Package 'heritability' reference manual

Title:	Marker-Based Estimation of Heritability Using Individual Plant or Plot Data
Description:	Implements marker-based estimation of heritability when observations on genetically identical replicates are available. These can be either observations on individual plants or plot-level data in a field trial. Heritability can then be estimated using a mixed model for the individual plant or plot data. For comparison, also mixed-model based estimation using genotypic means and estimation of repeatability with ANOVA are implemented. For illustration the package contains several datasets for the model species Arabidopsis thaliana.
Authors:	Willem Kruijer, with a contribution from Ian White (the internal function pin). Contains data collected by Padraic Flood and Rik Kooke.
Maintainer:	Willem Kruijer <[email protected]>
License:	GPL-3
Version:	1.4
Built:	2024-11-16 03:49:06 UTC
Source:	https://github.com/cran/heritability

Marker-Based Estimation of Heritability Using Individual Plant or Plot Data.

Description

The package implements marker-based estimation of heritability when observations on genetically identical replicates are available. These can be either observations on individual plants (e.g. in a growth chamber) or plot-level data in a field trial. The function marker_h2 estimates heritability using a mixed model for the individual plant or plot data, as proposed in Kruijer et al. For comparison, also mixed-model based estimation using genotypic means (marker_h2_means) and estimation of repeatability with ANOVA (repeatability) are implemented. For illustration the package contains several datasets for the model species Arabidopsis thaliana.

Author(s)

Willem Kruijer Maintainer: Willem Kruijer <[email protected]>

References

Kruijer, W. et al. (2015) Marker-based estimation of heritability in immortal populations. Genetics, Vol. 199(2), p. 1-20.

Examples

# A) marker-based estimation of heritability, given individual plant-data
# and a marker-based relatedness matrix:
data(LDV)
data(K_atwell)
# This may take up to 30 sec.
#out1 <- marker_h2(data.vector=LDV$LDV,geno.vector=LDV$genotype,
#                  covariates=LDV[,4:8],K=K_atwell)
#
# B) marker-based estimation of heritability, given genotypic means
# and a marker-based relatedness matrix:
data(means_LDV)
data(R_matrix_LDV)
data(K_atwell)
out2 <- marker_h2_means(data.vector=means_LDV$LDV,geno.vector=means_LDV$genotype,
                        K=K_atwell,Dm=R_matrix_LDV)
#
# C) estimation of repeatability using ANOVA:
data(LDV)
out3 <- repeatability(data.vector=LDV$LDV,geno.vector= LDV$genotype,
                      covariates.frame=as.data.frame(LDV[,3]))
# A) marker-based estimation of heritability, given individual plant-data
# and a marker-based relatedness matrix:
data(LDV)
data(K_atwell)
# This may take up to 30 sec.
#out1 <- marker_h2(data.vector=LDV$LDV,geno.vector=LDV$genotype,
#                  covariates=LDV[,4:8],K=K_atwell)
#
# B) marker-based estimation of heritability, given genotypic means
# and a marker-based relatedness matrix:
data(means_LDV)
data(R_matrix_LDV)
data(K_atwell)
out2 <- marker_h2_means(data.vector=means_LDV$LDV,geno.vector=means_LDV$genotype,
                        K=K_atwell,Dm=R_matrix_LDV)
#
# C) estimation of repeatability using ANOVA:
data(LDV)
out3 <- repeatability(data.vector=LDV$LDV,geno.vector= LDV$genotype,
                      covariates.frame=as.data.frame(LDV[,3]))

Bolting time and leaf width for the Arabidopsis hapmap population.

