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15,277 results for “GENETIC”
Data from: The genetic legacy of extreme exploitation in a polar vertebrate
<p>Microsatellite data (39 loci) from Antarctic fur seals and Subantarctic fur seals, used in the paper: "The genetic legacy of extreme exploitation in a polar vertebrate"</p> <p> </p> <p><strong>Abstract</strong></p> <p>Understanding the effects of human exploitation on the genetic composition of wild populations is important for predicting species persistence and adaptive potential. We therefore investigated the genetic legacy of large-scale commercial harvesting by reconstructing on a global scale the recent demographic history of the Antarctic fur seal (<em>Arctocephalus gazella</em>), a species that was hunted to the brink of extinction by 18<sup>th</sup> and 19<sup>th</sup> century sealers. Molecular genetic data from over 2,000 individuals, sampled from all eight major breeding colonies across the species᾿ circumpolar geographic distribution, show that at least four relict populations around Antarctica survived commercial hunting. Coalescent simulations suggest that all of these populations experienced severe bottlenecks down to effective population sizes of around 150–200. Nevertheless, comparably high levels of neutral genetic variability were retained as these declines are unlikely to have been strong enough to deplete allelic richness by more than around 15%. These findings suggest that even dramatic short-term declines need not necessarily result in major losses of diversity, and explain the apparent contradiction between the high genetic diversity of this species and its extreme exploitation history.</p> <p> </p> <p><strong>Funding</strong></p> <p>This research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in<br> the framework of a Sonderforschungsbereich (project numbers 316099922 and 396774617–TRR 212) and the<br> priority programme "Antarctic Research with Comparative Investigations in Arctic Ice Areas" SPP 1158 (project<br> number 424119118). It was also funded by Norwegian Antarctic Research Expeditions (NARE) programme.<br> This work contributes to the Ecosystems project of the British Antarctic Survey, Natural Environmental Research<br> Council, and is part of the Polar Science for Planet Earth Programme. The Department of Environmental Affairs<br> provided logistical support for research at Marion Island and the Department of Science and Technology of<br> South Africa provided funding through the National Research Foundation (NRF). We are grateful to Caroline<br> Bonin, Debbie Baird-Bower and Iain Staniland together with the seal biologists working within the Marion<br> Island Marine Mammal Programme for sample collection and logistics. We acknowledge support for the Article<br> Processing Charge by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld<br> University.</p>
Genome-wide association summary statistics of chronic musculoskeletal pain at four anatomic sites and their genetically independent components
<p>The dataset contains results of a genome-wide association study of distinct chronic musculoskeletal pain conditions: back pain, knee pain, neck pain, and hip pain. Additionally, there are genome-wide association summary statistics for four genetically independent components of pain conditions, listed above. For more details, please, read the paper XXX.</p> <p>All files contain association summary statistics for genome-wide association meta-analysis of the 265,000 white British individuals from the UK Biobank and additional 191,580 individuals of European Ancestry from the UK biobank (total N = 456,580). Cases and controls were defined based on questionnaire responses. First, participants responded to “Pain type(s) experienced in the last months” followed by questions inquiring if the specific pain had been present for more than 3 months. Those who reported back, neck or shoulder, hip, or knee pain lasting more than 3 months were considered chronic back, neck/shoulder, hip, and knee pain cases, respectively. Participants reporting no such pain lasting longer than 3 months were considered controls (regardless of whether they had another regional chronic pain, such as abdominal pain, or not). Individuals who preferred not to answer were excluded from the study. Besides this, we excluded individuals who reported more than 3 months of pain all over the body.</p> <p>The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilization of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank.