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.