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675 results for “introgression”
Genome evolution and introgression in the New Zealand mud snails Potamopyrgus estuarinus and Potamopyrgus kaitunuparaoa
<p>We have sequenced, assembled, and analyzed the nuclear and mitochondrial genomes and transcriptomes of <i>Potamopyrgus estuarinus</i> and <i>Potamopyrgus kaitunuparaoa</i>, two prosobranch snail species native to New Zealand that together span the continuum from estuary to freshwater.<i> </i>These two species are the closest known relatives of the freshwater species <i>P. antipodarum—</i>a model for studying the evolution of sex, host-parasite coevolution, and biological invasiveness—and thus provide key evolutionary context for understanding its unusual biology. The <i>P. estuarinus</i> and <i>P. kaitunuparaoa </i>genomes are very similar in size and overall gene content. Comparative analyses of genome content indicate that these two species harbor a near-identical set of genes involved in meiosis and sperm functions, including seven genes with meiosis-specific functions. These results are consistent with obligate sexual reproduction in these two species and provide a framework for future analyses of <i>P. antipodarum—</i>a species comprising both obligately sexual and obligately asexual lineages, each separately derived from a sexual ancestor. Genome-wide multigene phylogenetic analyses indicate that <i>P. kaitunuparaoa</i> is likely the closest relative to <i>P. antipodarum. </i>We nevertheless show that there has been considerable introgression between <i>P. estuarinus</i> and <i>P. kaitunuparaoa.</i> That introgression does not extend to the mitochondrial genome, which appears to serve as a barrier to hybridization between <i>P. estuarinus </i>and <i>P. kaitunuparaoa.</i> Nuclear-encoded genes whose products function in joint mitochondrial-nuclear enzyme complexes exhibit similar patterns of non-introgression, indicating that incompatibilities between the mitochondrial and the nuclear genome may have prevented more extensive gene flow between these two species.<i> </i> </p>
Archaic introgression for HGDP and 1000genomes in hg38
<p>These files contain the infered positions of introgressed archaic sequence in 1000genomes and HGDP datasets.</p> <p>The segments are identified using hmmix (https://github.com/LauritsSkov/Introgression-detection). Datasets are phased so segments are infered for each haplotype. Each datasets will have two files: a *segments.txt file and a *SNPS.txt file.</p> <p> </p> <p>> The columns in the segments.txt file are:</p> <p><strong>name: </strong>name of individidual<br><strong>haplotype:</strong> either hap1 or hap2 (if a genotype is 0|1 then 0 will be on hap1 and 1 will be on hap2)<br><strong>pop: </strong>population from HGDP or 1000 genomes<br><strong>region: </strong>region from HGDP or 1000 genomes - can be AMERICA, CENTRAL_SOUTH_ASIA, EAST_ASIA, EUROPE, MIDDLE_EAST or OCEANIA<br><strong>chrom:</strong> chromosome in hg38 - X chromosome is not included<br><strong>start</strong>: start coordinate of introgressed segment in hg38<br><strong>end</strong>: end coordinate of introgressed segment in hg38<br><strong>mean_prob:</strong> Mean posterior probability that a segment is archaic according to hmmix (I usually recommend doing a cutoff at 0.8)<br><strong>ND_type:</strong> Which sequenced archaic does the segments share more derived SNPs with. Can be Both, Denisova, Neanderthal or none<br><strong>snps:</strong> Number of derived SNPs on segment NOT seen in Sub saharan Africa<br><strong>admixpopvariants:</strong> How many derived SNPs are shared with a sequenced arhaic genome<br><strong>Altai:</strong> How many derived SNPs are shared with the Altai Neanderthal (Denisova5)<br><strong>Vindija:</strong> How many derived