</p> <p><strong>When using downloaded data, please cite the corresponding paper and this repository:</strong></p> <ol> <li>Tsepilov et al 2020</li> </ol> <p><strong>Funding:</strong></p> <p>The work of YSA and SZS was supported by the Russian Ministry of Education and Science under the 5-100 Excellence Programme and by the Federal Agency of Scientific Organizations via the Institute of Cytology and Genetics (project 0324-2019-0040). The work of YAT, ASSh, and EEE was supported by the Russian Foundation for Basic Research (project 19-015-00151). The contribution of LСK was funded by PolyOmica. Dr. Suri was supported by VA Career Development Award # 1IK2RX001515 from the United States (U.S.) Department of Veterans Affairs Rehabilitation Research and Development (RR&D) Service. Dr. Suri is a Staff Physician at the VA Puget Sound Health Care System. The contents of this work do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.</p> <p><strong>List of files:</strong></p> <ol> <li>Back_output_done.csv: GWAS summary statistics for the chronic back pain</li> <li>gpc1_output_done.csv: GWAS summary statistics for the GIP1</li> <li>gpc2_output_done.csv: GWAS summary statistics for the GIP2</li> <li>gpc3_output_done.csv: GWAS summary statistics for the GIP3</li> <li>gpc4_output_done.csv: GWAS summary statistics for the GIP4</li> <li>Hip_output_done.csv: GWAS summary statistics for the chronic hip pain</li> <li>Knee_output_done.csv: GWAS summary statistics for the chronic knee pain</li> <li>Neck_output_done.csv: GWAS summary statistics for the chronic neck pain</li> </ol> <p><strong>Column headers:</strong></p> <ol> <li>gwas_id: uninformative field</li> <li>rs_id: dbSNP rsID (GRCh37 build) </li> <li>snp_num: uninformative field</li> <li>chr: chromosome (GRCh37 build) </li> <li>bp: position (GRCh37 build) </li> <li>ea: effect allele (coded as "1")</li> <li>ra: reference allele (coded as "0")</li> <li>eaf: effect allele frequency</li> <li>af_ref: uninformative field</li> <li>beta: effect size of effect allele</li> <li>se: standard error of effect size</li> <li>p: P-value of association (without GC correction)</li> <li>n:Total sample size</li> <li>z: Z-statistic of association</li> <li>info: uninformative field</li> <li>af_outlier: uninformative field</li> <li>pz_outlier: uninformative field</li> </ol>
Data from: Genetic admixture increases phenotypic diversity in the nectar yeast Metschnikowia reukaufii,
<p>Raw data and supplementary files for the manuscript "Genetic admixture increases phenotypic diversity in the nectar yeast <em>Metschnikowia reukaufii</em>."</p> <p>-------------------</p> <p><strong>Table S5.xlsx </strong>-- Pairwise correlations between phenotypic traits of <em>Metschnikowia reukaufii</em>.</p> <p><strong>Table S6.xlsx</strong> -- Detailed results obtained in tests of phylogenetic signal for different phenotypic traits and indices of overall performance of <em>Metschnikowia reukaufii</em>.</p> <p><strong>Table S7.xlsx</strong> -- Detailed model fitting results obtained for phenotypic traits and indices of overall performance of <em>Metschnikowia reukaufii</em>.</p> <p><strong>mronlyvcf-renamed.vcf</strong> -- High coverage SNPs obtained from whole genome mapping of 73 <em>Metschnikowia reukaufii</em> strains to diploid reference (mean coverage = 47.9×, range 23 – 116×).</p> <p><strong>MR_phenotypes.xlsx</strong> -- Phenotypic data obtained for 73 <em>Metschnikowia reukaufii</em> strains.</p>
Phenotypic data related to genetic architecture of transmission stage production and virulence in schistosome parasites
<p>These data were generated related to the study of the <strong>Genetic architecture of transmission stage production and virulence in schistosome parasites</strong>.</p> <p><strong>Abstract:</strong> Both theory and experimental data from multiple pathogens suggest that the production of transmission stages should be strongly associated with virulence, but the genetic bases of parasite transmission/virulence traits are poorly understood. In the blood fluke <em>Schistosoma mansoni</em>, parasite genotypes show extensive variation in numbers of cercariae larvae shed from infected snails. Furthermore, high shedding parasites cause high mortality to snails while low shedding parasites cause low mortality, consistent with expected trade-offs between parasite transmission and virulence. To understand the genetic basis of transmission stage production/virulence, we conducted reciprocal crosses between schistosomes from two laboratory populations that differ 8-fold in cercarial shedding and in their virulence to inbred snail hosts. Each parasite generation, we determined four-week cercarial shedding profiles in inbred <em>Biomphalaria glabrata</em> snails infected with single parasite larvae. We sequenced the whole genome of the F0 parents and the exome of the F1 progeny and 188 F2 progeny from each cross, and used linkage mapping to reveal quantitative trait loci (QTLs) underlying transmission stage production. Cercarial production is polygenic: we found three major QTLs on chromosome 1, 3 and 5 (Log-of-the-odds (LOD) = 5.61, 8.19, 6.25) and two minor QTLs on chromosome 2 and 4. These QTLs act additively and explained 28.56% of the phenotypic variation in cercarial shedding. Alleles inherited from the high and low shedding parents were co-dominant at all QTLs, except for chr. 1 and chr. 4 where the “high cercarial shedding” allele is recessive. These results demonstrate that the genetic architecture of key traits directly relevant to schistosome ecology can be dissected using classical linkage mapping approaches, and set the stage for fine mapping and functional validation of the genes involved using the growing armory of functional and cell biology tools available for this parasite.