W572
Genomics and Reciprocal Illumination: The Study of Local Adaptation Across an Understudied Clade of Pines

Date: Saturday, January 10, 2015
Time: 11:00 AM
Room: Pacific Salon 6-7 (2nd Floor)
Andrew J. Eckert , Virginia Commonwealth University, Richmond, VA
Decades of research addressing the ecological genetics of forest tree populations, through use of common garden experimentation, have established that populations of forest trees are often adapted to their local site conditions. Missing from these studies, however, is the identification and characterization of the genetic materials conferring the observable fitness differences among trees that drive these patterns. Early studies using small sets of candidate genes to characterize genotype-phenotype maps for forest trees were promising, with numerous biologically plausible correlations discovered. Much of the genetic variation documented in common garden experimentation, however, remains unexplained. Newly published draft genome sequences for phylogenetically diverse conifer species offer novel sources of data with which to generate and test new hypotheses about these genetic materials and the trait architecture they comprise.

Inferential connections between phenotypes, where observable fitness differences are manifested dependent upon environment, and multilocus genotypes will benefit from explicit use of reciprocal illumination, a philosophical stance borrowed from phylogenetic inference in which novel hypotheses are evaluated against prevailing hypotheses using all of the available data. Here, I argue that intensive study of the species classified within Pinus subgenus Strobus will provide novel insights about the genetic architecture of local adaptation, unable to be answered as clearly in other conifer species, using the concept of reciprocal illumination. I illustrate this through use of several previously published and emerging datasets and highlight where genome level data will allow for a more nuanced and detailed examination of the processes driving observable patterns.