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255 results for “gwas”
GWAS output for early salt stress responses in Arabidopsis thaliana accessions
<p>The output of the Genome Wide Association Study for early responses to salt stress in Arabidopsis accessions. The "_gwas2029.rda" files contain associations found for each trait using single models, where the first part of file name describes the phenotype (e.g. ROUNDNESS), the first number describes the day after stress application, the subsequent C/S indicated whether the association was performed with the trait scored at Control / Salt Stress conditions respectively. The "mtmm_final.rda" files contain the associations found between two traits, indicated as above in the first part of the file name. </p>
Combining GWAS and FST-based approaches to identify targets of Borrelia-mediated selection in natural rodent hosts
Recent advances in high-throughput sequencing technologies provide opportunities to gain novel insights into the genetic basis of phenotypic trait variation. Yet to date, progress in our understanding of genotype-phenotype associations in non-model organisms in general and natural vertebrate populations in particular has been hampered by small sample sizes typically available for wildlife populations and a resulting lack of statistical power, as well as a limited ability to control for false positive signals. Here we propose to combine a genome-wide association (GWAS) and FST-based approach with population-level replication to partly overcome these limitations. We present a case study in which we used this approach in combination with Genotyping-by-Sequencing (GBS) SNP data to identify genomic regions associated with Borrelia afzelii resistance or susceptibility in the natural rodent host of this Lyme disease-causing spirochete, the bank vole (Myodes glareolus). Using this combined approach we identified four consensus SNPs located in exonic regions of the genes Slc26a4, Tns3, Wscd1 and Espnl, which were significantly associated with the voles' Borrelia infectious status within and across populations. Functional links between host responses to bacterial infections and most of these genes have previously been demonstrated in other rodent systems, making them promising new candidates for the study of evolutionary host responses to Borrelia emergence. Our approach is applicable to other systems and may facilitate the identification of genetic variants underlying disease resistance or susceptibility, as well as other ecologically relevant traits, in wildlife populations.
Genetic architecture of alcohol consumption identified by a genotype-stratified GWAS, and impact on esophageal cancer risk in Japanese
<p><span>An East Asian-specific variant on <em>aldehyde</em> <em>dehydrogenase</em> <em>2</em> (<em>ALDH2</em> rs671, G>A) is the major genetic determinant of alcohol consumption. We performed an rs671 genotype-stratified genome-wide association study (GWAS) meta-analysis in up to 175,672 Japanese individuals to uncover additional loci associated with alcohol consumption in an rs671-dependent manner. Three loci (<em>GCKR</em>, <em>KLB</em>, and <em>ADH1B</em>)</span> <span>satisfied the genome-wide significance threshold in wild-type homozygotes (GG), whereas six loci (<em>GCKR</em>, <em>ADH1B</em>, <em>ALDH1B1</em>, <em>ALDH1A1</em>, <em>ALDH2</em>, and <em>GOT2</em>) did so in heterozygotes (GA). Of these, five loci showed genome-wide significant interaction with rs671. Genetic correlation analyses revealed ancestry-specific genetic architecture in heterozygotes. Subsequent polygenic risk scoring depicted interactions highlighted by stratified GWAS. Further, most discovered loci showed significant effects on risk of esophageal cancer, a representative alcohol-related disease, and multiple other phenotypes. Our results identify the genotype-specific genetic architecture of alcohol consumption and reveal its potential impact on alcohol-related disease risk.</span></p>
EMMA-X GWAS results on S. pimpinellifolium RSA traits - part 3
