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343 results for “genomic divergence”
Genomics of extreme ecological specialists: multiple convergent evolution but no genetic divergence between ecotypes of Maculinea alcon butterflies
<p>Biotic interactions are often acknowledged as catalysers of genetic divergence and eventual explanation of processes driving species richness. We address the question, whether extreme ecological specialization is always associated with lineage sorting, by analysing polymorphisms in morphologically similar ecotypes of the myrmecophilous butterfly <em>Maculinea alcon</em>. The ecotypes occur in either hygric or xeric habitats, use different larval host plants and ant species, but no significant distinctive molecular traits have been revealed so far. We apply genome-wide RAD-sequencing to specimens originating from both habitats across Europe in order to get a view of the potential evolutionary processes at work. Our results confirm that genetic variation is mainly structured geographically but not ecologically — specimens from close localities are more related to each other than populations of each ecotype from distant localities. However, we found two loci for which the association with xeric versus hygric habitats is supported by segregating alleles, suggesting convergent evolution of habitat preference. Thus, ecological divergence between the forms probably does not represent an early stage of speciation, but may result from independent recurring adaptations involving few genes. We discuss the implications of these results for conservation and suggest preserving biotic interactions and main genetic clusters.</p>
Data from: Can the genomics of ecological speciation be predicted across the divergence continuum from host races to species? A case study in Rhagoletis
<p>Studies assessing the predictability of evolution typically focus on short-term adaptation within populations or the repeatability of change among lineages. A missing consideration in speciation research is to determine whether natural selection predictably transforms standing genetic variation within populations into differences between species. Here, we test whether host-related selection on diapause timing anticipates genome-wide differentiation during ecological speciation by comparing ancestral hawthorn and newly formed apple-infesting host races of <i>Rhagoletis pomonella </i>to their sibling species <i>R. mendax</i> that attacks blueberries. The responses of 57,857 single nucleotide polymorphisms in a diapause study on the hawthorn race strongly predicted the direction and magnitude of genomic divergence among the three flies at a field site in Fennville, Michigan, USA. As anticipated, the apple race and <i>R. mendax</i> show parallel changes in the frequencies of putative inversions on three chromosomes associated with the earlier fruiting times of apples and blueberries compared to hawthorns. A diapause experiment on <i>R. mendax</i> revealed compensatory mutations throughout the genome accounting for the earlier eclosion of blueberry, but not apple flies. Thus, a degree of predictability, although not complete, exists in the genomics of diapause across the ecological speciation continuum in <i>Rhagoletis</i>. The generality of this result is placed in the context of other similar systems.</p>
Data from: Gene flow, ancient polymorphism, and ecological adaptation shape the genomic landscape of divergence among Darwin's finches
Genomic comparisons of closely related species have identified "islands" of locally elevated sequence divergence. Genomic islands may contain functional variants involved in local adaptation or reproductive isolation and may therefore play an important role in the speciation process. However, genomic islands can also arise through evolutionary processes unrelated to speciation, and examination of their properties can illuminate how new species evolve. Here, we performed scans for regions of high relative divergence (FST) in 12 species pairs of Darwin's finches at different genetic distances. In each pair, we identify genomic islands that are, on average, elevated in both relative divergence (FST) and absolute divergence (dXY). This signal indicates that haplotypes within these genomic regions became isolated from each other earlier than the rest of the genome. Interestingly, similar numbers of genomic islands of elevated dXY are observed in sympatric and allopatric species pairs, suggesting that recent gene flow is not a major factor in their formation. We find that two of the most pronounced genomic islands contain the ALX1 and HMGA2 loci, which are associated with variation in beak shape and size, respectively, suggesting that they are involved in ecological adaptation. A subset of genomic island regions, including these loci, appears to represent anciently diverged haplotypes that evolved early during the radiation of Darwin's finches. Comparative genomics data indicate that these loci, and genomic islands in general, have exceptionally low recombination rates, which may play a role in their establishment.
Data from: Genomic landscapes of divergence among island bird populations: evidence of parallel adaptation but at different loci?
