DISCUSSION
The primary goal of this study was to provide an experimental test of
the extent to which genetic offsets, a correlative space-for-time
approach, can predict performance of populations exposed to new
environments. By transplanting individuals from their home environment
to the novel climate of the common gardens, we substituted space for
time as a proxy for rapid climate change. We found that genetic offsets
based on existing gene-environment relationships work well to predict
performance of populations experiencing new environments - and much
better than climate differences alone (Table 1). We view this finding as
encouraging preliminary evidence that genetic offsets may represent a
first order estimate of the degree of expected maladaptation of
populations exposed to novel environments. While our study considered
climate differences across geographic space, in principle our findings
should be relevant to temporal changes in climate as well. As such,
genetic offsets could provide a means to estimate aspects of
population-level vulnerability to climate change. Additional research is
warranted to determine the extent to which our findings are
generalizable to other systems and populations growing in natural
environments.
That genetic offsets outperformed naive climate distances is not
surprising and can be best understood by considering the similarities
and distinctions between these two methods. In many ways, genetic offset
share a conceptual foundation with climate transfer distances long used
in forestry (Mátyás, 1996).
The establishment of provenance trials, in which tree seed from multiple
locations are collected and grown in multiple sites, has allowed for
evaluation of tree performance as a function of differences in climate
between sources and planting sites (i.e., response functions derived
from climate transfer distances; Wang, Hamann, Yanchuk, O’Neill, &
Aitken, 2006; Wang, O’Neill, & Aitken, 2010). These experiments provide
excellent insight into the climate variables that best predict
phenotypic performance upon transfer to a new site, but are time and
labor-intensive, and not practical for most study systems. A simpler
approach is to delineate climate-based seed zones from which seeds
should be selected for restoration under the hypothesis that
maladaptation of seedlings is minimized (and production is maximized)
when movement of seeds is restricted to other sites with similar climate
(e.g., Bower, St Clair,
& Erickson, 2014; Pike et al., 2020). The distinction between the
“traditional” climate transfer distances used for seed zone
delineation and genetic offsets is simply that genetic offsets use
re-scaled climate distances based on the modeled associations with
(adaptive) genomic variation, whereas climate distances typically weigh
the included variables equally despite potential variation in their
adaptive importance. Existing gene-environment relationships described
by the fitted turnover functions from GF provide the mechanism that
allows proper weighting of different climate variables, based on how
allele frequencies are aligned with climate gradients. Gradients
strongly associated with genomic variation (and portions of these
gradients where genetic patterns change most rapidly) will have greater
contribution to genetic offsets than will unimportant variables (or
portions of gradients where allele frequencies generally are constant;
Capblancq, Fitzpatrick,
Bay, Exposito-Alonso, & Keller, 2020; Fitzpatrick & Keller, 2015).
This also fits well with a recent study in lodgepole pine, (Pinus
contorta ), in which the climate variables identified as important in
GEA models were strongly correlated (r = 0.9) with the climate
variables associated with phenotypic performance in a 20-year provenance
trial (Mahony et al., 2020).
This suggests that one of the realized benefits of GEA may be in
identifying which among a set of climate variables are most predictive
of local adaptation, which is the same principle being employed by GF to
weight different climate variables based on the strength of the genomic
association when calculating genetic offsets. The use of GEA plus
genetic offsets may prove useful for conservation planning in long-lived
species or those for which phenotypic information from experimental
assessment of field performance is lacking.
The finding that genetic offsets had good predictive power regardless of
whether they were based on sets of outlier SNPs or simply SNPs selected
at random from the genome (which surprisingly slightly outperformed
genetic offsets based on outlier SNPs) is harder to explain. One
explanation is if allele frequencies of the genome as a whole tend to be
aligned with the same environmental gradients that are important to
local adaptation (i.e., the gradients of adaptive and neutral genomic
background are parallel or proportional), then one could serve as an
adequate proxy for the other. If this is the case, then SNPs selected at
random should provide the same rank weighting of the climate gradients
as would outlier SNPs, which was generally the case in our study (Fig.
2, Supplementary Fig. S3). However, as mentioned above, the shapes of
the turnover functions also will influence genetic offsets. All else
being equal, larger genetic offsets will occur for populations
transferred between environments on either side of a threshold as
compared to populations transferred along flat portions of allele
turnover gradients. Assuming these nonlinearities reflect true signals
of local adaptation, we would then expect genetic offsets that
incorporate these patterns to outperform linear methods that do not. Our
findings do not support this expectation. In this study, the turnover
functions based on outlier SNPs often showed pronounced nonlinearities,
whereas those based on randomly sampling SNPs from the genomic
background tended to be more linear (Figs. 2, 3), yet genetic offsets
based on outliers tended to be strongly correlated with those from
random SNPs (Supplementary Figs. 6). Further, random SNPs slightly
outperformed outlier SNPs in explaining height growth in the common
gardens. Additional research is required to determine whether this
result is an artefact of our study or a more general pattern.
