| Title: | Genomic Prediction Using SNP Codata |
|---|---|
| Description: | Computes genomic breeding values using external information on the markers. The package fits a linear mixed model with heteroscedastic random effects, where the random effect variance is fitted using a linear predictor and a log link. The method is described in Mouresan, Selle and Ronnegard (2019) <doi:10.1101/636746>. |
| Authors: | Lars Ronnegard |
| Maintainer: | Lars Ronnegard <[email protected]> |
| License: | GPL |
| Version: | 1.43 |
| Built: | 2024-11-23 03:17:23 UTC |
| Source: | https://github.com/cran/CodataGS |
Computes genomic breeding values using external information on the markers. The package fits a linear mixed model with heteroscedastic random effects, where the random effect variance is fitted using a linear predictor and a log link. The method is described in Mouresan, Selle and Ronnegard (2019) <doi:10.1101/636746>.
The DESCRIPTION file:
| Package: | CodataGS |
| Type: | Package |
| Title: | Genomic Prediction Using SNP Codata |
| Version: | 1.43 |
| Date: | 2019-05-17 |
| Author: | Lars Ronnegard |
| Maintainer: | Lars Ronnegard <[email protected]> |
| Description: | Computes genomic breeding values using external information on the markers. The package fits a linear mixed model with heteroscedastic random effects, where the random effect variance is fitted using a linear predictor and a log link. The method is described in Mouresan, Selle and Ronnegard (2019) <doi:10.1101/636746>. |
| License: | GPL |
| Depends: | Matrix |
| NeedsCompilation: | no |
| Packaged: | 2019-05-17 12:09:05 UTC; lrn |
| Date/Publication: | 2019-05-17 15:40:07 UTC |
| Repository: | https://larsronn.r-universe.dev |
| RemoteUrl: | https://github.com/cran/CodataGS |
| RemoteRef: | HEAD |
| RemoteSha: | 86c42d095b486bb0ef550e568baded3d1f4e3ad8 |
Index of help topics:
CodataGS-package Genomic Prediction Using SNP Codata
MME Mixed model equations
Transform Transforms hat values
compute_GL Computes genomic relationship matrix
compute_phitau Computes models for the variance components
genomicEBV.w.codata Performs genomic prediction based on SNP
codata.
hat.transf Transforms hat values
scaleZ Scales the genotype matrix.
summary.CodataGS Summary method for CodataGS objects
This package performs genomic prediction based on SNP codata. The main function is genomicEBV.w.codata.
Lars Ronnegard
Maintainer: Lars Ronnegard <[email protected]>
This function computes the genomic relationship matrix, G, together with its matrix square root, L.
compute_GL(Z, w)compute_GL(Z, w)
Z |
Scaled matrix with genotype information |
w |
weights |
L |
Square root matrix of G |
svdVec |
Vectors in the Single Value Decomposition of G |
svdD |
Diagonal elements in the Single Value Decomposition of G |
wZt |
weights times the transpose of Z |
Lars Ronnegard
set.seed(1234) N <- 20 #Number of individuals k <- 30 #Number of SNPs with all marker positions including a QTL Z1 <- matrix(0, N, k ) Z2 <- matrix(0, N, k ) Z1[1:N, 1] <- rbinom(N, 1, 0.5) #Simulated phased SNP matrices Z2[1:N, 1] <- rbinom(N, 1, 0.5) LD.par <- 0.2 #A parameter to simulate LD. 0 gives full LD, and 0.5 no LD for (j in 2:k) { Z1[1:N, j] <- abs( Z1[1:N, j-1] - rbinom(N, 1, LD.par) ) Z2[1:N, j] <- abs( Z2[1:N, j-1] - rbinom(N, 1, LD.par) ) } Z <- Z1 + Z2 #Genotypic SNP matrix sim.res <- compute_GL(Z, w = rep(1,k))set.seed(1234) N <- 20 #Number of individuals k <- 30 #Number of SNPs with all marker positions including a QTL Z1 <- matrix(0, N, k ) Z2 <- matrix(0, N, k ) Z1[1:N, 1] <- rbinom(N, 1, 0.5) #Simulated phased SNP matrices Z2[1:N, 1] <- rbinom(N, 1, 0.5) LD.par <- 0.2 #A parameter to simulate LD. 0 gives full LD, and 0.5 no LD for (j in 2:k) { Z1[1:N, j] <- abs( Z1[1:N, j-1] - rbinom(N, 1, LD.par) ) Z2[1:N, j] <- abs( Z2[1:N, j-1] - rbinom(N, 1, LD.par) ) } Z <- Z1 + Z2 #Genotypic SNP matrix sim.res <- compute_GL(Z, w = rep(1,k))
This function computes the residual variance, the SNP variances and the linear predictor for the SNP variance model.