Description

Bolting time and leaf width for the Arabidopsis hapmap population

Usage

data(BT_LW_H)data(BT_LW_H)

Format

A data frame with phenotypic observations on bolting time and leaf width:

genotype: a factor, the levels being the accession or ecotype identifiers
BT: Bolting time, in number of days
LW: Leaf width
replicate: The replicate (or block) each plant is contained in (factor with levels 1 to 3)
rep1: numeric encoding of the factor replicate: equals 1 if the plant is in replicate 1 and 0 otherwise
rep2: numeric encoding of the factor replicate: equals 1 if the plant is in replicate 2 and 0 otherwise

Author(s)

Willem Kruijer <[email protected]>; experiments conducted by Rik Kooke <[email protected]>

References

Kruijer, W. et al. (2015) Marker-based estimation of heritability in immortal populations. Genetics, Vol. 199(2), p. 1-20.

Examples

data(BT_LW_H)
data(BT_LW_H)

Flowering time data taken from Atwell et al. (2010).

Description

Two data-frames containing individual plant data on flowering time under different conditions: LDV (Flowering time under long days and vernalization) and LD (Flowering time under long days, without vernalization).

Usage

data(LD); data(LDV)data(LD); data(LDV)

Format

Data-frames with flowering time observations, genotype and design information:

genotype: a factor, the levels being the accession or ecotype identifiers
LD: Flowering time under long days, in number of days
LDV: Flowering time under long days and vernalization, in number of days
replicate: The replicate (or block) each plant is contained in (factor with levels 1 to 6)
rep1: numeric encoding of the factor replicate: equals 1 if the plant is in replicate 1 and 0 otherwise
rep2: numeric encoding of the factor replicate: equals 1 if the plant is in replicate 2 and 0 otherwise
rep3: numeric encoding of the factor replicate: equals 1 if the plant is in replicate 3 and 0 otherwise
rep4: numeric encoding of the factor replicate: equals 1 if the plant is in replicate 4 and 0 otherwise
rep5: numeric encoding of the factor replicate: equals 1 if the plant is in replicate 5 and 0 otherwise

Details

All plants that had not flowered by the end of the experiment were given a phenotypic value of 200. Only accessions for which SNP-data are available are included here: 167 accessions in case of LD and 168 accessions in case of LDV.

References

Atwell, S., Y. S. Huang, B. J. Vilhjalmsson, G. Willems, M. Horton, et al. (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465: 627-631.
Kruijer, W. et al. (2015) Marker-based estimation of heritability in immortal populations. Genetics, Vol. 199(2), p. 1-20.

Examples

data(LD); data(LDV)
data(LD); data(LDV)

Marker-based relatedness matrices for 3 populations of Arabidopsis thaliana.

Description

Marker-based relatedness matrices based on the SNP-data from Horton et al. (2012). Three matrices are provided: (a) K_atwell, for the 199 accessions studied in Atwell et al. (2010). (b) K_hapmap, for a subset of 350 accessions taken from the Arabidopsis hapmap (Li et al., 2010). (c) K_swedish, for 304 Swedish accessions. All of these are part of the world-wide regmap of 1307 accessions, described in Horton et al. (2012).

Usage

data(K_atwell); data(K_hapmap); data(K_swedish)data(K_atwell); data(K_hapmap); data(K_swedish)

Format

Matrices whose row- and column names are the ecotype or seed-stock IDs of the accessions.

Details

The matrices were computed using equation (2.2) in Astle and Balding (2009); see also Goddard et al. (2009). The heritability-package does not contain functions to construct relatedness matrices from genotypic data, but such functions can be found in many other software packages. For example, GCTA (Yang et al., 2011), LDAK (Speed et al., 2012), Fast-LMM (Lippert, 2011) and GEMMA (Zhou and Stephens, 2012).

