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1,155 results for “loci”
Plasma circulating microRNA-expression quantitative trait loci (eQTLs) data in the Rotterdam Study
<p>The dataset contains GWAS summary statistics for 2,083 plasma circulating microRNAs, obtained from nearly 2,178 participants of the Rotterdam Study. The dataset includes three files, as outlined below:</p> <p><strong>File1: SNP_reference_file_maf0.01_Rsq0.7.txt</strong></p> <p>A reference file for SNPs with good imputation quality (Rsq > 0.7) and minor allele frequency > 0.01 among participants included in our GWAS in the Rotterdam Study (N=2,178). The headers are:</p> <p>SNP: rsID</p> <p>chr: chromosome number according to GRCh37</p> <p>bp: basepair position according to GRCh37</p> <p>effect_allele: effect allele</p> <p>other_allele: other allele</p> <p>eaf: effect allele frequency</p> <p><strong>File2: miReQTLs_1e-5_maf0.01_Rsq0.7.txt</strong></p> <p>Summary statistics for all SNPs significantly associated with 2083 miRNAs (p-value < 1e-5), filtered by minor allele frequency > 0.01 and Rsq > 0.7. The headers are:</p> <p>SNP: rsID</p> <p>beta: effect estimate</p> <p>se: standard error</p> <p>pval: p-value</p> <p>miRNA: miRNA ID</p> <p><strong>File3: miReQTLs_nominal_sig.csv.gz</strong></p> <p>Summary statistics for all SNPs nominally associated with 2083 miRNAs (p-value < 0.05). The headers are:</p> <p>RSID: SNP ID</p> <p>p-value: p-value</p> <p>phenotype: miRNA</p> <p>SE: standard error</p> <p>BETA: effect estimate</p> <p> </p> <p>The SNP allelic information and frequency can be found in the reference file (<strong>File1</strong>). </p> <p><br>For more information, please contact: m.ghanbari@erasmusmc.nl</p>
Arctic grayling neutral genomic microsatellite loci from the Kuparuk, the Sagavanirktok (primarily Oksrukuyik Creek) and the Itkillik (primarily the I-Minus outlet stream) watersheds, 2010-2014
Since 2009, The FISHSCAPE Project (National Science Foundation grants: 1719267, 1417754, and 0902153), based at Toolik Field Station, has monitored physical, chemical, and biological parameters within three watersheds: The Kuparuk (including Toolik Lake and Toolik outlet stream), The Sagavanirktok (primarily Oksrukuyik Creek, but also including sections of the Atigun River and Tea and Galbraith Lakes), and Itkillik (primarily the I-Minus outlet stream a tributary that that feeds into the Itkilik River). Goals of the FISHSCAPE project are to understand and predict the adaptability and persistence of a key Arctic species, the Arctic grayling (Thymallus arcticus), to changing climate and hydrology. Research questions include: (1) Does landscape structure determine movement within and among watersheds; (2) do populations adapt to stream characteristics at local and regional scales; and (3) will the relative adaptability of populations determine their persistence under future climate change. We used genetics to investigate population structure and landscape genetics for Arctic grayling. Adult and young-of-the-year fish were captured at sampling locations and coordinates and/or specific station locations were noted. Fin clip samples (adults) or whole fish (young-of-the-year) were collected and preserved in 95% ethanol until Deoxyribonucleic acid (DNA) was extracted. Polymerase chain reaction (PCR) products from neutral genomic microsatellite loci were scored and used to assess population genetic structure and other population parameters. Adult capture and movement data, including length, weight and Passive Integrated Transponder (PIT) tag information, can be found in a separate data package.
