Genomic and transcriptomic analyses reveal polygenic architecture for
ecologically-important functional traits in aspen (Populus
tremuloides Michx.)
Abstract
Intraspecific genetic variation in foundation species such as trembling
aspen shapes their impact on forest structure and function. Identifying
genes and genomic regions underlying ecologically relevant traits is key
to understanding that impact. Previous studies using genome-wide
association (GWA) analyses to identify candidate genes have identified
fewer genes than anticipated for highly heritable 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 40 functional traits
using genomic and transcriptomic analyses in an association mapping
population of aspen. A multi-marker association model revealed that most
traits displayed a polygenic architecture, with most variation explained
by loci with small effects (below the detection levels of single-marker
GWA methods). Consistent with a polygenic architecture, our
single-marker GWA analyses found only 35 significant SNPs in 22 genes
across 15 trait/trait combinations. Next, we used differential
expression analysis on a subset of aspen genets with divergent
concentrations of salicinoid phenolic glycosides (key defense traits).
This alternative method to traditional GWA discovered 1,243
differentially expressed genes for a polygenic trait. Soft clustering
analysis revealed three gene clusters (246 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 functional 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.