</p> <p> </p> <p>This dataset is made of 4 tables:</p> <ul> <li>F0_parental_populations.csv</li> <li>F1.csv</li> <li>F2.csv</li> <li>sex.tsv</li> </ul> <p> </p> <p><strong>F0_parental_populations.csv</strong></p> <p> </p> <p>This table contains the number of cercariae produced by each individual <em>Biomphalaria glabrata</em> Bg26 snails infected with single genotypes of <em>Schistosoma mansoni</em> parasite. We have compared the transmission stage production between two different populations of <em>S. mansoni</em> parasite. This dataset was originally published in Le Clec'h et al., 2019 (Striking differences in virulence, transmission and sporocyst growth dynamics between two schistosome populations. Parasites and Vectors. 2019 Oct 16;12(1):485. doi: 10.1186/s13071-019-3741-z).</p> <p> </p> <p>This table is made of 9 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample.</li> <li><strong>schistosoma_population</strong>: the population of schistosome used for the infection of the snail. Each snail was infected with a single parasite genotype. We have used SmLE (high shedder/highly virulent population) and SmBRE (low shedding/low virulent population).</li> <li><strong>Shed.1</strong>: the number of cercariae produced by each parasite genotype at the first shedding week (4 weeks after exposure to parasite).</li> <li><strong>Shed.2</strong>: the number of cercariae produced by each parasite genotype at the second shedding week (5 weeks after exposure to parasite).</li> <li><strong>Shed.3</strong>: the number of cercariae produced by each parasite genotype at the third shedding week (6 weeks after exposure to parasite).</li> <li><strong>Shed.4</strong>: the number of cercariae produced by each parasite genotype at the fourth shedding week (7 weeks after exposure to parasite).</li> <li><strong>sum</strong>: the sum of the cercariae produced by each parasite genotype over the 4 weeks of shedding (Shed.1 + Shed.2 + Shed.3 + Shed.4).</li> <li><strong>average</strong>: the average number of cercariae produced by each parasite genotype over the 4 weeks of shedding.</li> <li><strong>sex</strong>: the sex of each parasite genotype determined by PCR <sup>1</sup>.</li> </ul> <p> </p> <p><strong>F1.csv</strong></p> <p> </p> <p>This table contains the number of cercariae produced by each individual <em>Biomphalaria glabrata</em> Bg26 snails infected with single genotypes of F1 progeny from the cross SmLE x SmBRE (see the manuscript for details).</p> <p> </p> <p>This table is made of 11 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample.</li> <li><strong>cross</strong>: F1A or F1B cross. Each snail was infected with a single parasite genotype from either F1A or F1B progeny.</li> <li><strong>Shed.1</strong>: the number of cercariae produced by each parasite genotype at the first shedding week (4 weeks after exposure to parasite).</li> <li><strong>Shed.2</strong>: the number of cercariae produced by each parasite genotype at the second shedding week (5 weeks after exposure to parasite).</li> <li><strong>Shed.3</strong>: the number of cercariae produced by each parasite genotype at the third shedding week (6 weeks after exposure to parasite).</li> <li><strong>Shed.4</strong>: the number of cercariae produced by each parasite genotype at the fourth shedding week (7 weeks after exposure to parasite).</li> <li><strong>sum</strong>: the sum of the cercariae produced by each parasite genotype over the 4 weeks of shedding (Shed.1 + Shed.2 + Shed.3 + Shed.4).</li> <li><strong>average</strong>: the average number of cercariae produced by each parasite genotype over the 4 weeks of shedding.</li> <li><strong>PO</strong>: the total phenoloxidase activity in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>2</sup>.</li> <li><strong>Hb</strong>: the hemoglobin rate in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>3</sup>.</li> <li><strong>sex</strong>: the sex of each parasite genotype determined by PCR <sup>1</sup>.</li> </ul> <p> </p> <p><strong>F2.csv</strong></p> <p>This table contains the number of cercariae produced by each individual <em>Biomphalaria glabrata</em> Bg26 snails infected with single genotypes of F2 progeny from the cross SmLE x SmBRE (see the manuscript for details).</p> <p> </p> <p>This table is made of 10 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample.</li> <li><strong>cross</strong>: F2A or F2B cross. Each snail was infected with a single parasite genotype from either F2A or F2B progeny.