Variant, Metabolite and Source Data for: Population genomics uncover loci for trait improvement in the indigenous African cereal tef (Eragrostis tef)
<p>These files contain the variant and metabolome for a collection of 220 tef (<em>Eragrsotis tef)</em> accessions from an ethiopian diversity panel. The accessions were assembled and managed by the Ethiopian Institute of Agricultural Research (EIAR, Ethiopia). The variant data was produced at the John Innes Centre (UK). The metabolome data was produced at Aberystwyth University (UK). These dataset are described in Jones et al. (2024), <em>bioRxiv</em>, https://doi.org/10.1101/2024.09.30.615331. The source data for main figures in the publication are also included.</p> <p>The submission contains</p> <ol> <li>EIAR_filtered.vcf.gz: This is the variant data obtained from alignment of Illumina reads from all 220 teff accessions to the reference assembly of tef (Dabbi). Low quality variants were filtered out. This variant data was used for constructing the phylogenetic relationship between the accessions. The samples names corresponds to the DNA code in Supplementary Table S10 (Jones et al, 2024).</li> <li>pooled_EIAR_filtered.vcf.gz: After the phylogentic analysis described above, reads from accessions that were found to be genetically redundant were pooled before variant calling. This file was used for the SNP GWAS analysis. The samples names corresponds to the DNA code in Supplementary Table S10 (Jones et al, 2024).</li> <li> Metabolite_Profile.xlxs (source data for Figure 5): This file contains m/z feature intensities from untargeted metabolite fingerprinting using Flow Infusion Electrospray High-resolution Mass Spectrometry (FIE-HRMS). The sample names contains a combination of Location code and Plot number in Supplementary Table S10 e.g AT plot 1, CD plot 1, DZ plot 1, where AT, CD and DZ represent Alem Tena, Chefe Donsa and Debre Zeit, respectively. The data was used for the partial least squares discriminant analysis and differentially accumulated metabolites analysis presented in Figure 5.</li> <li>Source data: Numerical source data for graphs and charts in Figures 3 - 7.</li> <li>Tsedey TT2 Sequence from Improved Assembly: The 4A and 4B sequences around the TT2 orthologue in tef from the improved PacBio-based chromosome-scale assembly of tef. These sequences were used for plotting the LTR Copia alignments presented in Supplementary Figure 9. We thank Corteva for pre-publication access to this improved Tsedey genome assembly.</li> </ol>
Mapping of multiple complementary sex determination loci in a parasitoid wasp
<p>Files required to reproduce the analysis from the manuscript "Mapping of Multiple Complementary Sex Determination Loci in a Parasitoid Wasp" published in Genome Biology and Evolution (doi: 10.1093/gbe/evz219). The code is hosted on the github repository CSD_lfabarum github repository (https://github.com/cmdoret/CSD_lfabarum.</p>
Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (Brassica napus)
<p>Supplemental datasets associated with publication: Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (<em>Brassica napus</em>)</p> <p><strong>Abstract</strong></p> <ul> <li>Crops are affected by several pathogens, but these are rarely studied in parallel to identify common and unique genetic factors controlling diseases. Broad-spectrum quantitative disease resistance (QDR) is desirable for crop breeding as it confers resistance to several pathogen species.</li> <li>Here, we use associative transcriptomics (AT) to identify candidate gene loci associated with <em>Brassica napus</em> constitutive QDR to four contrasting fungal pathogens: <em>Alternaria brassicicola</em>, <em>Botrytis cinerea</em>, <em>Pyrenopeziza</em><em> brassicae</em> and <em>Verticillium longisporum. </em>We did not identify any loci associated with broad-spectrum QDR to fungal pathogens with contrasting lifestyles. Instead, we observed QDR dependent on the lifestyle of the pathogen—hemibiotrophic and necrotrophic pathogens had distinct QDR responses and associated loci, including some loci associated with early immunity. Furthermore, we identify a genomic deletion associated with resistance to <em>V. longisporum </em>and potentially broad-spectrum QDR.