Help for package simulMGF

Type:

Package

Title:

Simulate SNP Matrix, Phenotype and Genotypic Effects

Version:

0.1.1

Author:

Martin Nahuel Garcia [aut, cre]

Maintainer:

Martin Nahuel Garcia <garcia.martin@inta.gob.ar>

Description:

Simulate genotypes in SNP (single nucleotide polymorphisms) Matrix as random numbers from an uniform distribution, for diploid organisms (coded by 0, 1, 2), Sikorska et al., (2013) <doi:10.1186/1471-2105-14-166>, or half-sib/full-sib SNP matrix from real or simulated parents SNP data, assuming mendelian segregation. Simulate phenotypic traits for real or simulated SNP data, controlled by a specific number of quantitative trait loci and their effects, sampled from a Normal or an Uniform distributions, assuming a pure additive model. This is useful for testing association and genomic prediction models or for educational purposes.

License:

MIT + file LICENSE

Encoding:

UTF-8

URL:

https://github.com/mngar/simulMGF

RoxygenNote:

7.2.3

Suggests:

spelling

Language:

en-US

NeedsCompilation:

Packaged:

2023-03-28 17:10:40 UTC; Martin

Repository:

CRAN

Date/Publication:

2023-04-02 22:30:06 UTC

Function to simulate SNP matrix

Description

Simulate SNP matrix coded 0, 1 and 2; with random genotypes.

Usage

simGeno(Nind, Nmarkers)

Arguments

Nind

number of individuals to simulate.

Nmarkers

Nmarkers number of SNP markers to generate.

Value

a matrix of dimensions Nind x Nmarkers.

Author(s)

Martin Nahuel Garcia <orcid:0000-0001-5760-986X>

References

Wu, R., Ma, C., & Casella, G. (2007). Statistical genetics of quantitative traits: linkage, maps and QTL. Springer Science & Business Media.

Examples

#simulate 100 individuals and 1000 SNPs
set.seed(123)
simGeno(100, 1000)
#[1] "simG was generated"
dim(simG);simG[1:5,1:5]
#[1]  100 1000
#[,1] [,2] [,3] [,4] [,5]
#[1,]    0    1    0    2    2
#[2,]    2    0    2    0    0
#[3,]    1    1    1    2    2
#[4,]    2    2    1    2    1
#[5,]    2    1    1    1    1

Function to simulate phenotypes

Description

Simulate a phenotype from a genotype matrix with QTLs with random effects sampled from a Normal distribution.

Usage

simPheno(x, Nqtl, Esigma, Pmean, Perror)

Arguments

x

SNP matrix coded like 0 homozygous; 1 heterozygous; 2 homozygous

Nqtl

number of QTLs to simulate

Esigma

standard deviation of effects with distribution N~(0,Esigma^2)

Pmean

phenotype mean

Perror

standard deviation of error (portion of phenotype not explained by genomic information)

Value

An object of class list containing the trait, the markers associated and their effects.

pheno

vector with the trait values simulated.

QTN

column in the SNP matrix with the SNP associated.

Meffects

effects of the associated SNPs.

Author(s)

Martin Nahuel Garcia <orcid:0000-0001-5760-986X>

References

Wu, R., Ma, C., & Casella, G. (2007). Statistical genetics of quantitative traits: linkage, maps and QTL. Springer Science & Business Media.

Examples

set.seed(123)
simGeno(100, 1000)
#' #[1] "simG was generated"
simPheno(simG, 50, .8, 12, .5)
#[1] "simP was generated"
str(simP)
#List of 3
#$ pheno   : num [1:100, 1] 24 20.5 15.6 13.6 18.5 ...
#$ QTN     : int [1:50] 568 474 529 349 45 732 416 51 413 514 ...
#$ Meffects: num [1:50] 0.2396 -0.138 0.906 0.0186 1.0687 ...

Function to simulate full sib progeny genotype

Description

Simulate full sib progeny genotypes from the genotype of the parents (matrixes with the same dimensions). Pair of parents mating will be in the order of the matrixes. We assume that these are diploid organisms.

Usage

simulFS(x, y, Nprogeny)

Arguments

x

genotype matrix of a set of moms

y

genotype matrix of a set of dads

Nprogeny

Nprogeny number of progeny's genotypes to generate from each pair of parents

Value

a matrix of dimensions (nrow(x)*Nprogeny) x ncol(x)

Author(s)

Martin Nahuel Garcia <orcid:0000-0001-5760-986X>

References

Wu, R., Ma, C., & Casella, G. (2007). Statistical genetics of quantitative traits: linkage, maps and QTL. Springer Science & Business Media.

Examples

#simulate 100 individuals and 1000 SNPs
set.seed(123)
simGeno(100, 1000)
#[1] "simG was generated"
#simulate the genotype of 5 FS from 3 pairs of parents
simulFS(simG[1:3,],simG[4:6,],5)
#[1] "simulatedFS was generated"
 dim(simulatedFS)
#[1]   15 1000
# The first 5 individuals are progeny of mom 1 and dad 1, the second 5 individuals
# are progeny of mom 2 and dad 2, and so on.

Function to simulate half sib progeny genotypes

Description

Simulate half sib progeny from one genotyped parent assuming a random genotype for the other parental. We assume that these are diploid organisms.

