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176 results for “QTL”

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zenodo48/100

QTL summary statistics from the DIRECT consortium

These are the complete summary statistics for DIRECT genotype-phenotypes associations (QTLs). The project performed genotype-phenotype associations for gene expression (RNAseq), targeted proteins (Olink), targeted metabolites (Biocrates) and untargeted metabolites (Metabolon) derived from 3,029 blood and plasma samples from the DIRECT cohort. This submission includes supplementary files and nominal pvalues (as uncorrected pvalues) for all associations included in the manuscript. Trans associations included are typically limited to pvalues <1e-04. Network tables are also included, with information to load and use Cytoscape to visualize them. This is version 2, some files were missing on version 1.

opencc-by-4.0Feb 2021View details →

zenodo44/100

QTL Mapping for Resistance to Cankers Induced by Pseudomonas syringae pv. actinidiae (Psa) in a Tetraploid Actinidia chinensis Kiwifruit Population

Raw Illumina R1 sequence reads for individual plants  genotyped for the study entitled "QTL Mapping for Resistance to Cankers Induced by Pseudomonas syringae pv. actinidiae (Psa) in a Tetraploid Actinidia chinensis Kiwifruit Population" accepted in MDPI Pathogen journals, Special issue "Pseudomonas syringae Species Complex"

opencc-by-4.0Dec 2019View details →

zenodo44/100

QTL Mapping for Resistance to Cankers Induced by Pseudomonas syringae pv. actinidiae (Psa) in a Tetraploid Actinidia chinensis Kiwifruit Population

Raw Illumina R2 sequence reads for individual plants  genotyped for the study entitled "QTL Mapping for Resistance to Cankers Induced by Pseudomonas syringae pv. actinidiae (Psa) in a Tetraploid Actinidia chinensis Kiwifruit Population" accepted in MDPI Pathogen journals, Special issue "Pseudomonas syringae Species Complex"

opencc-by-4.0Dec 2019View details →

zenodo44/100

Strawberry volatile organic compounds metabolomic data and QTL study

This dataset contains the supplementary materials of the publication "Multivariate QTL approach reveals a major regulator of terpenoid production and other volatiles in strawberry" of the same authors. In this study we extracted volatile organic compounds from several strawberry samples and analysed their identity and abundance. We used this volatile data to perform an extensive multivariate QTL study, the results of which can be found in this dataset. All analysis, results and figures can be reproduced using the folder included in the supplementary data 1. If you want to reproduce part or all of our analysis, only download sup data 1.  If you only need one of our results or data table you can download them individually: <ul> <li>Sup data 2: abundances of volatile organic compounds from a biparental and diverse panel (GWAS) population.</li> <li>Sup data 3 and 4: p-value tables for all metabolites as well as multivariate traits (see publication for more information).</li> <li>Sup table 1: Summary of metabolite abundances and heritabilities across both populations.</li> <li>Sup tables 2 and 3: significant QTL signals for each trait individually and summarised per QTL locus.</li> <li>Sup table 4: previously reported VOC QTLs in strawberry, with positions imputed in the Royal Royce genome.</li> <li>Sup table 5: metadata about all the identified compounds on this and previous studies.</li> <li>Sup table 6: SNP array positions imputed in the "Royal Royce" genome assembly.</li> <li>Sup table 7: Number of markers per chromosome in this analysis.</li> </ul> <h3>Update 2025</h3> We updated the underlying code and datasets to reflect several revisions made to this work. Most notably, the QTL results have been reworked. They now do not include Blink or FarmCPU results (only mixed model results, obtained through statgenGWAS). Additionally, heritability estimations, QQ-plots and other figures have been added to the reproducible results code.

opencc-by-4.0Dec 2022View details →

zenodo40/100

Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits

Supporting data associated with the manuscript "Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits". These include: <ul> <li>StringTie2.438.GEUVADIS.gtf.gz - Assembled transcriptome using RNA-seq from 438 samples from the GEUVADIS project by StringTie2</li> <li>puQTL_nominal_output.txt.gz - puQTL results from QTLtools nominal pass</li> <li>puQTL_conditional_output.txt.gz - puQTL results from QTLtools conditional pass</li> <li>eQTL_nominal_output.txt.gz - eQTL results from QTLtools nominal pass</li> <li>eQTL_conditional_output.txt.gz - eQTL results from QTLtools conditional pass</li> </ul> See <a href="https://qtltools.github.io/qtltools/">https://qtltools.github.io/qtltools/</a> for column descriptions of QTL mapping results.

opencc-by-4.0Feb 2022View details →

zenodo40/100

Gene expression QTL mapping in stimulated iPSC-derived macrophages provides insights into common complex diseases.

