The long term goal is to genetically dissect complex traits and understand the relationship between naturally occurring genetic and phenotypic variation in forest trees. In this project the relationships between naturally occurring genetic and phenotypic variation in Pinus taeda L. will be identified. Loblolly pine, a gymnosperm, is well-positioned phylogenetically to help in understanding the evolution of adaptive traits in land plants. Specific objectives are to (1) identify all common variants in 5000 genes, creating a first-of-its-kind community resource of genomic sequence for loblolly pine, and (2) associate genetic variation at 1000 loci with traits of economic importance. The traits will include chemical and anatomical wood properties and resistance to disease. All DNA sequence data will be submitted immediately to public access databases, GenBank and TreeGenes (dendrome.ucdavis.edu). Genotypic and phenotypic data will be immediately deposited into TreeGenes. The overarching goal of community coalescence will be accomplished by integrating loblolly pine genomics and bioinformatics efforts across the United States and making them available through the public databases. Identification of specific genes controlling naturally occurring phenotypic variation will provide 1) a greater understanding of trait inheritance in plants, 2) an indirect selection tool for tree breeders, and 3) a set of potentially useful genetic reagents for genetic modification. By aggressively seeking to identify most of the major genes controlling specific wood property and disease related traits in loblolly pine, a significant breakthrough is anticipated in the understanding of the genetics of complex traits and the potential economic impact of indirect selection. Marker-aided-selection based on desired variants in genes controlling economic traits can be used across pedigrees within both breeding and natural populations. Such a tool would have immediate and beneficial ramifications for applied tree improvement programs by dramatically reducing testing costs and breeding cycle times. Ultimately, such information could be useful in guiding gene conservation efforts and enhancing our ability to cope with the growing challenges of climate change, shifting habitats and evolving pest populations.