</li> <li>This is the first time AT has been used for several pathosystems simultaneously to identify host genetic loci involved in broad-spectrum QDR. We highlight constitutively expressed candidate loci for broad-spectrum QDR with no antagonistic effects on susceptibility to the other pathogens studies as candidates for crop breeding. In conclusion, this study represents and advancement in our understanding if broad-spectrum QDR in <em>B. napus </em>and is a significant resource for the scientific community. </li> </ul> <p><strong>Description of data files</strong></p> <p><strong>Full dataset for input into AT analysis </strong>Full datasets (infection phenotypes for <em>A. brassicicola, B. cinerea, </em>or <em>V.longisporum, </em>ROS measurements for chitin, flg22, or elf18) and link to original <em>P. brassicae </em>dataset. These datasets were used for input into the Associative Transcriptomics pipeline (Nichols, 2022, <a href="https://github.com/bsnichols/GAGA. https://zenodo.org/badge/latestdoi/512807075">https://github.com/bsnichols/GAGA. https://zenodo.org/badge/latestdoi/512807075</a>). </p> <p><strong>Table S1 </strong>Mean, normalized phenotype data for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). These data were used for association transcriptomic analysis.<strong> </strong></p> <p><strong>Table S2 </strong>Full list of single nucleotide polymorphism (SNP) markers and significance levels from genome-wide association (GWA) analyses for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. The best fit model for GWA analysis is indicated in the tab title. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates SNP location along the chromosome; the y-axis indicates the -log10(p) (P value). Qqplots are included to demonstrate model fit.</p> <p><strong>Table S3</strong> Full list of gene expression markers (GEMs) and significance levels from GEM analyses for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates GEM location along the chromosome; the y-axis indicates the -log10(p) (P value). </p> <p><strong>Table S4 </strong>184 gene expression markers (GEMs) associated with chitin-induced ROS compared with GEMs associated with resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and<em> Verticillium longisporum</em>) and ROS response induced by flg22, and elf18. Lists correspond to Venn diagrams in Fig. 2. The first tab includes all 184 GEMs associated with chitin-induced ROS. The subsequent tabs include lists of shared GEMs associated with chitin-induced ROS response and each additional trait (quantitative disease resistance (QDR) to each fungal pathogen or additional PAMP-induced ROS responses). The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p><strong>Table S5</strong> Enrichment analyses to determine if the number of gene expression markers (GEMs) shared between different lists is greater than the number of GEMs that would be expected by chance (e.g., lists of quantitative disease resistance (QDR) GEMs for two fungal pathogens). The representation factor is the number of overlapping GEMs divided by the expected number of overlapping GEMs drawn from two independent groups (traits), considering the total number of GEMs sequenced (53884). A representation factor > 1 indicates more overlap than expected of two groups, a representation factor < 1 indicates less overlap than expected, and a representation factor of 1 indicates that the two groups by the number of genes expected for independent groups of genes. </p> <p><strong>Table S6 R</strong>esults from Weighted Co-expression Gene Network Analysis (WGCNA). The first tab indicates significant modules from WGCNA analysis. Black and magenta modules are associated with antagonistic effects on resistance/susceptibility to all four pathogens. The second tab includes a full list of the GEM markers (Table S3), which are in significant WGCNA modules. The third, fourth and, fifth tabs indicate all significant GEMs in the black module, GO terms associated with GEMs in the black module, and all GO terms associated with the black module, respectively. The sixth, seventh and, eighth tabs indicate all significant GEMs in the magenta module, GO terms associated with GEMs in the magenta module, and all GO terms associated with the magenta module, respectively.</p> <p><strong>Table S7 </strong>Shared gene expression markers (GEMs) associated with resistance to different pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>). Lists correspond to matrices and Venn diagrams in Fig. 3. The first tab includes all GEMs associated quantitative disease resistance (QDR) to the fungal pathogens. The subsequent tabs include lists of shared GEMs associated with QDR to two or more fungal pathogens. The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p><strong>Table S8 </strong>List of genes in linkage disequilibrium with the top marker for <em>Verticillium longisporum</em> resistance from genome-wide association (GWA) analysis on chromosome A09 (107 genes)(Tab 1) and the homoeologous region on C08 (Tab 2). Their percentage identity and query coverage in <em>Brassica napus</em> reference genotypes Quinta, Tapidor, Westar and Zhongshuang 11 compared to the <em>B. napus</em> pantranscriptome is indicated. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p> </p>
GWAS summary stats in "Genome-wide association meta-analysis identifies two novel loci associated with dental caries."