compute_phitau(dev, hv, devu, hvu, X.rand.disp)compute_phitau(dev, hv, devu, hvu, X.rand.disp)
dev |
Deviance values |
hv |
Hat values for the observed response values |
devu |
Deviance values computed for the random effects |
hvu |
Hat values for the random effects |
X.rand.disp |
Design matrix used in the linear predictor for the SNP variance model. |
var.e |
Residual variance |
phi |
Vector of SNP variances |
coef |
Fitted coefficients for the linear predictor in the SNP variance model |
Lars Ronnegard
set.seed(1234) N <- 20 #Number of individuals k <- 30 #Number of SNPs with all marker positions including a QTL #Simulated deviances and hat values dev <- rnorm(N)^2 hv <- runif(N, 0.1, 0.5) devu <- rnorm(k)^2 hvu <- runif(k, 0.1, 0.85) X.rand.disp <- matrix(1, k, 1) sim.res <- compute_phitau(dev, hv, devu, hvu, X.rand.disp)set.seed(1234) N <- 20 #Number of individuals k <- 30 #Number of SNPs with all marker positions including a QTL #Simulated deviances and hat values dev <- rnorm(N)^2 hv <- runif(N, 0.1, 0.5) devu <- rnorm(k)^2 hvu <- runif(k, 0.1, 0.85) X.rand.disp <- matrix(1, k, 1) sim.res <- compute_phitau(dev, hv, devu, hvu, X.rand.disp)
The main function of the package. The input includes response values, a design matrix for the fixed effects, a matrix with SNP genotype data and a design matrix for the SNP codata.
genomicEBV.w.codata(y, X, Z, X.SNPcodata, Z.test = NULL, max.iter = 100, conv.crit = 1e-5)genomicEBV.w.codata(y, X, Z, X.SNPcodata, Z.test = NULL, max.iter = 100, conv.crit = 1e-5)
y |
Response values |
X |
Design matrix for the fixed effects |
Z |
Genotype matrix with element values of 0, 1 or 2 |
X.SNPcodata |
Design matrix for the linear predictor of the SNP variances. |
Z.test |
An optional genotype matrix for a test data set. |
max.iter |
The maximum number of iterations |
conv.crit |
The value of the convergence criterion. |
By specifying the matrix Z.test in the input, the function computes predicted genomic breeding values for an out-of-sample data set.
gEBV |
Genomic breeding values |
predicted.gEBV |
Genomic breeding values based on the genotypes in |
w |
Computed SNP weights |
u |
Fitted SNP effects |
beta |
Fitted fixed effects |
disp.beta |
Fitted coefficients in the linear predictor for the SNP variance model |
Converge |
Shows whether the algorithm has converged or not |
iter |
The number of iterations used |
Lars Ronnegard
####### #Simulation part set.seed(1234) N <- 200 #Number of individuals k <- 300 #Number of SNPs with all marker positions including a QTL Z1 <- matrix(0, N, k ) Z2 <- matrix(0, N, k ) Z1[1:N, 1] <- rbinom(N, 1, 0.5) #Simulated phased SNP matrices Z2[1:N, 1] <- rbinom(N, 1, 0.5) LD.par <- 0.2 #A parameter to simulate LD. 0 gives full LD, and 0.5 no LD for (j in 2:k) { Z1[1:N, j] <- abs( Z1[1:N, j-1] - rbinom(N, 1, LD.par) ) Z2[1:N, j] <- abs( Z2[1:N, j-1] - rbinom(N, 1, LD.par) ) } Z <- Z1 + Z2 #Genotypic SNP matrix x1 <- c(rep(1,k/2), rep(0,k/2)) #An indicator for the SNPs. #The first k/2 SNPs and the last k/2 have different variances #Simulate linear predictor for the random effect variance lin.pred <- 0 + 2*x1 X.snp <- model.matrix( ~ x1 ) #Corresponding design matrix u <- rnorm(k, 0 , sqrt( exp(lin.pred) )) #Took the square root here because it is the SD that is specified. #and exp() because we are modelling a log link. u.scaled <- u/as.numeric( sqrt( var( crossprod(t(Z), u) )) ) #Scaled by the variance of the breeding values e <- rnorm(N) #A residual variance mu <- 0 y <- mu + crossprod(t(Z),u.scaled) + e ###### #Estimation part mod1 <- genomicEBV.w.codata(y = as.numeric(y), X = matrix(1, N, 1), Z = Z, X.SNPcodata = X.snp) #To fit gBLUP just specify X.SNPcodata = matrix(1, k, 1) cat("Correlation between true and estimated BV for the codata model:") cat(cor(crossprod(t(Z),u.scaled), mod1$gEBV), "\n")####### #Simulation