SNPs are shared with Vindija Neanderthal (Vindija33.19)<br><strong>Denisova:</strong> How many derived SNPs are shared with Denisova (Denisova3)<br><strong>Chagyrskaya:</strong> How many derived SNPs are shared with Chagyrskaya Neanderthal (Chagyrskaya8)<br><strong>variants:</strong> List of derived SNPs on segment NOT seen in Sub saharan Africa </p> <p> </p> <p>> The columns in the SNPS.txt file are:</p> <p><strong>chrom:</strong> chromosome in hg38 - X chromosome is not included<br><strong>pos:</strong> position of SNP in hg38 coordinates<br><strong>snptype:</strong> can be shared derived with archaic (DAV), in high LD <br><strong>ancestralbase:</strong> what is the ancestral base<br><strong>derivedbases: </strong>What is the derived base (there can be multiple but >99% are bilallelic)<br><strong>freq_in_dataset:</strong> Frequency of most common derived base (in percent so the number is between 0 and 100)<br><strong>ND:</strong> derived in either Denisovans only (ND01), Neanderthals only (ND10), derived in both Neanderthals and Denisovans (ND11) or none<br><strong>sharedwith: </strong>Which archaic genomes is the derived allele(s) shared with. This does not only include the four high coverage archaics</p> <p> </p> <p> </p>
Adaptive Introgression in Modern Human Circadian Rhythm Genes Datasets
<p><strong>README:</strong></p> <p>Modern human genetic data with evidence of adaptive introgression from Neanderthals or Denisovans within circadian rhythm genes. The data was generated from the phased gnomAD 1KGP + HGDP callset (Koenig <em>et al</em>., 2024) and introgressed segments were identified by SPrime (Browning <em>et al</em>., 2018). Genes of interest were downloaded from the Circadian Genome Database (CGDB) (Li <em>et al</em>., 2017). Additional variants, haplotypes, and genes that have been previously reported to influence circadian rhythm or chronotype that are thought to be derived from Neanderthals and Denisovans were compiled from Dannemann & Kelso (2017), McArthur et al. (2021), Dannemann et al. (2022), and Velazquez-Arcelay et al. (2023).</p> <p><strong>SPrime ND_Match Files</strong></p> <p>Raw SPrime identified files that we used for our entire analysis. These were modified to include the archaic allele, archaic allele frequency, and average introgressed segment allele frequency. Note that these have been lifted over (Hinrichs <em>et</em> <em>al</em>., 2006) from GRCh38 (hg38) to GRCh37 (hg19) coordinates to match the genome builds of the archaic samples used in our study. As such, any manually generated variant IDs (chromosome:position:ReferenceAllele_AlternativeAllele naming convention) may no longer match the position they are currently sitting on as they were generated with hg38 coordinates. However, all of these were subsequently filtered out of our final results and any proper SNP IDs (dbSNP labels) will be accurate.</p> <p><strong>Supplementary Tables</strong></p> <p>All supplementary tables have an associated README as the first sheet that explains in detail the contents.</p> <p><strong>NEXUS Files</strong></p> <p>NEXUS files were used to generate haplotype networks in PopArt (Leigh & Bryant, 2015). There is a larger, master haplotype file and a smaller subset file. The larger file contains 668 haplotypes from all populations generated in the phased gnomAD 1KGP + HGDP callset (Koenig <em>et al</em>., 2024) for the <em>SUSD1 </em>core haplotype. The smaller subset file is the top 50 haplotypes and ties based on frequency, all Oceanic haplotypes with frequencies of at least 2, and the Neanderthal and Denisovan haplotypes for <em>SUSD1</em>. </p> <p><strong>TRAITS file</strong></p> <p>Accompanies the NEXUS files to create pie graphs for the haplotype network and contains frequency counts of number of haplotypes per region.</p>