<p>Summary stats of the genome-wide meta-analysis for dental caries and periodontal diseases in our study (population A and B).</p> <p>Article "Genome-wide association meta-analysis identifies two novel loci associated with dental caries."</p> <p>https://doi.org/10.1186/s12903-024-04799-1<br><br></p>
Genome-wide association analyses identify novel Brugada syndrome risk loci and highlight a new mechanism of sodium channel regulation in disease susceptibility
<p>The Brugada syndrome GWAS summary statistics</p> <p>Brugada syndrome is a cardiac arrhythmia disorder associated with sudden death in young adults. With the exception of <em>SCN5A</em>, encoding the cardiac sodium channel Na<sub>V</sub>1.5, susceptibility genes remain largely unknown. We performed a genome-wide association meta-analysis comprising 2,820 unrelated cases with Brugada syndrome and 10,001 controls.</p> <p> </p>
Summary Statistics from "Meta-GWAS of PCSK9 levels detects two novel loci at APOB and TM6SF2"
<p>GWAMA summary statistics of PCSK9 levels using fixed-effect model. Genome-wide data is given for Europeans with statin adjustment and Europeans without statin treatment only (subset of the population). In addition, locus-wide data of the PCSK9 gene locus for African-Americans without statin treatment is listed.</p> <p>When using this data, please cite: Pott J, Gadin J, Theusch E, et al.. Meta-GWAS of PCSK9 levels detects two novel loci at APOB and TM6SF2. Hum Mol Genet. 2021 Sep 30:ddab279. doi: 10.1093/hmg/ddab279. PMID: 34590679</p> <p>All txt files contain the following columns:</p> <ul> <li>markername</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>ea (effect allele)</li> <li>oa (other allele)</li> <li>eaf (effect allele frequency)</li> <li>info (minimal info score across all used studies)</li> <li>nSamples (sample size per SNP)</li> <li>nStudies (number of studies)</li> <li>beta (effect estimate)</li> <li>se (standard error)</li> <li>p (p-value)</li> <li>I2 (SNP heterogeneity across studies)</li> <li>phenotype (phenotyp setting)</li> </ul>
Phenotypic variation and quantitative trait loci for resistance to southern anthracnose and clover rot in red clover
<p>Red clover (<em>Trifolium pratense</em> L.) is an important forage legume of temperate regions, particularly valued for its high yield potential and its high forage quality. Despite substantial breeding progress during the last decades, continuous improvement of cultivars is crucial to ensure yield stability in view of newly emerging diseases or changing climatic conditions. The high amount of genetic diversity present in red clover ecotypes, landraces and cultivars provides an invaluable, but often unexploited resource for the improvement of key traits such as yield, quality, and resistance to biotic and abiotic stresses.</p> <p>A collection of 397 red clover accessions was genotyped using a pooled genotyping-by-sequencing approach with 200 plants per accession. Resistance to the two most pertinent diseases in red clover production, southern anthracnose caused by <em>Colletotrichum trifolii</em>, and clover rot caused by <em>Sclerotinia trifoliorum, </em>was assessed using spray inoculation. The mean survival rate for southern anthracnose was 22.9% and the mean resistance index for clover rot was 34.0%. Genome-wide association analysis revealed several loci significantly associated with resistance to southern anthracnose and clover rot. Most of these loci are in coding regions. One quantitative trait locus (QTL) on chromosome 1 explained 16.8% of the variation in resistance to southern anthracnose. For clover rot resistance we found eight QTL, explaining together 80.2% of the total phenotypic variation. The SNPs associated with these QTL provide, once validated, a promising resource for marker-assisted selection in existing breeding programs, facilitating the development of novel cultivars with increased resistance against two devastating fungal diseases of red clover.</p>
Phage defence loci of Streptococcus thermophilus
<p><span>We describe the systematic identification and functional analysis of the phage resistome of <em>S. thermophilus </em>using a collection of 27 strains as representatives of the species. In addition to CRISPR-Cas and R/M systems, we uncover nine distinct phage-resistance systems including homologues of Kiwa, Gabija, Dodola, defence-associated sirtuins and abortive infection systems. The genes encoding several of these newly identified systems are located in proximity to the genetic determinants of CRISPR-Cas systems thus constituting apparent Phage Defence Islands. Other phage-resistance systems whose encoding genes are not co-located with genes specifying CRISPR-Cas systems may represent anchors to identify additional Defence Islands harbouring, as yet, uncharacterised phage defence systems. Remarkably, we estimate that up to 2.5 % of the genetic material of the analysed strains is dedicated to phage defence, highlighting that phage-host antagonism plays an important role in driving the evolution and shaping the composition of dairy streptococcal genomes.</span></p>
Variant dataset and code for "Population-level whole genome sequencing of Ascochyta rabiei identifies genomic loci associated with isolate aggressiveness"
<p>This dataset contains genetic variants (SNPs) of <em>Ascochyta rabiei</em> isolates and the R code used in their analysis to generate the results and figures described in the manuscript "<strong>Population-level whole genome sequencing of <em>Ascochyta rabiei</em> identifies genomic loci associated with isolate aggressiveness</strong>".</p> <div> <div> </div> </div>
Database for GWAS SVatalog: a visualization tool to aid fine-mapping of GWAS loci with structural variations.
