The interaction between sire and f was a significant term when fitted in the MANOVA of the nine morphometric traits (Fthirty-six,2208=1.451, P=0.041) but f fitted as a main effect was not (F9,549=0.903, P=0.523). MLH was not a significant term either as a main effect (Fnine,549=1.5, P=0.144) or as an interaction with sire (Fthirty-six,2208=0.715, P=0.896). Note that f and MLH were not fitted in the same model for either the univariate or the multivariate analyses.
Predictions to many other vertebrate communities
Along with the Coopworth sheep society, summation statistics in accordance with f and marker heterozygosity were collected for eleven most other communities. These types of research were up coming accustomed estimate the relationship coefficient between f and MLH (a) into markers which were entered the research inhabitants so far, and you can (b) if the one hundred indicators off imply heterozygosity 0.seven was typed. Rates are displayed inside the Dining table step one. The populace wherein MLH is actually the best predictor from f are Scandinavian wolves that have a supposed roentgen(H, f)=?0.71 in case the 30 recorded microsatellites were blogged and a supposed r(H, f)= ?0.90 when the one hundred loci was indeed published. The population wherein MLH was poor on predicting f try the new collared .jpg)
flycatchers (Ficedula albicollis) for the Swedish Area away from Gotland, that have a supposed roentgen(H, f)=?0.08 whether your about three documented microsatellites have been published and a supposed r(H, f)=?0.32 if the 100 loci had been wrote. Essentially, heterozygosity won’t offer strong rates from f, no matter if one hundred loci is actually wrote. For example, the new asked roentgen(H, f) was weakened than –0.5 for five of your own twelve communities and you can weakened than just ?0.seven getting nine of communities.
In seven of the populations, r(H, f) had actually been estimated, enabling a comparison between expected and observed correlation coefficients (Table 1). In Scandinavian wolves and Large Ground Finches, the observed and expected correlation coefficients were almost identical. In four of the five other populations, r(H, f)observed was weaker than r(H, f)expected, perhaps due to errors in estimation of f (see Discussion).
Discussion
The primary objective of this study was to establish if and when MLH can be used as a robust surrogate for individual f. A theoretical model and empirical data both suggest that the correlation between MLH and f is weak unless the study population exhibits unusually high variance in f. The Coopworth sheep data set used in this study comprised a considerably larger number of genotypes (590 individuals typed at 138 loci) than any similar study, yet MLH was only weakly correlated to individual f. Furthermore, f explained significant variation in a number of morphometric traits (typically 1–2% of the overall trait variance), but heterozygosity did not. From equation (5), it can be seen that the expected correlation between trait value and MLH is the product of the correlation coefficient between f and the trait (hereafter r(W, f)) and r(H, f). Estimates of the proportion of phenotypic trait variation explained by f are scarce, although from the limited available data 2% seems a typical value (see for example Kruuk et al, 2002; this paper, Table 2). Assuming r(W, f) 2 =0.02, and given the median value of r(H, f)=?0.21 reported in Table 1, a crude estimate of average r(W, H) is 0.03, which is equivalent to MLH explaining <0.1% of trait variance. These findings are consistent with a recent meta-analysis that reported a mean r(W, H) of 0.09 for life history traits and 0.01 for morphometric traits (Coltman and Slate, 2003). In summary, MLH is a poor replacement for f, such that very large sample sizes are required to detect variance in inbreeding in most populations.
