Assessing per-transcript level signatures of adaptive evolution
We evaluated the among population component of additive genetic variance
(QST ) for each transcript given our quantitative
genetic study design (Spitze, 1993). Under neutrality,QST should be similar to its genome-wide
equivalent (FST ) (Leinonen et al ., 2008).
Under spatially divergent selection pressures driving local adaptation,
however, Q ST should statistically exceedF ST. Using the per transcript variance components
obtained from equation (1), we estimated QST as:
\(Q_{\text{ST}}=\ \frac{\sigma_{a}^{2}}{6\sigma_{w}^{2}\ +\ \sigma_{a}^{2}}\), (2)
where \(\sigma_{a}^{2}\) represents the among population variance
component and \(\sigma_{w}^{2}\) represents the within population
variance component. The constant 6 originates from our assumption that
the mixed-sib design is a 50:50 mixture of half and full siblings
(Gilbert & Whitlock, 2015). Confidence intervals (CI, 95%) per
transcript per garden were obtained using 1000 parametric bootstrap
estimates generated using the bootMer function implemented in the
lme4 v.1.1-28 package in R (Bates et al ., 2015). To address our
hypotheses concerning garden specific patterns of adaptive
differentiation at the per-transcript level (H1 & H2), we compared the
0.025 quantile of QST for each transcript with
the 0.95 quantile of FST and identified
transcripts exhibiting signatures of local adaptation. Since we are
primarily interested in the architecture underlying GEI, subsequent
analyses and our main results focus on three QSTcategories: (a) conditionally adaptive in cold garden (Cold-condA),
transcripts with QST >FST only in high elevation garden; (b)
conditionally adaptive in the warm garden (Warm-condA), transcripts withQST > FST only in low elevation garden; and (c) adaptive plasticity (Ad-Pl),
transcripts with QST >FST across both gardens and a significantQST reaction norm (p < 0.05)
assessed using a t -test (Fig. 2). For eachQST category, we performed gene ontology (GO)
enrichment analyses using a hypergeometric test with 1000 permutations
to compute the family-wise error rate as implemented in the GOfuncR
v1.12 package (Grote, 2020) in R. The background for GO enrichment
analyses was the full set of GO terms across all annotated and
non-contaminant transcripts from EnTAP (Table S1; Methods S1).