part set.seed(1234) N <- 200 #Number of individuals k <- 300 #Number of SNPs with all marker positions including a QTL Z1 <- matrix(0, N, k ) Z2 <- matrix(0, N, k ) Z1[1:N, 1] <- rbinom(N, 1, 0.5) #Simulated phased SNP matrices Z2[1:N, 1] <- rbinom(N, 1, 0.5) LD.par <- 0.2 #A parameter to simulate LD. 0 gives full LD, and 0.5 no LD for (j in 2:k) { Z1[1:N, j] <- abs( Z1[1:N, j-1] - rbinom(N, 1, LD.par) ) Z2[1:N, j] <- abs( Z2[1:N, j-1] - rbinom(N, 1, LD.par) ) } Z <- Z1 + Z2 #Genotypic SNP matrix x1 <- c(rep(1,k/2), rep(0,k/2)) #An indicator for the SNPs. #The first k/2 SNPs and the last k/2 have different variances #Simulate linear predictor for the random effect variance lin.pred <- 0 + 2*x1 X.snp <- model.matrix( ~ x1 ) #Corresponding design matrix u <- rnorm(k, 0 , sqrt( exp(lin.pred) )) #Took the square root here because it is the SD that is specified. #and exp() because we are modelling a log link. u.scaled <- u/as.numeric( sqrt( var( crossprod(t(Z), u) )) ) #Scaled by the variance of the breeding values e <- rnorm(N) #A residual variance mu <- 0 y <- mu + crossprod(t(Z),u.scaled) + e ###### #Estimation part mod1 <- genomicEBV.w.codata(y = as.numeric(y), X = matrix(1, N, 1), Z = Z, X.SNPcodata = X.snp) #To fit gBLUP just specify X.SNPcodata = matrix(1, k, 1) cat("Correlation between true and estimated BV for the codata model:") cat(cor(crossprod(t(Z),u.scaled), mod1$gEBV), "\n")
Transforms hat values between the SNP-BLUP model and the gBLUP model.
hat.transf(C22, transf, vc, k, N, w)hat.transf(C22, transf, vc, k, N, w)
C22 |
Submatrix of the inverse of the LHS in the MME |
transf |
A transformation matrix. |
vc |
Genetic variance |
k |
Number of SNPs |
N |
Number of individuals |
w |
SNP weights |
Transformed hat values
Lars Ronnegard
A fast version of the Henderson's mixed model equations (MME)
MME(y, X, Z, var.e, var.u)MME(y, X, Z, var.e, var.u)
y |
Response |
X |
Design matrix for fixed effects |
Z |
Design matrix for the random effects |
var.e |
Residual variance |
var.u |
Genetic variance |
beta |
Estimates of fixed effects |
v |
Fitted random effects |
hv |
Hat values |
dev |
Deviances |
Lars Ronnegard
Scales the genotype matrix so that ZZ' gives the genomic relationship matrix.
scaleZ(Z, freq1)scaleZ(Z, freq1)
Z |
Genotype matrix with element values 0, 1 and 2 |
freq1 |
Optional input parameter with allele frequencies. A vector of length equal to the number of columns in Z. |
Z |
Scaled genotype matrix |
Lars Ronnegard
####### #Simulation part set.seed(1234) N <- 200 #Number of individuals k <- 300 #Number of SNPs with all marker positions including a QTL Z1 <- matrix(0, N, k ) Z2 <- matrix(0, N, k ) Z1[1:N, 1] <- rbinom(N, 1, 0.5) #Simulated phased SNP matrices Z2[1:N, 1] <- rbinom(N, 1, 0.5) LD.par <- 0.2 #A parameter to simulate LD. 0 gives full LD, and 0.5 no LD for (j in 2:k) { Z1[1:N, j] <- abs( Z1[1:N, j-1] - rbinom(N, 1, LD.par) ) Z2[1:N, j] <- abs( Z2[1:N, j-1] - rbinom(N, 1, LD.par) ) } Z <- Z1 + Z2 #Genotypic SNP matrix sim.res <- scaleZ(Z)####### #Simulation part set.seed(1234) N <- 200 #Number of individuals k <- 300 #Number of SNPs with all marker positions including a QTL Z1 <- matrix(0, N, k ) Z2 <- matrix(0, N, k ) Z1[1:N, 1] <- rbinom(N, 1, 0.5) #Simulated phased SNP matrices Z2[1:N, 1] <- rbinom(N, 1, 0.5) LD.par <- 0.2 #A parameter to simulate LD. 0 gives full LD, and 0.5 no LD for (j in 2:k) { Z1[1:N, j] <- abs( Z1[1:N, j-1] - rbinom(N, 1, LD.par) ) Z2[1:N, j] <- abs( Z2[1:N, j-1] - rbinom(N, 1, LD.par) ) } Z <- Z1 + Z2 #Genotypic SNP matrix sim.res <- scaleZ(Z)
A summary method for the object class CodataGS
## S3 method for class 'CodataGS' summary(object, ...)## S3 method for class 'CodataGS' summary(object, ...)
object |
A CodataGS object |
... |
arguments not used |
Provides a concise summary of CodataGS objects.