References

W. Astle and D.J. Balding (2009) Population Structure and Cryptic Relatedness in Genetic Association Studies. Statistical Science, Vol. 24, No. 4, 451-471.
Atwell, S., Y. S. Huang, B. J. Vilhjalmsson, G. Willems, M. Horton, et al. (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465: 627-631.
Goddard, M.E., Naomi R. Wray, Klara Verbyla and Peter M. Visscher (2009) Estimating Effects and Making Predictions from Genome-Wide Marker Data. Statistical Science, Vol. 24, No. 4, 517-529.
Horton, M. W., A. M. Hancock, Y. S. Huang, C. Toomajian, S. Atwell, et al. (2012) Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel. Nature Genetics 44: 212-216.
Li, Y., Y. Huang, J. Bergelson, M. Nordborg, and J. O. Borevitz (2010) Association mapping of local climate-sensitive quantitative trait loci in arabidopsis thaliana. PNAS vol. 107, number 49.
Lippert, C., J. Listgarten, Y. Liu, C.M. Kadie, R.I. Davidson, et al. (2011) FaST linear mixed models for genome-wide association studies. Naure methods 8: 833-835.
Speed, D., G. Hemani, M. R. Johnson, and D.J. Balding (2012) Improved heritability estimation from genome-wide snps. the American journal of human genetics 91: 1011-1021.
Yang, J., S.H. Lee, M.E. Goddard, and P.M. Visscher (2011) GCTA: a tool for genomewide complex trait analysis. the American journal of human genetics 88: 76-82.
Zhou, X., and M. Stephens, (2012) Genome-wide efficient mixed-model analysis for association studies. Nature genetics 44: 821-824.

Examples

data(K_atwell)
data(K_hapmap)
data(K_swedish)
data(K_atwell)
data(K_hapmap)
data(K_swedish)

Arabidopsis leaf area data for the hapmap and Swedish regmap population.

Description

Arabidopsis leaf area data for the hapmap and Swedish regmap population.

Usage

data(LA_H); data(LA_S)data(LA_H); data(LA_S)

Format

Data frame with leaf area observations:

genotype: a factor, the levels being the accession identifiers
LA13_H: Leaf area 13 days after sowing, in numbers of pixels (hapmap)
LA13_S: Leaf area 13 days after sowing, in numbers of pixels (Swedish regmap)
replicate: The replicate (or block) each plant is contained in (factor with levels 1 to 4)
rep1: numeric encoding of the factor replicate: equals 1 if the plant is in replicate 1 and 0 otherwise
rep2: numeric encoding of the factor replicate: equals 1 if the plant is in replicate 2 and 0 otherwise
rep3: numeric encoding of the factor replicate: equals 1 if the plant is in replicate 3 and 0 otherwise
x: The within image x-coordinate of the plant. A factor with levels 1 2 3
y: The within image y-coordinate of the plant. A factor with levels 1 2 3 4
x1: numeric encoding of the factor x: equals 1 if the plant is in position 1 and 0 otherwise
x2: numeric encoding of the factor x: equals 1 if the plant is in position 2 and 0 otherwise
y1: numeric encoding of the factor y: equals 1 if the plant is in position 1 and 0 otherwise
y2: numeric encoding of the factor y: equals 1 if the plant is in position 2 and 0 otherwise
y3: numeric encoding of the factor y: equals 1 if the plant is in position 3 and 0 otherwise

Author(s)

Willem Kruijer <[email protected]>; experiments conducted by Padraic Flood <[email protected]>

References

Kruijer, W. et al. (2015) Marker-based estimation of heritability in immortal populations. Genetics, Vol. 199(2), p. 1-20.

Examples

data(LA_H); data(LA_S)
data(LA_H); data(LA_S)

Compute a marker-based estimate of heritability, given phenotypic observations at individual plant or plot level.

Description

Given a genetic relatedness matrix and phenotypic observations at individual plant or plot level, this function computes REML-estimates of the genetic and residual variance and their standard errors, using the AI-algorithm (Gilmour et al. 1995). Based on this, heritability estimates and confidence intervals are given (the estimator $h_r^2$ in Kruijer et al.).

