prog = 2,
ratio = 0.5)User-designed-pedigree mating needs a specific user designed pedigree to control mating process. Pedigree should at least start with generation 2. Please make sure that paternal id and maternal id can be found in the last generation. Note that the individuals in the pedigree data file do not need to be sorted by the date of birth, and the missing value can be replaced by NA or 0. Single-breed methods needs only one genotype matrix, you can generate a random genotype matrix by setting num.prog or use your own genotype matrix by setting rawgeno1 = your_own_matrix.
# user-designed-pedigree mating
simer.list <-
simer(num.gen = 5,
verbose = verbose,
outpath = outpath,
input.map = input.map,
rawgeno1 = rawgeno, # use your own genotype matrix
# num.ind = 100,
mtd.reprod = "userped",
userped = userped) # input your own pedigreeTwo-way cross method needs two genotype matrice of different two breeds. You can input your own genotype matrix by parameters rawgeno1 and rawgeno2. If any of these two is NULL, SIMER will generates a random one.
# two-way cross
simer.list <-
simer(num.gen = 5,
verbose = verbose,
outpath = outpath,
input.map = input.map,
rawgeno1 = rawgeno, # use your own genotype matrix
rawgeno2 = NULL,
# num.ind = 100,
mtd.reprod = "singcro",
num.prog = 2,
ratio = 0.5)Three-way cross method needs three genotype matrice of different three breeds. You can input your own genotype matrix by parameters rawgeno1, rawgeno2, and rawgeno3. If any of these three is NULL, SIMER will generates a random one. In triple-cross method, you can set litter size of the single-cross of population2 and population3 by prog.tri.
# three way cross
simer.list <-
simer(num.gen = 5,
verbose = verbose,
outpath = outpath,
input.map = input.map,
rawgeno1 = rawgeno, # use your own genotype matrix
rawgeno2 = NULL,
rawgeno3 = NULL,
# num.ind = 100,
mtd.reprod = "tricro",
num.prog = 2,
prog.tri = 2,
ratio = 0.5)Four-way cross method needs four genotype matrice of different four breeds. You can input your own genotype matrix by parameters rawgeno1, rawgeno2, rawgeno3, and rawgeno4. If any of these four is NULL, SIMER will generates a random one. In four-way cross method, you can set litter size of the first two two-way cross by prog.doub.
# four way cross
simer.list <-
simer(num.gen = 5,
verbose = verbose,
outpath = outpath,
input.map = input.map,
rawgeno1 = rawgeno, # use your own genotype matrix
rawgeno2 = NULL,
rawgeno3 = NULL,
rawgeno4 = NULL,
# num.ind = 100,
mtd.reprod = "doubcro",
num.prog = 2,
prog.doub = 2,
ratio = 0.5)Back-cross method needs two different breeds. You can input your own genotype matrix by parameters rawgeno1 and rawgeno2. If any of these two is NULL, SIMER will generates a random one. Back-cross is similar to two-way cross but with some differences: 1. Back-cross is multi-generation mating; 2. The first base population is fixed in every generations. In back-cross method, you can set litter size of two-way cross in every generation by a vector prog.back.
# Back-cross
simer.list <-
simer(num.gen = 5,
verbose = verbose,
outpath = outpath,
input.map = input.map,
rawgeno1 = rawgeno, # use your own genotype matrix
rawgeno2 = NULL,
# num.ind = 100,
mtd.reprod = "backcro",
num.prog = 2,
prog.back = rep(2, 5),
ratio = 0.5)After a total reproduction process, further work can be done. For breeders, they want to predict their breeding plans. To save a lot of money and time, simulation will assist them to make comparison on different breeding plans.
read.selgeno(), function to make comparison on breeding plans:
pop, total population information
selPath, the path of breeding plans
outpath, path of output files
Breeding plans should be stored on different files respectively. Filenames must begin with breeding_plan, such like breeding_plan01.txt. For now, SIMER supports different breeding plans on generation, family_index, within_family_index, and sex.