Supporting data and codes for: A new biological species in the Mercurialis annua polyploid complex: functional divergence in inflorescence morphology, hybrid sterility and possible introgression
<p>This GitHub repository includes R codes and datasets for the paper: A new biological species in the Mercurialis annua polyploid complex: functional divergence in inflorescence morphology, hybrid sterility and possible introgression</p>
Adaptive introgression from maize has facilitated the establishment of teosinte as a noxious weed in Europe
<p>This is the total genotyoping matrix we used for the analyses.<br> The first line of the file contains the identifiers of the samples and each subsequent line the genotype at each SNP The first column contains the identifier of the SNPs.</p> <p>Genotype data for the 70 French teosintes was combined with published and available data for the following material: 40 accessions of Spanish teosintes (1), 314 accessions of parviglumis (2, 3), 332 accessions of mexicana (2, 3), 94 maize landraces from Meso- and Central-America (4) and 155 maize inbred lines from North-America and Europe (5)</p> <ol> <li> <p>Trtikova M, Lohn A, Binimelis R, Chapela I, Oehen B, Zemp N, Widmer A, Hilbeck A (2017) Teosinte in Europe – searching for the origin of a novel weed. Scientific Reports 7, 1560. DOI: https://doi.org/10.1038/s41598-017-01478-w</p> </li> <li> <p>Aguirre-Liguori JA, Tenaillon MI, Vásquez-Lobo A, Gaut BS, Jaramillo-Correa JP, Montes-Hernandez S, Souza V, Eguiarte LE (2017) Connecting genomic patterns of local adaptation and niche suitability in teosintes. Molecular Ecology 26, 4226-4240. DOI: https://doi.org/10.1111/mec.14203</p> </li> <li> <p>Pyhäjärvi T, Hufford MB, Mezmouk S, Ross-Ibarra J (2013) Complex patterns of local adaptation in teosinte. Genome Biology and Evolution 5, 1594–1609. DOI: https://doi.org/10.1093/gbe/evt109</p> </li> <li> <p>Takuno S, Ralph P, Swarts K, Elshire RJ, Glaubitz JC, Buckler ES, Hufford MB, Ross-Ibarra J (2015) Independent molecular basis of convergent highland adaptation in maize. Genetics 200, 1297–1312. DOI: https://doi.org/10.1534/genetics.115.17832</p> </li> <li> <p>Unterseer S, Pophaly SD, Peis R, Westermeier P, Mayer M, Seidel MA, Haberer G, Mayer KFX, Ordas B, Pausch H, Tellier A, Bauer , Schön CC (2016) A comprehensive study of the genomic differentiation between temperate Dent and Flint maize. Genome Biology 17, 137. DOI: https://doi.org/10.1186/s13059-016-1009-x</p> </li> </ol>
Interspecific introgression and widespread intraspecific gene flow in a clade of tropical and subtropical seabirds
<p>The mechanisms that restrict gene flow between populations and facilitate population differentiation and speciation vary across the tree of life. In systems where physical barriers to gene flow are dynamic over time and space, such as many marine species, genetic introgression may be a major factor in the speciation process. In sympatric species of seabirds, hybridization has been frequently observed but few studies have investigated patterns of introgression. We used whole-genome sequence data to test for interspecific introgression between five pairs of tropical and subtropical seabird species and to test for gene flow within species across major land mass barriers and ocean basins. We found evidence for introgression between: blue-footed (<em>Sula</em> <em>nebouxii</em>) and Peruvian boobies (<em>S. variegata</em>); masked (<em>S. dactylatra</em>) and Nazca boobies (<em>S. granti</em>); and blue-footed and Nazca boobies. We found no evidence of introgression between blue-footed and brown boobies (<em>S. leucogaster</em>), or masked and brown boobies, despite observed hybridization between these species. We also found evidence for gene flow across several major land masses in three pantropical species: red-footed (<em>S. sula</em>), brown, and masked boobies. Finally, we report mixed evidence for ancient introgression between brown boobies and the ancestor of blue-footed, Peruvian, masked, and Nazca boobies. Our work indicates (1) that interspecific introgression has shaped contemporary booby diversity in the eastern Pacific, and (2) that contemporary physical barriers to gene flow between booby colonies are not absolute. Our findings contribute novel insights to the growing body of evidence that introgression is a widespread evolutionary process.</p>
