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I have 6 locations, 3 replications, 7 blocks, and 49 entries in a full diallel experiment. I am trying to find out the heritability for each trait in each location separately. So, here I have used the following R code to calculate broad sense heritability from variance components:

# Code 

library(lme4)
library(Matrix)
a<-read.csv("sadore.csv", header = T, na.strings = "NA")
summary(a)
head(a)
str(a) 

a1=lmer(Yield~(1|Entry)+ (1|Rep)+(1|Rep:Block), data=a)
summary(a1)

#formula
h2= v(Entry)/v(Entry)+v(Rep)/3+v(Rep:Block)/21+ v(Error)/63 

But after that, I got heritability which is super high and not reasonable. I know I am doing something wrong here. So, is there any wrong in my formula or code?

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  • $\begingroup$ Can you print the output of summary(a1) as well as the value you calculated for h2? $\endgroup$ – AdamO May 14 '18 at 12:58
  • $\begingroup$ Random effects: Groups Name Variance Std.Dev. Entry (Intercept) 1.99551 1.4126 Rep:Block (Intercept) 0.08888 0.2981 Rep (Intercept) 0.00000 0.0000 Residual 0.46240 0.6800 Number of obs: 147, groups: Entry, 49; Rep:Block, 21; Rep, 3 Fixed effects: Estimate Std. Error t value (Intercept) 13.3431 0.2194 60.81 this is the output and I have got 0.9942344 for h2 after calculation which seems suspicious. This I have calculated from raw data and there I presumed to have some outliers. $\endgroup$ – S. Dutta May 15 '18 at 17:26
  • $\begingroup$ Please copy/paste as an edit into your question and use the code indentation to accept fixed width. $\endgroup$ – AdamO May 15 '18 at 19:28

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