Adaptive divergence under gene flow along an environmental gradient in two coexisting stickleback species

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Host habitat rather than evolutionary history explains gut microbiome diversity in sympatric stickleback species

Host-associated microbiota can influence host phenotypic variation, fitness and potential to adapt to local environmental conditions. In turn, both host evolutionary history and the abiotic and biotic environment can influence the diversity and composition of microbiota. Yet, to what extent environmental and host-specific factors drive microbial diversity remains largely unknown, limiting our understanding of host-microbiome interactions in natural populations. Here, we compared the intestinal microbiota between two phylogenetically related fishes, the three-spined stickleback (Gasterosteus aculeatus) and the nine-spined stickleback (Pungitius pungitius) in a common landscape. Using amplicon sequencing of the V3-V4 region of the bacterial 16S rRNA gene, we characterised the α and β diversity of the microbial communities in these two fish species from both brackish water and freshwater habitats. Across eight locations, α diversity was higher in the nine-spined stickleback, suggesting a broader niche use in this host species. Habitat was a strong determinant of β diversity in both host species, while host species only explained a small fraction of the variation in gut microbial composition. Strong habitat-specific effects overruled effects of geographic distance and historical freshwater colonisation, suggesting that the gut microbiome correlates primarily with local environmental conditions. Interestingly, the effect of habitat divergence on gut microbial communities was stronger in three-spined stickleback than in nine-spined stickleback, possibly mirroring the stronger level of adaptive divergence in this host species. Overall, our results show that microbial communities reflect habitat divergence rather than colonisation history or dispersal limitation of host species

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Adaptive divergence under gene flow along an environmental gradient in two coexisting stickleback species

publishedVersio

Data_Sheet_1_Host habitat rather than evolutionary history explains gut microbiome diversity in sympatric stickleback species.pdf

Host-associated microbiota can influence host phenotypic variation, fitness and potential to adapt to local environmental conditions. In turn, both host evolutionary history and the abiotic and biotic environment can influence the diversity and composition of microbiota. Yet, to what extent environmental and host-specific factors drive microbial diversity remains largely unknown, limiting our understanding of host-microbiome interactions in natural populations. Here, we compared the intestinal microbiota between two phylogenetically related fishes, the three-spined stickleback (Gasterosteus aculeatus) and the nine-spined stickleback (Pungitius pungitius) in a common landscape. Using amplicon sequencing of the V3-V4 region of the bacterial 16S rRNA gene, we characterised the α and β diversity of the microbial communities in these two fish species from both brackish water and freshwater habitats. Across eight locations, α diversity was higher in the nine-spined stickleback, suggesting a broader niche use in this host species. Habitat was a strong determinant of β diversity in both host species, while host species only explained a small fraction of the variation in gut microbial composition. Strong habitat-specific effects overruled effects of geographic distance and historical freshwater colonisation, suggesting that the gut microbiome correlates primarily with local environmental conditions. Interestingly, the effect of habitat divergence on gut microbial communities was stronger in three-spined stickleback than in nine-spined stickleback, possibly mirroring the stronger level of adaptive divergence in this host species. Overall, our results show that microbial communities reflect habitat divergence rather than colonisation history or dispersal limitation of host species.</p

Novel genomic resources for shelled pteropods: a draft genome and target capture probes for <i>Limacina bulimoides</i>, tested for cross-species relevance

BACKGROUND: Pteropods are planktonic gastropods that are considered as bio-indicators to monitor impacts of ocean acidification on marine ecosystems. In order to gain insight into their adaptive potential to future environmental changes, it is critical to use adequate molecular tools to delimit species and population boundaries and to assess their genetic connectivity. We developed a set of target capture probes to investigate genetic variation across their large-sized genome using a population genomics approach. Target capture is less limited by DNA amount and quality than other genome-reduced representation protocols, and has the potential for application on closely related species based on probes designed from one species. RESULTS: We generated the first draft genome of a pteropod, Limacina bulimoides, resulting in a fragmented assembly of 2.9 Gbp. Using this assembly and a transcriptome as a reference, we designed a set of 2899 genome-wide target capture probes for L. bulimoides. The set of probes includes 2812 single copy nuclear targets, the 28S rDNA sequence, ten mitochondrial genes, 35 candidate biomineralisation genes, and 41 non-coding regions. The capture reaction performed with these probes was highly efficient with 97% of the targets recovered on the focal species. A total of 137,938 single nucleotide polymorphism markers were obtained from the captured sequences across a test panel of nine individuals. The probes set was also tested on four related species: L. trochiformis, L. lesueurii, L. helicina, and Heliconoides inflatus, showing an exponential decrease in capture efficiency with increased genetic distance from the focal species. Sixty-two targets were sufficiently conserved to be recovered consistently across all five species. CONCLUSION: The target capture protocol used in this study was effective in capturing genome-wide variation in the focal species L. bulimoides, suitable for population genomic analyses, while providing insights into conserved genomic regions in related species. The present study provides new genomic resources for pteropods and supports the use of target capture-based protocols to efficiently characterise genomic variation in small non-model organisms with large genomes