Genomic landscape of introgression from the ghost lineage in a gobiid fish uncovers the generality of forces shaping hybrid genomes
<p>Extinct lineages can leave legacies in the genomes of extant lineages through ancient introgressive hybridization. The patterns of genomic survival of these extinct lineages provide insight into the role of extinct lineages in current biodiversity. However, our understanding of the genomic landscape of introgression from extinct lineages remains limited due to challenges associated with locating the traces of unsampled "ghost" extinct lineages without ancient genomes. Herein, we conducted population genomic analyses on the East China Sea (ECS) lineage of <em>Chaenogobius annularis</em>, which was suspected to have originated from ghost introgression, with the aim of elucidating its genomic origins and characterizing its landscape of introgression. By combining phylogeographic analysis and demographic modeling, we demonstrated that the ECS lineage originated from ancient hybridization with an extinct ghost lineage. Forward simulations based on the estimated demography indicated that the statistic <em>γ</em> of the HyDe analysis can be used to distinguish the differences in local introgression rates in our data. Consistent with introgression between extant organisms, we found reduced introgression from extinct lineage in regions with low-recombination rates and with functional importance, thereby suggesting a role of linked selection that has eliminated the extinct lineage in shaping the hybrid genome. Moreover, we identified enrichment of repetitive elements in regions associated with ghost introgression, which was hitherto little-known but was also observed in the reanalysis of published data on introgression between extant organisms. Overall, our findings underscore the unexpected similarities in the characteristics of introgression landscapes across different taxa, even in cases of ghost introgression.</p>
FIGURE 5 in Cryptic diversity and gene introgression of Moinidae (Crustacea: Cladocera) in Nigeria
FIGURE 5 Haplotype network of Moinidae lineages within species, based on the mitochondrial COI gene (478 bp). Each circle represents a unique haplotype and its size reflects the number of sequences. Segment sizes within circles indicate the distribution of haplotypes among different regions (color key to regions is on the left side of the figure). The lineage ID s are shown in columns relating to the species-delimitation methods, and those newly detected from Nigeria are indicated in colored squares. The number of marks on connecting lines shows the number of mutations separating haplotypes.
FIGURE 4 in Cryptic diversity and gene introgression of Moinidae (Crustacea: Cladocera) in Nigeria
FIGURE 4 Bayesian phylogenetic tree of the (a) ITS-1 region (677 bp) and (b) ITS-2 region (955 bp) of Moinidae lineages from Nigeria. Only posterior probabilities> 0.70 are shown. The lineage ID s are shown in columns relating to the species-delimitation methods, and those newly detected from Nigeria are indicated in colored squares. The mismatch assignments by COI and ITS-1 are in bold and highlighted with an asterisk. For abbreviations of country names refer to Fig. 3.
