Genomic and transcriptomic analyses reveal polygenic architecture for
ecologically important traits in aspen (Populus tremuloides
Michx.)
Abstract
Intraspecific genetic variation in foundation species such as aspen
(Populus tremuloides Michx.) shapes their impact on forest
structure and function. Identifying genes underlying ecologically
important traits is key to understanding that impact. Previous studies
using single-locus genome-wide association (GWA) analyses to identify
candidate genes have identified fewer genes than anticipated for highly
heritable quantitative traits. Mounting evidence suggests that polygenic
control of quantitative traits is largely responsible for this “missing
heritability” phenomenon. Our research characterized the genetic
architecture of 35 ecologically important traits using a common garden
of aspen through genomic and transcriptomic analyses. A multilocus
association model revealed that most traits displayed a polygenic
architecture, with most variation explained by loci with small effects
(likely below the detection levels of single-locus GWA methods).
Consistent with a polygenic architecture, our single-locus GWA analyses
found only 38 significant SNPs in 22 genes across 15 traits. Next, we
used differential expression analysis on a subset of aspen genets with
divergent concentrations of salicinoid phenolic glycosides (key defense
traits). This complementary method to traditional GWA discovered 1,243
differentially expressed genes for a polygenic trait. Soft clustering
analysis revealed three gene clusters (241 candidate genes) involved in
secondary metabolite biosynthesis and regulation. Our results support
the omnigenic model that complex traits are largely controlled by many
small effect loci, most of which may not have obvious connections to the
traits of interest. Our work reveals that ecologically important traits
governing higher-order community- and ecosystem-level attributes of a
foundation forest tree species have complex underlying genetic
structures and will require methods beyond traditional GWA analyses to
unravel.