<p>GWAS SVatalog is a novel visualization tool and database for structural variants (SV) found in a predominantly European population of 101 individuals with Cystic Fibrosis (CF). Aside from the CF-causing variants on chromosome 7 and the LD block in which they lie, the remainder of the genome is comparable to a the 1000 Genomes healthy European population. This data is a collection of SV calls and their linkage disequilibrium (LD) statistics with GWAS-significant SNPs reported in the GWAS Catalog.</p> <p> </p> <p>The goal of this project is to provide a resource to aid fine mapping of GWAS loci using SVs. GWAS loci are generally identified by SNPs which account for an incomplete proportion of genetic variation and phenotypic heritability. Their relevance to the phenotype might be limited, tagging other polymorphisms, such as SVs, that could be the cause of the association signal. To leverage this data to its full potential, visit the <a href="https://svatalog.research.sickkids.ca/" target="_blank" rel="noopener">GWAS SVatalog</a> web tool. Here, interactive visualizations can illustrate SVs identified in high LD with GWAS-significant SNPs, suggesting putative causal variation that could guide additional functional investigation.</p> <p> </p> <p>For more information on how to use GWAS SVatalog, visit the<a href="https://gwas-svatalog-docs.readthedocs.io/en/latest/index.html" target="_blank" rel="noopener noreferrer"> documentation</a>.</p> <p> </p> <p>This project was accomplished in collaboration with the <a href="https://lab.research.sickkids.ca/strug/" target="_blank" rel="noopener">Strug Lab</a> at <a href="https://www.sickkids.ca/en/" target="_blank" rel="noopener">The Hospital for Sick Children (SickKids)</a>, <a href="https://www.tcag.ca/" target="_blank" rel="noopener">The Center for Applied Genomics (TCAG)</a>, and <a href="https://www.utoronto.ca/" target="_blank" rel="noopener">University of Toronto</a>.</p>
Data for: Bivariate Genome-Wide Association Scan Identifies 6 Novel Loci Associated With Lipid Levels and Coronary Artery Disease.
