# 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

1 2 |

### 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 |

`covariates` |
A data-frame or matrix with optional covariates, the rows corresponding to
the phenotypic observations in |

`K` |
A genetic relatedness or kinship matrix, typically marker-based.
Must have row- and column-names corresponding to the levels of |

`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 |

`h2` |
When |

### Details

Given phenotypic observations

*Y_{ij}*for genotypes*i=1,...,n*and replicates*j = 1,...,n_i*, the mixed model*Y_{ij} = μ + 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*σ_A^2 K*, where*σ_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*σ_E^2*. Under certain assumptions (see Speed*et al.*2012) the marker- or chip-heritability*h^2 = σ_A^2 / (σ_A^2 + σ_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} β*, where*X_{ij}*is the row vector with observations on*k*extra covariates and the vector*β*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

*(σ_A^2,σ_E^2) -> σ_A^2 / (σ_A^2 + σ_E^2)*or to the function*(σ_A^2,σ_E^2) -> log(σ_A^2 / σ_E^2)*. In the latter case, a confidence interval for*log(σ_A^2 / σ_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.

### See Also

For marker-based estimation of heritability using genotypic means, see
`marker_h2_means`

.

### Examples

1 2 3 4 5 6 7 8 9 10 | ```
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)
``` |