</li> <li><strong>Shed.1</strong>: the number of cercariae produced by each parasite genotype at the first shedding week (4 weeks after exposure to parasite).</li> <li><strong>Shed.2</strong>: the number of cercariae produced by each parasite genotype at the second shedding week (5 weeks after exposure to parasite).</li> <li><strong>Shed.3</strong>: the number of cercariae produced by each parasite genotype at the third shedding week (6 weeks after exposure to parasite).</li> <li><strong>Shed.4</strong>: the number of cercariae produced by each parasite genotype at the fourth shedding week (7 weeks after exposure to parasite).</li> <li><strong>sum</strong>: the sum of the cercariae produced by each parasite genotype over the 4 weeks of shedding (Shed.1 + Shed.2 + Shed.3 + Shed.4)</li> <li><strong>average</strong>: the average number of cercariae produced by each parasite genotype over the 4 weeks of shedding.</li> <li><strong>PO</strong>: the total phenoloxidase activity in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>2</sup>.</li> <li><strong>Hb</strong>: the hemoglobin rate in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>3</sup>.</li> </ul> <p> </p> <p><strong>sex.csv</strong></p> <p> </p> <p>This table contains the <em>in silico</em> sexing of F0 parents, F1 parents and F2 progeny of <em>S. mansoni</em> parasites.</p> <p>This table is made of 4 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample</li> <li><strong>read_depth</strong>: the read depth ratio between the Z-linked and pseudo-autosomal regions.</li> <li><strong>ratio</strong>: computed ratio between the Z-linked and pseudo-autosomal regions.</li> <li><strong>sex</strong>: the sex of each parasite genotype determined <em>in silico</em>: a ratio around 1 corresponds to a male carrying two Z chromosomes while a ratio around 0.5 corresponds to a female carrying only one Z chromosome.</li> </ul> <p><strong>Notes:</strong></p> <p><sup>1</sup>. Le Clec’h W, Chevalier F et al. Real-time PCR for sexing Schistosoma mansoni cercariae. Mol Biochem Parasitol. Jan-Feb 2016; 205(1-2):35-8.doi: 10.1016/j.molbiopara.2016.03.010. Epub 2016 Mar 26.</p> <p><sup>2</sup>. Le Clec’h W et al. Characterization of hemolymph phenoloxidase activity in two Biomphalaria snail species and impact of Schistosoma mansoni infection. Parasit Vectors. 2016 Jan 22; 9:32.doi: 10.1186/s13071-016-1319-6.</p> <p><sup>3</sup>. Le Clec'h et al. Striking differences in virulence, transmission and sporocyst growth dynamics between two schistosome populations. Parasit Vectors. 2019 Oct 16; 12(1):485. doi: 10.1186/s13071-019-3741-z.</p>
Genetic Relationships Between Terminal Shoot Length, Number of Flushes and Height in a Four-Year-old Progeny Test of Pinus brutia Ten.
<p><strong>Description of the data</strong></p> <p>A total of 188 plus trees were selected from eight natural seed stands of <em>Pinus brutia</em> in the Aegean region of Turkey. The number of trees selected per seed stand (provenance) varied between 7 to 53 trees. Open-pollinated seeds were collected from plus trees in 1998 and in 1999. In addition, six checklots consisting of bulk seeds from natural seed stands were included in the study to estimate genetic gain and link the progeny tests across different breeding zones in the Aegean region.</p> <p>Open-pollinated progeny tests were established at three locations in the Aegean region of Turkey (Hisaronu, Izmir, and Kinik) in March 2000. One-year-old bare-root seedlings were used in the study. Randomized complete block design with four-tree row plots was used in all sites. For each plus tree (female parent), about 72 half-sib progenies were planted across three test sites. The Hisaronu site had four blocks, while two other sites had seven blocks each. Each parent tree was represented by 16 half-sib progenies at the Hisaronu site and 28 progenies in the other two sites when the trials were planted. The spacing among seedlings was 2 x 3 m at each site. Each block was split into four sets (sets in replications) to accommodate a large number of trees, with checklots included in every set. In total, about 166 half-sib progenies and checklots were planted in each block.</p> <p>At the end of the first growing year after planting, survival was assessed. It was about 52% at the Hisaronu site. Dead seedlings at the Hisaronu site were replaced with 1083 two-year-old seedlings of the same families, which were grown in a nursery near Marmaris in the Aegean region. The other two sites had 91% (İzmir) and 94% (Kinik) survival. At age four after planting (2004), tree height (cm), terminal shoot length (cm), and the number of flushes were measured. In total, approximately 12100 trees were assessed across the three locations. </p>
Whole genome sequencing of Turkish genomes reveals functional private alleles and impact of genetic interactions with Europe, Asia and Africa.
