Here is an example code using bivariate GREML to estimate genetic correlation in R:

library(lme4) library(lmerTest)

Load the data for two traits

data <- read.csv("data.csv")

Fit bivariate GREML model

model <- glmer(cbind(trait1, trait2) ~ 1 + (1|id), data = data, family = binomial)

Estimate genetic correlation

corr <- summary(model)$varcor$id[2]/(sqrt(summary(model)$varcor$id[1])*sqrt(summary(model)$varcor$id[3]))

Print genetic correlation

print(corr)

Note: This code assumes that the data is in a CSV file named "data.csv" and has columns named "id", "trait1", and "trait2". The bivariate GREML model is fitted using the glmer function from the lme4 package, with a binomial family specified for the response variable. The genetic correlation is estimated by dividing the variance of the random effect for id in trait2 by the product of the square roots of the variances of the random effects for id in trait1 and trait2.