> # get path of breeding plan
> selPath <- system.file("extdata", "01breeding_plan", package = "simer")
> # show filename format
> dir(selPath)
[1] "breeding_plan01.txt" "breeding_plan02.txt" "breeding_plan03.txt" "ReadMe.txt"
> # show file contents format
> fn1 <- file.path(selPath, "breeding_plan01.txt")
> bp1 <- read.delim(fn1, header = TRUE)
> # ###NOTE###
> # 1. any generation can be choosed within num.gen.
> # 2. any sex(1 represents sir 2 represents dam) can be choosed.
> # 3. any family index can be choosed in all family. 1:5 means
> # that the first 5 family will be chosen.
> # 4. any infamily index can be choosed in every family.
> # 5. numbers should be separated by comma(",").
> # 6. ":" can choose serial number, "1:3,7" represents "1 2 3 7".
> # 7. "all" will choose all of options in a category.
> bp1
generation family_index within_family_index sex
1 1 1:3,7 all 1
2 2 1:3,7 all 1
3 3 1:3,7 all 1After a total reproduction process, breeding plans comparison can be done.
# random-mating
simer.list <-
simer(num.gen = 10,
verbose = verbose,
outpath = outpath,
input.map = input.map,
rawgeno1 = rawgeno, # use your own genotype matrix
# num.ind = 100,
mtd.reprod = "randmate",
num.prog = 2,
ratio = 0.5)
pop <- simer.list$pop
out.pop <- read.selgeno(pop = pop, selPath = selPath, outpath = outpath)
# make comparison on breeding plans
plan1 <- out.pop$plan1
plan2 <- out.pop$plan2
plan3 <- out.pop$plan3
summary(plan1)
summary(plan2)
summary(plan3)In this part, calculation of population size and different ourput methods will be introduced.
simer(), main function:
num.gen, number of generations in simulation
replication, replication index of simulation
verbose, whether to print detail
out, prefix of output file name
outpath, path of output files
out.format, format of output, "numeric" or "plink"
seed.sim, random seed of a simulation process
seed.map, random seed of map file
out.geno.gen, indice of generations of output genotype
out.pheno.gen, indice of generations of output phenotype
The following is the method of obtaining population size of every generation. Every elements in cound.ind are population size in this generation respectively.
# parameters that controls population size of every generations
nind <- 40
num.gen <- 10
ratio <- 0.5
sel.on <- TRUE # whether selection exsits
ps <- 0.8
ps <- ifelse(sel.on, ps, 1)
num.prog <- 2
mtd.reprod <- "randmate"
# populations of the first generation
basepop <- getpop(nind = nind, from = 1, ratio = ratio)
pop2 <- getpop(nind = nind, from = 41, ratio = ratio)
pop3 <- getpop(nind = nind, from = 81, ratio = ratio)
pop4 <- getpop(nind = nind, from = 121, ratio = ratio)
# calculate number of individuals in every generation
count.ind <- rep(nind, num.gen)
if (mtd.reprod == "clone" || mtd.reprod == "dh" || mtd.reprod == "selfpol") {
if (num.gen > 1) {
for(i in 2:num.gen) {
count.ind[i] <- round(count.ind[i-1] * (1-ratio) * ps) * num.prog
}
}
} else if (mtd.reprod == "randmate" || mtd.reprod == "randexself") {
if (num.gen > 1) {
for(i in 2:num.gen) {
count.ind[i] <- round(count.ind[i-1] * (1-ratio) * ps) * num.prog
}
}
} else if (mtd.reprod == "singcro") {
sing.ind <- round(nrow(pop2) * ps) * num.prog
count.ind <- c(nrow(basepop), nrow(pop2), sing.ind)
} else if (mtd.reprod == "tricro") {
dam21.ind <- round(nrow(pop2) * ps) * prog.tri
tri.ind <- round(dam21.ind * (1-ratio) * ps) * num.prog
count.ind <- c(nrow(basepop), nrow(pop2), nrow(pop3), dam21.ind, tri.ind)
} else if (mtd.reprod == "doubcro") {
sir11.ind <- round(nrow(pop2) * ps) * prog.doub
dam22.ind <- round(nrow(pop4) * ps) * prog.doub
doub.ind <- round(dam22.ind * (1-ratio) * ps) * num.prog
count.ind <- c(nrow(basepop), nrow(pop2), nrow(pop3), nrow(pop4), sir11.ind, dam22.ind, doub.ind)
} else if (mtd.reprod == "backcro") {
count.ind[1] <- nrow(basepop) + nrow(pop2)
if (num.gen > 1) {
count.ind[2] <- round(nrow(pop2) * ps) * num.prog
for(i in 3:num.gen) {
count.ind[i] <- round(count.ind[i-1] * (1-ratio) * ps) * num.prog
}
}
}Simulation of multiple populations can be realized by "for" in R and replication. Random seed of simulation is random in every replication and random seed of map is fixed. In every replication, you can set your own random seed of simulation and random seed of map by seed.geno| and seed.map respectively.