<p>BACKGROUND:</p> <p>Turkey is a crossroads of major population movements throughout history and has been a hotspot of cultural interactions. Several studies have investigated the complex population history of Turkey through a limited set of genetic markers. However, to date, there have been no studies to assess the genetic variation at the whole genome level using whole genome sequencing. Here, we present whole genome sequences of 16 Turkish individuals resequenced at high coverage (32×-48×).</p> <p>RESULTS:</p> <p>We show that the genetic variation of the contemporary Turkish population clusters with South European populations, as expected, but also shows signatures of relatively recent contribution from ancestral East Asian populations. In addition, we document a significant enrichment of non-synonymous private alleles, consistent with recent observations in European populations. A number of variants associated with skin color and total cholesterol levels show frequency differentiation between the Turkish populations and European populations. Furthermore, we have analyzed the 17q21.31 inversion polymorphism region (MAPT locus) and found increased allele frequency of 31.25% for H1/H2 inversion polymorphism when compared to European populations that show about 25% of allele frequency.</p> <p>CONCLUSION:</p> <p>This study provides the first map of common genetic variation from 16 western Asian individuals and thus helps fill an important geographical gap in analyzing natural human variation and human migration. Our data will help develop population-specific experimental designs for studies investigating disease associations and demographic history in Turkey.</p>
Plotting sex differences in genetics of waist-hip ratio
<p>For plotting results of genome-wide association study results on waist-hip ratio, originally by Shungin et al 2015 doi: 10.1038/nature14132, to be used in a commissioned review article for eLS http://www.els.net/WileyCDA/ , to be published soon, and currently available as an unreviewed, un-edited pre-print on bioarxiv http://dx.doi.org/10.1101/063651</p>
Phlorest phylogeny derived from De Filippo et al. 2012 'Bringing together linguistic and genetic evidence to test the Bantu expansion'
<p>Cite the source of the dataset as:</p> <blockquote> <p>De Filippo, C., Bostoen, K., Stoneking, M., & Pakendorf, B. (2012). Bringing together linguistic and genetic evidence to test the Bantu expansion. Proceedings of the Royal Society B: Biological Sciences, 279(1741), 3256–3263. doi:10.1098/rspb.2012.0318</p> </blockquote>
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>
Robust genetic codes enhance protein evolvability
<p>The repository contains all data for our manuscript on protein evolvability under rewired genetic codes: https://www.biorxiv.org/content/10.1101/2023.06.20.545706v1</p> <p>The corresponding code is available on GitHub: https://github.com/parizkh/rewired_codes_landscapes</p>
Data from: Evolutionary potential and constraints in an aposematic species: Genetic correlations between warning coloration and fitness components in wood tiger moths
<p>Phenotypic data and pedigrees of two laboratory populations of wood tiger moths (<em>Arctia plantaginis</em>) of Finnish (=FIN) and Estonian (=EST) ancestry.</p> <p><strong>Pedigree: </strong><br>ID: individual identifier<br>sire = Father<br>dam=mother</p> <p><strong>Pheno.data: </strong><br>ID: individual identifier<br>Sex: 1=male; 2=female<br>hatchingdate: date when larva hatched<br>pupadate: date of pupation<br>adultdate: date of exclusion<br>Pupa.Weight: weight of pupa [mg]<br>Female.Colour = hindwing colour of females. In this species hindwing colour in females varies continuously from yellow to red. It was quantified by visual matching of hinwdings against a colour scale ranging from 1 = yellow to 6 = red. <br>Signal.Size = larva signal size. Larvae show an orange patch of variable size on the back of their black body. The size is given as number of segments<br>Egg.N = egg number produced by the individual<br>Off.N = offspring number. Larvae were counted 2-3 weeks after egg laying</p> <p> </p>
GWAS Summary Statistics for Publication: Identifying novel genetic and phenotypic associations to genomic features by leveraging off-target reads in exome sequencing data
