2013 journal article

The Evolutionary Genetics of the Genes Underlying Phenotypic Associations for Loblolly Pine (Pinus taeda, Pinaceae)

GENETICS, 195(4), 1353-+.

By: A. Eckert*, J. Wegrzyn*, J. Liechty*, J. Lee*, W. Cumbie, J. Davis*, B. Goldfarb n, C. Loopstra* ...

co-author countries: United States of America 🇺🇸
author keywords: association mapping; complex traits; Pinus taeda; natural selection; population genomics
MeSH headings : Evolution, Molecular; Expressed Sequence Tags; Genes, Plant; Genetic Loci; Multifactorial Inheritance; Phenotype; Pinus taeda / genetics
Source: Web Of Science
Added: August 6, 2018

Abstract A primary goal of evolutionary genetics is to discover and explain the genetic basis of fitness-related traits and how this genetic basis evolves within natural populations. Unprecedented technological advances have fueled the discovery of genetic variants associated with ecologically relevant phenotypes in many different life forms, as well as the ability to scan genomes for deviations from selectively neutral models of evolution. Theoretically, the degree of overlap between lists of genomic regions identified using each approach is related to the genetic architecture of fitness-related traits and the strength and type of natural selection molding variation at these traits within natural populations. Here we address for the first time in a plant the degree of overlap between these lists, using patterns of nucleotide diversity and divergence for >7000 unique amplicons described from the extensive expressed sequence tag libraries generated for loblolly pine (Pinus taeda L.) in combination with the >1000 published genetic associations. We show that loci associated with phenotypic traits are distinct with regard to neutral expectations. Phenotypes measured at the whole plant level (e.g., disease resistance) exhibit an approximately twofold increase in the proportion of adaptive nonsynonymous substitutions over the genome-wide average. As expected for polygenic traits, these signals were apparent only when loci were considered at the level of functional sets. The ramifications of this result are discussed in light of the continued efforts to dissect the genetic basis of quantitative traits.