# random-mating
rep <- 2
for (i in 1:rep) {
simer.list <-
simer(num.gen = 3,
replication = i, # set index of replication
verbose = verbose,
outpath = outpath,
input.map = input.map,
# rawgeno1 = rawgeno, # use your own genotype matrix
num.ind = 100,
mtd.reprod = "randmate",
num.prog = 2,
ratio = 0.5)
}SIMER won't output files by default. A series of files with prefix out = simer output when specify a exsited out path by outpath. File output format can be "numeric" or "plink" by out.format.
# set a output path
outpath = getwd()
# "numeric" format
simer.list <-
simer(num.gen = 3,
verbose = verbose,
outpath = outpath,
out.format = "numeric",
input.map = input.map,
# rawgeno1 = rawgeno, # use your own genotype matrix
num.ind = 100,
mtd.reprod = "randmate",
num.prog = 2,
ratio = 0.5)
# "plink" format
simer.list <-
simer(num.gen = 3,
verbose = verbose,
outpath = outpath,
out.format = "plink",
input.map = input.map,
# rawgeno1 = rawgeno, # use your own genotype matrix
num.ind = 100,
mtd.reprod = "randmate",
num.prog = 2,
ratio = 0.5)Output of genotype and phenotype can be generation-selective by out.geno.gen and out.pheno.gen. For example, out.geno.gen = 3:5 and out.pheno.gen = 1:5 represent outputting genotype from generation3 to generation5 and outputting phenotype from generation1 to generation5.
# set output generations of genotype and phenotype
out.geno.gen <- 3:5
out.pheno.gen <- 1:5
simer.list <-
simer(num.gen = 5,
verbose = verbose,
outpath = outpath,
out.geno.gen = out.geno.gen,
out.pheno.gen = out.pheno.gen,
input.map = input.map,
# rawgeno1 = rawgeno, # use your own genotype matrix
num.ind = 100,
mtd.reprod = "randmate",
num.prog = 2,
ratio = 0.5)SIMER outputs data including population information, marker effects, trait information, genotype, genotypic id, genotypic map, and selection intensity. Note that several result files with prefix out = "simer" will be generated. Firstly, a result path will be generated. The number at the beginning represents the total individuals number and the ending character is the format of output ("num_Simer_Data_format"). Secondly, you will see a path named "replication1". It is the first replication of simulation and you can get numerous different replications by setting parameter replication. In "replication1", results are following:
simer.geno.id contains indice of genotyped individuals
simer.geno.bin and simer.geno.desc contain genotype matrix of all individuals
simer.map contains input map with block information and recombination information
simer.ped contains pedigree of individuals
simer.phe contains phenotype of individuals
Population information contains generation, individual indice, family indice, within-family indice, sire indice, dam indice, sex, and phenotpye.
> pop <- simer.list$pop
> head(pop)
gen index fam infam sir dam sex pheno
1 1 1 1 1 0 0 1 -1.043716
2 1 2 2 2 0 0 1 0.551076
3 1 3 3 3 0 0 1 -5.727581
4 1 4 4 4 0 0 1 5.825591
5 1 5 5 5 0 0 1 4.430751
6 1 6 6 6 0 0 1 15.059641Marker effects is a list with selected markers and effects of markers.
