Functionally related genes have similar adaptive patterns
In our analysis of gene ontology enrichment, we focused on biological
processes related with response to abiotic stress factors such as
drought, cold and heat. Generally, we found that there was
overrepresentation of important biological GO terms associated with
adaptation in similar ways, consistent with expectations under our third
hypothesis. For instance, a GO term associated with
TSEAS, GO:0009416, is related to light stimulus. Some of
the SNPs associated with this GO term have been previously highlighted
for the annotated functional results (e.g., JAR02659). A GO term related
to karrikin stimulus was found associated with TMIN(GO:0080167). Karrikins are a group of phytohormones that control
several aspects of plant germination and growth and can be found in the
smoke from wildfires (Nelson et al., 2012). This is especially important
in SWWA where there is an ongoing shift to warmer and drier climatic
conditions, and consequent increases in fire frequency in this fire
prone environment (Dey et al., 2019, Kala et al., 2020). The term
GO:1901566 was found for a high number of SNPs (21) that were associated
with PWQ. This term is related to organonitrogen
compound biosynthetic process, a broad biological process that involves
chemical reactions and pathways related to nitrogen metabolism. Organic
nitrogen metabolism is a vital process for plant physiology and its
regulation has been shown to be dependent on abiotic factors, such as
temperature and water availability (Zielke et al., 2002; Gundale et al.,
2012). GO term SNPs associated with TMIN and
PWQ showed the highest deviance explained by the GDM
analysis. High allelic turnover is observed for two SNPs in
TMIN, JAR03088 and JAR05151, and an even greater
magnitude for two SNPs in PWQ, JAR00476 and JAR11797.
The SNP JAR00476 in particular explained more deviance than any other
SNP linked to GO terms; and its functional annotation is associated with
a MADS-box protein SOC1-like, involved in flowering regulation (Lee et
al., 2000) and shown to be responsive to abiotic factors such as cold
temperatures (Sheldon et al., 2006). Many plant functional traits are
polygenic, involving complex interactions controlled by multiple genes,
so it is also expected that patterns of climate adaptation are also the
result of combined effects from several alleles of small-effect
(Wadgymar et al., 2017). Indeed, climatic variables are expected to not
be the main driver for variation in some candidate SNPs, as the genes
associated can be pleiotropic and may be under selection from other
biotic or abiotic factors. For example, although precipitation and
temperature are consistently highlighted as key factors influencing
plant’ distribution and ecology, soil properties greatly affect these
settings, as water availability depends on the interaction between
climatic variables and soil characteristics (Piedallu et al., 2013). The
identification and understanding of adaptive genetic variations might
then be improved by including other relevant biotic factors such as soil
characteristics. Nevertheless, by hierarchically categorising gene
functions, we were able to find consistent adaptive patterns across the
distribution, highlighting polygenic adaptations to climate variables in
this species.