Connectivity patterns of QST transcripts and a
multivariate perspective on GEI
The positioning of traits within a co-expression network relies on their
patterns of connectivity with other traits. Identifying where adaptive
traits fall within the co-expression network can help us understand
whether adaptive evolution proceeds through large pleiotropic effects or
through smaller fine tuning of weakly pleiotropic traits (Jordanet al ., 2004; Des Marais et al ., 2017; Josephs et
al ., 2017). Additionally, comparing co-expression networks across
environments allows identification of modules targeted by selection and
those that are un-altered by changing environmental conditions, which we
investigated by calculating three measures of connectivity for each
expression trait. These reflected intramodular connectivity (kWithin),
connectivity to all transcripts disregarding module membership in the
network (kTotal) and the difference between inter- and intra-modular
connectivity (kDiff). Specifically, to evaluate garden-specific patterns
of GEI (H2) from a multivariate perspective, we performed comparative
analyses using 10,000 permutations with the WGCNA package (Langfelderet al ., 2011). We used two aggregate summary statistics to
declare a module as being preserved across gardens.
Zsummary represents a normalised value of various
connectivity and density-based measures following a permutation test
procedure, while medianRank simply scores each module based on
the observed preservation statistic. Following Langfelder et al .
(2011), we used both Zsummary andmedianRank for inference of module preservation. We declared
modules as preserved across gardens if their
Zsummary scores were higher than 10 andmedianRank scores were below 5.
To assess whether weakly pleiotropic traits dominate the architecture of
adaptive evolution (H4), we implemented the network characterization
approach developed by Mähler et al. (2017) to define the core of a
module based on the top 10% of transcripts with the highest kTotal. We
determined if the observed relationship betweenQST categories and connectivity was significantly
different than expected by chance by performing 10,000 permutations. Our
permuted sets were matched on bins of expression levels to be
representative of the core transcripts since expression levels are often
associated with connectivity in a co-expression network (Fig. S4).