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54 results for “hybrid incompatibilities”
Datasets for polygenic mechanisms of hybrid incompatibility in butterflies
<p><strong>Version 1.2 includes data that are missing in the previous versions.</strong></p> <p> </p> <p>Note: relevant scripts can also be found at</p> <p>https://github.com/tzxiong/2022_Papilio_HybridIncompatibilityMapping</p> <p>======================================================<br>Description of source data and scripts for all figures<br>======================================================</p> <p>==== MAIN FIGURES ====</p> <p>Fig. 1</p> <p> - Panel A<br> * Schematic figure, no source data are provided</p> <p> - Panel B<br> * Schematic figure, no source data are provided</p> <p> - Panel C<br> * Source data folder(s):<br> SourceData/Fig1/Fig1C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 5</p> <p> - Panel D<br> * Source data folder(s):<br> SourceData/Fig1/Fig1D<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 5</p> <p> - Panel E<br> * Source data folder(s):<br> SourceData/Fig1/Fig1E<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 2</p> <p> - Panel F<br> * Source data folder(s):<br> SourceData/Fig1/Fig1F<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 2</p> <p>Fig. 2</p> <p> - Panels A-G<br> * Source data folder(s): <br> SourceData/Fig2+S1toS2 <br> * The "Raw" folder contains unedited images.<br> * Two edited images used in Fig2 is also included for each subfigure.</p> <p> - Panels H-L<br> * Source data folder(s): <br> SourceData/Fig2+S1toS2 <br> * The "Raw" folder (unzipped) contains unedited confocal data in .czi format.<br> * Edited images are included with both monochrome and merged versions.</p> <p>Fig. 3</p> <p> - Panel A<br> * Source data folder(s):<br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> D(DB): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.1<br> B(BD): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 2.3</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> D(DB): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.1<br> B(BD): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.2</p> <p> - Panel C<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> D(DB): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.1<br> B(BD): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.2</p> <p> - Panel D<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Run all of Sections 4.1 and 4.2</p> <p> - Panel E<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Run all of Sections 4.1 and 4.2</p> <p>Fig. 4</p> <p> - Note 1: For Heliconius analysis, all data are from SourceData/Fig4-Heliconius+S11C/dat.4.qtl.lumped.csv. This file contains Heliconius ovary dysgenesis data from https://doi.org/10.1111/mec.16272</p> <p> - Panel A (Heliconius)<br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.1<br> <br> - Panel A (Papilio) <br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.2</p> <p> - Panel B (Heliconius)<br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.1<br> <br> - Panel B (Papilio)<br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.2</p> <p> - Panel C (Heliconius) <br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.2<br> <br> - Panel C (Papilio) <br> * Source data folder(s):<br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.3</p> <p> - Panel D<br> * Schematic figure, no source data are provided<br> <br> - Panel E (Heliconius) <br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.3<br> <br> - Panel E (Papilio) <br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.3<br> <br> - Panel F (Heliconius)<br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.3<br> <br> - Panel F (Papilio) <br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.3<br> <br> <br> <br>==== SUPPLEMENTARY FIGURES ====</p> <p>Fig. S1-S2</p> <p> * Source data folder(s): <br> SourceData/Fig2+S1toS2 <br> * The "Raw" folder (unzipped) contains unedited confocal data in .czi format.<br> * Edited images are included with both monochrome and merged versions.</p> <p>Fig. S3</p> <p> * Source data folder(s): <br> SourceData/FigS3<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 1<br> * Note: Source data file 04.0_IBD.NgsRelate.zip contains results from the NGSRelate software.<br> </p> <p>Fig. S4</p> <p> * Source data folder(s): <br> SourceData/FigS4<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 2<br> * Note 1: Source data file CorrectedReferenceGenome.zip is the corrected reference genome used for all analyses. It is in .fasta format.<br> * Note 2: Source data file DenovoMarkerOrder_on_CorrectedRefGenome.zip contains all outputs from the LepMap3/OrderMarkers2 module that uses genotype likelihoods and pedigree information to generate a new marker order. Use script "OrderMarkers2_ReOrder.sh" from the script repo.<br> </p> <p>Fig. S5-S7</p> <p> * Source data folder(s): <br> SourceData/FigS5toS7<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 2<br> * Note: The source data file PedigreeAncestryInGrandparentalPhase.zip contains all outputs from the LepMap3/OrderMarkers2 module that uses genotype likelihoods and pedigree information to impute ancestry at each marker. Ancestry is phased according to the sex of grandparents. Use script "OrderMarkers2.sh" from the GitHub repo.