Comparative and Evolutionary Genomics of Forest Trees

Abstract

AbstractComparative and evolutionary genomic approaches can identify genes regulating biological processes, and describe how those genes have been modified through speciation to produce phenotypic variation. These approaches have the potential to address fundamental issues of forest biology, including the regulation of biological traits important to industry and conservation, but have not been widely applied because of technical limitations. Here, we argue that powerful “next generation” DNA sequencing technologies now make comparative and evolutionary genomic approaches not only tractable for basic biological research in trees, but also have the potential to be more informative and cost effective than traditional, one-species-at-a-time approaches. However, designing effective comparative studies for forest trees requires careful consideration of the evolutionary relationships of tree species and biological traits important to forest biology.This chapter first provides an introduction to comparative and evolutionary genomics, followed by a brief review of some of the general features of the evolution and diversification of forest tree species. Next, two biological processes are discussed that are fundamental to forest trees: wood formation and perennial growth. We examine the varied evolutionary histories of these biological processes, and how these histories relate to the comparative genomic approaches used to research the genes and mechanisms underlying these processes. The chapter is concluded with discussion of practical issues that must be addressed to fully enable this new and powerful direction in forest genomics research, as well as how comparative genomics could support future research and applications for forest management.KeywordsNext Generation SequencingForest TreeShoot Apical MeristemSecondary XylemWood FormationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.