<p>Summary of Bivariate GWAS scan results reported in:<br> <a href="https://pubmed.ncbi.nlm.nih.gov/30525989/">Bivariate Genome-Wide Association Scan Identifies 6 Novel Loci Associated With Lipid Levels and Coronary Artery Disease. </a>Siewert KM, Voight BF. Circ Genom Precis Med. 2018 Dec;11(12):e002239. doi: 10.1161/CIRCGEN.118.002239.</p> <p>PMID: 30525989 </p>
Hypergraph Factorisation Expression Quantitative Trait Loci
<p>Please cite:</p> <pre><code>Hypergraph factorisation for multi-tissue gene expression imputation. Vinas Torne, Ramon and Joshi, Chaitanya K. and Georgiev, Dobrik and Lin, Phillip and Dumitrascu, Bianca* and Gamazon, Eric* and Lio, Pietro*. *Co-corresponding authors. </code></pre>
Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits
<p>Genetic association studies have identified hundreds of independent genetic signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a relevant T2D tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million variants. We anticipate that deep learning methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies.</p>
A two-tier bioinformatic pipeline to develop probes for target capture of nuclear loci with applications in Melastomataceae
<p><b><i>Premise of the study</i></b><b>: </b>Putatively single-copy nuclear (SCN) loci, identified using genomic resources of closely related species, are ideal for phylogenomic inference. However, suitable genomic resources are not available for many clades, including Melastomataceae. We introduce a versatile approach to identify SCN loci for clades with few genomic resources and use it to develop probes for target enrichment<i> </i>in the distantly related <i>Memecylon</i> and <i>Tibouchina</i> (Melastomataceae).</p> <p><b><i>Methods</i></b>: We present a two-tiered pipeline. First, we identified putatively SCN loci using MarkerMiner and transcriptomes from distantly related species in Melastomataceae. Published loci and genes of functional significance were added (384 total loci). Second, using HybPiper, we retrieved 689 homologous template sequences for these loci using genome-skimming data from within the focal clades.</p> <p><b><i>Results</i></b>: We sequenced 193 loci from both <i>Memecylon</i> and <i>Tibouchina</i>, with probes designed from 56 template sequences successfully targeting sequences in both clades. Probes designed from genome-skimming data within a focal clade were more successful than probes designed from other sources.</p> <p><b><i>Discussion: </i></b>Our pipeline successfully identified and targeted SCN loci in <i>Memecylon </i>and <i>Tibouchina</i>, enabling phylogenomic studies in both clades and potentially across Melastomataceae. This pipeline could be easily applied to other clades with few genomic resources. </p>
Placental Expression Quantitative Trait Loci In An East Asian Population
<p>Analysis script, full eQTL summary statistics, and fine-mapping statistics of article "Placental Expression Quantitative Trait Loci In An East Asian Population". This data contains workflow and result of 102 East Asian placental expression quantitative trait loci analysis.</p><p>Genotype from 102 cord blood used in the analysis is also included. Variants with minor allele frequencies less than 0.01 was filtered out to prevent personnel identification.</p>
Data from: Do genetic loci that cause reproductive isolation in the lab inhibit gene flow in nature?
<p>The genetic dissection of reproductive barriers between diverging lineages provides enticing clues into the origin of species. One strategy uses linkage analysis in experimental crosses to identify genomic locations involved in phenotypes that mediate reproductive isolation. A second framework searches for genomic regions that show reduced rates of exchange across natural hybrid zones. It is often assumed that these approaches will point to the same loci, but this assumption is rarely tested. In this perspective, we discuss the factors that determine whether loci connected to postzygotic reproductive barriers in the laboratory are inferred to reduce gene flow in nature. We synthesize data on the genetics of postzygotic isolation in house mice, one of the most intensively studied systems in speciation genetics. In a rare empirical comparison, we measure the correspondence of loci tied to postzygotic barriers via genetic mapping in the laboratory and loci at which gene flow is inhibited across a natural hybrid zone. We find no evidence that the two sets of loci overlap beyond what is expected by chance. In light of these results, we recommend avenues for empirical and theoretical research to resolve the potential incongruence between the two predominant strategies for understanding the genetics of speciation.</p>
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>
Polysaccharide utilization loci in Bacteroides determine population fitness and community-level interactions
<p>Polysaccharide utilization loci (PULs) in the human gut microbiome have critical roles in shaping human health and ecological dynamics. We develop a CRISPR-FnCpf1-RecT genome-editing tool to study 23 PULs in the highly abundant species <em>B. uniformis</em> (BU). We identify the glycan-degrading functions of multiple PULs and elucidate transcriptional coordination between PULs that enables the population to adapt to the loss of PULs. Exploiting a pooled BU mutant barcoding strategy, we demonstrate that the <em>in vitro</em> fitness and the colonization ability of BU in the murine gut is enhanced by deletion of specific PULs and modulated by glycan availability. We show that BU PULs can mediate complex glycan-dependent interactions with butyrate producers that depend on the mechanism of degradation and the butyrate producer glycan utilizing ability. In sum, PULs are major determinants of community dynamics and butyrate production and can provide a selective advantage or disadvantage depending on the nutritional landscape. </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.