Usage

simulHS(x, Nprogeny)

Arguments

x

genotype matrix of a set of moms

Nprogeny

number of progeny's genotypes to simulate for each mom

Details

The function assume: a diploid organism; mendelian segregation of alleles; and independent segregation.

Value

a matrix of dimensions (nrow(x)*Nprogeny) x ncol(x)

Author(s)

Martin Nahuel Garcia <orcid:0000-0001-5760-986X>

References

Wu, R., Ma, C., & Casella, G. (2007). Statistical genetics of quantitative traits: linkage, maps and QTL. Springer Science & Business Media.

Examples

#' #simulate 100 individuals and 1000 SNPs
set.seed(123)
simGeno(100, 1000)
#[1] "simG was generated"
#simulate the genotype of 3 sets 5 HS (one set by mom)
simulHS(simG[1:3,],5)
#[1] "simulatedHS was generated"
dim(simulatedHS)
#[1]   15 1000

Function to simulate a SNP matrix, a phenotypic trait and the effects of associated SNPs.

Description

This function simulate a SNP matrix (coded as 0, 1, 2) and traits with a selected number of QTLs and their effects that will be sampled from a Normal distribution.

Usage

simulN(Nind, Nmarkers, Nqtl, Esigma, Pmean, Perror)

Arguments

Nind

number of individuals to simulate.

Nmarkers

number of SNP markers to generate.

Nqtl

number of QTLs controlling the trait.

Esigma

standard deviation of effects with distribution N~(0,Esigma^2).

Pmean

phenotype mean.

Perror

standard deviation of error (portion of phenotype not explained by genomic information).

Details

Genotypic data is simulated as the round value sampled from an uniform distribution with interval (-.5,2.5). Phenotypic data are obtained as a linear function defined by:

y = Pmean + \sum QTN*Meffects + Perror

Value

An object of class list containing the SNP matrix, the trait, the markers associated and their effects.

geno

SNP matrix generated.

pheno

vector with the trait values simulated.

QTN

column in the SNP matrix with the SNP associated.

Meffects

effects of the associated SNPs.

Note

The genotype is simulated in the same way of simGeno function. The trait, QTLs and their effects are simulated in the same way of simPheno function.

Author(s)

Martin Nahuel Garcia <orcid:0000-0001-5760-986X>

References

Wu, R., Ma, C., & Casella, G. (2007). Statistical genetics of quantitative traits: linkage, maps and QTL. Springer Science & Business Media.

Examples

set.seed(123)
simulN(100, 1000, 50, .9, 12, .5)
#[1] "nsimout was generated"
str(nsimout)
#List of 4
#$ geno    : num [1:100, 1:1000] 0 2 1 2 2 0 1 2 1 1 ...
#$ pheno   : num [1:100, 1] 25.4 21.6 16 13.8 19.4 ...
#$ QTN     : int [1:50] 568 474 529 349 45 732 416 51 413 514 ...
#$ Meffects: num [1:50] 0.2696 -0.1552 1.0192 0.0209 1.2023 ...

Function to simulate a random SNP matrix, phenotype and QTLs with their effects

Description

This function simulate a SNP matrix (coded as 0, 1, 2) and traits with a selected number of QTLs and their effects that will be sampled from a Uniform distribution.

Usage

simulU(Nind, Nmarkers, Nqtl, Pmean, Perror)

Arguments

Nind

number of individuals to simulate.

Nmarkers

number of SNP markers to generate.

Nqtl

number of QTLs controlling the trait.

Pmean

phenotype mean.

Perror

standard deviation of error (portion of phenotype not explained by genomic information).

Value

An object of class list containing the SNP matrix, the trait, the markers associated and their effects.

geno

SNP matrix generated.

pheno

vector with the trait values simulated.

QTN

column in the SNP matrix with the SNP associated.

Meffects

effects of the associated SNPs.

Author(s)

Martin Nahuel Garcia <orcid:0000-0001-5760-986X>

References

Wu, R., Ma, C., & Casella, G. (2007). Statistical genetics of quantitative traits: linkage, maps and QTL. Springer Science & Business Media.

Examples

set.seed(123)
simulU(100, 1000, 50, 12, .5)
#[1] "usimout was generated"
str(usimout)
#List of 4
#$ geno    : num [1:100, 1:1000] 0 2 1 2 2 0 1 2 1 1 ...
#$ pheno   : num [1:100, 1] 10.3 14.7 11.8 10.2 13.1 ...
#$ QTN     : int [1:50] 568 474 529 349 45 732 416 51 413 514 ...
#$ Meffects: num [1:50] 0.2355 0.0158 -0.1369 -0.1246 0.7426 ...

Function to simulate SNP matrix

Description

Usage

Arguments

Value

Author(s)

References

Examples

Function to simulate phenotypes

Description

Usage

Arguments

Value

Author(s)

References

Examples

Function to simulate full sib progeny genotype

Description

Usage

Arguments

Value

Author(s)

References

Examples

Function to simulate half sib progeny genotypes

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Function to simulate a SNP matrix, a phenotypic trait and the effects of associated SNPs.

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

See Also

Examples

Function to simulate a random SNP matrix, phenotype and QTLs with their effects

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples