Broad-sense heritability in plant breeding
Author: Maria Belen Kistner & Flavio Lozano-Isla
Description: How to calculate the heritability in plant breeding.

# Broad-sense heritability (H2)

Broad-sense heritability (H2) is defined as the proportion of phenotypic variance that is attributable to an overall genetic variance for the genotype (Schmidt et al., 2019b). There are usually additional interpretations associated with H2: (i) It is equivalent to the coefficient of determination of a linear regression of the unobservable genotypic value on the observed phenotype; (ii) It is also the squared correlation between predicted phenotypic value and genotypic value; and (iii) It represents the proportion of the selection differential (S) that can be realized as the response to selection (R) (Falconer and Mackay, 2005).

There are two main reasons why heritability on an entry-mean basis is of interest in plant breeding (Schmidt et al., 2019a):

1. It is plugged into the breeder’s Equation to predict the response to selection.
2. It is a descriptive measure used to assess the usefulness and precision of results from cultivar evaluation trials.

## Breeder´s equation

ΔG=H2S Where:

- ΔG is the genetic gain
- S is the mean phenotypic value of the selected genotypes, expressed as a deviation from the population mean.

# Usual Problems

In practice, most trials are conducted in a multienvironment trial (MET) presente unbalanced data as not all cultivars are tested at each environment or simply when plot data is lost or when the number of replicates at each location varies between genotypes (Schmidt et al., 2019b). However, the standard method for estimating heritability implicitly assumes balanced data, independent genotype effects, and homogeneous variances.

# How calculate the Heritability?

According Schmidt et al. (2019a), the variance components could be calculated in two ways:

## 1) Two stages approach

For the two stage approach, in the first stage each experiment is analyzed individually according their experiment design (Lattice, CRBD, etc) (Zystro et al., 2018). And for the second stage environments are denotes a year-by-location interaction. This approach assumes a single variance for genotype-by-environment interactions (GxE), even when multiple locations were tested across multiple years (Buntaran et al., 2020).

### Model

yikt=μ + Gi+Et+GxEit+εikt

### Phenotypic variance

$$\sigma_p^2=\sigma_g^2+\frac{\sigma_{g\cdot e}^2}{n_e}+\frac{\sigma_{\varepsilon}^2}{n_e\cdot n_r}$$

## 2) One stage approach

For the one stage approach only one model is used for the MET analysis. The environmental effects are included via separate year, and location main interaction effects.

yikt=μ+Gi+Ym+Eq+YxEmq+GxYim+GxEiq+GxYxEimq+εikmq

### Phenotypic variance

$$\sigma_p^2=\sigma_g^2+\frac{\sigma_{g\cdot e}^2}{n_e}+\frac{\sigma_{g\cdot y}^2}{n_y}+\frac{\sigma_{g\cdot y\cdot e}^2}{n_y\cdot n_e}+\ \frac{\sigma_{\epsilon}^2}{n_e\cdot n_y\cdot n_r}$$

# Differentes heritability calculations

Differentes heritability calculation
Standart | Cullis | Piepho
--- | --- | ---
$H^2=\frac{\sigma_g^2}{\sigma_p^2}=\frac{\Delta G}{S}$ | $H_{Cullis}^2=1-\frac{\overline{V}_{\Delta..}^{^{BLUP}}}{2\cdot\sigma_g^2}$ | $H_{Piepho}^2=\frac{\sigma_g^2}{\sigma_g^2+\frac{\overline{V}_{\Delta..}^{BLUE}}{2}}$

# Heritability function in the package

For calculate the standard heritability in MET experiments the number of location and replication should be include manually in the function `H2cal()`. In the case of difference number of replication in each experiments, take the maximum value (often done in practice) (Schmidt et al., 2019b).

For remove the outliers the function implemented is the Method 4 used for Bernal-Vasquez et al. (2016): Bonferroni-Holm using re-scaled MAD for standardizing residuals (BH-MADR).