</p> <p>Fig. S8</p> <p> - Panel A <br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.1</p> <p> - Panel B <br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 2.3</p> <p> - Panel C<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> See previous two panels</p> <p>Fig. S9</p> <p> - Panel A<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.1</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.2</p> <p>Fig. S10</p> <p> - Panel A<br> * Source data folder(s):<br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.1</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.2</p> <p>Fig. S11</p> <p> - Panel A<br> * Source data folder(s): <br> SourceData/FigS11AB<br> * Source code:<br> Data in SpeciesAncestry.B0D1.zip can be directly visualized to get the figure</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/FigS11AB<br> * Source code:<br> Data in SpeciesAncestry.B0D1.zip can be directly visualized to get the figure<br> * Note: This file contains ancestry at each marker phased according to species (bianor=0, dehaanii=1). These data are directly transformed from files in SourceData/FigS5toS7/PedigreeAncestryInGrandparentalPhase.zip.</p> <p> - Panel C<br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.4</p> <p>Fig. S12</p> <p> * Source data folder(s): <br> No source data are needed<br> * Source code:<br> SourceData/Code03_PolygenicGhostQTL.ipynb</p> <p>Fig. S13</p> <p> - Panel A<br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.3</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.2</p> <p>Fig. S14</p> <p> * Source data folder(s): <br> No source data are needed<br> * Source code:<br> SourceData/Code03_PolygenicGhostQTL.ipynb</p> <p>Fig. S15</p> <p> - Panel A<br> * Source data folder(s): <br> SourceData/FigS15/FigS15A<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 2.1</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/FigS15/FigS15B<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 2.2</p> <p>Fig. S16</p> <p> * Source data folder(s): <br> SourceData/FigS16<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 3<br> </p> <p>==== OTHER SOURCE DATA & SUMMARY OF SOURCE CODE FOLDERS====</p> <p>LepMap3-SourceData</p> <p> * LepMap3_SourceData-Family_Info_Finalized_withPseudoGrandParents_transposed.txt<br> <br> This file is the pedigree file ready-to-use in LepMap3. Note that it contains pseudo grandparents for families missing grandparents in sequencing. Pseudo grandparents are simply created from fixed SNPs in all existing grandparents and adding them to the original vcf files containing genotype likelihoods.</p> <p> * LepMap3_SourceData-vcf_files.zip<br> <br> The vcf files containing genotype likelihoods for LepMap3 to use. Note that it contains the aforementioned pseudo grandparents.</p> <p><br>Code.NGSRelate</p> <p> * Code used for inferring kinship from low-coverage sequencing data</p> <p>Code.LepMap3<br> <br> * Code used for all LepMap3 analysis</p> <p>Code.JupyterLab</p> <p> * Code used for all Julia and R analysis in .ipynb format</p> <p><br> </p>
Data from: Hybrid incompatibility between D. virilis and D. lumei is stronger in the presence of transposable elements
<p>Mismatches between parental genomes in selfish elements are frequently hypothesized to underlie hybrid dysfunction and drive speciation. However, because the genetic basis of most hybrid incompatibilities is unknown, testing the contribution of selfish elements to reproductive isolation is difficult. Here we evaluated the role of transposable elements (TEs) in hybrid incompatibilities between Drosophila virilis and D. lummei by experimentally comparing hybrid incompatibility in a cross where active TEs are present in D. virilis (TE+) and absent in D. lummei, to a cross where these TEs are absent from both D. virilis (TE-) and D. lummei genotypes. Using genomic data, we confirmed copy number differences in TEs between the D. virilis (TE+) strain and the D. virilis (TE-) strain and D. lummei. We observed F1 postzygotic reproductive isolation exclusively in the interspecific cross involving TE+ D. virilis but not in the cross involving TE- D. virilis. This precisely mirrors the intraspecies dysgenic phenotype where teste atrophy only occurs when TE+ D. virilis is the paternal parent. A series of backcross experiments, designed to account for alternative models of hybrid incompatibility, showed that both F1 hybrid incompatibility and intrastrain dysgenesis is consistent with the action of TEs rather than other, genic, interactions. A further Y-autosome interaction contributes to additional, sex-specific, inviability in one direction of this cross combination. These experiments demonstrate that TEs that cause intraspecies dysgenesis can increase reproductive isolation between closely related lineages, thereby adding to the processes that consolidate speciation.</p>
On the impermanence of species: The collapse of genetic incompatibilities in hybridizing populations
