HardyWeinberg (version 1.6.1)

HardyWeinberg-package: Statistical Tests and Graphics for Hardy-Weinberg Equilibrium

Description

Contains tools for exploring Hardy-Weinberg equilibrium (Hardy, 1908; Weinberg, 1908) <doi:10.1126/science.28.706.49> for bi and multi-allelic genetic marker data. All classical tests (chi-square, exact, likelihood-ratio and permutation tests) with bi-allelic variants are included in the package, as well as functions for power computation and for the simulation of marker data under equilibrium and disequilibrium. Routines for dealing with markers on the X-chromosome are included (Graffelman & Weir, 2016) <doi:10.1038/hdy.2016.20>, including Bayesian procedures. Some exact and permutation procedures also work with multi-allelic variants. Special test procedures that jointly address Hardy-Weinberg equilibrium and equality of allele frequencies in both sexes are supplied, for the bi and multi-allelic case. Functions for testing equilibrium in the presence of missing data by using multiple imputation are also provided. Implements several graphics for exploring the equilibrium status of a large set of bi-allelic markers: ternary plots with acceptance regions, log-ratio plots and Q-Q plots.

Details

Package:	HardyWeinberg
Type:	Package
Version:	1.6.1
Date:	2018-05-28
License:	GPL Version 2 or later.

The most important function of the package is HWTernaryPlot that can be used to create ternary plots with acceptance regions for HWE. Other routines implement statistical tests for HWE such as HWChisq and HWLratio.

References

Weir, B.S. (1996) Genetic Data Analysis II. Sinauer Associates, Massachusetts.

Graffelman, J. and Morales, J. (2008) Graphical tests for Hardy-Weinberg equilibrium based on the ternary plot. Human Heredity 65(2):77-84. http://dx.doi.org/10.1159/000108939.

Graffelman, J. (2015) Exploring Diallelic Genetic Markers: The HardyWeinberg Package. Journal of Statistical Software 64(3): 1-23. http://www.jstatsoft.org/v64/i03/.

Examples

# NOT RUN {
library(HardyWeinberg)

# draw random SNPs from a population that is in HWE

set.seed(123)

m <- 100 # number of markers
n <- 100 # sample size

X <- HWData(n,m)
out <- HWTernaryPlot(X,100,region=1,vertex.cex=2,signifcolour=TRUE)

# }