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Gene regulatory divergence is thought to play an important role in adaptation, yet its extent and underlying mechanisms remain largely elusive for local adaptation with gene flow. Local adaptation is widespread in marine species despite generally high connectivity and is often associated with tightly linked genomic architectures, such as chromosomal inversions. To investigate gene regulatory evolution under gene flow and the role of inversions associated with local adaptation to a steep thermal gradient, we generated RNA-seq data from Atlantic silversides (Menidia menidia) from two locally adapted populations and their F1 hybrids, reared under two temperatures. We found substantial divergence in gene expression and thermal plasticity between populations, with up to 31% of genes being differentially expressed. Reduced thermal plasticity, temperature-dependent gene misexpression, and the disruption of coexpression networks in hybrids point toward a role of regulatory incompatibilities in local adaptation, particularly under colder temperatures. Chromosomal inversions show an accumulation of regulatory incompatibilities but are not consistently enriched for differentially expressed genes. Together, these results suggest that gene regulation can diverge substantially among populations despite gene flow, partly due to the accumulation of temperature-dependent regulatory incompatibilities within inversions.

 

Abstract The levels and distribution of standing genetic variation in a genome can provide a wealth of insights about the adaptive potential, demographic history, and genome structure of a population or species. As structural variants are increasingly associated with traits important for adaptation and speciation, investigating both sequence and structural variation is essential for wholly tapping this potential. Using a combination of shotgun sequencing, 10x Genomics linked reads and proximity-ligation data (Chicago and Hi-C), we produced and annotated a chromosome-level genome assembly for the Atlantic silverside (Menidia menidia)—an established ecological model for studying the phenotypic effects of natural and artificial selection—and examined patterns of genomic variation across two individuals sampled from different populations with divergent local adaptations. Levels of diversity varied substantially across each chromosome, consistently being highly elevated near the ends (presumably near telomeric regions) and dipping to near zero around putative centromeres. Overall, our estimate of the genome-wide average heterozygosity in the Atlantic silverside is among the highest reported for a fish, or any vertebrate (1.32–1.76% depending on inference method and sample). Furthermore, we also found extreme levels of structural variation, affecting ∼23% of the total genome sequence, including multiple large inversions (> 1 Mb and up to 12.6 Mb) associated with previously identified haploblocks showing strong differentiation between locally adapted populations. These extreme levels of standing genetic variation are likely associated with large effective population sizes and may help explain the remarkable adaptive divergence among populations of the Atlantic silverside. 

Low‐coverage whole genome sequencing (lcWGS) has emerged as a powerful and cost‐effective approach for population genomic studies in both model and nonmodel species. However, with read depths too low to confidently call individual genotypes, lcWGS requires specialized analysis tools that explicitly account for genotype uncertainty. A growing number of such tools have become available, but it can be difficult to get an overview of what types of analyses can be performed reliably with lcWGS data, and how the distribution of sequencing effort between the number of samples analysed and per‐sample sequencing depths affects inference accuracy. In this introductory guide to lcWGS, we first illustrate how the per‐sample cost for lcWGS is now comparable to RAD‐seq and Pool‐seq in many systems. We then provide an overview of software packages that explicitly account for genotype uncertainty in different types of population genomic inference. Next, we use both simulated and empirical data to assess the accuracy of allele frequency, genetic diversity, and linkage disequilibrium estimation, detection of population structure, and selection scans under different sequencing strategies. Our results show that spreading a given amount of sequencing effort across more samples with lower depth per sample consistently improves the accuracy of most types of inference, with a few notable exceptions. Finally, we assess the potential for using imputation to bolster inference from lcWGS data in nonmodel species, and discuss current limitations and future perspectives for lcWGS‐based population genomics research. With this overview, we hope to make lcWGS more approachable and stimulate its broader adoption.

 

Due to pervasive gene flow and admixture, simple bifurcating trees often do not provide an accurate representation of relationships among diverging lineages, but limited resolution in the available genomic data and the spatial distribution of samples has hindered detailed insights regarding the evolutionary and demographic history of many species and populations. In this issue ofMolecular Ecology, Foote et al. (2019) combine a powerful sampling design with novel analytical methods adopted from human genetics to describe previously unrecognized patterns of recurrent vicariance and admixture among lineages in the globally distributed killer whale (Orcinus orca). Based on sequence data from modern samples alone, they discover clear signatures of ancient admixture with a now extinct “ghost” lineage, providing one of the first accounts of archaic introgression in a nonhominid species. Coupling a cost‐effective sequencing strategy with novel analytical approaches, their paper provides a roadmap for advancing inference of evolutionary history in other nonmodel species, promising exciting times ahead for our field.