<p>The results of GWAS run through EMMA-X pipeline, on root system architecture traits of S. pimpinellifolim (wild tomato) exposed to control and salt stress conditions on agar plates. Traits are abbreviated as: LRnG for increase in lateral root number, LRno for lateral root number, MR.GR for main root growth rate, MRL for main root length. The treatments are abbreviated with C or S for control or 100 mM NaCl treatments. The numbers in file name (0-4) indicate the days post transfer to agar plates containing treatment. </p>
GWAS on salt stress responses in root system architecture parameters of wild tomato (S. pimpinellifolium)
<p>The population of +/2 200 accessions of wild tomato was screened with the protocol described <a href="https://www.protocols.io/view/studying-root-system-architecture-changes-in-tomat-2mqgc5w">here</a> with the only exception that the plants were transferred 4 days after germination (rather than 3 - described in the protocol). The images were analyzed using the <a href="https://smartroot.github.io/">SmartRoot</a> for days 0, 1, 2, 3, and 4 after transfer to treatment plates (0 or 100 mM NaCl, 1/4 MS, 0.5% sucrose, 0.1% MES, 1% Dashin agar). The data analysis was performed as described <a href="https://rpubs.com/mjulkowska/BIGpimp_RSA_salt">here</a>, while the pareto front calculations were done according to Chandrasekhar & Julkowska paper (<a href="https://www.biorxiv.org/content/10.1101/2021.08.12.456185v1">preprint here</a>). The GWAS was performed using the ASReml script similar to <a href="https://onlinelibrary.wiley.com/doi/10.1111/tpj.15310">Awlia et al. (2021)</a>. </p>
Clonal hematopoiesis GWAS summary statistics
<p>Datasets linked to the pre-print titled, "Genome-wide analyses of 200,453 individuals yields new insights into the causes and consequences of clonal hematopoiesis", which is available at <a href="https://doi.org/10.1101/2022.01.06.22268846">https://doi.org/10.1101/2022.01.06.22268846</a></p> <p>To estimate odds ratios from BOLT-LMM betas and standard errors please use the formula provided <a href="https://alkesgroup.broadinstitute.org/BOLT-LMM/BOLT-LMM_manual.html#x1-5500010.2">here</a>. The formula requires the number of controls (= 173,918 for each of the five GWAS) and the numbers of cases (10,203 overall-CH, 5,185 DNMT3A-CH, 2,042 TET2-CH, 4,049 large-CH, and 6,154 small-CH).</p>
Data Integration to Identify and Prioritize Potential Regulatory Variants for Rheumatoid Arthritis in a Post-GWAS era
<p>The Data set contain data related to the SNPs in linkage disequilibrium with published GWAS rheumatoid arthritis SNPs and the subsequent annotation and pathway analysis files performed to identify putative 'missed' variants of rheumatoid arthritis. </p>
GWAS pyrethroid-resistant Aedes aegypti
<p><span><span><span><span><span><span><span><span><span><span><span>Genome-wide association studies (GWAS) use genetic polymorphism across the genomes of individuals with distinct characteristics to identify genotype-phenotype associations. In mosquitoes, complex traits such as vector competence and insecticide resistance could benefit from GWAS use. We used the <i>Ae. aegypti</i> 50k SNP chip to genotype populations with different levels of pyrethroid resistance from Northern Brazil. Pyrethroids are widely used worldwide to control mosquitoes and other agricultural pests, and their intensive use led to the selection of resistance phenotypes in many insects including mosquitoes. For <i>Ae. aegypti</i>, resistance phenotypes are mainly associated with several mutations in the voltage-gated sodium channel, known as knockdown resistance (<i>kdr</i>). We phenotyped those populations with the WHO insecticide bioassay using deltamethrin impregnated papers, genotyped the <i>kdr</i> alleles using qPCR, and the whole genomic regions with the SNP chip. We identified single-nucleotide polymorphisms (SNPs) directly associated with resistance and one epistatic SNP pair. We also observed that the novel SNPs correlated