Accuracy of dominant markers for estimation of relatedness and heritability in an experimental stand of Prosopis alba (Leguminosae)

The estimation of genetic components of phenotypic variance is based on the resemblance between relatives. In natural populations of most forest tree species without genealogical information, a possible alternative approach is the use of relatedness estimates obtained indirectly from molecular marker data. Heritability (h (2)) is then estimated from the covariance of estimated relatedness and phenotypic resemblance. In a stand of Prosopis alba planted in 1991 in Argentina, relatedness was estimated for all individual pairs of trees by means of the information proceeding from 128 dominant markers (57 AFLPs and 71 ISSRs) and compared with estimates obtained from six microsatellite loci previously studied. We empirically compared the accuracy of different relatedness estimators based on dominant markers proposed by Lynch and Milligan (Mol Ecol 3:91-99, 1994), Hardy (Mol Ecol 12:1577-1588, 2003), Wang (Mol Ecol 13:3169-3178, 2004), and Ritland (Mol Ecol 14:3157-3165, 2005). Heritabilities of 13 quantitative traits were then estimated from the regression of pairwise phenotypic distances on pairwise relatedness according to Ritland (Genet Res 67:175-185, 1996a). Relatedness inferred from molecular markers was in all cases significantly correlated with actual relatedness, although Ritland's estimator showed the highest bias but the lowest variance. Dominant marker-based h (2) estimates were evidently downwards biased and showed poor correlation with those based on family records. In conclusion, the use of dominant molecular markers evidently produces much greater underestimates of h (2) than from using co-dominant ones, attributable to the lower accuracy in the indirect estimation of relatedness coefficient. Many traits with enough genetic variability as to respond readily to selection would remain undetected; only those with very high heritability would show significant h (2) estimates.