Usage

marker_h2(data.vector, geno.vector, covariates = NULL, K, alpha = 0.05,
          eps = 1e-06, max.iter = 100, fix.h2 = FALSE, h2 = 0.5)
marker_h2(data.vector, geno.vector, covariates = NULL, K, alpha = 0.05,
          eps = 1e-06, max.iter = 100, fix.h2 = FALSE, h2 = 0.5)

Arguments

`data.vector`	A vector of phenotypic observations. Needs to be of type numeric. May contain missing values.
`geno.vector`	A vector of genotype labels, either a factor or character. This vector should correspond to `data.vector`, and hence needs to be of the same length.
`covariates`	A data-frame or matrix with optional covariates, the rows corresponding to the phenotypic observations in `data.vector` and `geno.vector`. May contain missing values. Factors are not allowed, and need to be encoded by columns of type numeric or integer. The data-frame or matrix should not contain an intercept, which is included by default.
`K`	A genetic relatedness or kinship matrix, typically marker-based. Must have row- and column-names corresponding to the levels of `geno.vector`
`alpha`	Confidence level, for the 1-alpha confidence intervals.
`eps`	Numerical precision, used as convergence criterion in the AI-algorithm.
`max.iter`	Maximal number of iterations in the AI-algorithm.
`fix.h2`	Compute the log-likelihood and inverse AI-matrix for a fixed heritability value. Default is `FALSE`.
`h2`	When `fix.h2` is `TRUE`, the value of the heritability. Must be of type numeric, between 0 and 1.

Details

Given phenotypic observations $Y_{ij}$ for genotypes $i=1,...,n$ and replicates $j = 1,...,n_i$ , the mixed model $Y_{ij} = \mu + G_i + E_{ij}$ is assumed. The vector of additive genetic effects $(G_1,...,G_n)'$ follows a multivariate normal distribution with mean zero and covariance $\sigma_A^2 K$ , where $\sigma_A^2$ is the additive genetic variance, and $K$ is a genetic relatedness matrix derived from a dense set of markers. The errors $E_{ij}$ are independent and normally distributed with variance $\sigma_E^2$ . Under certain assumptions (see Speed et al. 2012) the marker- or chip-heritability $h^2 = \sigma_A^2 / (\sigma_A^2 + \sigma_E^2)$ equals the narrow-sense heritability.
It is assumed that the genetic relatedness matrix $K$ is scaled such that $trace(P K P) = n - 1$ , where $P$ is the projection matrix $I_n - 1_n 1_n' / n$ , for the identity matrix $I_n$ and $1_n$ being a column vector of ones. If this is not the case, $K$ is automatically scaled prior to fitting the mixed model.
The model can optionally include a term $X_{ij} \beta$ , where $X_{ij}$ is the row vector with observations on $k$ extra covariates and the vector $\beta$ contains their effects. In this case the argument covariates should be the (N x k) matrix or data-frame with rows $X_{ij}$ (N being the total number of observations). Observations where either $Y_{ij}$ or any of the covariates is missing are discarded.
Confidence intervals for heritability are constructed using the delta-method and the inverse AI-matrix. The delta-method can be applied either directly to the function $(\sigma_A^2,\sigma_E^2) -> \sigma_A^2 / (\sigma_A^2 + \sigma_E^2)$ or to the function $(\sigma_A^2,\sigma_E^2) -> log(\sigma_A^2 / \sigma_E^2)$ . In the latter case, a confidence interval for $log(\sigma_A^2 / \sigma_E^2)$ is obtained, which is back-transformed to a confidence interval for heritability. This approach (proposed in Kruijer et al.) has the advantage that intervals are always contained in the unit interval.
The AI-algorithm is run for max.iter iterations. If by then there is no convergence a warning is printed and the current estimates are returned.