with the known <i>kdr</i> genotypes, although on different chromosomes or not in close physical proximity to the voltage-gated sodium channel gene. In addition, a pairwise comparison of resistance and susceptible mosquitoes from each population revealed differentiated genomic regions not associated with pyrethroid resistance. These new bi-allelic markers can be used to genotype other populations along with <i>kdr </i>alleles to understand their worldwide distribution. The functional roles of the genes near the newly discovered SNPs require new studies to determine if they act synergistically with <i>kdr</i> alleles or reduce the fitness cost of maintaining resistant alleles.</span></span></span></span></span></span></span></span></span></span></span></p>
ANÁLISE FUNCIONAL DE GENES PARA CARACTERÍSTICAS REPRODUTIVAS EM SUÍNOS: DO GWAS AO PÓS-GWAS- APÊNDICES E ANEXOS
<p>As características reprodutivas, tais como o número de tetos, uniformidade e tamanho de leitegada, são essenciais para os programas de melhoramento animal devido á importância para a cadeia produtiva, pois influenciam na habilidade materna das porcas e pode afetar o número de leitões desmamados. Assim, objetivou-se a identificação e anotação funcional de genes candidatos associados a características reprodutivas em suínos. A identificação dos genes candidatos foi realizada por meio de uma revisão sistemática entre dois avaliadores independentes que utilizaram o mecanismo de busca na “<em>Web of Science”.</em> As consultas de pesquisa utilizaram combinações de palavras-chave seguindo os critérios: termos relacionados às características avaliadas (“<em>number of teats”, “teats number”, “total number born”, “litter uniformity”, “uniformity of the litter</em>”); tipos de teste de associação (“<em>gwas”, “genome wide association</em>”); e espécie (“<em>pig</em>”). Para a anotação de variantes alvo foi utilizado o banco de dados público do <em>Ensembl </em>contendo as variantes estruturais de suínos. Em seguida, foram classificadas de acordo com sua potencial função e concatenadas com os estudos prévios de GWAS. Os genes obtidos por meio da revisão sistemática de GWAS para as características reprodutivas de suínos, que possuíram variantes na região 5’UTR e/ou codificante, foram utilizados par a construção das redes gênicas de processos biológicos e Gene-Fatores de Transcrição. Da revisão sistemática um total de quatorze artigos foram selecionados, e o processo de anotação do gene resultou em um total de 2.077 genes candidatos. Após combinados com a lista de genes associados com às variantes estruturais, restaram 306 genes com suas potenciais variantes estruturais. Três genes para a característica de tamanho de leitegada foram priorizados como os genes mais candidatos (<em>GRID2</em>, <em>GRIK3 </em>e <em>PALB2)</em>, com seus processos biológicos enriquecidos (ex: via de sinalização de receptores ionotrópicos de glutamato e crescimento de blastócito). Para a característica de número de tetos, seis genes foram destacados como os genes mais candidatos (<em>GHR</em>, <em>IFT80, </em><em>FSTL3, SKOR1, SMURF1</em> e <em>AKT3</em>), e seus respectivos processos biológicos (ex: receptor do hormônio de crescimento, regulação da via de sinalização de BMP - <em>Bone </em><em>Morphogenetic Proteins</em> e proliferação de vasos sanguíneos). Com as redes Gene-Fatores de Transcrição, identificamos os principais FTs e os genes candidatos mais relevantes para o tamanho de leitegada (<em>PALB2</em> e <em>GRID2)</em> e para número de tetos (<em>RIN, LTBP2</em> e <em>COL6A6)</em><em>. </em>Com isso, a partir deste estudo, identificamos os genes mais promissores associados ao tamanho de leitegada e número de tetos, que poderão ser usados nos estudos de genética e melhoramento animal, pois seus mecanismos genéticos regulatórios, estão envolvidos na expressão fenotípica dessas características, influenciando no desempenho reprodutivo e produtividade suinícola. A descoberta destes genes, podem trazer novas considerações para o conhecimento atual da arquitetura genética dessas características, uma vez que, os marcadores associados a estes genes, podem ser atribuídos com pesos mais elevados na seleção genômica e validados em populações específicas.</p> <p> </p>
