Estimating genome-wide heterozygosity: effects of demographic history and marker type

Abstract

Heterozygosity-fitness correlations (HFCs) are often used to link individual genetic variation to differences in fitness. However, most studies examining HFCs find weak or no correlations. Here, we derive broad theoretical predictions about how many loci are needed to adequately measure genomic heterozygosity assuming different levels of identity disequilibrium (ID), a proxy for inbreeding. We then evaluate the expected ability to detect HFCs using an empirical data set of 200 microsatellites and 412 single nucleotide polymorphisms (SNPs) genotyped in two populations of bighorn sheep (Ovis canadensis), with different demographic histories. In both populations, heterozygosity was significantly correlated across marker types, although the strength of the correlation was weaker in a native population compared with one founded via translocation and later supplemented with additional individuals. Despite being bi-allelic, SNPs had similar correlations to genome-wide heterozygosity as microsatellites in both populations. For both marker types, this association became stronger and less variable as more markers were considered. Both populations had significant levels of ID; however, estimates were an order of magnitude lower in the native population. As with heterozygosity, SNPs performed similarly to microsatellites, and precision and accuracy of the estimates of ID increased as more loci were considered. Although dependent on the demographic history of the population considered, these results illustrate that genome-wide heterozygosity, and therefore HFCs, are best measured by a large number of markers, a feat now more realistically accomplished with SNPs than microsatellites.

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

“…95% quantiles crossed zero) [all markers: r HHC = -0.06, 95% CI = -0.068-0.196; g 2 = 0.001, 95% CI = 0.002-0.006, P = 0.17]. The expected power to detect HFCs according to Miller et al (2014) was small (r 2 = 0.06). Parental heterozygosity was not significantly associated with offspring heterozygosity (Table 1).…”

“…We did not detect positive heterozygosity-heterozygosity correlations (HHCs; Balloux et al 2004) for our microsatellite markers set, also the parameter g 2 gave no indication for inbreeding in our population, giving no support to the general effect hypothesis. However, according to formulae from Miller et al (2014) the power of our markers to estimate inbreeding in our study system was rather small. It must be kept in mind that the nonsignificant values should not be misinterpreted as disproving general effects, as the effects of weak inbreeding are more readily detected on the phenotypic level than on the level of a limited number of markers (Szulkin 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). Then, we assessed the expected power of our microsatellite markers set to detect general-effect HFCs using formulae from Miller et al (2014;eqn 5).…”

Direct and indirect causal effects of heterozygosity on fitness-related traits in Alpine ibex

“…As ID is considered a definite consequence of inbreeding and the proximal cause of HFCs, in the absence of ID (if g 2 ¼ 0), HFCs are not expected to arise [13]. Indeed, following the formula proposed by Miller et al [81], when g 2 ¼ 0, the power to detect HFCs also becomes 0. However, other studies found HFCs despite having no evidence for ID [10,82].…”

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

“…(2009) have suggested that the use of fitness‐correlated traits that are only weakly influenced by inbreeding may also hamper detection of HFCs, as might the varied demographic history of populations under study (see also Canal, Serrano, & Potti, 2014; Miller et al. 2014). Further, using a small number of loci to calculate heterozygosity may fail to represent heterozygosity across the genome, which is a critical assumption of the inbreeding interpretation of HFCs (Miller & Coltman, 2014; but see Brommer, Kekkonen, & Wikstrom, 2015).…”