<p>Species pairs often become genetically incompatible during divergence, which is an important source of reproductive isolation. An idealized picture is often painted where incompatibility alleles accumulate and fix between diverging species. However, recent studies have shown both that incompatibilities can collapse with ongoing hybridization, and that incompatibility loci can be polymorphic within species. This paper suggests some general rules for the behavior of incompatibilities under hybridization. In particular, we argue that redundancy of genetic pathways can strongly affect the dynamics of intrinsic incompatibilities. Since fitness in genetically redundant systems is unaffected by introducing a few foreign alleles, higher redundancy decreases the stability of incompatibilities during hybridization, but also increases tolerance of incompatibility polymorphism within species. We use simulations and theories to show that this principle leads to two types of collapse: in redundant systems, exemplified by classical Dobzhansky-Muller incompatibilities, collapse is continuous and approaches a quasi-neutral polymorphism between broadly sympatric species, often as a result of isolation-by-distance. In non-redundant systems, exemplified by coevolution among genetic elements, incompatibilities are often stable, but can collapse abruptly with spatial traveling waves. As both types are common, the proposed principle may be useful in understanding the abundance of genetic incompatibilities in natural populations.</p>
Genotype, phenotype and linkage data for Mimulus parishii x M. cardinalis hybrid incompatibility study
<p>The evolution of genomic incompatibilities causing postzygotic barriers to hybridization is a key step in species divergence. Incompatibilities take two general forms – structural divergence between chromosomes leading to severe hybrid sterility in F<sub>1</sub> hybrids and epistatic interactions between genes causing reduced fitness of hybrid gametes or zygotes (Dobzhansky-Muller incompatibilities). Despite substantial recent progress in understanding the molecular mechanisms and evolutionary origins of both types of incompatibility, how each behaves across multiple generations of hybridization remains relatively unexplored. Here, we use genetic mapping in F<sub>2</sub> and RIL hybrid populations between the phenotypically divergent but naturally hybridizing monkeyflowers <em>Mimulus cardinalis</em> and <em>M. parishii</em> to characterize the genetic basis of hybrid incompatibility and examine its changing effects over multiple generations of experimental hybridization. In F<sub>2</sub>s, we found severe hybrid pollen inviability (< 50% reduction vs. parental genotypes) and pseudolinkage caused by a reciprocal translocation between Chromosomes 6 and 7 in the parental species. RILs retained excess heterozygosity around the translocation breakpoints, which caused substantial pollen inviability when interstitial crossovers had not created compatible heterokaryotypic configurations. Strong transmission ratio distortion and inter-chromosomal linkage disequilibrium in both F<sub>2</sub>s and RILs identified a novel two-locus genic incompatibility causing sex-independent gametophytic (haploid) lethality. The latter interaction eliminated three of the expected nine F<sub>2</sub> genotypic classes via F<sub>1</sub> gamete loss without detectable effects on the pollen number or viability of F<sub>2</sub> double heterozygotes. Along with the mapping of numerous milder incompatibilities, these key findings illuminate the complex genetics of plant hybrid breakdown and are an important step toward understanding the genomic consequences of natural hybridization in this model system.</p>
On the impermanence of species: The collapse of genetic incompatibilities in hybridizing populations
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Data from: Admixture mapping reveals evidence for multiple mitonuclear incompatibilities in swordtail fish hybrids
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Genotype, phenotype and linkage data for Mimulus parishii x M. cardinalis hybrid incompatibility study
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Data from: Hybrid incompatibility between D. virilis and D. lumei is stronger in the presence of transposable elements
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Data from: Selfish evolution of cytonuclear hybrid incompatibility in Mimulus
Intraspecific coevolution between selfish elements and suppressors may promote interspecific hybrid incompatibility, but evidence of this process is rare. Here, we use genomic data to test alternative models for the evolution of cytonuclear hybrid male sterility in Mimulus. In hybrids between Iron Mountain (IM) Mimulus guttatus × Mimulus nasutus, two tightly linked M. guttatus alleles (Rf1/Rf2) each restore male fertility by suppressing a local mitochondrial male-sterility gene (IM-CMS). Unlike neutral models for the evolution of hybrid incompatibility loci, selfish evolution predicts that the Rf alleles experienced strong selection in the presence of IM-CMS. Using whole-genome sequences, we compared patterns of population-genetic variation in Rf at IM to a neighbouring population that lacks IM-CMS. Consistent with local selection in the presence of IM-CMS, the Rf region shows elevated FST, high local linkage disequilibrium and a distinct haplotype structure at IM, but not at Cone Peak (CP), suggesting a recent sweep in the presence of IM-CMS. In both populations, Rf2 exhibited lower polymorphism than other regions, but the low-diversity outliers were different between CP and IM. Our results confirm theoretical predictions of ubiquitous cytonuclear conflict in plants and provide a population-genetic mechanism for the evolution of a common form of hybrid incompatibility.