####### #Simulation part set.seed(1234) N <- 200 #Number of individuals k <- 300 #Number of SNPs with all marker positions including a QTL Z1 <- matrix(0, N, k ) Z2 <- matrix(0, N, k ) Z1[1:N, 1] <- rbinom(N, 1, 0.5) #Simulated phased SNP matrices Z2[1:N, 1] <- rbinom(N, 1, 0.5) LD.par <- 0.2 #A parameter to simulate LD. 0 gives full LD, and 0.5 no LD for (j in 2:k) { Z1[1:N, j] <- abs( Z1[1:N, j-1] - rbinom(N, 1, LD.par) ) Z2[1:N, j] <- abs( Z2[1:N, j-1] - rbinom(N, 1, LD.par) ) } Z <- Z1 + Z2 #Genotypic SNP matrix x1 <- c(rep(1,k/2), rep(0,k/2)) #An indicator for the SNPs. #The first k/2 SNPs and the last k/2 have different variances #Simulate linear predictor for the random effect variance lin.pred <- 0 + 2*x1 X.snp <- model.matrix( ~ x1 ) #Corresponding design matrix u <- rnorm(k, 0 , sqrt( exp(lin.pred) )) #Took the square root here because it is the SD that is specified. #and exp() because we are modelling a log link. u.scaled <- u/as.numeric( sqrt( var( crossprod(t(Z), u) )) ) #Scaled by the variance of the breeding values e <- rnorm(N) #A residual variance mu <- 0 y <- mu + crossprod(t(Z),u.scaled) + e ###### #Estimation part mod1 <- genomicEBV.w.codata(y = as.numeric(y), X = matrix(1, N, 1), Z = Z, X.SNPcodata = X.snp) summary(mod1)####### #Simulation part set.seed(1234) N <- 200 #Number of individuals k <- 300 #Number of SNPs with all marker positions including a QTL Z1 <- matrix(0, N, k ) Z2 <- matrix(0, N, k ) Z1[1:N, 1] <- rbinom(N, 1, 0.5) #Simulated phased SNP matrices Z2[1:N, 1] <- rbinom(N, 1, 0.5) LD.par <- 0.2 #A parameter to simulate LD. 0 gives full LD, and 0.5 no LD for (j in 2:k) { Z1[1:N, j] <- abs( Z1[1:N, j-1] - rbinom(N, 1, LD.par) ) Z2[1:N, j] <- abs( Z2[1:N, j-1] - rbinom(N, 1, LD.par) ) } Z <- Z1 + Z2 #Genotypic SNP matrix x1 <- c(rep(1,k/2), rep(0,k/2)) #An indicator for the SNPs. #The first k/2 SNPs and the last k/2 have different variances #Simulate linear predictor for the random effect variance lin.pred <- 0 + 2*x1 X.snp <- model.matrix( ~ x1 ) #Corresponding design matrix u <- rnorm(k, 0 , sqrt( exp(lin.pred) )) #Took the square root here because it is the SD that is specified. #and exp() because we are modelling a log link. u.scaled <- u/as.numeric( sqrt( var( crossprod(t(Z), u) )) ) #Scaled by the variance of the breeding values e <- rnorm(N) #A residual variance mu <- 0 y <- mu + crossprod(t(Z),u.scaled) + e ###### #Estimation part mod1 <- genomicEBV.w.codata(y = as.numeric(y), X = matrix(1, N, 1), Z = Z, X.SNPcodata = X.snp) summary(mod1)
The function calls the hat.transf function.
Transform(X, L, var.e, var.u, v, svdVec, svdD, wZt, w)Transform(X, L, var.e, var.u, v, svdVec, svdD, wZt, w)
X |
Design matrix for the fixed effects |
L |
Square root matrix of the genomic relationship matrix, G |
var.e |
Residual variance |
var.u |
Genetic variance |
v |
Random effects |
svdVec |
Vector from the Single Value Decomposition of G |
svdD |
Diagonal elements of the Single Value Decomposition of G |
wZt |
Weights times the transpose of the scaled genotype matrix |
w |
Fitted SNP weights |
u |
SNP effects |
qu |
Hat values for the SNP effects |
Lars Ronnegard