Many disease-associated variants are thought to be regulatory but are not present in existing catalogues of expression quantitative trait loci (eQTL). We hypothesise that these variants may regulate expression in specific biological contexts, such as stimulated immune cells. Here, we used human iPSC-derived macrophages to map eQTLs across 24 cellular conditions. We found that 76% of eQTLs detected in at least one stimulated condition were also found in naive cells. The percentage of response eQTLs (reQTLs) varied widely across conditions (3.7% - 28.4%), with reQTLs specific to a single condition being rare (1.11%). Despite their relative rarity, reQTLs were overrepresented (p=0.05, Fisher's exact test) among disease-colocalizing eQTLs. We nominated an additional 21.7% of disease effector genes at GWAS loci via colocalization of reQTLs, with 38.6% of these not found in the Genotype–Tissue Expression (GTEx) catalogue. Our study highlights the diversity of genetic effects on expression and demonstrates how condition-specific regulatory variation can enhance our understanding of common disease risk alleles.

opencc-by-4.0May 2023View details →

zenodo40/100

GWAS and GTEx QTL integration

# Data usage policy When using this data, you must acknowledge the source by citing the publication "Widespread dose-dependent effects of RNA expression and splicing on complex diseases and traits" (https://doi.org/10.1101/814350).   <pre># GTEx GWAS integration This package contains the application of several GWAS-QTL integration methods. The results were analyzed in [this preprint](https://www.biorxiv.org/content/10.1101/814350v1) about GTEx v8 application to several GWAS traits. ``` . |-- colocalization | |-- coloc | | `-- coloc_enloc_priors_eqtl.tar.gz | |-- enloc | | |-- enloc_eqtl_eur.tar.gz | | `-- enloc_sqtl_eur.tar.gz | `-- eur_ld.bed.gz |-- prediction_models | |-- gtex_v8_expression_mashr_snp_smultixcan_covariance.txt.gz | |-- gtex_v8_splicing_mashr_snp_smultixcan_covariance.txt.gz | |-- mashr_eqtl.tar | `-- mashr_sqtl.tar |-- smr | |-- SMR_gtex_v8_README.txt | `-- SMRresults_GTEx_v8_peQTL5e-08.tar.gz |-- smultixcan | |-- smultixcan_eqtl.tar.gz | `-- smultixcan_sqtl.tar.gz `-- spredixcan |-- spredixcan_eqtl.tar.gz `-- spredixcan_sqtl.tar.gz ``` You can uncompress gzipped tarball packages `*.tar.gz` in a UNIX command line with an instruction such as: ```bash tar -xzvpf smultixcan_eqtl.tar.gz ``` , and the tar packages (`*.tar`) with an analogous instruction: ```bash tar -xvpf mashr_eqtl.tar ``` ## Preliminaries **Finemapping** results are contained in a separate release due to size constraints. GWAS summary statistics for 114 traits were harmonized and imputed to GTEx v8 variants with MAF>0.01 using only european samples. (summary imputation software [here](https://github.com/hakyimlab/summary-gwas-imputation)). Some of the following analyses used the full set of 114 traits, while some focused only on 87 traits whose imputed associations showed no deflation (the imputation algorithm is conservative, and studies with too few available variants have a depleted distribution of association p-values after imputation). The harmonized and imputed GWAS summary statistics are contained in a separate release due to size constraints. For completeness' sake, the imputed summary statistics look like: ``` variant_id panel_variant_id chromosome position effect_allele non_effect_allele current_build frequency sample_size zscore pvalue effect_size standard_error imputation_status n_cases rs554008981 chr1_13550_G_A_b38 chr1 13550 A G hg38 0.017316017316017316 336474 -2.2919929353647097 0.021906050841240293 NA NA imputed NA rs201055865 chr1_14671_G_C_b38 chr1 14671 C G hg38 0.012987012987012988 336474 -0.9559192804440632 0.33911301727494103 NA NA imputed NA ... ``` The GWAS were split in approximately independent LD regions (Berisa-Pickrell)/ GWAS regions are defined in `eur_ld.bed.gz` (note that a few of them are ill-defined in hg38 and where ignored; only completely defined regions were used). ## Colocalization ### Enloc ENLOC ([see fotware here](https://github.com/xqwen/integrative)) was run for sQTLs and eQTLs using individuals of european ancestry and DAP-G QTL enrichment results on 87 traits. Result files are included in `enloc_eqtl_eur.tar.gz` and `enloc_sqtl_eur.tar.gz` Each file contains a particular tissue-trait combination. Each row details colocalization between a GWAS region (Berisa-Pickrell) and gene's or intron's cis-window. A region might overlap multiple genes/introns or viceversa. Each ENLOC file contains the following columns: * gwas_locus: GWAS LD region * molecular_qtl_trait: gene or intron * locus_gwas_pip: posterior inclusion probability of variants in the GWAS LD region * locus_rcp: regional colocalization probability (main colocalization measure) * lead_coloc_SNP: snp with highest RCP * lead_snp_rcp: rcp of the lead coloc snp ### Coloc Coloc ([see software here](https://cran.r-project.org/web/packages/coloc/index.html)) was