<p>This dataset contains summary statistics for genome-wide association studies (GWAS) conducted on genomic features derived from off-target reads in whole-exome sequencing (WES) data. The study utilized tools like Seeing Beyond the Target (SBT) and ImReP to construct novel phenotypic features from unmapped reads in ~50,000 participants in the UK Biobank. Features include mitochondrial DNA (mtDNA) copy number, ribosomal DNA (rDNA) copy number (5S, 18S, 28S), immune repertoire metrics (e.g., T-cell receptor alpha diversity), and microvial genome load (viral and fungal).</p> <p>Summary statistics can be used for replication studies, meta-analyses, or further exploration of these phenotypes.</p>
The complete reference genome for grapevine (Vitis vinifera L.) genetics and breeding
<div>PN40024, a highly homozygous inbred line originating from ‘Helfensteiner’, was used for T2T genome assembly. In total, 21 Gb (21 024 461 524 bp, ∼42× coverage) HiFi reads were generated by the PacBio platform. For the preliminary assembly, hifiasm was used to assemble the HiFi reads. We then used MUMmer and the 12X.v0 genome version (V. vinifera genome assembly 12X.v0 to order the 38 contigs into 19 chromosomes.</div> <p> The PN_T2T genome size was finally generated (494.87 Mb), being 69 Mb longer than 12X.v0 using the same statistical method. The k-mer metric was used to evaluate genomic homozygosity, estimated at 99.8%. The BUSCO for this genome is up to 98.5%.</p> <p>The PN40024.T2T genome assembly: PN.fa</p> <p>The PN40024.T2T gene annotation: PN_T2T.v5.1.gff3</p> <p>The PN40024.T2T TE annotation: PN_T2T_TE.gff</p> <p>The PN40024.T2T centromere annotation: PN.trf.gff3</p> <p>The PN40024.T2T protein sequence: PN_protein.fa</p> <p>The PN40024.T2T cds sequence: PN40024.cds.fa</p> <p>Comparison of gene annotation among PN_T2T and PN_T2T.v5.1, 12X.v0, 12X.v2, PN40024.v4, PN40024.v4.1: correlation.list.txt</p> <p>Mitochondrial assembly sequence of PN40024: PN_T2T_mit.fa</p> <p>Annotation of mitochondrial assembly for PN40024:PN_T2T_mit.gff3</p> <p>Chloroplast assembly sequence of PN40024: PN_T2T_chl.fa</p> <p>Annotation of chloroplast assembly for PN40024: PN_T2T_chl.gff3</p> <p>Citation: </p> <p>Please cite this paper when using the data of PN_T2T for your publications.</p> <p>Xiaoya Shi, Shuo Cao, Xu Wang, Siyang Huang, Yue Wang, Zhongjie Liu, Wenwen Liu, Xiangpeng Leng, Yanling Peng, Nan Wang, Yiwen Wang, Zhiyao Ma, Xiaodong Xu, Fan Zhang, Hui Xue, Haixia Zhong, Yi Wang, Kekun Zhang, Amandine Velt, Komlan Avia, Daniela Holtgräwe, Jérôme Grimplet, José Tomás Matus, Doreen Ware, Xinyu Wu, Haibo Wang, Chonghuai Liu, Yuling Fang, Camille Rustenholz, Zongming Cheng, Hua Xiao, Yongfeng Zhou, The complete reference genome for grapevine (<em>Vitis vinifera</em> L.) genetics and breeding, <em>Horticulture Research</em>, Volume 10, Issue 5, May 2023, uhad061, <a href="https://doi.org/10.1093/hr/uhad061">https://doi.org/10.1093/hr/uhad061</a></p>
Data from: Enamel proteins reveal biological sex and genetic variability within southern African Paranthropus
<p>This dataset contains the sequences of Paranthropus robustus, first described in 'Enamel proteins reveal biological sex and genetic variability within southern African Paranthropus', as well as the reference data and all the results from the analysis of those sequences.</p> <p><strong>Folders and Sub-Folders:</strong></p> <p><strong>- Paranthropus_Raw_AA_Sequences_Unaligned: </strong>Contains 2 fasta files. Paranthropus_Unaligned.fasta contains all the Paranthropus robustus sequences that were used for all of the analyses. Paranthropus_Unaligned_UNFILTERED.fasta contains all the Paranthropus robusts sequences <strong>before </strong><strong>filtering </strong>for SAP quality/confidence. These sequences were not used in any of the analyses, but are provided here for openness. </p> <p> </p> <p> </p> <p><strong>-</strong> <strong>Reference_Datasets</strong>: Contains 3 fasta files. Each fasta file is a reference dataset used in at least one analysis. The identity and origin of each sample is described in the supplementary document of the publication.</p> <p> </p> <p> </p> <p><strong>- Phylogenetic_Analysis_Datasets_and_Trees: </strong>Contains the following <strong>five folders</strong></p> <p> - <strong>Paranthropus_Alignments_All_Datasets</strong>: Contains three folders. Each folder contains the aligned and I/L corrected MSAs (Multiple Sequence Alignments) of Paranthropus robustus and a reference dataset.</p> <p> - <strong>Paranthropus_Diversity_Dataset_Trees_Results</strong>: Contains all analysis done using the 'diversity' reference dataset. Contains one folder for each protein, which includes the protein alignment and the phylogenetic tree of that protein. Additionally a folder named 'CONCATENATED' contains the concatenated alignemnts and trees. The BEAST2-STARBEAST3 folder contains the Starbeast3 analysis, including the xml, output log file, output trees and the input taxon set file.