Quote of the Month

"Even apparently similar adaptations may be built from genetically different components."
-Theodosius Dobzhansky

Liriodendron tulipifera
The genetic architecture of locally adapted phenotypes is a multifaceted body of knowledge. It includes the identities, effect sizes, interactions, genomic distribution, and environmental dependency of the genetic variants contributing to variation in fitness among individual trees. Given this complexity, myriad research questions can be asked. For example, how do woody plants adapt to their environments? What are the genes that facilitate this process? How are these genes organized within the genomes of woody plants? Our research addresses all three of these questions using a mixture of experimental and observational approaches.
Experimental approaches are based on the concept of common gardens and the decomposition of phenotypic variance observed in standardized environments into its genetic and environmental components. Choice of the phenotypic trait to examine is crucial and our research has focused on several different phenotypic traits related to the survival component of total lifetime fitness (e.g. growth, water-use efficiency, wood properties, cold tolerance & disease resistance). For conifers, common gardens that we use are constructed from seeds collected within natural populations, so as to form collections of half-sibling families (at least this is what we assume). From this collection of half-sibling families, we can estimate numerous quantitative genetic parameters such as heritability (i.e. the proportion of phenotypic variance explainable by genetic variance), QST (i.e. the proportion of additive genetic variance due to population identifiers), and the additive genetic value of maternal trees (e.g. maternal tree BLUPs). The latter forms the basis of the phenotypic data used to associate with genetic marker data using either population-based approaches, such as association genetics, or family-based approaches, such as quantitative trait locus mapping. The outcomes of these studies are genomic regions that are candidates for the genes or genomic regions comprising the genetic architecture for the phenotypic trait of interest. Along with the identity of the genomic regions comprising the genetic architecture of a trait, this approach also provides effect size estimates for each candidate region. A large fraction of our  publications utilize data derived from common gardens, with the bulk of them dealing with the relationship between genotypes and phenotypes for Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), foxtail pine (Pinus balfouriana Grev. & Balf.), and loblolly pine (Pinus taeda L.). Experimental approaches based on common gardens are also amenable to more detailed experimentation, such as application of treatments to specific families to test specific hypotheses or development into reciprocal transplantation studies (Kawecki and Ebert 2004). Although not yet implemented in most of our research, this is a natural extension once the groundwork has been laid using traditional common gardens.
Pinus pungens
 Observational approaches typically involve rigorous sampling within natural populations with the ultimate goal of making inferences directly from genetic marker data about local or lineage-wide patterns of non-neutral evolution. The logic employed in this approach is to acknowledge that phenotypic traits are the entity upon which natural selection acts, but that we as scientists do not know what these are. This approach is thus implicitly agnostic with respect to which phenotypic traits cause variation in fitness among individuals. As such, inferences in this framework are largely those associated with outlier analysis, where patterns of genetic diversity at a particular locus, which are identified as outliers with respect to some neutral model or a set of neutral loci, are assumed to have been generated by some form of natural selection. We have used and continue to use several different statistical approaches to identify these outliers: environmental association analysis (e.g. Eckert et al. 2010), FST outlier analysis (e.g. Eckert et al. 2009), and McDonald-Kreitman type analyses (e.g. Eckert et al. 2013a). All of these approaches have as their sole result a set of outlier loci that cannot be explained by neutral models, which if identified correctly should be enriched for variation at genomic regions contributing to fitness-variation among trees. An active area of research in our laboratory is to relate these loci to those identified using the experimental approaches outlined previously. Preliminary results indicate that there may be a fundamental disconnect between results from these two approaches that derives ultimately from the true genetic architecture for the phenotypic traits driving local or lineage-wide adaptation (e.g. Eckert et al. 2013b). Recently, we have also become interested in the relationship of genetic architecture of phenotypic traits identified as locally adapted and the underlying phylogeny linking closely related species. For example, is the genetic architecture for the same phenotypic trait (e.g. cone serotiny or bark thickness) comprised of the same genomic regions or different regions in different species? Does the degree of shared architecture correlate with the underlying phylogeny or are these phenotypes the result of convergent evolution at the level of the genetic architecture? These questions form the basis of Dr. Friedline’s recent postdoctoral work in our laboratory, which is focusing on the genetic architecture of bark thickness, which is a fire-related trait (He et al. 2012), across four closely related pine species distributed across the southeastern United States.


  • Alberto, F. J., S. N. Aitken, R. Alia, S. C. Gonzalez-Martinez, H. Hanninen, A. Kremer, F. Lefevre, T. Lenormand, S. Yeaman, R. Whettan, et al. 2013. Potential for evolutionary responses to climate change - Evidence from tree populations. Global Change Biology 19: 1645-1661.
  • Eckert, A. J., A. D. Bower, B. Pande, K. D. Jermstad, K. V. Krutovsky, J. B. St. Clair and D. B. Neale. 2009. Association genetics of coastal Douglas fir (Pseudotsuga menziesii var. menziesii, Pinaceae). I. Cold-hardiness related traits. Genetics 182: 1289-1302.
  • Eckert, A. J., J. van Heerwaarden, J. L. Wegrzyn, C. D. Nelson, J. Ross-Ibarra, S. C. González-Martínez and D. B. Neale. 2010. Patterns of population structure and environmental associations to aridity across the range of loblolly pine (Pinus taeda L., Pinaceae). Genetics 185: 969-982.
  • Eckert, A. J., J. L. Wegrzyn, J. D. Liechty, J. M. Lee, W. P. Cumbie, J. M. Davis, B. Goldfarb, C. A. Loopstra, S. R. Palle, T. Quesada, C. H. Langley, and D. B. Neale. 2013a. The evolutionary genetics of the genes underlying phenotypic associations for loblolly pine (Pinus taeda, Pinaceae). Genetics 195: 1353-1372.
  • Eckert, A. J., A. D. Bower, K. D. Jermstad, J. L. Wegrzyn, B. J. Knauss, J. V. Syring, and D. B. Neale. 2013b. Multilocus analyses reveal little evidence for lineage wide adaptive evolution within major clades of soft pines (Pinus subgenus Strobus). Molecular Ecology 22: 5635-5650.
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