FIGURE 3 in Cryptic diversity and gene introgression of Moinidae (Crustacea: Cladocera) in Nigeria
FIGURE 3 Bayesian phylogenetic tree and species- delimitation of Moinidae from Southeast Nigeria, based on the mitochondrial COI gene (478 bp). A single representative of each haplotype (for reference sequences see Supplementary Table S1) is included in the tree. Codes of Moinidae haplotypes from Nigeria are provided in Table 1. Only posterior probabilities> 0.70 are shown. The numbers in the bands relating to the bPTP method indicate the statistical support (PP) for lineage membership. The lineage ID s are shown in columns relating to the species-delimitation methods, and the newly detected lineages from Nigeria are indicated in colored squares. Abbreviations of country names in which each haplotype was detected are, BO: Bolivia, CA: Canada, CN: China, CZ: Czech Republic, HU: Hungary, IN: India, JP: Japan, KZ: Kazakhstan, KR: Korea, MX: Mexico, MN: Mongolia, NG: Nigeria, RU: Russia, TH: Thailand, UA: Ukraine, US: U.S.A. Downloaded from Brill.com 12/12/2023 04:27:13PM via Open Access. This is an open access article distributed under the terms of the CC BY 4.0 license. https://creativecommons.org/licenses/by/4.0/
FIGURE 2 in Cryptic diversity and gene introgression of Moinidae (Crustacea: Cladocera) in Nigeria
FIGURE 2 Morphology of Moinidae from Southeast Nigeria. Monia cf. micrura from the Nome Pool 2, Amaho: lateral view of (a) parthenogenetic female, (b) male and (c) ephippial female; (d) antenna II, (e) postabdomen (f) valve and (g) postero-ventral margin of valve of the parthenogenetic female. Monia cf. macrocopa, parthenogenetic female from Nome Pool 1: (h) lateral view, (i) antenna II, (j) limb I, (k) postabdomen and (l) valve. Moinodaphnia macleayi, parthenogenetic female from Adanni Opanda Rd Pool 1: (m) lateral view, (n) antenna II, (o) postabdomen and (p) valve. Scale bars 0.1 mm.
FIGURE 1 in Cryptic diversity and gene introgression of Moinidae (Crustacea: Cladocera) in Nigeria
FIGURE 1 Geographic locations of sampling for Moinidae in Southeast Nigeria. Solid black circles indicate locations where moinids were present, empty circles indicate locations where no moinids were detected. Large colored circles near solid black circles represent the distribution of COI lineages. For abbreviations of location names, refer to Table 1.
Population size differences can lead to biases in phylogenetic inference and introgression detection in the presence of purifying selection
<p>Phylogenetic reconstruction and introgression detection rely on an assumption about the probability distribution of gene tree topologies. Recently, evidence has emerged that population size differences can affect the probability distribution of gene tree topologies in the presence of purifying selection. Here, using the population genetic simulator SLiM, we provide evidence that in the presence of purifying selection, population size differences can lead to biases in phylogenetic inference. We also provide evidence that in the presence of purifying selection, population size differences can cause statistics used for introgression detection to exhibit patterns resembling those caused by introgression. In addition, we present a theoretical analysis showing that the occurrence of population size–dependent gene tree distributions is an inherent consequence of purifying selection. Our work underscores the importance of considering the potential confounding effect of purifying selection on phylogenetic inference and introgression detection.</p>
Selection shapes the genomic landscape of introgressed ancestry in a pair of sympatric sea urchin species
<p>A growing number of recent studies have demonstrated that introgression is common across the tree of life. However, we still have a limited understanding of the fate and fitness consequence of introgressed variation at the whole-genome scale across diverse taxonomic groups. Here, we implemented a phylogenetic hidden Markov model to identify and characterize introgressed genomic regions in a pair of well-diverged, non-sister sea urchin species: <em>Strongylocentrotus</em> <em>pallidus</em> and <em>S. droebachiensis</em>. Despite the old age of introgression, a sizable fraction of the genome (1% - 5%) exhibited introgressed ancestry, including numerous genes showing signals of historical positive selection that may represent cases of adaptive introgression. One striking result was the overrepresentation of hyalin genes in the identified introgressed regions despite observing considerable overall evidence of selection against introgression. There was a negative correlation between introgression and chromosome gene density, and two chromosomes were observed with considerably reduced introgression. Relative to the non-introgressed genome-wide background, introgressed regions had significantly reduced nucleotide divergence (<em>d</em><sub>XY</sub>) and overlapped fewer protein-coding genes, coding bases, and genes with a history of positive selection. Additionally, genes residing within introgressed regions showed slower rates of evolution (<em>d</em><sub>N</sub>, <em>d</em><sub>S</sub>, <em>d</em><sub>N</sub>/<em>d</em><sub>S</sub>) than random samples of genes without introgressed ancestry. Overall, our findings are consistent with widespread selection against introgressed ancestry across the genome and suggest that slowly evolving, low-divergence genomic regions are more likely to move between species and avoid negative selection following hybridization and introgression.</p>
Fig. 4 in Altai Mountains - cradle of hybrids and introgressants: A case study in Veronica subg. Pseudolysimachium (Plantaginaceae)
Fig. 4. STRUCTURE results showing the probability of ancestry of each individual (horizontal axis) to each of K = 2 populations (vertical axis) in all the five scenarios. A, Veronica spicata × V. pinnata; B, V. incana and V. longifolia; C, V. longifolia and V. porphyriana; D & E, V. pinnata and V. porphyriana involving putative hybrids of V. ×schmakovii and V. ×sessiliflora. Details of the exact posterior probabilities of each putative hybrid individual and their corresponding parents are given in suppl. Table S3.
