Rhh: an R extension for estimating multilocus heterozygosity and heterozygosity–heterozygosity correlation

Abstract

Individual multilocus heterozygosity estimates based on a limited number of loci are expected to correlate only weakly with the inbreeding level of an individual. Before using multilocus heterozygosity estimates in studies of inbreeding, their ability to capture information on inbreeding in the given setting should be tested. A convenient method for this is to compute the heterozygosity-heterozygosity correlation, i.e. the mean correlation between multilocus heterozygosity estimates calculated from random samples of loci, which should be positive if multilocus heterozygosity carries a signature of inbreeding. Rhh is an extension package for the statistical software r that estimates this correlation and calculates three measures of individual multilocus heterozygosity: homozygosity by loci, internal relatedness and standardized heterozygosity. The extension package is available through the CRAN (http://cran.r-project.org) and has a homepage at http://www.helsinki.fi/biosci/egru/research/software.

Age‐dependent, negative heterozygosity–fitness correlations and local effects in an endangered Caribbean reptile, Iguana delicatissima

“…All HFC studies use at least one of these three MLH measures (Chapman et al., 2009; Grueber et al., 2011), and by including all three measures, our results can be more readily compared to other studies. Calculations for SH, IR, and HL were performed in the R (R Core Team 2016) package Rhh v. 1.0.2 (Alho, Välimäki, & Merilä, 2010). SH of adults and hatchlings was fitted to a normal curve, and the goodness of fit was calculated using a Shapiro–Wilk test to assess normality.…”

Heterozygosity–fitness correlations in blue tit nestlings (Cyanistis caeruleus) under contrasting rearing conditions

“…We computed a heterozygosity parameter as 1-HL. HL was calculated using Rhh, an extension package for R (Alho et al 2010). We tested for identity disequilibrium with two approaches in R (R Development Core Team 2013) using the function in the 'inbreedR' package (Stoffel et al 2016).…”

“…All the markers were randomly divided into two subsets of fifteen loci that were separately used to calculate multi-locus heterozygosity and then the correlation coefficient between the heterozygosity of the two subsets was calculated. If neutral markers such as microsatellites carry information about genome-wide levels of heterozygosity, then comparing two random subsets of such markers should yield a positive, significant correlation (Balloux et al 2004;Alho et al 2010). We ran 1000 randomizations of the markers for heterozygosity by loci (HL) estimate of heterozygosity and estimated the average HHC coefficient (r) and the 95% confidence intervals (Alho et al 2010).…”

“…If neutral markers such as microsatellites carry information about genome-wide levels of heterozygosity, then comparing two random subsets of such markers should yield a positive, significant correlation (Balloux et al 2004;Alho et al 2010). We ran 1000 randomizations of the markers for heterozygosity by loci (HL) estimate of heterozygosity and estimated the average HHC coefficient (r) and the 95% confidence intervals (Alho et al 2010). To detect identity disequilibrium (correlations in heterozygosity among loci) due to variance in inbreeding, we also calculated the parameter, g 2 , because this gives a more powerful statistic than HHC (Szulkin et al 2010).…”

Genome‐wide comparisons reveal a clinal species pattern within a holobenthic octopod—the Australian Southern blue‐ringed octopus, Hapalochlaena maculosa (Cephalopoda: Octopodidae)

“…To assess individual genome‐wide diversity and inbreeding measures, standardized multilocus heterozygosity (sMLH) and internal relatedness (IR) were calculated for all individuals using the R package Rhh (Alho, VÄLIMÄKI, & MERILÄ, 2010). The 1 − proportion of shared alleles ( A S ) individual distance was calculated for each individual pair using the “propShared” command in adegenet (Jombart, 2008).…”