</p> <p> - <strong>Paranthropus_Representative_Dataset_Trees_Results:</strong> Contains all analysis done using the 'representative' reference dataset. Contains one folder for each protein, which includes the protein alignment and the phylogenetic tree of that protein. Additionally a folder named 'CONCATENATED' contains the concatenated alignemnts and trees. The BEAST2 folder contains the time-calibrated BEAST2 analysis, including the xml, output log file, output trees. The folder Distance_Matrix contains the generated distance matrix and the Rscript used to generate the heatmap from it.</p> <p> - <strong>Paranthropus_Independent_Dataset_Trees_Results: </strong>Contains all nexus files and tree-figures used in the analysis of the 'independent' reference dataset. </p> <p> - <strong>Tree_Figures: </strong>Contains three sub-folders and an additional figure. Each sub-folder contains the phylogenetic tree figures generated using one of the three reference datasets.</p>
Highly parallel genomic selection response in replicated Drosophila melanogaster populations with reduced genetic variation
<p>Many adaptive traits are polygenic and frequently more loci contributing to the phenotype are segregating than needed to express the phenotypic optimum. Experimental evolution with replicated populations adapting to a new controlled environment provides a powerful approach to study polygenic adaptation. Since genetic redundancy often results in non-parallel selection responses among replicates, we propose a modified Evolve and Resequence (E&R) design that maximizes the similarity among replicates. Rather than starting from many founders, we only use two inbred <em>Drosophila melanogaster</em>strains and expose them to a very extreme, hot temperature environment (29°C). After 20 generations, we detect many genomic regions with a strong, highly parallel selection response in 10 evolved replicates. The X chromosome has a more pronounced selection response than the autosomes, which may be attributed to dominance effects. Furthermore, we find that the median selection coefficient for all chromosomes is higher in our two-genotype experiment than in classic E&R studies. Since two random genomes harbor sufficient variation for adaptive responses, we propose that this approach is particularly well-suited for the analysis of polygenic adaptation.</p> <p>See the README.txt file to get a description of the uploaded files. Scripts.zip contains annotated command lines and scripts for the project (see internal README.txt file).</p>
Data for "Unfolding the structural stability of nanoalloys via symmetry-constrained genetic algorithm and neural network potential"
<p><strong>PtNi_alloy_eam.db</strong> is the dataset (ase.db object) consisting of 55982 intially sampled Pt-Ni alloy structures with EAM energies and forces.</p> <p><strong>PtNi_alloy_dft.db</strong> is the dataset (ase.db object) consisting of the final 6828 resampled Pt-Ni alloy structures with DFT energies and forces calculated by VASP. This is the training set for the NNP, and could be very useful for fitting other machine learning models.</p> <p><strong>PtNi_nanoalloy_vertices_nnp.db</strong> is the dataset (ase.db object) consisting of all the vertices (stable structures) on the convex hulls obtained from NNP-based SCGA runs on 36 Pt-Ni nanoalloy systems. The energies are given by the NNP. Additional information such as mixing energy, motif and symmetry axis are also saved in the dataset and can be queried by the 'data' keyword. An xyz format trajectory of these stable structures is also uploaded.</p> <p>All the input files and scripts for hybrid MC-MD simulations, QBC resampling, DFT calculations, NNP training, NNP-based SCGA runs and convex hull analysis are provided in <strong>inputs_and_scripts.zip</strong>.</p>
LA1141 × OH8245 inbred backcross (IBC) single nucleotide polymorphism (SNP) markers for genetic studies
<p>The LA1141 × OH8245 157 polymorphic SNP markers from an optimized tomato panel Sim et al., 2012 were used for linkage map construction in the BC<sub>2</sub>S<sub>3</sub> IBC and composite interval mapping QTL analysis. Genetic map position and physical position corresponding to Sl4.0 (Hosmani et al., 2019), and flanking sequences are provided.</p>
Exploring the Pocillopora cryptic diversity: a new genetic lineage in the western Indian Ocean or remnants from an ancient one?