Supplementary Information for "Performance evaluation of adaptive introgression classification methods"
<p>Supplementary information : supplementary figures and tables from "<em>Performance evaluation of adaptive introgression classification methods</em>", Romieu <em>et al., </em>2024 manuscript. ROC values, curves and score value by non-AI windows type for various demographic scenarios.</p>
Demographic history and natural selection shape patterns of deleterious mutation load and barriers to introgression across Populus genome
<p><br> Abbreviation of species names in each folder: Palb, P. alba; Pade, P. adenopoda; Pdav, P. davidiana; Ptra, P. tremula; Ptrs, P. tremuloides; Prot, P. rotundifolia; Pqio,P. qiongdaoensis.</p> <p>1. FST<br> Relative divergence (FST) for pairwise species comparisons was calculated for all sites with 100 Kbp non-overlapping windows. </p> <p>2. dxy<br> Absolute divergence (dxy) was calculated for all sites with 100 Kbp non-overlapping windows. </p> <p>3. Nucleotide diversity<br> Nucleotide diversity (π) was calculated for all sites with 100 Kbp non-overlapping windows. </p> <p>4. Derived allele frequency<br> The derived frequencies of 4 different functional categories. Each folder contains seven Populus resluts</p> <p>5. Derived_allele_statistics<br> The statistics of homozygous and heterozygous derived alleles for loss of function, deleterious, tolerated and synonymous variants for each individual. The last two individuals in each file are outgroups </p> <p>6. dsuite-dinvestigate<br> The outputs of 10 trios using program Dinvestigate from Dsuite. The sliding window is 50 SNPs, and the step is 20 SNPs.</p> <p>7. Recombination rate<br> The result of population-scaled recombination rate was calculated by LDhat v2.2.</p> <p>8. Volcanofinder<br> Genome-wide scans of introgression sweeps within each species was implemented using VolcanFinder v.1.0 with the Model over 10 Kbp non-overlapping windows.</p> <p>9. ihh12<br> phased SNPs were used to computed ihh12 by selscan v1.3.0. </p> <p>10 populus162.phased.recode.vcf.gz<br> SNPs were phased with Beagle v.4.1 for the 162 non-hybrid individuals.</p> <p>11 populus227.snp.rm_indel.para_filter.biallelic.GQ30.max_miss20.bed.recode.vcf.gz <br> The vcf of 227 Populus samples. </p>
Phylogenomics, introgression, and demographic history of South American true toads (Rhinella)
<p>The effects of genetic introgression on species boundaries and how they affect species' integrity and persistence over evolutionary time have received increased attention. The increasing availability of genomic data has revealed contrasting patterns of gene flow across genomic regions, which impose challenges to inferences of evolutionary relationships and of patterns of genetic admixture across lineages. By characterizing patterns of variation across thousands of genomic loci in a widespread complex of true toads (<em>Rhinella</em>), we assess the true extent of genetic introgression across species thought to hybridize to extreme degrees based on natural history observations and multi-locus analyses. Comprehensive geographic sampling of five large-ranged Neotropical taxa revealed multiple distinct evolutionary lineages that span large geographic areas and, at times, distinct biomes. The inferred major clades and genetic clusters largely correspond to currently recognized taxa; however, we also found evidence of cryptic diversity within taxa. While previous phylogenetic studies revealed extensive mito-nuclear discordance, our genetic clustering analyses uncovered several admixed individuals within major genetic groups. Accordingly, historical demographic analyses supported that the evolutionary history of these toads involved cross-taxon gene flow both at ancient and recent times. Lastly, ABBA-BABA tests revealed widespread allele sharing across species boundaries, a pattern that can be confidently attributed to genetic introgression as opposed to incomplete lineage sorting. These results confirm previous assertions that the evolutionary history of <em>Rhinella</em> was characterized by various levels of hybridization even across environmentally heterogeneous regions, posing exciting questions about what factors prevent complete fusion of diverging yet highly interdependent evolutionary lineages.</p>
