Assessing the impact of pedigree attributes on the validity of quantitative genetic parameter estimates.

Abstract

Investigating the evolution of complex traits in nature requires accurate assessment of their genetic basis. Quantitative genetic (QG) modeling is frequently applied to estimate the additive genetic variance (VA) in traits, combining phenotypic and pedigree data from a sample of individuals. Whether reconstructed from social links or molecular markers, empirical pedigrees differ in completeness, genealogical error rates, and other attributes that can impact QG estimation. Here we investigate this impact using human genealogical data for 6 French-Canadian (FC) populations originating from the same genetic founding source but differing in their pedigrees' attributes. First, we simulated phenotypic values along pedigrees and under different trait architectures and "true" parameter values (e.g., VA). Then we fitted mixed effects "animal" models to these simulated data, to assess how QG estimation was impacted by pedigree attributes. Our results show that pedigree size and depth were important determinants of the precision, but not accuracy, of genetic parameter estimates. In contrast, pedigree completeness and entropy, 2 attributes related to the density of genealogical links, were not clearly associated with the performance of parameter estimation. Noticeably, a slight increase in the genealogical error rate was sufficient to cause a detectable underestimation of VA. Including maternal genetic effects in the simulations led to a slight underestimation of VA with pedigrees of smaller size and depth. Despite originating from the same genetic source, the 6 pedigrees yielded wide variations in QG estimates under identical conditions. These findings highlight the importance of sensitivity analyses in pedigree-based genetic studies on natural populations.