Another primary goal of our study was to explore differences between GF
models fit to different sets of outlier SNPs. There are numerous ways to
detect statistical outlier SNPs, and, as was the case in this study, it
is not uncommon for different methods to identify different SNPs as
outliers, leaving some uncertainty regarding which SNPs are false vs.
true positives, and therefore which SNPs truly are associated with
climate adaptation and thus most informative from a predictive
standpoint. By fitting GF models to different sets of outlier SNPs, we
can ask: To what extent do different sets of outlier SNPs produce
different inferences? We found that although the different outlier
methods detected different sets outlier SNPs (Fig. 1), GF models fit to
different sets of outliers from bayenv2 , lfmm , and
GF-X were similar in terms of variable importance ranking (thoughR 2 values differed, Supplementary Figs. 3 and
5), the general shapes of the turnover functions (Figs. 2, 3), and
therefore, the predicted spatial patterns of genetic variation (Fig. 4),
and by extension, the predicted genetic offsets (Supplementary Fig. 6).
GF models fit to SNPs selected at random or those selected using allele
frequencies uncorrected for population structure (GF-Raw) also generally
followed the same pattern of variable importance ranking as other
outlier detection methods, but given that these methods selected a large
proportion of unique SNPS, they produced turnover functions and
predicted spatial patterns that differed from each other and frombayenv2 , lfmm , and GF-X . The similarity in variable
importance ranking and predictions from different sets of outlier SNPs
would arise if (1) the outlier SNPs they shared in common tended to have
strong relationships with climate (and therefore would have greater
contribution to the fitted turnover functions from GF;
(Ellis et al., 2012) and/or
(2) the outlier SNPs unique to each method tended to have similar
relationships (i.e., shapes of turnover functions) with climate. We have
evidence for both possibilities. The shapes and cumulative importance of
the turnover functions for the outlier SNPs unique to bayenv2 ,lfmm , and GF-X were similar (Fig. 3) and the totalR 2 from GF models increased for SNPs as their
outlier status was shared among an increasing number of detection
methods (Supplementary Fig. S7). Outliers unique to a single method
likely represent a mix of false positive SNPs along with some true
positives that may be better detected by one method over another,
although these are difficult to separate in real data. Our experimental
design and sampling strategy were specifically chosen to minimize false
positives arising from demographic history, and our simulations testing
GF-Raw suggested a low type I error rate under a simple scenario of
isolation by distance. However, under more complex demographic histories
we would expect GF-Raw to be prone to false positives because it does
not have an internal control for neutral population structure. Given
this, and the observed reduction in unique outliers identified by GF
before and after correcting for population relatedness (i.e., GF-Raw vs.
GF-X ), we advocate fitting GF only to allele frequencies that
have been properly corrected for demographic history.
In terms of outlier detection, it is notable that GF-X detected
the fewest outliers overall and the fewest outliers unique to that
method (Fig. 1). Unlike bayenv2 and lfmm , GF is
multivariate, can accommodate interactions between variables, and
assumes no parametric form of the allele frequency ~
environment relationship (although it does assume monotonicity).
Therefore, GF-X may be less prone to the multiple testing problem
inherent in univariate methods, or to outlier loci arising due to
departures from the assumed linear model. As such, the combination of GF
run on standardized allele frequencies produced by bayenv2 as
done in this study (GF-X ) could provide a more holistic approach
to multivariate outlier detection that is robust to the shape of the
allele frequency ~ environment relationship, while also
correcting for finite sampling and population structure. Because GF
reports an R 2 for each predictor variable in
the model as well as for the model as a whole, it also provides a means
to consider outlier status from the context of individual climate
gradients as well as more comprehensively. Taken together, we feel GF
warrants further study as a useful outlier detection method under simple
demographic histories, or when provided with allele frequencies that
have been corrected for population relatedness; especially for systems
under strong, linear selection and intermediate migration (supplementary
Figure S2).
While still a new and largely untested method, GF is increasingly being
applied to genomic studies, including quantifying population-level
climate change vulnerability. However, concerns have been raised about
the application of genetic offsets in this capacity, especially for
mobile organisms with short generation times
(Fitzpatrick, Keller, et al.,
2018). Common garden experiments are not perfect proxies for climate
change or organisms in natural environments, but our results suggest
that existing genetic patterns across space and associated genetic
offsets may be informative for predictions across time as well - even if
these predictions are based on neutral genetic patterns. Given the
inherent complexities, for most any organism it will be challenging to
predict the exact nature of genomic change in response to
environmental change. However, for some organisms, it may be possible to
use existing gene-environment relationships to develop adequate
assessments of the magnitude of expected genomic change based on
genetic offsets, which can provide a proxy for population-level exposure
to climate change.