<p>When populations colonise new environments they may be exposed to novel selection pressures but also suffer from extensive genetic drift due to founder effects, small population sizes, and limited interpopulation gene flow. Genomic approaches enable us to study how these factors drive divergence, and disentangle neutral effects from differentiation at specific loci due to selection. Here, we investigate patterns of genetic diversity and divergence using whole-genome resequencing (> 22X coverage) in Berthelot's pipit (<em>Anthus berthelotii</em>), a passerine endemic to the islands of three north Atlantic archipelagos. Strong environmental gradients, including in pathogen pressure, across populations in the species range, make it an excellent system in which to explore traits important in adaptation and/or incipient speciation. Firstly, we quantify how genomic divergence accumulates across the speciation continuum, i.e., among Berthelot's pipit populations, between subspecies across archipelagos, and between Berthelot's pipit and its mainland ancestor, the tawny pipit (<em>Anthus campestris</em>). Across these colonisation timeframes (2.1 million – <em>ca.</em> 8,000 years ago), we identify highly differentiated loci within genomic islands of divergence and conclude that the observed distributions align with expectations for non-neutral divergence. Characteristic signatures of selection are identified in loci associated with craniofacial/bone and eye development, metabolism, and immune response between population comparisons. Interestingly, we find limited evidence for repeated divergence of the same loci across the colonisation range but do identify different loci putatively associated with the same biological traits in different populations, likely due to parallel adaptation. Incipient speciation across these island populations, in which founder effects and selective pressures are strong, may therefore be repeatedly associated with morphology, metabolism, and immune defence.</p>
Supplementary Data of "Introgression between highly divergent sea squirt genomes: an adaptive breakthrough?"
<p><strong>Datasets used in the analyses (see Table S5 for a detailed description).</strong></p> <p>► Dataset #1: phased SNPs with offspring.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle.vcf<br> ► Dataset #2: all SNPs with missing data.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_parents_oNA5.vcf<br> ► Dataset #3a: phased SNPs.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle_parents.vcf<br> ► Dataset #3b: CDS version of “phased SNPs”.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle_parents_SNP_CHRall.orf.cds<br> ► Dataset #3c: FASTA version of “phased SNPs”.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle_parents_SNP_chr5.sub_${START}-${END}.fasta<br> ► Dataset #4: ancestry informative phased SNPs.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle_parents.frq.fixed.vcf<br> ► Dataset #5: all SNPs.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_parents_oNA.vcf<br> ► Dataset #6: all polarized SNPs.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_parents_raw_refine_HQ_edwardsi_oNA3_polar.vcf<br> ► Dataset #7: unfiltered mapping files.<br> ciona_bwa-mapping_${IND}_bwa_mem_mdup_IR_chromosome5:700000-1500000_sorted_nodup.bam</p>
Supplementary Information of "Introgression between highly divergent sea squirt genomes: an adaptive breakthrough?"
<p><strong>Supplementary Figures</strong></p> <p><strong>Figure S1</strong> Population genetic statistics calculated in non-overlapping 10 Kb windows along the 14 chromosomes in the sea squirt genome.<br> <strong>Figure S2</strong> <em>C. robusta</em> introgression into <em>C. intestinalis</em> shown across the 14 chromosomes.<br> <strong>Figure S3</strong> Population genetic statistics of the <em>C. robusta</em> introgressed coding sequences.<br> <strong>Figure S4 </strong>ABBA-BABA introgression patterns using<em> C. edwardsi </em>as an outgroup.<br> <strong>Figure S5</strong> Inference of the divergence history between <em>C. robusta</em> and <em>C. intestinalis</em> with moments.<br> <strong>Figure S6 </strong>Selection tests.<br> <strong>Figure S7 </strong><em>C. robusta</em> ancestry along chromosome 5 in <em>C. intestinalis</em> individuals.<br> <strong>Figure S8</strong> Neighbor-joining trees of 50 Kb windows framing the “missing data region” (grey band) at the center of the chromosome 5 hotspot.<br> <strong>Figure S9</strong> Copy number variation at candidate SNPs in the introgression hotspot on chromosome 5 (700 Kb - 1.5 Mb).