Data from: Partial genotyping at polymorphic markers can improve heritability estimates in sibling groups
Gauzere, J.; Oddou-Muratorio, S.; Gay, L.; Klein, E. K. (2016), Data from: Partial genotyping at polymorphic markers can improve heritability estimates in sibling groups, Dryad, Dataset, https://doi.org/10.5061/dryad.k25qf
Accurate estimates of heritability (h²) are necessary to assess adaptive responses of populations and evolution of fitness-related traits in changing environments. For plants, h² estimates generally rely on maternal progeny designs, assuming that offspring are either half-sibs or unrelated. However, plant mating systems often depart from half-sib assumptions, this can bias h² estimates. Here, we investigate how to accurately estimate h² in non-model species through the analysis of sibling designs with a moderate genotyping effort. We performed simulations to investigate how microsatellite marker information available for only a subset of offspring can improve h² estimates based on maternal progeny designs in presence of non-random mating, inbreeding in the parental population or maternal effects. We compared the basic family method, considering or not adjustments based on average relatedness coefficients, and methods based on the animal model. The animal model was used with average relatedness information, or with hybrid relatedness information: associating one-generation pedigree and family assumptions, or associating one-generation pedigree and average relatedness coefficients. Our results highlighted that methods using marker-based relatedness coefficients performed as well as pedigree-based methods in presence of non-random mating (i.e. unequal male reproductive contributions, selfing), offering promising prospects to investigate in situ heritabilities in natural populations. In presence of maternal effects, only the use of pairwise relatednesses through pedigree information improved the accuracy of h² estimates. In that case the amount of father-related offspring in the sibling design is the most critical. Overall, we showed that the method using both one-generation pedigree and average relatedness coefficients was the most robust to various ecological scenarios.