Characterizing a lethal mitonuclear incompatibility in naturally hybridizing Xiphophorus swordtails
<p><span>The evolution of reproductive barriers is the first step in the formation of new species and can help us understand the diversification of life on Earth. These reproductive barriers often take the form of "hybrid incompatibilities," where alleles derived from two different species no longer interact properly in hybrids</span><span>. Theory predicts that hybrid incompatibilities may be more likely to arise at rapidly evolving genes</span><span> and that incompatibilities involving multiple genes should be common</span><span>, but there has been sparse empirical data to evaluate these predictions. Here, we describe a mitonuclear incompatibility involving three genes in physical contact within respiratory Complex I </span><span>of </span><span>naturally hybridizing swordtail fish species. Individuals homozygous for mismatched protein combinations fail to complete embryonic development or die as juveniles, while those heterozygous for the incompatibility have reduced </span><span>Complex I </span><span>function and unbalanced representation of parental alleles in the mitochondrial proteome. We find that the impacts of different genetic interactions on survival are non-additive, highlighting subtle complexity in the genetic architecture of hybrid incompatibilities. </span><span>Finally, we</span><span> document the evolutionary history of the genes involved, showing </span><span>signals of accelerated</span> <span>evolution and the first</span> <span>case of </span><span>an incompatibility transferred between species via hybridization. </span></p>
Data from: Differential gene expression and mitonuclear incompatibilities in fast- and slow-developing inter-population Tigriopus californicus hybrids
<p>Mitochondrial functions are intimately reliant on proteins and RNAs encoded in both the nuclear and mitochondrial genomes, leading to inter-genomic coevolution within taxa. Hybridization can break apart coevolved mitonuclear genotypes, resulting in decreased mitochondrial performance and reduced fitness. This hybrid breakdown is an important component of outbreeding depression and early-stage reproductive isolation. However, the mechanisms contributing to mitonuclear interactions remain poorly resolved. Here we scored variation in developmental rate (a proxy for fitness) among reciprocal F2 inter-population hybrids of the intertidal copepod <em>Tigriopus californicus</em>, and used RNA sequencing to assess differences in gene expression between fast- and slow-developing hybrids. In total, differences in expression associated with developmental rate were detected for 2,925 genes, whereas only 135 genes were differentially expressed as a result of differences in mitochondrial genotype. Up-regulated expression in fast developers was enriched for genes involved in chitin-based cuticle development, oxidation-reduction processes, hydrogen peroxide catabolic processes, and mitochondrial respiratory chain complex I. In contrast, up-regulation in slow developers was enriched for DNA replication, cell division, DNA damage, and DNA repair. Eighty-four nuclear-encoded mitochondrial genes were differentially expressed between fast- and slow-developing copepods, including twelve subunits of the electron transport system (ETS) which all had higher expression in fast developers than in slow developers. Nine of these genes were subunits of ETS complex I. Our results emphasize the major roles that mitonuclear interactions within the ETS, particularly in complex I, play in hybrid breakdown, and resolve strong candidate genes for involvement in mitonuclear interactions.</p>
Data from: Selfish evolution of cytonuclear hybrid incompatibility in Mimulus
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Characterizing a lethal mitonuclear incompatibility in naturally hybridizing Xiphophorus swordtails
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Data from: Differential gene expression and mitonuclear incompatibilities in fast- and slow-developing inter-population Tigriopus californicus hybrids
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Endosperm-based incompatibilities in hybrid monkeyflowers
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Data from: Genetic incompatibilities in reciprocal hybrids between populations of Tigriopus californicus with low to moderate mitochondrial sequence divergence
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Integrative genomic phylogeography reveals signs of mitonuclear incompatibility in a natural hybrid goby population
<p>Hybridization between divergent lineages generates new allelic combinations. One mechanism that can hinder the formation of hybrid populations is mitonuclear incompatibility, i.e. dysfunctional interactions between proteins encoded on the nuclear and mitochondrial genomes (mitogenomes) of diverged lineages. Theoretically, selective pressure due to mitonuclear incompatibility can affect genotypes in a hybrid population in which nuclear genomes and mitogenomes from divergent lineages admix. To directly and thoroughly observe this key process, we <i>de novo</i> sequenced the 747 Mb genome of the coastal goby, <i>Chaenogobius annularis</i>, and investigated its integrative genomic phylogeographics using RNA‐sequencing, RAD‐sequencing, genome re‐sequencing, whole mitogenome sequencing, amplicon‐sequencing, and small RNA‐sequencing. <i>Chaenogobius annularis</i> populations have been geographically separated into Pacific Ocean (PO) and Sea of Japan (SJ) lineages by past isolation events around the Japanese archipelago. Despite the divergence history and potential mitonuclear incompatibility between these lineages, the mitogenomes of the PO and SJ lineages have coexisted for generations in a hybrid population on the Sanriku Coast. Our analyses revealed accumulation of nonsynonymous substitutions in the PO‐lineage mitogenomes, including two convergent substitutions, as well as signals of mitochondrial lineage‐specific selection on mitochondria‐related nuclear genes. Finally, our data implied that a microRNA gene was involved in resolving mitonuclear incompatibility. Our integrative genomic phylogeographic approach revealed that mitonuclear incompatibility can affect genome evolution in a natural hybrid population.</p>
Data from: A rare exception to Haldane's rule: Are X chromosomes key to hybrid incompatibilities?
