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Abstract Recombination is central to genetics and to evolution of sexually reproducing organisms. However, obtaining accurate estimates of recombination rates, and of how they vary along chromosomes, continues to be challenging. To advance our ability to estimate recombination rates, we present Hi-reComb, a new method and software for estimation of recombination maps from bulk gamete chromosome conformation capture sequencing (Hi-C). Simulations show that Hi-reComb produces robust, accurate recombination landscapes. With empirical data from sperm of five fish species we show the advantages of this approach, including joint assessment of recombination maps and large structural variants, map comparisons using bootstrap, and workflows with trio phasing vs. Hi-C phasing. With off-the-shelf library construction and a straightforward rapid workflow, our approach will facilitate routine recombination landscape estimation for a broad range of studies and model organisms in genetics and evolutionary biology. Hi-reComb is open-source and freely available at https://github.com/millanek/Hi-reComb. 

Abstract Gene co-expression networks are a widely used tool for summarizing transcriptomic variation between individuals, and for inferring the transcriptional regulatory pathways that mediate genotype–phenotype relationships. However, these co-expression networks must be interpreted with caution, as they can arise from multiple processes. Here, we investigate one such process, using simulations to demonstrate that hybridization and gene flow between populations can greatly modify co-expression networks. Admixture between populations produces correlated expression between genes experiencing linkage disequilibrium. This correlated expression does not reflect functional relationships between genes but rather depends on migration rates and physical linkage on chromosomes. Given the prevalence of gene flow and hybridization between divergent populations in nature, these introgression effects likely represent a significant force in network evolution, even in populations where hybridization is historical rather than contemporary. These findings emphasize the critical importance of considering both evolutionary history and genomic architecture when analyzing gene co-expression networks in natural populations. 

ABSTRACT Many terrestrial ectotherms have gone to great evolutionary lengths to adapt to long cold winters; some have even evolved the ability to tolerate the freezing of most of the extracellular fluid in the body. Now, however, high‐elevation and high‐latitude winters are experiencing an accelerated period of warming. Specialised winter adaptations that promoted fitness in a seasonally frozen environment may soon be superfluous or even maladaptive. We ask whether winter adaptations include changes in immune functions, and whether changing winter conditions could exert disparate effects on populations of a wide‐ranging terrestrial ectotherm, the wood frog (Lithobates sylvaticus). By rearing wood frogs from ancestral winter environments that vary in length and temperature in a common garden, and reciprocally exposing post‐metamorphic frogs to unfrozen and frozen artificial winter conditions in the lab, we were able to decompose transcriptomic differences in ventral skin gene expression into those that were environmentally induced (responsive to temperature) and genetically determined and those that varied as an interaction between the genotype and environment. We found that frogs from harsh ancestral winter environments constitutively upregulated immune processes, including cellular immunity, inflammatory processes and adaptive immune processes, as compared to frogs from mild ancestral winter environments. Further, we saw that the expression of several genes varied in an interaction between the genotype and artificial winter. We suggest that just as winter climates likely served as the selective force resulting in remarkable winter adaptations such as freeze tolerance, they may have also induced constitutive changes in immune gene expression. 

ABSTRACT Dispersal can affect individual‐level fitness and population‐level ecological and evolutionary processes. Factors that affect dispersal could therefore have important eco‐evolutionary implications. Here, we investigated the extent to which an inflammation and tissue repair response—peritoneal fibrosis—which is known to restrict movement, could influence dispersal by conducting a mark‐recapture experiment in a lake in Alaska with threespine stickleback (Gasterosteus aculatus). A subset of captured stickleback were injected with aluminium phosphate to experimentally induce fibrosis (‘treatment group’), and another subset were injected with saline or received no injection—both of which do not induce fibrosis (‘control group’). We released all fish at one introduction point and re‐sampled stickleback throughout the lake for 8 days. We recaptured 123 individuals (n = 47 fibrosis treatment;n = 76 control) and dissected them to determine fibrosis levels. Overall, fibrosis did not affect dispersal. Some compelling (but not statistically significant) trends suggest that early‐stage inflammation may affect dispersal, providing opportunities for future work. By showing that effects on dispersal are not important side effects of fibrosis, these findings improve our understanding of the ecological implications of immune responses. 

Abstract Eco‐evolutionary experiments are typically conducted in semi‐unnatural controlled settings, such as mesocosms; yet inferences about how evolution and ecology interact in the real world would surely benefit from experiments in natural uncontrolled settings. Opportunities for such experiments are rare but do arise in the context of restoration ecology—where different “types” of a given species can be introduced into different “replicate” locations. Designing such experiments requires wrestling with consequential questions. (Q1) Which specific “types” of a focal species should be introduced to the restoration location? (Q2) How many sources of each type should be used—and should they be mixed together? (Q3) Whichspecificsource populations should be used? (Q4) Which type(s) or population(s) should be introduced into which restoration sites? We recently grappled with these questions when designing an eco‐evolutionary experiment with threespine stickleback (Gasterosteus aculeatus) introduced into nine small lakes and ponds on the Kenai Peninsula in Alaska that required restoration. After considering the options at length, we decided to use benthic versus limnetic ecotypes (Q1) to create a mixed group of colonists from four source populations of each ecotype (Q2), where ecotypes were identified based on trophic morphology (Q3), and were then introduced into nine restoration lakes scaled by lake size (Q4). We hope that outlining the alternatives and resulting choices will make the rationales clear for future studies leveraging our experiment, while also proving useful for investigators considering similar experiments in the future. 

Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype  →  expression  →  phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype  →  network  →  phenotype mechanism. Here, we develop a network-based method, called spectral network quantitative trait loci analysis (snQTL), to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback Gasterosteus aculeatus data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.