Understanding the Evolution and Stability of the G-Matrix

Abstract

The G-matrix summarizes the inheritance of multiple, phenotypic traits. The stability and evolution of this matrix are important issues because they affect our ability to predict how the phenotypic traits evolve by selection and drift. Despite the centrality of these issues, comparative, experimental, and analytical approaches to understanding the stability and evolution of the G-matrix have met with limited success. Nevertheless, empirical studies often find that certain structural features of the matrix are remarkably constant, suggesting that persistent selection regimes or other factors promote stability. On the theoretical side, no one has been able to derive equations that would relate stability of the G-matrix to selection regimes, population size, migration, or to the details of genetic architecture. Recent simulation studies of evolving G-matrices offer solutions to some of these problems, as well as a deeper, synthetic understanding of both the G-matrix and adaptive radiations.

Repeated modification of early limb morphogenesis programmes underlies the convergence of relative limb length inAnolislizards

“…Within a theoretical context, developmental constraints must align with genetic constraints to have a lasting evolutionary impact [64]. Although certainly possible in theory, Anolis is a very old genus (estimated age is at least 40 million years [30]); finding conserved genetic architectures over such great periods of time might be considered surprising ( [65][66][67][68] and references therein; but see [69] for evidence of conserved phenotypic integration over long evolutionary timescales in anoles).…”

Comparing G: multivariate analysis of genetic variation in multiple populations

“…Although G-matrices are highly conserved among some populations (see Arnold et al (2008)), they have also been demonstrated to rapidly diverge among both natural populations and experimental treatments (Cano et al, 2004;Doroszuk et al, 2008;Hine et al, 2009;Johansson et al, 2012). Laboratory manipulations have demonstrated that G can evolve rapidly in response to drift (Phillips et al, 2001), and that selection can drive rapid and repeatable evolution of G (Blows and Higgie, 2003;Hine et al, 2011).…”

“…Numerous empirical studies have taken a comparative approach to determine evolutionary rates, and the processes affecting G (Steppan et al, 2002;Arnold et al, 2008). Although G-matrices are highly conserved among some populations (see Arnold et al (2008)), they have also been demonstrated to rapidly diverge among both natural populations and experimental treatments (Cano et al, 2004;Doroszuk et al, 2008;Hine et al, 2009;Johansson et al, 2012).…”

“…These studies have demonstrated that G will evolve to the greatest extent in small populations, under weak correlational selection on traits, through directional selection against major axes of G, and when mutational correlation among traits is low. Simulated parameter ranges are based on information from nature (see Arnold et al (2008)), but nonetheless, we typically lack information on multivariate mutation and selection, and on migration in specific natural populations and for multivariate trait sets of interest. It remains an empirical question whether G typically varies among populations in ways that will impact on their future responses to selection.…”

Evolution of the additive genetic variance–covariance matrix under continuous directional selection on a complex behavioural phenotype

“…However, general answers from simulations seem unlikely to emerge because it is extremely complex to consider all of the relevant parameters involved across the whole range of relevant parameter values (e.g. population size, number of loci, number of alleles, distribution of allelic effects, dominance, epistasis, mutation rate, strength of selection [11]). Therefore, if and how selection influences G remains largely an empirical question [11][12][13].…”