dewlap-associated SNPs
To determine whether SNPs associated with dewlap traits are also
involved in local adaptation of invasive A. sagrei populations,
we combined F ST outlier analyses and
genotype-environment association analyses. TheF ST outlier approach aimed to determine whether
dewlap-associated SNPs make a large contribution to population genetic
differentiation in invasive A. sagrei. We followed the approach
described in Bock et al. (2021) for the same set of populations as those
used for dewlap measurements here. Briefly, we used VCFtools (v. 0.1.16;
Danecek et al., 2011) to calculate SNP-based F STamong 15 independent population pairs found in our dataset. For this, we
relied on the full LD-filtered SNP set. We then comparedF ST values for all dewlap-associated SNPs withF ST values observed for an equal number of
randomly selected non-dewlap SNPs. For this, we used a linear model in
R, which had population pair and locus category (i.e., dewlap or
non-dewlap SNP) as predictor variables, and F STvalues as the response variable. As well, we designated asF ST outliers those values that were in the top
5% of the empirical distribution, for each of the 15 population pairs.
We then asked whether SNPs that are strongly associated with dewlap
traits (i.e., those with the lowest inferred GWAS P value) are
also repeatedly classified as F ST outliers in
multiple independent population pairs.
The genotype-environment association (GEA) analyses aimed to determine
whether dewlap-associated SNPs are also associated with environmental
variables that are important from the perspective of dewlap signal
effectiveness. We followed the approach described in DeVos et al. (2023)
and used a latent factor mixed model (LFMM), as implemented in thelfmm R package (v. 1.1; Frichot et al., 2013). To correct for the
confounding effect of population structure, we set K = 2, which
corresponds to the main genetic subdivision in our dataset (see
‘Population structure’ results below). Similar to the GWAS analyses
described above, we adjusted the GEA P values based on the
genomic inflation factor. We then relied on the qvalue R package
(v. 2.30.0; Storey et al., 2015) to convert P values to qvalues and to identify genome-wide significant SNPs based on a false
discovery rate (FDR) of 5%. We used canopy openness as the
environmental variable, given evidence of correlation between this
metric and several of the dewlap traits (see ‘Associations between the
dewlap, genetic ancestry, and the environment’ results below), and
because this metric was considerably more variable than temperature or
precipitation across our study populations (Figure S3). Finally, we
compared the canopy openness GEA results with the standard GWAS results
for dewlap total brightness. We focused on this trait for two reasons.
First, dewlap brightness was correlated with canopy openness, as might
be expected under local adaptation. Second, the GWAS for this trait
revealed several ancestry-independent loci. Thus, we could exclude the
confounding effects of genomic background, which may occur for loci
identified using the ancestry-specific GWAS.