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inbreedR : an R package for the analysis of inbreeding based on genetic markers
Author(s) -
Stoffel Martin A.,
Esser Mareike,
Kardos Marty,
Humble Emily,
Nichols Hazel,
David Patrice,
Hoffman Joseph I.
Publication year - 2016
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12588
Subject(s) - inbreeding , biology , loss of heterozygosity , microsatellite , evolutionary biology , genetics , linkage disequilibrium , snp , genetic load , statistics , single nucleotide polymorphism , allele , population , genotype , mathematics , demography , gene , sociology
Summary Heterozygosity–fitness correlations (HFCs) have been widely used to explore the impact of inbreeding on individual fitness. Initially, most studies used small panels of microsatellites, but more recently with the advent of next‐generation sequencing, large SNP datasets are becoming increasingly available and these provide greater power and precision to quantify the impact of inbreeding on fitness. Despite the popularity of HFC studies, effect sizes tend to be rather small. One reason for this may be low variation in inbreeding levels among individuals. Using genetic markers, it is possible to measure variance in inbreeding through the strength of correlation in heterozygosity across marker loci, termed identity disequilibrium (ID). ID can be quantified using the measure g 2 , which is also a central parameter in HFC theory that can be used within a wider framework to estimate the direct impact of inbreeding on both marker heterozygosity and fitness. However, no software exists to calculate g 2 for large SNP datasets nor to implement this framework. inbreedR is an R package that provides functions to calculate g 2 based on microsatellite and SNP markers with associated P ‐values and confidence intervals. Within the framework of HFC theory, inbreedR also estimates the impact of inbreeding on marker heterozygosity and fitness. Finally, inbreedR implements user‐friendly simulations to explore the precision and magnitude of estimates based on different numbers of genetic markers. We hope this package will facilitate good practice in the analysis of HFCs and help to deepen our understanding of inbreeding effects in natural populations.