Genome-wide sequence data show no evidence of hybridization and introgression among pollinator wasps associated with a community of Panamanian strangler figs
<p>The specificity of pollinator host choice influences opportunities for reproductive isolation in their host plants. Similarly, host plants can influence opportunities for reproductive isolation in their pollinators. For example, in the fig and fig wasp mutualism, offspring of fig pollinator wasps mate inside the inflorescence that the mothers pollinate. Although often host specific, multiple fig pollinator species are sometimes associated with the same fig species, potentially enabling hybridization between wasp species. Here we study the 19 pollinator species (<em>Pegoscapus</em> spp.) associated with an entire community of 16 Panamanian strangler fig species (<em>Ficus</em> subgenus <em>Urostigma</em>, section <em>Americanae</em>) to determine whether the previously documented history of pollinator host switching and current host sharing predicts genetic admixture among the pollinator species, as has been observed in their host figs. Specifically, we use genome-wide ultraconserved element (UCE) loci to estimate phylogenetic relationships and test for hybridization and introgression among the pollinator species. In all cases, we recover well-delimited pollinator species that contain high interspecific divergence. Even among pairs of pollinator species that currently reproduce within syconia of shared host fig species, we found no evidence of hybridization or introgression. This is in contrast to their host figs, where hybridization and introgression have been detected within this community, and more generally, within figs worldwide. Consistent with general patterns recovered among other obligate pollination mutualisms (<em>e.g.</em>, yucca moths and yuccas), our results suggest that while hybridization and introgression are processes operating within the host plants, these processes are relatively unimportant within their associated insect pollinators.<br> </p>
Local adaptation and archaic introgression shape global diversity at human structural variant loci
<p>Supporting data associated with the manuscript "Local adaptation and archaic introgression shape global diversity at human structural variant loci". These include:</p> <ul> <li>structural variant genotypes (Paragraph; <a href="https://github.com/Illumina/paragraph">https://github.com/Illumina/paragraph</a>)</li> <li>eQTL mapping results (fastqtl permutation pass; see <a href="http://fastqtl.sourceforge.net/">http://fastqtl.sourceforge.net/</a> for column descriptions)</li> <li>eQTL fine-mapping results (CAVIAR; see <a href="http://genetics.cs.ucla.edu/caviar/index.html">http://genetics.cs.ucla.edu/caviar/index.html</a>)</li> <li>structural variant selection scan results (Ohana; <a href="https://github.com/jade-cheng/ohana">https://github.com/jade-cheng/ohana</a>)</li> </ul> <p>Description of files in this directory:</p> <p><strong>Structural variant genotypes</strong></p> <p><code>SVs_paragraphFormat.vcf.gz</code> - merged long-read structural variant calls</p> <p><code>SVs_1KGP_pgGTs.vcf.gz</code> - genotypes for 1000 Genomes samples in VCF format</p> <p><strong>eQTL mapping results</strong></p> <p><code>fastqtl_out.txt</code> - results from