Computing heritability

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Here we show how to compute narrow-sense heritability from a simple linear animal model. Narrow-sense heritability, or heritability for short, can be defined as the ratio of additive genetic variance over phenotypic variance. In our case, we have modelled only the mean, the additive genetic variance and the residual variance, so heritability is: $ h^2 = V_A / V_P = V_A / (V_A + V_R)$.

We still use the gryphon dataset with birth_weight as the response, and MCMCglmm.

phenotypicdata <- read.csv("data/gryphon.csv")
pedigreedata <- read.csv("data/gryphonped.csv")

library(MCMCglmm)

inverseAmatrix <- inverseA(pedigree = pedigreedata)$Ainv

We re-run the model we used previously:

prior1.2 <- list(
  G = list(G1 = list(V = 1, nu = 0.002)),
  R = list(V = 1, nu = 0.002)
)

model1.2 <- MCMCglmm(birth_weight ~ 1, #Response and Fixed effect formula
                   random = ~id, # Random effect formula
          ginverse = list(id = inverseAmatrix), # correlations among random effect levels (here breeding values)
          data = phenotypicdata, # data set
          prior = prior1.2, # explicit prior for the random effect and residuals
          burnin = 10000, nitt = 30000, thin = 20) # run the model for longer compare to the default

summary(model1.2)

## 
##  Iterations = 10001:29981
##  Thinning interval  = 20
##  Sample size  = 1000 
## 
##  DIC: 3912.138 
## 
##  G-structure:  ~id
## 
##    post.mean l-95% CI u-95% CI eff.samp
## id     3.406    2.227    4.668    379.1
## 
##  R-structure:  ~units
## 
##       post.mean l-95% CI u-95% CI eff.samp
## units     3.851    2.886     4.91    444.5
## 
##  Location effects: birth_weight ~ 1 
## 
##             post.mean l-95% CI u-95% CI eff.samp  pMCMC    
## (Intercept)     7.591    7.323    7.878    888.6 <0.001 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

One could get a rough calculation of heritability using the values in the summary, but it is much better to apply (i.e., integrate) the calculation on the full posterior distribution. It is actually quite simple in MCMCglmm and in general an advantage of Bayesian approaches.

We extract the vector of additive genetic variance posterior values (there are 1000 of them), stored in $VCV[,"id"] and the vector of residual variance posterior values (also 1000 of them), stored in $VCV[,"units"] and compute the 1000 posterior values of heritabilities as:

h2_full_posterior <- model1.2$VCV[, "id"] / (model1.2$VCV[, "id"] + model1.2$VCV[, "units"])

We can then look at the trace and distribution of heritability:

plot(h2_full_posterior)

at point estimates:

mean(h2_full_posterior)

## [1] 0.4681105

median(h2_full_posterior)

## [1] 0.4645474

posterior.mode(h2_full_posterior)

##      var1 
## 0.4945087

and at credible intervals:

HPDinterval(h2_full_posterior) # default 95% interval

##          lower     upper
## var1 0.3280542 0.6111049
## attr(,"Probability")
## [1] 0.95

HPDinterval(h2_full_posterior, prob = 0.98) # another arbitrary interval with 98% probability

##          lower     upper
## var1 0.3002651 0.6515297
## attr(,"Probability")
## [1] 0.98