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.