Value

A list with the following components:

va: REML-estimate of the (additive) genetic variance.
ve: REML-estimate of the residual variance.
h2: Plug-in estimate of heritability: $va / (va + ve)$ .
conf.int1: 1-alpha confidence interval for heritability.
conf.int2: 1-alpha confidence interval for heritability, obtained by application of the delta method on a logarithmic scale.
inv.ai: The inverse of the average information (AI) matrix.
loglik: The log-likelihood.

Author(s)

Willem Kruijer.

References

Gilmour et al. Gilmour, A.R., R. Thompson and B.R. Cullis (1995) Average Information REML: An Efficient Algorithm for Variance Parameter Estimation in Linear Mixed Models. Biometrics, volume 51, number 4, 1440-1450.
Kruijer, W. et al. (2015) Marker-based estimation of heritability in immortal populations. Genetics, Vol. 199(2), p. 1-20.
Speed, D., G. Hemani, M. R. Johnson, and D.J. Balding (2012) Improved heritability estimation from genome-wide snps. the American journal of human genetics 91: 1011-1021.

Examples

data(LD)
data(K_atwell)
# Heritability estimation for all observations:
#out <- marker_h2(data.vector=LD$LD,geno.vector=LD$genotype,
#                 covariates=LD[,4:8],K=K_atwell)
# Heritability estimation for a randomly chosen subset of 20 accessions:
set.seed(123)
sub.set <- which(LD$genotype %in% sample(levels(LD$genotype),20))
out <- marker_h2(data.vector=LD$LD[sub.set],geno.vector=LD$genotype[sub.set],
                 covariates=LD[sub.set,4:8],K=K_atwell)
data(LD)
data(K_atwell)
# Heritability estimation for all observations:
#out <- marker_h2(data.vector=LD$LD,geno.vector=LD$genotype,
#                 covariates=LD[,4:8],K=K_atwell)
# Heritability estimation for a randomly chosen subset of 20 accessions:
set.seed(123)
sub.set <- which(LD$genotype %in% sample(levels(LD$genotype),20))
out <- marker_h2(data.vector=LD$LD[sub.set],geno.vector=LD$genotype[sub.set],
                 covariates=LD[sub.set,4:8],K=K_atwell)

Compute a marker-based estimate of heritability, given genotypic means.

Description

Given a genetic relatedness matrix and genotypic means, this function computes REML-estimates of the genetic and residual variance and their standard errors, using the AI-algorithm (Gilmour et al. 1995). Based on this, heritability estimates and confidence intervals are given (the estimator $h_m^2$ in Kruijer et al.).

Usage

marker_h2_means(data.vector, geno.vector, K, Dm=NULL, alpha = 0.05, eps = 1e-06,
       max.iter = 100, fix.h2 = FALSE, h2 = 0.5, grid.size=99)
marker_h2_means(data.vector, geno.vector, K, Dm=NULL, alpha = 0.05, eps = 1e-06,
       max.iter = 100, fix.h2 = FALSE, h2 = 0.5, grid.size=99)

Arguments

`data.vector`	A vector of phenotypic observations, typically genotypic means. Needs to be of type numeric. May contain missing values.
`geno.vector`	A vector of genotype labels, either a factor or character. This vector should correspond to `data.vector`, and hence needs to be of the same length.
`K`	A genetic relatedness or kinship matrix, typically marker-based. Must have row- and column-names corresponding to the levels of `geno.vector`
`Dm`	Covariance of the genotypic means contained in data.vector; see details. Should be of class matrix, with row- and column-names corresponding to the levels of `geno.vector`
`alpha`	Confidence level, for the 1-alpha confidence intervals.
`eps`	Numerical precision, used as convergence criterion in the AI-algorithm.
`max.iter`	Maximal number of iterations in the AI-algorithm.
`fix.h2`	Compute the log-likelihood and inverse AI-matrix for a fixed heritability value. Default is `FALSE`.
`h2`	When `fix.h2` is `TRUE`, the value of the heritability. Must be of type numeric, between 0 and 1.
`grid.size`	If the AI-algorithm has not converged after `max.iter` iterations, the likelihood is computed on the grid of heritability values 1/(`grid.size`+1),...,`grid.size`/(`grid.size`+1); see details.

