Introduction
Highly dispersive species like Pacific lamprey (Entosphenus
tridentatus ) present an evolutionary conundrum for adaptation.
Adaptation is facilitated when particular combinations of gene variants
that confer optimal fitness in an environment can be passed on to the
next generation. However, high rates of gene flow can impede inheritance
of these optimal combinations of gene variants via the action of
recombination. Yet there is evidence from Pacific lamprey and other
dispersive species that local adaptation may occur despite these high
rates of gene flow. For example, Pacific lamprey body size is correlated
with upstream migration distance in the Columbia River (Keefer et
al . 2009; Hess et al. 2014) and traits in other dispersive species
appear to be optimized for specific environments within their broader
range (Asaduzzaman et al. 2019, Miller et al. 2019, Phairet al. 2019).
Genomic architecture appears to be one factor that can influence local
adaptation in highly dispersive species. In general, the closer two
genes occur in the genome the smaller the chance for recombination
events that may separate an optimal combination of variants (Yeaman and
Whitlock 2011). Inversions resist recombination between inverted
haplotypes and can effectively lock an optimal combination of variants
together over longer distances within the inverted segment (sometimes
referred to as a supergene); the fitness conferred by these inversions
can help maintain them as a polymorphism in a population through both
forces of balancing and divergent selection (Wellenreuther and
Bernatchez 2018, Faria et al. 2019). In Pacific lamprey, if there
are particular phenotypes that have a polygenic basis and confer
differential fitness across environments, we might expect to identify
long polymorphic intervals of DNA sequence.
Several traits in Pacific lamprey have been found to have a genetic
basis. These include body size, reproductive migration-timing (Hesset al. 2014, 2015), and advanced maturity of females at onset of
freshwater migration (i.e. ocean-maturing versus river-maturing
ecotypes, Parker et al. 2019). There also appears to be evidence
for statistical linkage of multiple loci that show high divergence in
the species’ range (Hess et al. 2013). One thing that is unclear
is whether range-wide divergence that has been observed can be explained
by phenotype-by-genotype associations reported thus far. Phenotypic
traits are often interrelated, which can obscure the true target of
selection (Powell and MacGregor 2011). Testing a large variety of
phenotypic trait associations with genotypes at different sites in the
species’ range can help to disentangle these correlations and help
elucidate the true target of selection. Once phenotype-by-genotype
associations are confirmed across geographic sites, these relationships
can be exploited to extrapolate a phenotype across large geographic
areas in which only genotypes have been measured. This genetic tool then
becomes a powerful predictor and can generate hypothesis testing
frameworks to guide future studies aimed to validate these predicted
phenotypic distributions across the range and elucidate factors driving
regional optimization of these traits.
In this study we addressed four major objectives: 1) Divergence
mapping : Test whether previously observed genomic divergence across the
species’ range is either concentrated or diffusely organized in the
genome, 2) Association testing : Test phenotypic trait
associations with genotypes across geographic sites to identify robust
phenotype-by-genotype relationships, 3) Association mapping : Test
whether phenotypic-by-genotype associations mapped to the genome can
explain genomic divergence across the species’ range, 4)Extrapolation of spatiotemporal phenotypic distributions : Use
candidate SNP genotypic distributions across time and space to
characterize the ecological niche of life history traits. Our findings
supported a high concentration of genomic divergence to regions within
four chromosomes, referred to as genomic islands. Two of these four
genomic islands showed robust correlation with maturity and body-size
traits and could be used to predict their spatiotemporal distributions
across the species’ range.