h2_mzdz {gap}

Heritability estimation according to twin correlations

Description

Heritability estimation according to twin correlations

Usage

h2_mzdz(
  mzDat = NULL,
  dzDat = NULL,
  rmz = NULL,
  rdz = NULL,
  nmz = NULL,
  ndz = NULL,
  selV = NULL
)

Arguments

mzDat

a data frame for monzygotic twins (MZ).

dzDat

a data frame for dizygotic twins (DZ).

rmz

correlation for MZ twins.

rdz

correlation for DZ twins.

nmz

sample size for MZ twins.

ndz

sample size for DZ twins.

selV

names of variables for twin and cotwin.

Details

Given MZ/DZ data or their correlations and sample sizes, it obtains heritability and variance estimates under an ACE model as in doi:10.1038/s41562-023-01530-y and Keeping (1995).

Value

A data.frame with variables h2, c2, e2, vh2, vc2, ve2.

References

Elks CE, den Hoed M, Zhao JH, Sharp SJ, Wareham NJ, Loos RJ, Ong KK (2012). “Variability in the heritability of body mass index: a systematic review and meta-regression.” Front Endocrinol (Lausanne), 3, 29. ISSN 1664-2392, doi:10.3389/fendo.2012.00029.

Keeping ES (1995). Introduction to statistical inference, Dover books on mathematics, Dover edition. Dover Publications, New York. ISBN 9780486685021.

Examples

## Not run: 
library(mvtnorm)
set.seed(12345)
mzm <- as.data.frame(rmvnorm(195, c(22.75,22.75),
                     matrix(2.66^2*c(1, 0.67, 0.67, 1), 2)))
dzm <- as.data.frame(rmvnorm(130, c(23.44,23.44),
                     matrix(2.75^2*c(1, 0.32, 0.32, 1), 2)))
mzw <- as.data.frame(rmvnorm(384, c(21.44,21.44),
                     matrix(3.08^2*c(1, 0.72, 0.72, 1), 2)))
dzw <- as.data.frame(rmvnorm(243, c(21.72,21.72),
                     matrix(3.12^2*c(1, 0.33, 0.33, 1), 2)))
selVars <- c('bmi1','bmi2')
names(mzm) <- names(dzm) <- names(mzw) <- names(dzw) <- selVars
ACE_CI <- function(mzData,dzData,n.sim=5,selV=NULL,verbose=TRUE)
{
  ACE_obs <- h2_mzdz(mzDat=mzData,dzDat=dzData,selV=selV)
  cat("\n\nheritability according to correlations\n\n")
  print(format(ACE_obs,digits=3),row.names=FALSE)
  nmz <- nrow(mzData)
  ndz <- nrow(dzData)
  r <- data.frame()
  for(i in 1:n.sim)
  {
    cat("\rRunning # ",i,"/", n.sim,"\r",sep="")
    sampled_mz <- sample(1:nmz, replace=TRUE)
    sampled_dz <- sample(1:ndz, replace=TRUE)
    mzDat <- mzData[sampled_mz,]
    dzDat <- dzData[sampled_dz,]
    ACE_i <- h2_mzdz(mzDat=mzDat,dzDat=dzDat,selV=selV)
    if (verbose) print(ACE_i)
    r <- rbind(r,ACE_i)
  }
  m <- apply(r,2,mean,na.rm=TRUE)
  s <- apply(r,2,sd,na.rm=TRUE)
  allr <- data.frame(mean=m,sd=s,lcl=m-1.96*s,ucl=m+1.96*s)
  print(format(allr,digits=3))
}
ACE_CI(mzm,dzm,n.sim=500,selV=selVars,verbose=FALSE)
ACE_CI(mzw,dzw,n.sim=500,selV=selVars,verbose=FALSE)

## End(Not run)