<br> <strong>Figure S10</strong> Structural analysis of the “missing data region” on chromosome 5 (from 1,009,000 to 1,055,000 bp).</p> <p> </p> <p><strong>Supplementary Tables</strong></p> <p><strong>Table S1</strong> Sample information.<br> <strong>Table S2 </strong>Correlation between chromosomes of the individual <em>C. robusta </em>ancestry fraction.<br> <strong>Table S3</strong> Demographic results with moments – excluding chromosome 5.<br> <strong>Table S4</strong> Demographic results with moments – including chromosome 5.<br> <strong>Table S5 </strong>Description of the Supplementary Data.</p> <p> </p> <p><strong>Supplementary Scripts</strong></p> <p><em>Bioinformatic pipeline used for genotyping and haplotyping.</em></p> <p><strong>Script #1</strong>: prepare the reference genome for BWA and GATK.<br> reference_bwa_GATK_CF.sh<br> <strong>Script #2</strong>: mapping the reads to the reference with BWA.<br> mapping_bwa-mem_CF.sh<br> <strong>Script #3</strong>: indel realignment with GATK.<br> indel_realignment_CF.sh<br> <strong>Script #4</strong>: individual variant calling in gVCF format with GATK.<br> snpindel_callingGVCF_raw_CF.sh<br> <strong>Script #5</strong>: joint genotyping with GATK.<br> joint_genotyping_raw_CF.sh<br> <strong>Script #6</strong>: genotype refinement with GATK.<br> genotype_refinement_raw_CF.sh<br> <strong>Script #7</strong>: SNPs and indels recalibration with GATK.<br> snpindel_recalibration_CF.sh<br> <strong>Script #8</strong>: genotype refinement after recalibration with GATK.<br> genotype_refinement_recal_CF.sh<br> <strong>Script #9</strong>: genotype correction.<br> phase_by_transmission_correctCalling_CF@2020.sh<br> <strong>Script #10</strong>: phasing with GATK and BEAGLE.<br> phase_by_transmission_clean_CF@2020.sh</p> <p><em>Pipeline used for the demographic inferences with moments.</em></p> <p><strong>Script #11</strong>: define the demographic models.<br> moments_models_2pop_bb_parallel_folded_2periods.py<br> <strong>Script #12</strong>: run the demographic inferences.<br> moments_inference_dualanneal_bb_parallel_folded_2periods_bounds.py</p>
Genomic variation in the Black-throated Green Warbler (Setophaga virens) suggests divergence in a disjunct Atlantic Coastal Plain population (S. v. waynei)
<p>We used whole-genome resequencing to estimate genetic distinctiveness in the Black-throated Green Warbler (Setophaga virens)—including S. v. waynei—a putative subspecies that occupies a narrow disjunct breeding range along the Atlantic Coastal Plain. Despite detecting low-global differentiation (FST = 0.027) across the entire species, the principal components analysis of genome-wide differences shows the main axis of variation separates S. v. waynei from all other S. v. virens samples. We also estimated a low-migration rate for S. v. waynei, but found them to be most similar to another disjunct population from the Piedmont of North Carolina, and detected evidence of a historical north-to-south geographic dispersal among the entire species. New World wood warblers (family: Parulidae) can exhibit strong phenotypic differences among species, particularly, in song and plumage; however, within-species variation in these warblers—often designated as subspecies—is much more subtle. The existence of several isolated Black-throated Green Warbler populations across its eastern North American breeding range offers an excellent opportunity to further understand the origin, maintenance, and conservation status of subspecific populations. Our results, combined with previously documented ecological and morphological distinctiveness, support that S. v. waynei be considered a distinct and recognized subspecies worthy of targeted conservation efforts.</p>
Figure 5. Bayesian phylogeny, with species divergence age estimates reconstructed with BEAST using all the 26 in Complete mitochondrial genomes from museum specimens clarify millipede evolution in the Eastern Arc Mountains
Figure 5. Bayesian phylogeny, with species divergence age estimates reconstructed with BEAST using all the 26 mitochondrial genomes generated in this study. The dataset was supplemented with Thyropygus sp. and Abacion magnum as outgroups, derived from GenBank. GenBank accession numbers are provided in parentheses. Blue bars indicate the 95% highest probability density intervals for node ages. Age estimation for lineage divergence was based on a general arthropod mitochondrial DNA substitution rate and should be considered with caution. *Thyropygus sp. (red font) is very likely to be a misidentification; for more information, see the Discussion.
