inbreedR provides functions and workflows for the analysis of
inbreeding and heterozygosity-fitness correlations (HFCs) based on
molecular markers such as microsatellites and SNPs. In case of genomic
data, it’s most useful for lower density datasets where it is unclear
whether genotyped markers represent genome-wide diversity / inbreeding.
It has four main application areas:
Quantifying variance in inbreeding through estimation of identitiy disequilibria (g2), heterozygosity-heterozygosity correlations (HHC) and variance in standardized multilocus heterozygosity (sMLH)
Calculating g2 for small and large SNP datasets. The use of
data.table and parallelization speed up bootstrapping and
Estimating central parameters within HFC theory, such as the influence of inbreeding on heterozygosity and fitness, and their confidence intervals.
Exploring the sensitivity of these measures towards the number of genetic markers using simulations
You can install the stable version of
inbreedR from CRAN with:
Or the development version from GitHub with:
# install.packages("remotes") remotes::install_github("mastoffel/inbreedR", build_vignettes = TRUE, dependencies = TRUE) # manual browseVignettes("inbreedR")
If you find a bug, please report a minimal reproducible example in the issues.
To get started read the vignette:
vignette("inbreedR_step_by_step", package = "inbreedR")
Stoffel, M. A., Esser, M., Kardos, M., Humble, E., Nichols, H., David, P., & Hoffman, J. I. (2016). inbreedR: an R package for the analysis of inbreeding based on genetic markers. Methods in Ecology and Evolution, 7(11), 1331-1339.
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