run using prior probabilities estimated from QTL enrichment of GWAS variants (computed via ENLOC). Results for eQTL are available in `coloc_enloc_priors_eqtl.tar.gz`. Each file contains results for a trait-tissue combination. Columns are: * gene_id: gene or intron id * p0: probability that neither QTL nor GWAS contain a causal variant * p1: probability that only GWAS contains a causal variant * p2: probability that only QTL has a causal variant * p3: probability that GWAS and QTL have a causal variant and it's distinct * p4: probability that GWAS and QTL have a causal variant and it's the same (main colocalization measure) ## PrediXcan `mashr_eqtl.tar` and `mashr_sqtl.tar` contain prediction models (trained on expression or splicing data respectively, for 49 GTEx tissues) and LD compilations to be used with PrediXcan, S-PrediXcan, MultiXcan and S-MultiXcan. For every tissue, the `mashr_{tissue}.db` file is a SQLite file with the prediction model definitions. `mashr_{tissue}.txt.gz` is a gzipped-text file with the upper triangular matrices of covariance between snps within a gene/intron prediction model. Many variants in these models don't have an rsid. To fully leverage the information in these models, it is advised to at least harmonize to GTEx variants, and if possible impute as we did [here](https://github.com/hakyimlab/summary-gwas-imputation). ### S-PrediXcan S-PrediXcan was run for the 114 harmonized and imputed traits, on eQTL and sQTL mashr prediction models. All of the GWAS traits had the same format, so that the following format parameters were used with S-PrediXcan: ``` --snp_column panel_variant_id --effect_allele_column effect_allele --non_effect_allele_column non_effect_allele --zscore_column zscore \ --keep_non_rsid --additional_output --model_db_snp_key varID \ ``` Each file is a CSV, with each row containing a gene/intron association at a given trait-tissue combination: * gene: ENSEMBLE ID or intron id * gene_name: HUGO name or intron id * zscore: predicted association z-score * effect_size: estimated effect size * pvalue: association p-value * var_g: estimated variance of predicted expression or splicing * pred_perf_r2: prediction model cross-validated performance * pred_perf_pval: prediction model cross-validated performance * pred_perf_qval: deprecated, empty field left for compatibility * n_snps_used: number of snps in the intersection of GWAS and model * n_snps_in_cov: number of snps in the LD compilation * n_snps_in_model: number of snps in the model * best_gwas_p: smallest p-value acros GWAS snps used in this model * largest_weight: largest prediction model weight ### S-Multixcan S-MultiXcan results were generated from the above S-PrediXcan results. Each fiel contains multi-tissue associations for a given trait: * gene: ENSEMBLE ID or intron id * gene_name: HUGO name or intron id * pvalue: multi-tissue association p-value * n: number of models avialble for this gene/intron * n_indep: number of independent components of variation in predicted expression/splicing (surviving principal components) * p_i_best: highest single-tissue p-value (S-PrediXcan) * t_i_best: tissue of highest p-value * p_i_worst: lowest single-tissue p-value (S-PrediXcan) * t_i_worst: tissue of lowest p-value * eigen_max: maximum eigenvalue of SVD * eigen_min: minimum eigenvalue of SVD * eigen_min_kept: smallest eigenvalue retained after discarding smallest variations * z_min: minimum single-tissue z-score * z_max: maximum single-tissue z-score * z_mean: mean single-tissue zscre * z_sd: standard deviation of the single-tissue z-scores * tmi: trace of M * M_i where M is predicted expression/splicing covariance across tissues for a gene, and M_i is its SVD pseudo-inverse * status: computation status, 0 if no errors ## SMR See `SMR_gtex_v8_README.txt` for details.</pre>     # Disclaimer The data is provided "as is", and the authors assume no responsibility for errors or omissions.   The User assumes the entire risk associated with its use of these data.   The authors shall not be held liable for any use or misuse of the data described and/or contained herein.   The User bears all responsibility in determining whether these data are fit for the User's intended use.   The information contained in these data is not better than the original sources from which they were derived, and both scale and accuracy may vary across the data set.   These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate.     The user is responsible to comply with any data usage policy from the original GWAS studies; refer to the list of traits described [here](https://www.biorxiv.org/content/10.1101/814350v1) to identify their respective Consortia's requirements. THE DATA IS PROVIDED WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATA OR THE USE OR OTHER DEALINGS IN THE DATA.

opencc-by-4.0Oct 2019View details →

zenodo40/100

Full expression and splicing QTL summary statistics for three human spinal cord segments