Evolution of the correlated genomic variation landscape across a divergence continuum in the genus Castanopsis
<p>The heterogeneous landscape of genomic variation has been well documented in population genomic studies. However, disentangling the intricate interplay of evolutionary forces influencing the genetic variation landscape over time remains challenging. In this study, we assembled a chromosome-level genome for <em>Castanopsis eyrei</em> and sequenced the whole genomes of 276 individuals from 12 <em>Castanopsis</em> species, spanning a broad divergence continuum. We found highly correlated genomic variation landscapes across these species. Furthermore, variations in genetic diversity and differentiation along the genome were strongly associated with recombination rates and gene density. These results suggest that long-term linked selection and conserved genomic features have contributed to the formation of a common genomic variation landscape. By examining how correlations between population summary statistics change throughout the species divergence continuum, we determined that background selection alone does not fully explain the observed patterns of genomic variation; the effects of recurrent selective sweeps must be considered. We further revealed that extensive gene flow has significantly influenced patterns of genomic variation in <em>Castanopsis</em> species. The estimated admixture proportion correlated positively with recombination rate and negatively with gene density, supporting a scenario of selection against gene flow. Additionally, putative introgression regions exhibited strong signals of positive selection, an enrichment of functional genes, and reduced genetic burdens, indicating that adaptive introgression has played a role in shaping the genomes of hybridizing species. This study provides insights into how different evolutionary forces have interacted in driving the evolution of the genomic variation landscape.</p>
FIGURE 5 in The Mitochondrial Genome of Allonautilus (Mollusca: Cephalopoda): Base Composition, Noncoding-Region Variation, and Phylogenetic Divergence
FIGURE 5. Phylogenetic relationships among some cephalopod species (and their orders) based on mitochondrial DNA sequences. Parsimony phylogram is based on COX and ATPase genes for cephalopod species whose mitogenomes have been sequenced; Katharina tunicata was used as an outgroup (not shown). Bootstrap values are shown along branches.
FIGURE 2 in The Mitochondrial Genome of Allonautilus (Mollusca: Cephalopoda): Base Composition, Noncoding-Region Variation, and Phylogenetic Divergence
FIGURE 2. Arrangement of the mitogenome of Allonautilus scrobiculatus; the lengths of the individual genes are drawn approximately to scale. Genes encoding on the same strand as CO1 are shown (in white) on the outer portion of the circular genome and are transcribed in the clockwise direction. Genes on the other strand are transcribed in the counterclockwise direction and are indicated on the inner portion of the genome and shaded in blue; the nine largest noncoding regions (20 bp or greater) are shown in gray.
FIGURE 4 in The Mitochondrial Genome of Allonautilus (Mollusca: Cephalopoda): Base Composition, Noncoding-Region Variation, and Phylogenetic Divergence
FIGURE 4. Base composition of the major genes (excluding tRNAs) in the mitogenome of Allonautilus. Plusstrand (+) defined as the coding strand for CO1.
FIGURE 1 in The Mitochondrial Genome of Allonautilus (Mollusca: Cephalopoda): Base Composition, Noncoding-Region Variation, and Phylogenetic Divergence
FIGURE 1. Allonautilus differs from Nautilus in the size and shape of the umbilicus, type of periostracum, and texture of the hood (e.g., Saunders et al., 1987). A. Allonautilus scrobiculatus, Little Ndrova Island, Papua New Guinea, AMNH 101045. B. Nautilus macromphalus, New Caledonia, AMNH 94104.
FIGURE 3 in The Mitochondrial Genome of Allonautilus (Mollusca: Cephalopoda): Base Composition, Noncoding-Region Variation, and Phylogenetic Divergence
FIGURE 3. Architecture of the large noncoding region of extant nautilid mitogenomes. Features are shown for the strand on which CO1 is coded. Nautilus macromphalus (Boore, 2006) was characterized by a microsatellite, six copies of a 62 bp repeat (R1–R6), and a poly-T monomer; in Allonautilus, the microsatellite and first copy of the repeat were missing in one individual (indel pattern A), and an additional repeat was missing in two other individuals (indel pattern B); all individuals possessed the poly-T monomer (blue bar). The frequency of T and G nucleotides varied considerably through the noncoding region: their frequencies, in a 100 bp sliding window, are shown in the lower panel.