Details

Given phenotypic observations $Y_{i}$ for genotypes $i=1,...,n$ , the mixed model $Y_{i} = \mu + G_i + E_{i}$ is assumed. Typically, the $Y_{i}$ are genotypic means or BLUEs obtained from fitting a linear (mixed) model to the raw data, containing several plants or plots for each genotype. The vector of additive genetic effects $(G_1,...,G_n)'$ follows a multivariate normal distribution with mean zero and covariance $\sigma_A^2 K$ , where $\sigma_A^2$ is the additive genetic variance, and $K$ is a genetic relatedness matrix derived from a dense set of markers. The vector of errors $(E_1,...,E_n)'$ follows a multivariate normal distribution with mean zero and covariance $\sigma_E^2 D_m$ , where $D_m$ is the covariance of the means obtained from the initial analysis. In case of a completely randomized design with $r_i$ replicates for genotypes $i=1,...,n$ , $D_m$ is diagonal with elements $1 / r_i$ . Under certain assumptions (see Speed et al. 2012) the marker- or chip-heritability $h^2 = \sigma_A^2 / (\sigma_A^2 + \sigma_E^2)$ equals the narrow-sense heritability.
As in the marker_h2 function, it is assumed that the genetic relatedness matrix $K$ is scaled such that $trace(P K P) = n - 1$ , where $P$ is the projection matrix $I_n - 1_n 1_n' / n$ , for the identity matrix $I_n$ and $1_n$ being a column vector of ones. If this is not the case, $K$ is automatically scaled prior to fitting the mixed model.
No covariates can be included, as it is assumed that these are available at plant- or plot level, and accounted for in the genotypic means.
The resulting heritability estatimes are less accurate than those obtained from individual plant or plot data, and the likelihood can be monotone in $h^2 = \sigma_A^2 / (\sigma_A^2 + \sigma_E^2)$ . If the AI-algorithm has not converged after max.iter iterations, the likelihood is computed on the grid of heritability values 1/(grid.size+1),...,grid.size/(grid.size+1)
As in the marker_h2 function, confidence intervals for heritability are constructed using the delta-method and the inverse AI-matrix. The delta-method can be applied either directly to the function $(\sigma_A^2,\sigma_E^2) -> \sigma_A^2 / (\sigma_A^2 + \sigma_E^2)$ or to the function $(\sigma_A^2,\sigma_E^2) -> log(\sigma_A^2 / \sigma_E^2)$ . In the latter case, a confidence interval for $log(\sigma_A^2 / \sigma_E^2)$ is obtained, which is back-transformed to a confidence interval for heritability. This approach (proposed in Kruijer et al.) has the advantage that intervals are always contained in the unit interval.

Value

A list with the following components:

va: REML-estimate of the (additive) genetic variance.
ve: REML-estimate of the residual variance.
h2: Plug-in estimate of heritability: $va / (va + ve)$ .
conf.int1: 1-alpha confidence interval for heritability.
conf.int2: 1-alpha confidence interval for heritability, obtained by application of the delta method on a logarithmic scale.
inv.ai: The inverse of the average information (AI) matrix.
loglik: The log-likelihood.
loglik.vector: Empty numeric vector if the AI-algorthm converged within $max.iter$ iterations. Otherwise it contains the log-likelihood on a grid.

Author(s)

Willem Kruijer.

References

Gilmour et al. Gilmour, A.R., R. Thompson and B.R. Cullis (1995) Average Information REML: An Efficient Algorithm for Variance Parameter Estimation in Linear Mixed Models. Biometrics, volume 51, number 4, 1440-1450.
Kruijer, W. et al. (2015) Marker-based estimation of heritability in immortal populations. Genetics, Vol. 199(2), p. 1-20.
Speed, D., G. Hemani, M. R. Johnson, and D.J. Balding (2012) Improved heritability estimation from genome-wide snps. the American journal of human genetics 91: 1011-1021.

