estimH2means: Broad-sense heritability

In timflutre/rutilstimflutre: Timothee Flutre's personal R code

Broad-sense heritability

Description

Estimate broad-sense heritability (squared correlation between predicted and true genotypic effects) on an entry-mean basis:

1. via the classical formula for balanced data sets (see Falconer and Mackay, or the introduction of Piepho and Mohring (2007)): H2 = var.g / var.p, where var.p = var.g + var.ge / m + var.e / (r x m) with "m" the number of trials and "r" the number of replicates per trial (for unbalanced data sets, the mean number of trials and replicates per trial are used);

2. via the formula of Oakey et al (2006) for unbalanced data sets: H2 = 1 - trace(G^-1 C_zz) / m, with "m" the number of genotypes.

Usage

estimH2means(
  dat,
  colname.resp,
  colname.trial = "year",
  vc,
  geno.var.blups = NULL
)

Arguments

dat	data frame of input data after missing data have been excluded, with columns named colname.resp, "geno" and colname.trial
colname.resp	name of the column containing the response
colname.trial	name of the column identifying the trials (e.g. "year", "year_irrigation", etc)
vc	data frame of variance components with columns "grp" and "vcov" (i.e. formatted as as.data.frame(VarCorr()) from the "lme4" package); grp="Residual" for "var.e"
geno.var.blups	vector of variances of empirical BLUPs of the genotypic effects, g, assuming g ~ MVN(0, G) where G = sigma_g^2 I_m; if not provided, the estimator of Oakey et al won't be computed

Value

list with the mean number of trials, the mean number of replicates per trial, the broad-sense heritability (classical estimator from Falconer and Mackay, as well as optionally the one from Oakey et al), and a function to compute summary statistics whch can be used for estimating confidence intervals by bootstrap

Author(s)

Timothee Flutre

timflutre/rutilstimflutre documentation built on Feb. 7, 2024, 8:17 a.m.