> effs <- simer.list$effs
> str(effs)
List of 2
$ mrk1: int [1:18] 4006 4950 10416 18070 21899 23817 26038 28886 29437 30012 ...
$ eff1:List of 1
..$ eff.a: num [1:18] -0.0144 -5.7868 -1.2436 1.2436 -3.4893 ...Trait information is displayed by generation in single-breed reproduction or by breed in multiple-breeds reproduction. In every generation (or breed), it contains trait information (variance components and heritability), effect information (phenotype decomposition), phenotype information (TBV, TGV, pEBVs, gEBVs, ssEBVs, phenotype), and others.
> trait <- simer.list$trait
> str(trait)
List of 5
$ gen1:List of 4
..$ info.tr :List of 3
.. ..$ Vg: num 23.3
.. ..$ Ve: num 63
.. ..$ h2: num 0.27
..$ info.eff :'data.frame': 40 obs. of 2 variables:
.. ..$ ind.a : num [1:40] -6.46 6.46 6.46 6.46 0 ...
.. ..$ ind.env: num [1:40] 5.419 -5.912 -12.191 -0.638 4.431 ...
..$ info.pheno:'data.frame': 40 obs. of 3 variables:
.. ..$ TBV : num [1:40] -6.46 6.46 6.46 6.46 0 ...
.. ..$ TGV : num [1:40] -6.46 6.46 6.46 6.46 0 ...
.. ..$ pheno: num [1:40] -1.044 0.551 -5.728 5.826 4.431 ...
..$ qtn.a : num [1:18, 1:40] 1 1 1 1 1 1 1 1 1 1 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:40] "V1" "V3" "V5" "V7" ...
$ gen2:List of 4
..$ info.tr :List of 3
.. ..$ Vg: num 41.7
.. ..$ Ve: num 113
.. ..$ h2: num 0.27
..$ info.eff :'data.frame': 32 obs. of 2 variables:
.. ..$ ind.a : num [1:32] 5.17 1.29 6.46 6.46 6.46 ...
.. ..$ ind.env: num [1:32] 10.8 -8.61 -2.41 1.8 -5.04 ...
..$ info.pheno:'data.frame': 32 obs. of 3 variables:
.. ..$ TBV : num [1:32] 5.17 1.29 6.46 6.46 6.46 ...
.. ..$ TGV : num [1:32] 5.17 1.29 6.46 6.46 6.46 ...
.. ..$ pheno: num [1:32] 15.97 -7.31 4.06 8.26 1.43 ...
..$ qtn.a : num [1:18, 1:32] 0 -1 0 0 0 0 0 0 0 0 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:32] "V17" "V18" "t1" "t2" ...
$ gen3:List of 4
..$ info.tr :List of 3
.. ..$ Vg: num 17.5
.. ..$ Ve: num 36.5
.. ..$ h2: num 0.324
..$ info.eff :'data.frame': 26 obs. of 2 variables:
.. ..$ ind.a : num [1:26] -1.525 -1.525 0 0.609 6.463 ...
.. ..$ ind.env: num [1:26] 3.29 2.31 -6.94 5.97 15.28 ...
..$ info.pheno:'data.frame': 26 obs. of 3 variables:
.. ..$ TBV : num [1:26] -1.525 -1.525 0 0.609 6.463 ...
.. ..$ TGV : num [1:26] -1.525 -1.525 0 0.609 6.463 ...
.. ..$ pheno: num [1:26] 1.77 0.781 -6.936 6.576 21.743 ...
..$ qtn.a : num [1:18, 1:26] 0 0 0 0 0 0 0 0 0 0 ...
..
17AE
..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:26] "t2" "t2" "t2" "t1" ...
$ gen4:List of 4
..$ info.tr :List of 3
.. ..$ Vg: num 15.8
.. ..$ Ve: num 32
.. ..$ h2: num 0.33
..$ info.eff :'data.frame': 20 obs. of 2 variables:
.. ..$ ind.a : num [1:20] -2.808 -0.242 9.085 -2.013 4.052 ...
.. ..$ ind.env: num [1:20] 3.96 2.64 -2.9 -2.65 2.2 ...
