Annotation and gene ontology
Full annotation results for SNPs associated with each variable are given
in Supporting Information (Table S2, tabs 6–10). Of the 2,336 unique
candidate SNPs associated with the climate variables, 1,440 SNPs were
linked to functionally annotated genes (Blast -score
> 60), which represents 10.6% of the total candidates set
(13,534). TMAX delivered the highest amount of linked
functionally annotated genes (474), followed by PWQ(312), TSEAS (237), PMA (214) and
TMIN (203). Most of these candidate SNPs were linked to
functionally annotated genes (Table 2) . For example, JAR00198,
associated with both TSEAS and TMIN, was
located in a trans-cinnamate 4-monooxygenase (TCMO) gene; JAR00662,
associated with TSEAS, was found in a UPF0496 protein
gene; two SNPS associated with TMAX, JAR00038 and
JAR00207 were found on transcription repressor MYB6 and transcription
factor MYB44 genes respectively. For PMA, JAR02395 was
located in a peroxidase 72 gene; and for PWQ, JAR00273
was located in a 10 kDa chaperonin gene.
Gene ontology enrichment analysis explored how groups of annotated SNPs
relate to similar functions (Table S2, tabs 11-15). Several enriched GO
terms in the biological process category are highlighted (Table 3): a GO
term associated with response to light stimulus (GO:0009416) was found
with the SNPs related to TSEAS. Genes associated with
this GO term are linked to cellular response processes (in terms of
components movement, enzyme production, and secretion and protein
expression) from abiotic stimulus, specifically electromagnetic
radiation and light. A GO term related to karrikin stimulus was found
associated with TMIN (GO:0080167). As for
PMA and PWQ, GO terms with high counts
of SNPs were found for each variable (GO:0044763 and GO:1901566,
respectively) as well as a term related to UV response (GO:0009411)
associated with PMA.
Landscape Modelling
The SNPs associated with enriched GO terms (Table 3) were used in a GDM
analysis to measure allelic turnover across climatic gradients (Figure
4). The patterns of allelic turnover varied by climatic variable:
overall, GDM showed small to moderate response, in terms of deviance
explained. The GO term associated with PWQ explained
more deviance on the SNPs group (n = 21 ; 21.22%, Figure 4e) than any
other climate variable association using GO-groups of SNPs, followed by
GO terms associated with TMIN (n =15 ; 14.27%, Figure
4c). TSEAS, TMAX and PMAshowed a similar deviance for allelic turnover composition
(<5% for each group of SNPs). A specific SNP associated with
PWQ , JAR00476, explained the highest deviance (35.5%)
of all the GO terms groups of SNPs used for the GDM modelling. We also
applied a GDM analysis to all individual SNPs associated with the 5
climatic variables (Figure S4), and the SNP that explained the highest
deviance for each variable was selected to display spatial patterns of
allelic turnover (Figure 5): TSEAS – JAR00269 (39.2%);
TMAX – JAR11943 (25.5%); TMIN –
JAR01172 (16.8%); PMA – JAR10596 (21.9%) and
PWQ – JAR06621 (36.9%). The SNP associated with
TSEAS (JAR00269) explained more deviance than any other
in the whole dataset across the 5 climate variables, followed by a SNP
associated with PWQ (JAR06621). There is rapid turnover
noticeable for the three temperature variables from the coastal to
eastern populations in the north of the range, and more gradual turnover
from the northern populations to the southern populations (Figure 5a, b,
c). But even among the three temperature variables, there are major
differences in adaptive patterns. For instance, while
TSEAS and TMAX display a similar rapid
turnover from the coastal to eastern populations in the north of the
range, and fairly gradual turnover from the northern populations to the
southern populations, TMIN follows the same trend in the
northern region, but a rapid turnover is present between the coastal and
inland populations in the south region. In contrast, the precipitation
variables showed rapid turnover in the southern or central parts of the
distribution, and more gradual turnover in the northern distribution
(Figure 5d, e). In southern areas, PWQ shows a rapid
turnover between coastal and inland southern populations, while
PMA shows a more gradual pattern in this region.