FUNCTIONAL ANALYSIS OF GENES FOR REPRODUCTIVE TRAITS IN PIGS: FROM GWAS TO POST-GWAS
<p>Reproductive traits, such as number of teats, uniformity and litter size, are essential for animal breeding programs due to the importance for the production chain, since they influence the maternal ability of sow and can affect the number of weaned piglets. Our objective was to identify candidate genes associated with reproductive traits in pigs, using GWAS data, from a systematic review combined with sequencing data, to build networks of biological processes and Gene-TFs (transcription factors networks from the identified genes in order to highlight the most candidate genes for litter size, uniformity and number of teats. In the systematic review only peer-reviewed articles were used, with descriptors related to the evaluated traits, and selected based on eligibility criteria. Fourteen papers were selected and classified into groups for functional analysis of gene networks with 2077 candidate genes identified. After combining with the list of genes presenting known structural variants in the 5'UTR and/or coding region, 306 genes remained to be used to build the networks of biological processes and TFs gene networks, highlighting processes associated with litter size (e.g., ionotropic glutamate receptor signaling pathway and blastocyte growth) and teats number (e.g., growth hormone receptor, regulation of the BMP - Bone Morphogenetic Proteins signaling pathway and blood vessel proliferation). Three most candidate genes for litter size trait (<em>GRID2</em>and <em>PALB2</em>) and six most candidate genes (<em>GHR, IFT80,</em> <em>FSTL3, SKOR1, SMURF1</em> and <em>AKT3</em>) for teats number were prioritized. TF associated with candidate genes were also identified for litter size (<em>PALB2</em> and <em>GRID2</em>) and teat number (<em>RIN, LTBP2</em> and <em>COL6A6</em>). Thus, it is suggested that the genes and TFs presented in this study may play an important role in the traits studied, being important for genetic studies and animal breeding. The highlighted genes may bring new considerations to the current knowledge of the genetic architecture of these traits, since the markers associated with these genes can be assigned higher weights in genomic selection and validated in specific populations.</p>
Combining GWAS and population genomic analyses to characterize coevolution in a legume-rhizobia symbiosis
<p>The mutualism between legumes and rhizobia is clearly the product of past coevolution. However, the nature of ongoing evolution between these partners is less clear. To characterize the nature of recent coevolution between legumes and rhizobia, we used population genomic analysis to characterize selection on functionally annotated symbiosis genes as well as on symbiosis gene candidates identified through a two-species association analysis. For the association analysis, we inoculated each of 202 accessions of the legume host <em>Medicago truncatula</em> with a community of 88 <em>Ensifer meliloti</em> strains. Multi-strain inoculation, which better reflects the ecological reality of rhizobial selection in nature than single-strain inoculation, allows strains to compete for nodulation opportunities and host resources and for hosts to preferentially form nodules and provide resources to some strains. We found extensive host by symbiont, <em>i.e.</em>, genotype-by-genotype, effects on rhizobia fitness and some annotated rhizobia genes bear signatures of recent positive selection. However, neither genes responsible for this variation nor annotated host symbiosis genes are enriched for signatures of either positive or balancing selection. This result suggests that stabilizing selection dominates selection acting on symbiotic traits and that variation in these traits is under mutation-selection balance. Consistent with the lack of positive selection acting on host genes, we found that among-host variation in growth was similar whether plants were grown with rhizobia or N-fertilizer, suggesting that the symbiosis may not be a major driver of variation in plant growth in multi-strain contexts.</p>