Examples

data(means_LDV)
data(R_matrix_LDV)
data(K_atwell)
out <- marker_h2_means(data.vector=means_LDV$LDV,geno.vector=means_LDV$genotype,
                       K=K_atwell,Dm=R_matrix_LDV)
# Takes about a minute:
#data(means_LD)
#data(R_matrix_LD)
#out <- marker_h2_means(data.vector=means_LD$LD,geno.vector=means_LD$genotype,
#                       K=K_atwell,Dm=R_matrix_LD)
# The likelihood is monotone increasing:
#plot(x=(1:99)/100,y=out$loglik.vector,type="l",ylab="log-likelihood",lwd=2,
#     main='',xlab='h2',cex.lab=2,cex.axis=2.5)
data(means_LDV)
data(R_matrix_LDV)
data(K_atwell)
out <- marker_h2_means(data.vector=means_LDV$LDV,geno.vector=means_LDV$genotype,
                       K=K_atwell,Dm=R_matrix_LDV)
# Takes about a minute:
#data(means_LD)
#data(R_matrix_LD)
#out <- marker_h2_means(data.vector=means_LD$LD,geno.vector=means_LD$genotype,
#                       K=K_atwell,Dm=R_matrix_LD)
# The likelihood is monotone increasing:
#plot(x=(1:99)/100,y=out$loglik.vector,type="l",ylab="log-likelihood",lwd=2,
#     main='',xlab='h2',cex.lab=2,cex.axis=2.5)

Flowering time from Atwell et al. (2010): accession means.

Description

Accession means for the flowering time data contained in LD and LDV.

Usage

data(means_LD); data(means_LDV)data(means_LD); data(means_LDV)

Format

Data-frames with flowering time means:

genotype: a factor, the levels being the accession or ecotype identifiers
LD: Flowering time under long days, in number of days
LDV: Flowering time under long days and vernalization, in number of days

Details

Following Kruijer et al. (appendix A) these means were defined as the least-squares estimate for the factor accession, in a linear model containing both accession and replicate effects. Consequently there are differences compared to Atwell et al. (2010), where just the arithmetic averages are considered.

References

Atwell, S., Y. S. Huang, B. J. Vilhjalmsson, G. Willems, M. Horton, et al. (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465: 627-631.
Kruijer, W. et al. (2015) Marker-based estimation of heritability in immortal populations. Genetics, Vol. 199(2), p. 1-20.

Examples

data(means_LD)
data(means_LDV)
data(means_LD)
data(means_LDV)

Covariance matrix of the accession means for flowering time.

Description

Covariance matrices of the accession means for flowering time contained in means_LD and means_LDV, derived from the Atwell et al. (2010) data.

Usage

data(R_matrix_LDV);data(R_matrix_LD)data(R_matrix_LDV);data(R_matrix_LD)

Format

Matrix whose row- and column names are the ecotype-IDs of the accessions contained in LD and LDV.

Details

The matrix was computed as in Kruijer et al., Appendix A.

References

Atwell, S., Y. S. Huang, B. J. Vilhjalmsson, G. Willems, M. Horton, et al. (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465: 627-631.
Kruijer, W. et al. (2015) Marker-based estimation of heritability in immortal populations. Genetics, Vol. 199(2), p. 1-20.