..$ info.pheno:'data.frame': 20 obs. of 3 variables:
.. ..$ TBV : num [1:20] -2.808 -0.242 9.085 -2.013 4.052 ...
.. ..$ TGV : num [1:20] -2.808 -0.242 9.085 -2.013 4.052 ...
.. ..$ pheno: num [1:20] 1.15 2.4 6.19 -4.66 6.26 ...
..$ qtn.a : num [1:18, 1:20] 1 1 1 0 0 1 1 1 0 1 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:20] "V18" "t2" "t1" "t1" ...
$ gen5:List of 4
..$ info.tr :List of 3
.. ..$ Vg: num 12.1
.. ..$ Ve: num 28.4
.. ..$ h2: num 0.299
..$ info.eff :'data.frame': 16 obs. of 2 variables:
.. ..$ ind.a : num [1:16] 3.79 4.377 0.551 0.551 7.68 ...
.. ..$ ind.env: num [1:16] -1.22 -5.5 -7.99 8.79 3.1 ...
..$ info.pheno:'data.frame': 16 obs. of 3 variables:
.. ..$ TBV : num [1:16] 3.79 4.377 0.551 0.551 7.68 ...
.. ..$ TGV : num [1:16] 3.79 4.377 0.551 0.551 7.68 ...
.. ..$ pheno: num [1:16] 2.57 -1.12 -7.44 9.34 10.78 ...
..$ qtn.a : num [1:18, 1:16] -1 -1 -1 0 0 0 -1 -1 -1 -1 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:16] "t2" "t1" "t2" "t2" ...In genotype matrix, each row represents a marker and each column represents a individual.
> geno <- simer.list$geno
> geno[1:6, 1:6]
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 1 1 1 0 0
[2,] 1 1 1 1 0 0
[3,] 1 1 1 1 0 0
[4,] 1 1 1 1 0 0
[5,] 1 1 1 1 0 0
[6,] 1 1 1 1 0 0Genotypic id is the indice of genotyped individuals.
> genoid <- simer.list$genoid
> head(genoid)
[1] 73 74 75 76 77 78In SNP map information, the first column is SNP name, the second column is Chromosome ID, the third column is phsical position, the fourth column is REF, the fifth column is ALT, the sixth column is block ID, and the seventh is recombination information ("1" represents making recombination and "0" represents not).
> map <- simer.list$map
> head(map)
SNP Chrom BP REF ALT block recom
[1,] "1_10673082" "1" " 10673082" "T" "C" "1" "1"
[2,] "1_10723065" "1" " 10723065" "A" "G" "1" "1"
[3,] "1_11407894" "1" " 11407894" "A" "G" "1" "1"
[4,] "1_11426075" "1" " 11426075" "T" "C" "1" "1"
[5,] "1_13996200" "1" " 13996200" "T" "C" "1" "1"
[6,] "1_14638936" "1" " 14638936" "T" "C" "1" "1" Selection intensity is calculated according to ratio of selected individuals.
> si <- simer.list$si
> si
[1] 0.3499524For SIMER:
Hope it will be coming soon!
For ADI model:
Kao, Chenhung, et al. "Modeling Epistasis of Quantitative Trait Loci Using Cockerham's Model." Genetics 160.3 (2002): 1243-1261.
For build.cov:
B. D. Ripley "Stochastic Simulation." Wiley-Interscience (1987): Page 98.
🆘 Question1: Failing to install "devtools":
ERROR: configuration failed for package ‘git2r’
removing ‘/Users/acer/R/3.4/library/git2r’
ERROR: dependency ‘git2r’ is not available for package ‘devtools’
removing ‘/Users/acer/R/3.4/library/devtools’
😋 Answer: Please try following codes in terminal:
apt-get install libssl-dev/unstable
🆘 Question2: When installing packages from Github with "devtools", an error occurred:
Error in curl::curl_fetch_disk(url, x$path, handle = handle): Problem with the SSL CA cert (path? access rights?)
😋 Answer: Please try following codes and then try agian.
library(httr)
set_config(config(ssl_verifypeer = 0L))Questions, suggestions, and bug reports are welcome and appreciated. ➡️