Formatted Public GWAS Summary Statistics for 16 Traits and LD pruned SNP sets
<p>This data set includes 16 files with formatted GWAS summary statistic and a csv file gwas_info.csv. The csv provides the original download link and publication for each study. The data in this repository were created by downloading raw summary statistics for each study and processing them using Joe Marcus' GWAS pipeline (https://github.com/jhmarcus/gwass). The resulting data set have consistent allele orientation and column headers making them convenient for analysis. We use them in an MR analysis of pairs of GWAS traits described in Section 2.3 of Morrison et al (2019) (https://www.biorxiv.org/content/10.1101/682237v3) and here https://jean997.github.io/cause/gwas_pairs.html.</p> <p>New Sep 2022: I have added LD pruned SNP sets for each pair of traits.For each exposure/outcome pair, the set of snps in</p> <p>snps_<exposure>__<outcome>.txt</p> <p>were generated by LD pruning using LD estimated using LD Shrink (available https://zenodo.org/record/1464357/) at a threshold of r^2 < 0.1. LD pruning was performed using the ld_prune function in the cause R package (github.com/jean997/cause). These are the SNP sets used in the analysis in the paper</p>
Summary-level results from the AFGen, GWAS and educational attainment GWAS
<p>Merged summary-level results to calculate genetic correlations between various traits. The columns are as follows:</p> <ul> <li>SNP: rs identifier for the SNP</li> <li>AFGen.EUR.Z: z-score from the AFGen GWAS of atrial fibrillation performed in European-ancestry samples</li> <li>EduYrs.Z: z-score from the educational attainment GWAS (data available from LDHub)</li> <li>afib.broad.Z: z-scores from the SiGN GWAS of atrial fibrillation</li> <li>allstroke.Z: z-scores from the SiGN GWAS of all stroke </li> <li>CCScCEmajor.Z: z-scores from the SiGN GWAS of CE stroke (defined by CCSc)</li> <li>CCScCRYPTCE.Z: z-scores from the SiGN GWAS of cryptogenic/CE minor stroke (defined by CCSc)</li> <li>CCScINCUNC.Z: z-scores from the SiGN GWAS of incomplete/unclassified stroke (defined by CCSc)</li> <li>CCScLAA.Z: z-scores from the SiGN GWAS of LAA stroke (defined by CCSc)</li> <li>CCScSAO.Z: z-scores from the SiGN GWAS of SAO stroke (defined by CCSc)</li> <li>CCScUNDETER.Z: z-scores from the SiGN GWAS of undetermined stroke (defined by CCSc)</li> <li>CCSpCEmajincl.Z: z-scores from the SiGN GWAS of CE stroke (defined by CCSp)</li> <li>CCSpCryptoincl.Z: z-scores from the SiGN GWAS of cryptogenic stroke (defined by CCSp) </li> <li>CCSpLAAmajincl.Z: z-scores from the SiGN GWAS of LAA stroke (defined by CCSp)</li> <li>CCSpSAOmajincl.Z: z-scores from the SiGN GWAS of SAO stroke (defined by CCSp)</li> <li>toastCE.Z: z-scores from the SiGN GWAS of CE stroke (defined by TOAST)</li> <li>toastLAA.Z: z-scores from the SiGN GWAS of LAA stroke (defined by TOAST)</li> <li>toastSAO.Z: z-scores from the SiGN GWAS of SAO stroke (defined by TOAST)</li> <li>toastUNDETER.Z: z-scores from the SiGN GWAS of undetermined stroke (defined by TOAST)</li> </ul> <p>CCSc, CCS Causative subtyping system; CCSp, CCS Phenotypic subtyping system; TOAST, TOAST subtyping system; CE, cardioembolic stroke; LAA, large artery atherosclerosis; SAO, small artery occlusion.</p> <p> </p>
aurora: A Machine Learning GWAS Tool For Analyzing Microbial Habitat Adaptation
<p>This upload contains compressed raw data to rerun examples in the main text of <em>aurora </em>and the first version of<em> aurora </em>used in the paper.</p>
Genetics of Cardiac Aging Implicate Organ-Specific Variation - GWAS Summary Stats and Model Weights
Open the record for dataset details and reuse information.