The prevalence of Haldane's rule suggests that sex chromosomes commonly have a key role in reproductive barriers and speciation. However, the majority of research on Haldane's rule has been conducted in species with conventional sex determination systems (XY and ZW) and exceptions to the rule have been understudied. Here we test the role of X-linked incompatibilities in a rare exception to Haldane's rule for female sterility in field cricket sister species (Teleogryllus oceanicus and T. commodus). Both have an XO sex determination system. Using three generations of crosses, we introgressed X chromosomes from each species onto different, mixed genomic backgrounds to test predictions about the fertility and viability of each cross type. We predicted that females with two different species X chromosomes would suffer reduced fertility and viability compared with females with two parental X chromosomes. However, we found no strong support for such X-linked incompatibilities. Our results preclude X–X incompatibilities and instead support an interchromosomal epistatic basis to hybrid female sterility. We discuss the broader implications of these findings, principally whether deviations from Haldane's rule might be more prevalent in species without dimorphic sex chromosomes.
Data from: Hybrid speciation by sorting of parental incompatibilities in Italian sparrows
Speciation by hybridization is emerging as a significant contributor to biological diversification. Yet, little is known about the relative contributions of (i) evolutionary novelty and (ii) sorting of preexisting parental incompatibilities to the build-up of reproductive isolation under this mode of speciation. Few studies have addressed empirically whether hybrid animal taxa are intrinsically isolated from their parents, and no study has so far investigated by which of the two aforementioned routes intrinsic barriers evolve. Here, we show that sorting of preexisting parental incompatibilities contributes to intrinsic isolation of a hybrid animal taxon. Using a genomic cline framework, we demonstrate that the sex-linked and mito-nuclear incompatibilities isolating the homoploid hybrid Italian sparrow at its two geographically separated hybrid-parent boundaries represent a subset of those contributing to reproductive isolation between its parent species, house and Spanish sparrows. Should such a sorting mechanism prove to be pervasive, the circumstances promoting homoploid hybrid speciation may be broader than currently thought, and indeed there may be many cryptic hybrid taxa separated from their parent species by sorted, inherited incompatibilities.
Analysis of ancestry heterozygosity suggests that hybrid incompatibilities in threespine stickleback are environment-dependent
<p>Hybrid incompatibilities occur when interactions between opposite-ancestry alleles at different loci reduce the fitness of hybrids. Most work on incompatibilities has focused on those that are 'intrinsic', meaning they affect viability and sterility in the laboratory. Theory predicts that ecological selection can also underlie hybrid incompatibilities, but tests of this hypothesis using sequence data are scarce. In this article, we compiled genetic data for F<sub>2</sub> hybrid crosses between divergent populations of threespine stickleback fish (<em>Gasterosteus aculeatus</em> L.) that were born and raised in either the field (semi-natural experimental ponds) or the laboratory (aquaria). Because selection against incompatibilities results in elevated ancestry heterozygosity, we tested the prediction that ancestry heterozygosity will be higher in pond-raised fish compared to those raised in aquaria. We found that ancestry heterozygosity was elevated by approximately 3% in crosses raised in ponds compared to those raised in aquaria. Additional analyses support a phenotypic basis for incompatibility and suggest that environment-specific single-locus heterozygote advantage is not the cause of selection on ancestry heterozygosity. Our study provides evidence that, in stickleback, a coarse—albeit indirect—signal of environment-dependent hybrid incompatibility is reliably detectable and suggests that extrinsic incompatibilities can evolve before intrinsic incompatibilities.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.