fastQTL permutation pass; see <a href="http://fastqtl.sourceforge.net/">http://fastqtl.sourceforge.net/</a> for column descriptions</p> <p><code>caviar_out.txt</code> - results from fine-mapping SNPs and SVs at significant SV eQTL loci with CAVIAR. Description of columns:</p> <ul> <li>query_sv: SV that was a significant eQTL and underwent fine-mapping</li> <li>gene_id: gene exhibiting an expression association with the query_sv</li> <li>var_id: variant (SNV or SV) that was tested for expression association with the above gene in the fine-mapping analysis</li> <li>var_in_credible_causal_set: Boolean variable denoting whether the above variant is in the 95% credible causal set</li> <li>prob_in_pcausal_set: the amount that this variant contributes to 95% credible causal set</li> <li>causal_post_prob: the posterior probability that the variant is causal in the expression association</li> </ul> <p><strong>Structural variant selection scan results</strong></p> <p><code>chr21_pruned_50_Q.matrix</code> - admixture proportion matrix (generated by Ohana; <a href="https://github.com/jade-cheng/ohana">https://github.com/jade-cheng/ohana</a>)</p> <p><code>chr21_pruned_50_F.matrix</code> - matrix of inferred ancestral allele frequencies (generated by Ohana)</p> <p><code>chr21_pruned_50_C.matrix</code> - matrix of ancestry component covariances (generated by Ohana) Entries of the matrix can be modified to produce "selection hypothesis" matrices where allele frequencies are allowed to vary in one ancestry component (<a href="https://github.com/jade-cheng/ohana/wiki/Population-or-ancestry-specific-selection-scan">https://github.com/jade-cheng/ohana/wiki/Population-or-ancestry-specific-selection-scan</a>).</p> <p><code>selscan_50_k8_p*.txt.gz</code> - raw output of Ohana selscan (see <a href="https://github.com/jade-cheng/ohana">https://github.com/jade-cheng/ohana</a>)</p> <p><code>selscan_res.txt.gz</code> - Ohana selection scan results. These results have been filtered to exclude SVs that have low genotyping rates (<50% of samples), violate Hardy-Weinberg equilibrium expectations (excess of heterozygotes) in more than half of populations, or have extreme global log likelihood estimate (LLE) values. Description of columns:</p> <ul> <li>ID: SV ID</li> <li>#CHROM: SV chromosome</li> <li>POS: SV start position</li> <li>SVLEN: SV length (negative for deletions)</li> <li>step: number of steps needed to interpolate between genome-wide and selection hypothesis models</li> <li>lle_ratio: likelihood ratio statistic (LRS) of the genome-wide vs. selection hypothesis model</li> <li>global-lle: log likelihood of the genome-wide model</li> <li>local-lle: log likelihood of the selection hypothesis model</li> <li>f-pop0: inferred allele frequency in ancestry component 0</li> <li>f-pop1: inferred allele frequency in ancestry component 1</li> <li>f-pop2: inferred allele frequency in ancestry component 2</li> <li>f-pop3: inferred allele frequency in ancestry component 3</li> <li>f-pop4: inferred allele frequency in ancestry component 4</li> <li>f-pop5: inferred allele frequency in ancestry component 5</li> <li>f-pop6: inferred allele frequency in ancestry component 6</li> <li>f-pop7: inferred allele frequency in ancestry component 7</li> <li>ancestry_component: ancestry component tested by the selection hypothesis model. Note that we have added 1 to the ancestry component numbers to match the terminology used in paper (which orders the components from 1-8 rather than 0-7 for interpretability)</li> <li>snp_perc: SV's percentile in the LRS distribution for frequency-matched SNPs</li> <li>p_nominal: nominal p-value calculated from the likelihood ratio</li> <li>p_adj: adjusted p-value calculated from the likelihood ratio</li> </ul> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.