## Load packages

    library(inti)

## H2cal function

    dt <- inti::potato
    hr <- H2cal(data = dt
              , trait = "stemdw"
              , gen.name = "geno"
              , rep.n = 5
              , fixed.model = ~ 0 + (1|bloque) + geno
              , random.model = ~ 1 + (1|bloque) + (1|geno)
              , emmeans = TRUE
              , plot_diag = FALSE
              , outliers.rm = TRUE
              )

## Model information

    hr$model %>% summary()
    #> Linear mixed model fit by REML ['lmerMod']
    #> Formula: stemdw ~ 1 + (1 | bloque) + (1 | geno)
    #>    Data: dt.rm
    #> Weights: weights
    #>
    #> REML criterion at convergence: 796.1
    #>
    #> Scaled residuals:
    #>      Min       1Q   Median       3Q      Max
    #> -2.38440 -0.64247 -0.08589  0.57452  2.84508
    #>
    #> Random effects:
    #>  Groups   Name        Variance Std.Dev.
    #>  geno     (Intercept) 19.960   4.4677
    #>  bloque   (Intercept)  0.110   0.3316
    #>  Residual              9.411   3.0677
    #> Number of obs: 148, groups:  geno, 15; bloque, 5
    #>
    #> Fixed effects:
    #>             Estimate Std. Error t value
    #> (Intercept)    12.51       1.19   10.51

## Variance components

    hr$tabsmr %>% kable(caption = "Variance component table")

Variance component table
trait | rep | geno | env | year | mean | std | min | max | V.g | V.e | V.p | repeatability | H2.s | H2.p | H2.c
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
stemdw | 5 | 15 | 1 | 1 | 12.59867 | 4.749994 | 2.818 | 22.302 | 19.96002 | 9.410932 | 21.84221 | 0.913828 | 0.913828 | 0.9502395 | 0.9533473

## Best Linear Unbiased Estimators (BLUEs)

    hr$blues %>% kable(caption = "BLUEs")

BLUEs
geno | stemdw | SE | df | lower.CL | upper.CL
--- | --- | --- | --- | --- | ---
G01 | 15.73200 | 1.030325 | 119.7830 | 13.6919903 | 17.77201
G02 | 10.12100 | 1.030325 | 119.7830 | 8.0809903 | 12.16101
G03 | 9.69500 | 1.030325 | 119.7830 | 7.6549903 | 11.73501
G04 | 15.17700 | 1.030325 | 119.7830 | 13.1369903 | 17.21701
G05 | 12.87106 | 1.086433 | 122.5189 | 10.7204483 | 15.02167
G06 | 22.30200 | 1.030325 | 119.7830 | 20.2619903 | 24.34201
G07 | 2.81800 | 1.030325 | 119.7830 | 0.7779903 | 4.85801
G08 | 10.42300 | 1.030325 | 119.7830 | 8.3829903 | 12.46301
G09 | 15.66800 | 1.030325 | 119.7830 | 13.6279903 | 17.70801
G10 | 9.24200 | 1.030325 | 119.7830 | 7.2019903 | 11.28201
G11 | 6.42500 | 1.030325 | 119.7830 | 4.3849903 | 8.46501
G12 | 16.11100 | 1.030325 | 119.7830 | 14.0709903 | 18.15101
G13 | 14.62900 | 1.030325 | 119.7830 | 12.5889903 | 16.66901
G14 | 16.29700 | 1.030325 | 119.7830 | 14.2569903 | 18.33701
G15 | 11.46900 | 1.030325 | 119.7830 | 9.4289903 | 13.50901

## Best Linear Unbiased Predictors (BLUPs)

    hr$blups %>% kable(caption = "BLUPs")

BLUPs
geno | stemdw
--- | ---
G01 | 15.587018
G02 | 10.228658
G03 | 9.821839
G04 | 15.057007
G05 | 12.843686
G06 | 20.631268
G07 | 3.254483
G08 | 10.517060
G09 | 15.525899
G10 | 9.389236
G11 | 6.699074
G12 | 15.948953
G13 | 14.533681
G14 | 16.126578
G15 | 11.515963

## Outliers

    hr$outliers$fixed %>% kable(caption = "Outliers fixed model")

Outliers fixed model
| index | bloque | geno | stemdw | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
68 | 68 | IV | G05 | 80.65 | 60.36709 | 18.84505 | 0 | 0 | 0 | OUTLIER

    hr$outliers$random %>% kable(caption = "Outliers random model")

Outliers random model
| index | bloque | geno | stemdw | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
68 | 68 | IV | G05 | 80.65 | 61.39925 | 18.886677 | 0.0000000 | 0.0000000000 | 0.0000000 | OUTLIER
100 | 100 | IV | G06 | 33.52 | 12.02340 | 3.698449 | 0.0002169 | 0.0002169207 | 0.0323212 | OUTLIER

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