<p>Cryptic species and lineages have been widely reported during the last decades, particularly in the marine realm. Misidentifications and ignoring species complexes imply many consequences, notably biasing biodiversity and connectivity assessments, which in turn mislead our understanding of ecosystems and impact the effective design and management of conservation plans. Focusing on the Indo-Pacific coral genus <em>Pocillopora</em>, playing key roles in reef ecosystems as one of the main bio-constructors, we report the first <em>Pocillopora</em> PSH16 (ORF53; <em>sensu</em> Gélin et al. 2017, Mol Phylogenet Evol 109:430–446) colonies (<em>N</em> = 19) in the western Indian Ocean (Nosy Tanikely, Madagascar), 6,000 km further from its current distribution. Colonies were identified according to their mitochondrial Open Reading Frame (ORF) haplotype and Bayesian assignment tests based on 13-microsatellite genotypes. Additionally, we performed genetic structure and diversity analyses with sympatric colonies from other <em>Pocillopora</em> species and <em>Pocillopora</em> PSH16 colonies from the tropical southwestern Pacific, revealing (1) a weak clonal richness, (2) a weak genetic diversity and (3) a relative isolation for the newly reported PSH16 colonies. These colonies thus represent either a new, distinct and uncommon, genetic lineage, or isolated remnants of a wider one. In any case, unless specific management measures are implemented, their long-term maintenance seems compromised due to restricted gene flow within a restricted pool of genes.</p> <p> </p> <p>This dataset contains the microsatellite genotypes analysed (98 <em>Pocillopora</em> colonies × 13 loci + ORF). Missing data are encoded as "?". The sampling marine province and the population are indicated for each individual.</p>
The genetic basis of structural colour variation in mimetic Heliconius butterflies
<p>Raw USAXS data from discal region of <em>Heliconius </em>butterflies (<em>H. erato </em>and<em> H. melpomene</em>). The data comes from wings of individuals of two intercross families, one from each species and was used to estimate scale structure variation and a QTL analysis.</p>
The contribution of genetic and environmental effects to Bergmann's rule and Allen's rule in house mice
<p>Data associated with the manuscript, "The contribution of genetic and environmental effects to Bergmann's rule and Allen's rule in house mice".</p> <p><strong>Abstract</strong>: Distinguishing between genetic, environmental, and genotype-by-environment effects is central to understanding geographic variation in phenotypic clines. Two of the best-documented phenotypic clines are Bergmann's rule and Allen's rule, which describe larger body sizes and shortened extremities in colder climates, respectively. Although numerous studies have found inter- and intraspecific evidence for both ecogeographic patterns, we still have a poor understanding of the extent to which these patterns are driven by genetics, environment, or both. Here, we measured the genetic and environmental contributions to Bergmann's rule and Allen's rule across introduced populations of house mice (<em>Mus musculus domesticus</em>) in the Americas. First, we documented clines for body mass, tail length, and ear length in natural populations, and found that these conform to both Bergmann's rule and Allen's rule. We then raised descendants of wild-caught mice in the lab and showed that these differences persisted in a common environment and are heritable, indicating that they have a genetic basis. Finally, using a full-sib design, we reared mice under warm and cold conditions. We found very little plasticity associated with body size, suggesting that Bergmann's rule has been shaped by strong directional selection in house mice. However, extremities showed considerable plasticity, as both tails and ears grew shorter in cold environments. These results indicate that adaptive phenotypic plasticity as well as genetic changes underlie major patterns of clinal variation in house mice and likely facilitated their rapid expansion into new environments across the Americas.</p> <p>Supplemental data files are provided below.</p> <p>Code associated with the analysis of these data can be found on GitHub at <a href="https://github.com/malballinger/Ballinger_allenbergmann_AmNat_2021">https://github.com/malballinger/Ballinger_allenbergmann_AmNat_2021</a>.</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.