Quantitative genetics and population genomics of repeated complex trait evolution

PROJECT SUMMARY Complex phenotypes are suites of adaptive traits that contribute to a shared function. An important challenge is understanding how such phenotypes evolve and are maintained in populations. Theory predicts that the evolution of complex traits and their preservation in the face of homogenizing gene flow will depend on the genetic architecture of trait divergence, the types and sources of genetic variation, and the presence of reproductive isolating barriers. However, we lack empirical systems to rigorously test these theoretical expectations. While investigations into the genetic basis of repeated phenotypic evolution in emerging model taxa provide an opportunity whether, and how often, evolution proceeds through similar genetic processes, studies of repeated evolution have often involved comparison of only two or three lineages, and rarely have studied complex traits. My research group will bridge these gaps by investigating evolutionary genetics of floral pollination syndrome, a complex adaptation, in the plant genus Penstemon. In this genus, a novel syndrome phenotype has evolved at least 20 independent times. My group will initiate research directions aimed at systematically quantifying genetic and genomic features governing the evolution and maintenance of complex trait diversity, leveraging the exceptional parallelism in Penstemon. We will examine the genetic and genomic architecture of adaptive divergence using replicate QTL analyses, determine which elements of complex traits display genetic parallelism, assess whether standing variation or gene flow act as a source of genetic variation, and examine how complex trait variation is maintained in recently diverged species using population genomic data. This integrative work will contribute to the development of a general framework for understanding whether, and through what mechanisms, phenotypic evolution is constrained by genetic mechanisms. In other words, such data will illuminate the inherent flexibility and limits of an organism's genome to adapt to novel environmental challenges.