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hwe.jags

Hardy-Weinberg equlibrium test for a multiallelic marker using JAGS


Description

Hardy-Weinberg equilibrium test

Usage

hwe.jags(k,n,delta=rep(1/k,k),lambda=0,lambdamu=-1,lambdasd=1,
              parms=c("p","f","q","theta","lambda"),...)

Arguments

k

number of alleles

n

a vector of k(k+1)/2 genotype counts

delta

initial parameter values

lambda

initial parameter values

lambdamu

initial parameter values

lambdasd

initial parameter values

parms

monitored parmeters

...

parameters passed to jags, e.g., n.chains, n.burnin, n.iter

Details

This function performs Bayesian Hardy-Weinberg equilibrium test, which mirrors hwe.hardy, another implementation for highly polymorphic markers when asymptotic results do not hold.

Value

The returned value is a fitted model from jags().

References

Wakefield J (2010). Bayesian methods for examining Hardy-Weinberg equilibrium. Biometrics 66:257-265

Author(s)

Jing Hua Zhao, Jon Wakefield

See Also

Examples

## Not run: 
ex1 <- hwe.jags(4,c(5,6,1,7,11,2,8,19,26,15))
print(ex1)
ex2 <- hwe.jags(2,c(49,45,6))
print(ex2)
ex3 <- hwe.jags(4,c(0,3,1,5,18,1,3,7,5,2),lambda=0.5,lambdamu=-2.95,lambdasd=1.07)
print(ex3)
ex4 <- hwe.jags(9,c(1236,120,3,18,0,0,982,55,7,249,32,1,0,12,0,2582,132,20,1162,29,
                    1312,6,0,0,4,0,4,0,2,0,0,0,0,0,0,0,115,5,2,53,1,149,0,0,4),
                delta=c(1,1,1,1,1,1,1,1,1),lambdamu=-4.65,lambdasd=0.21)
print(ex4)
ex5 <- hwe.jags(8,n=c(
         3,
         4, 2,
         2, 2, 2,
         3, 3, 2, 1,
         0, 1, 0, 0, 0,
         0, 0, 0, 0, 0, 1,
         0, 0, 1, 0, 0, 0, 0,
         0, 0, 0, 2, 1, 0, 0, 0))
print(ex5)

# Data and code accordining to the following URL,
# http://darwin.eeb.uconn.edu/eeb348-notes/testing-hardy-weinberg.pdf
hwe.jags.ABO <- function(n,...)
{
  hwe <- function() {
     # likelihood
     pi[1] <- p.a*p.a + 2*p.a*p.o
     pi[2] <- 2*p.a*p.b
     pi[3] <- p.b*p.b + 2*p.b*p.o
     pi[4] <- p.o*p.o
     n[1:4] ~ dmulti(pi[],N)
     # priors
     a1 ~ dexp(1)
     b1 ~ dexp(1)
     o1 ~ dexp(1)
     p.a <- a1/(a1 + b1 + o1)
     p.b <- b1/(a1 + b1 + o1)
     p.o <- o1/(a1 + b1 + o1)
  }
  hwd <- function() {
     # likelihood
     pi[1] <- p.a*p.a + f*p.a*(1-p.a) + 2*p.a*p.o*(1-f)
     pi[2] <- 2*p.a*p.b*(1-f)
     pi[3] <- p.b*p.b + f*p.b*(1-p.b) + 2*p.b*p.o*(1-f)
     pi[4] <- p.o*p.o + f*p.o*(1-p.o)
     n[1:4] ~ dmulti(pi[],N)
     # priors
     a1 ~ dexp(1)
     b1 ~ dexp(1)
     o1 ~ dexp(1)
     p.a <- a1/(a1 + b1 + o1)
     p.b <- b1/(a1 + b1 + o1)
     p.o <- o1/(a1 + b1 + o1)
     f ~ dunif(0,1)
  }
  N <- sum(n)
  ABO.hwe <- R2jags::jags(list(n=n,N=N),,c("pi","p.a","p.b","p.o"),hwe,...)
  ABO.hwd <- R2jags::jags(list(n=n,N=N),,c("pi","p.a","p.b","p.o","f"),hwd,...)
  invisible(list(hwe=ABO.hwe,hwd=ABO.hwd))
}

hwe.jags.ABO.results <- hwe.jags.ABO(n=c(862, 131, 365, 702))
hwe.jags.ABO.results

## End(Not run)

gap

Genetic Analysis Package

v1.2.3-1
GPL (>= 2)
Authors
Jing Hua Zhao and colleagues with inputs from Kurt Hornik and Brian Ripley
Initial release
2021-4-21

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