Examples

data(R_matrix_LD); data(R_matrix_LDV)
data(R_matrix_LD); data(R_matrix_LDV)

ANOVA-based estimates of repeatability

Description

Given a population where each genotype is phenotyped for a number of genetically identical replicates (either individual plants or plots in a field trial), the repeatability or intra-class correlation can be estimated by $V_g / (V_g + V_e)$ , where $V_g = (MS(G) - MS(E)) / r$ and $V_e = MS(E)$ . In these expressions, $r$ is the number of replicates per genotype, and $MS(G)$ and $MS(E)$ are the mean sums of squares for genotype and residual error obtained from analysis of variance. In case $MS(G) < MS(E)$ , $V_g$ is set to zero. See Singh et al. (1993) or Lynch and Walsh (1998), p.563. When the genotypes have differing numbers of replicates, $r$ is replaced by $\bar r = (n-1)^{-1} (R_1 - R_2 / R_1)$ , where $R_1 = \sum r_i$ and $R_2 = \sum r_i^2$ . Under the assumption that all differences between genotypes are genetic, repeatability equals broad-sense heritability; otherwise it only provides an upper-bound for broad-sense heritability.

Usage

repeatability(data.vector, geno.vector, line.repeatability = FALSE,
              covariates.frame = data.frame())
repeatability(data.vector, geno.vector, line.repeatability = FALSE,
              covariates.frame = data.frame())

Arguments

`data.vector`	A vector of phenotypic observations. Needs to be of type numeric. May contain missing values.
`geno.vector`	A vector of genotype labels, either a factor or character. This vector should correspond to `data.vector`, and hence needs to be of the same length.
`line.repeatability`	If `TRUE`, the line-repeatability or line-heritability $\sigma_G^2 / (\sigma_G^2 + \sigma_E^2 / r)$ is estimated, otherwise (the default) the repeatability at plot- or plant level, which is $\sigma_G^2 / (\sigma_G^2 + \sigma_E^2)$ .
`covariates.frame`	A data-frame with additional covariates, the rows corresponding to `geno.vector` and the phenotypic observations in `data.vector`. May contain missing values. Each column can be numeric or a factors.

Value

A list with the following components:

repeatability: the estimated repeatability.
gen.variance: the estimated genetic variance.
res.variance: the estimated residual variance.
line.repeatability: whether repeatability was estimated at the individual plant or plot level (the default), or at the level of genotypic means (in the latter case, line.repeatability=TRUE)
average.number.of.replicates: The average number of replicates. See the description above.
conf.int: Confidence interval for repeatability. See Singh et al. (1993) or Lynch and Walsh (1998)

Author(s)

Willem Kruijer [email protected]

References

Kruijer, W. et al. (2015) Marker-based estimation of heritability in immortal populations. Genetics, Vol. 199(2), p. 1-20.
Lynch, M., and B. Walsh (1998) Genetics and Analysis of Quantitative Traits. Sinauer As- sociates, 1st edition.
Singh, M., S. Ceccarelli, and J. Hamblin (1993) Estimation of heritability from varietal trials data. Theoretical and Applied Genetics 86: 437-441.

Examples

repeatability(data.vector=rep(rnorm(26),each=5) + rnorm(5*26),
              geno.vector=rep(letters,each=5))
repeatability(data.vector=rep(rnorm(26),each=5) + rnorm(5*26),
              geno.vector=rep(letters,each=5))

Package 'heritability'

Help Index

Marker-Based Estimation of Heritability Using Individual Plant or Plot Data.

Description

Author(s)

References

Examples

Bolting time and leaf width for the Arabidopsis hapmap population.

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Flowering time data taken from Atwell et al. (2010).

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Marker-based relatedness matrices for 3 populations of Arabidopsis thaliana.

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Arabidopsis leaf area data for the hapmap and Swedish regmap population.

Description

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Author(s)

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Examples

Compute a marker-based estimate of heritability, given phenotypic observations at individual plant or plot level.

Description

Usage

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Author(s)

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Examples

Compute a marker-based estimate of heritability, given genotypic means.

Description

Usage

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Author(s)

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Examples

Flowering time from Atwell et al. (2010): accession means.

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Covariance matrix of the accession means for flowering time.

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Examples

ANOVA-based estimates of repeatability

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