Investigating the evolutionary dynamics of complex traits in nature requires the accurate assessment of their genetic architecture. Using a quantitative genetic (QG) modeling approach (e.g., animal model), relatedness information from a pedigree combined with phenotypic measurements can be used to infer the amount of additive genetic variance in traits. However, pedigree information from natural systems is not perfect and might contain errors or be of low quality. Published sensitivity analyses revealed a limited impact of expected error rates on parameter estimates. However, natural systems will differ in many respects (e.g., mating system, data availability, pedigree structure), thus it can be inappropriate to generalize outcomes from one system to another. French-Canadian (FC) genealogies are extensive and deep-rooted (up to 9 generations in this study) making them ideal to study how the quality and properties (e.g., errors, completeness) of pedigrees affect QG estimates. We conducted simulation analyses to infer the reliability of QG estimates using FC pedigrees and how it is impacted by genealogical errors and variation in pedigree structure. Broadly, results show that pedigree size and depth are important determinants of precision but not of accuracy. While the mean genealogical entropy (based on missing links) seems to be a good indicator of accuracy. Including a shared familial component into the simulations led to on average a 46% overestimation of the additive genetic variance. This has crucial implications for evolutionary studies aiming to estimate QG parameters given that many traits of interest, such as life history, exhibit important non-genetic sources of variation.
Evidence from natural populations shows that changes in environmental conditions can cause rapid modifications in the evolutionary potential of phenotypes, partly through genotype-by-environment interactions (G×E). Therefore, the overall rate of microevolution should depend on fluctuations in environmental conditions, even when directional selection is sustained over several generations. We tested this hypothesis in a preindustrial human population that experienced a microevolutionary change in age at first reproduction (AFR) of mothers, using the annual infant mortality rate (IMR) as an indicator of environmental conditions during their early life. Using quantitative genetics analyses, we found that G×Es explained a nonnegligible fraction of the additive genetic variance in AFR and in relative fitness, as well as of the genetic covariance between AFR and fitness (i.e., the Robertson-Price covariance). The covariance was stronger for individuals exposed to unfavorable early-life environmental conditions. Our results unravel the presence of G×Es in an important life history trait and its impact on the rate of microevolution, which appears to have been sensitive to short-term fluctuations in local environmental conditions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.