Whole genome demographic models indicate divergent effective population size histories shape contemporary genetic diversity gradients in a montane bumble bee
<p>Understanding historical range shifts and population size variation provides important context for interpreting contemporary genetic diversity. Methods to predict changes in species distributions and model changes in effective population size (N<sub>e</sub>) using whole genomes make it feasible to examine how temporal dynamics influence diversity across populations. We investigate N<sub>e</sub> variation and climate-associated range shifts to examine the origins of a previously observed latitudinal heterozygosity gradient in the bumble bee <em>Bombus</em> <em>vancouverensis</em> Cresson (Hymenoptera: Apidae: <em>Bombus</em> Latreille) in western North America. We analyze whole genomes from a latitude-elevation cline using sequentially Markovian coalescent models of N<sub>e</sub> through time to test whether relatively low diversity in southern high-elevation populations is a result of long-term differences in N<sub>e</sub>. We use Maxent models of the species range over the last 130,000 years to evaluate range shifts and stability. N<sub>e</sub> fluctuates with climate across populations, but more genetically diverse northern populations have maintained greater Ne over the late Pleistocene and experienced larger expansions with climatically favorable time periods. Northern populations also experienced larger bottlenecks during the last glacial period which matched the loss of range area near these sites, however, bottlenecks were not sufficient to erode diversity maintained during periods of large N<sub>e</sub>. A genome sampled from an island population indicated a severe postglacial bottleneck, indicating that large recent post-glacial declines are detectable if they have occurred. Genetic diversity was not related to niche stability or glacial-period bottleneck size. Instead, spatial expansions and increased connectivity during favorable climates likely maintain diversity in the north while restriction to high elevations maintains relatively low diversity despite greater stability in southern regions. Results suggest genetic diversity gradients reflect long-term differences in N<sub>e</sub> dynamics and also emphasize the unique effects of isolation on insular habitats for bumble bees. Patterns are discussed in the context of conservation under climate change.</p>
High heterogeneity in genomic differentiation between phenotypically divergent songbirds: A test of mitonuclear co-introgression
<p>Comparisons of genomic variation among closely related species often show more differentiation in mitochondrial DNA (mtDNA) and sex chromosomes than in autosomes, a pattern expected due to the differing effective population sizes and evolutionary dynamics of these genomic components. Yet, introgression can cause species pairs to deviate dramatically from general differentiation trends. The yellowhammer (<em>Emberiza</em> <em>citrinella</em>) and pine bunting (<em>E</em>. <em>leucocephalos</em>) are hybridizing avian sister species that differ greatly in appearance and moderately in nuclear DNA, but that show no mtDNA differentiation. This discordance is best explained by adaptive mtDNA introgression—a process that can select for co-introgression at nuclear genes with mitochondrial functions (mitonuclear genes). To better understand these discordant differentiation patterns and characterize nuclear differentiation in this system, we investigated genome-wide differentiation between allopatric yellowhammers and pine buntings and compared it to what was seen previously in mtDNA. We found significant nuclear differentiation that was highly heterogeneous across the genome, with a particularly wide differentiation peak on the sex chromosome Z. We further investigated mitonuclear gene co-introgression between yellowhammers and pine buntings and found support for this process in the direction of pine buntings into yellowhammers. Genomic signals indicative of co-introgression were common in mitonuclear genes coding for subunits of the mitoribosome and electron transport chain complexes. Such introgression of mitochondrial DNA and mitonuclear genes provides a possible explanation for the patterns of high genomic heterogeneity in genomic differentiation seen among some species groups.</p>
Data from: Genomic landscapes of divergence among island bird populations: evidence of parallel adaptation but at different loci?
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Genomic variation in the Black-throated Green Warbler (Setophaga virens) suggests divergence in a disjunct Atlantic Coastal Plain population (S. v. waynei)
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Data from: Gene flow, ancient polymorphism, and ecological adaptation shape the genomic landscape of divergence among Darwin's finches
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Data from: Inversions contribute disproportionately to parallel genomic divergence in dune sunflowers
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