UK Biobank GWAS of WHRadjBMI in combined, women only, and men only
<p>GWAS of WHRadjBMI in UK Biobank. GWAS was performed in BOLT-LMM and full details can be found here: <a href="https://www.ncbi.nlm.nih.gov/pubmed/30239722">https://www.ncbi.nlm.nih.gov/pubmed/30239722</a> (the paper is open access).</p> <p>Columns:</p> <ul> <li>SNP, SNP identifier</li> <li>CHR, chromosome</li> <li>BP, position (hg19)</li> <li>GENPOS, genetic position</li> <li>ALLELE1, tested allele</li> <li>ALLELE0, other allele</li> <li>A1FREQ, frequency of the tested allele</li> <li>INFO, imputation quality (info score)</li> <li>CHISQ_LINREG, chi-square from linear regression</li> <li>P_LINREG, p-value from linear regression</li> <li>BETA, beta (effect) from the linear mixed model</li> <li>SE, standard error from the linear mixed model</li> <li>CHISQ_BOLT_LMM_INF, chi-square from linear mixed model</li> <li>P_BOLT_LMM_INF, p-value from the linear mixed model</li> </ul> <p> </p>
genotyping matrix for common bean GWAS.
<p>These files inculded twenty phenotypic characteristics were evaluated on 628 accessions. Of these, 15 were quantitative and two were binary traits. Yield-related traits that were evaluated across four locations for three different years.In addition, associations with qualitative traits and 3 datasets for diseases in the greenhouse and insect infestation in a seed chamber were also assessed.</p> <p>In additational, also included genotyping matrix for common bean GWAS.</p>
UK Biobank GWAS of WHR in combined, women only, and men only
<p>GWAS of WHR in UK Biobank. GWAS was performed in BOLT-LMM and full details can be found here: <a href="https://www.ncbi.nlm.nih.gov/pubmed/30239722">https://www.ncbi.nlm.nih.gov/pubmed/30239722</a> (the paper is open access).</p> <p>Columns:</p> <ul> <li>SNP, SNP identifier</li> <li>CHR, chromosome</li> <li>BP, position (hg19)</li> <li>GENPOS, genetic position</li> <li>ALLELE1, tested allele</li> <li>ALLELE0, other allele</li> <li>A1FREQ, frequency of the tested allele</li> <li>INFO, imputation quality (info score)</li> <li>CHISQ_LINREG, chi-square from linear regression</li> <li>P_LINREG, p-value from linear regression</li> <li>BETA, beta (effect) from the linear mixed model</li> <li>SE, standard error from the linear mixed model</li> <li>CHISQ_BOLT_LMM_INF, chi-square from linear mixed model</li> <li>P_BOLT_LMM_INF, p-value from the linear mixed model</li> </ul>
UK Biobank GWAS of WHRadjBMI and WHR in combined, women only, and men only (unrelated, self-identified white British)
<p>GWAS of WHRadjBMI and WHR in UK Biobank, using unrelated self-identified white British samples only.</p> <p>GWAS was performed in BOLT-LMM and full details can be found here: <a href="https://www.ncbi.nlm.nih.gov/pubmed/30239722">https://www.ncbi.nlm.nih.gov/pubmed/30239722</a> (the paper is open access).</p> <p>Columns:</p> <ul> <li>SNP, SNP identifier</li> <li>CHR, chromosome</li> <li>BP, position (hg19)</li> <li>GENPOS, genetic position</li> <li>ALLELE1, tested allele</li> <li>ALLELE0, other allele</li> <li>A1FREQ, frequency of the tested allele</li> <li>INFO, imputation quality (info score)</li> <li>CHISQ_LINREG, chi-square from linear regression</li> <li>P_LINREG, p-value from linear regression</li> <li>BETA, beta (effect) from the linear mixed model</li> <li>SE, standard error from the linear mixed model</li> <li>CHISQ_BOLT_LMM_INF, chi-square from linear mixed model</li> <li>P_BOLT_LMM_INF, p-value from the linear mixed model</li> </ul>
A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals (GWAS)
<p>GWAS summary statistics accompanying manuscript "A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals".</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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.