This dataset is part of the manuscript: "Integrative genetic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes" by Jack Humphrey, et al. The files below contain nominal and permuted quantitative trait loci (QTL) associations between common genetic variants derived from whole genome sequencing and either gene expression or splicing phenotypes generated from RNA-seq of post-mortem spinal cord sections. All QTLs were mapped with TensorQTL. Nominal association files are coordinate sorted, bgzip-compressed and tabix-indexed tab-separated variable files. Top association files are gzip-compressed tab-separated variable files. Sample sizes for each dataset are as follows: Cervical spinal cord n = 216. Lumbar spinal cord n = 197. Thoracic spinal cord n = 68. Description of files: CervicalSpinalCord_expression_peer30_gene.cis_qtl.txt.gz - Top per-gene eQTL summary statistics from cervical spinal cord  CervicalSpinalCord_expression_peer30_gene.cis_qtl_nominal_tabixed.tsv.gz - Full nominal eQTL summary statistics from cervical spinal cord  CervicalSpinalCord_splicing_peer15_cluster.cis_qtl.txt.gz - Top per-cluster sQTL summary statistics from cervical spinal cord CervicalSpinalCord_splicing_peer15_gene.cis_qtl_nominal_tabixed.tsv.gz - Full nominal sQTL summary statistics from cervical spinal cord LumbarSpinalCord_expression_peer30_gene.cis_qtl.txt.gz - Top per-gene eQTL summary statistics from lumbar spinal cord LumbarSpinalCord_expression_peer30_gene.cis_qtl_nominal_tabixed.tsv.gz - Full nominal eQTL summary statistics from lumbar spinal cord LumbarSpinalCord_splicing_peer10_cluster.cis_qtl.txt.gz - Top per-cluster sQTL summary statistics from lumbar spinal cord LumbarSpinalCord_splicing_peer10_gene.cis_qtl_nominal_tabixed.tsv.gz - Full nominal sQTL summary statistics from lumbar spinal cord ThoracicSpinalCord_expression_peer10_gene.cis_qtl.txt.gz - Top per-gene eQTL summary statistics from thoracic spinal cord ThoracicSpinalCord_expression_peer10_gene.cis_qtl_nominal_tabixed.tsv.gz - Full nominal eQTL summary statistics from thoracic spinal cord ThoracicSpinalCord_splicing_peer5_cluster.cis_qtl.txt.gz - Top per-cluster sQTL summary statistics from thoracic spinal cord ThoracicSpinalCord_splicing_peer5_gene.cis_qtl_nominal_tabixed.tsv.gz - Full nominal sQTL summary statistics from thoracic spinal cord als_snps_maf0.01_alleles.tsv.gz - Allele information for all SNPs tested in the eQTL and sQTL analysis Table columns are formatted as follows: Nominal QTL results include all SNP-gene pairs tested using either a 1Mb window from each side of the transcription start site (TSS) of the gene (eQTLs) or 100kb either side of the middle of the splicing cluster (sQTLs). Table columns are formatted as follows: <ul> <li>phenotype_id - ensembl ID of the gene tested (GENCODE v30) for eQTL, or the ID of the splice junction for sQTLs, splicing junction position, splicing cluster id, and gene Ensembl id (GENCODE v30),separated by a colon</li> <li>variant_id - SNP tested for association (rsid or chr:position:ref:alt)</li> <li>tss_distance - distance of the SNP to the gene transcription start site (TSS)</li> <li>maf - minor allele frequency in cohort</li> <li>ma_samples - number of samples carrying the minor allele</li> <li>ma_count - total number of minor alleles across individuals</li> <li>pval_nominal - nominal P-value from linear regression</li> <li>slope - slope of the linear regression</li> <li>slope_se - standard error of the slope</li> </ul> Permuted QTL results include only the top SNP-gene association for each gene (eQTL) or cluster (sQTL). Table columns are formatted as follows: <ul> <li>phenotype_id - ensembl ID of the gene tested (GENCODE v30), or the coordinates of the cluster, formatted as coordinate, splicing cluster id, and gene Ensembl id (GENCODE v30), separated by a colon</li> <li>num_var - total number of variants tested in cis</li> <li>beta_shape1 - first parameter value of the fitted beta distribution</li> <li>beta_shape2 - second parameter value of the fitted beta distribution</li> <li>true_df - effective degrees of freedom the beta distribution approximation</li> <li>pval_true_df - empirical P-value for the beta distribution approximation</li> <li>variant_id - ID of the top variant (rsid or chr:position:ref:alt)</li> <li>tss_distance - distance of the SNP to the gene transcription start site (TSS)</li> <li>ma_samples - number of samples carrying the minor allele</li> <li>ma_count - total number of minor alleles across individuals</li> <li>maf -minor allele frequency in MiGA cohort</li> <li>ref_factor - flag indicating if the alternative allele is the minor allele in the cohort (1 if AF <= 0.5, -1 if not)</li> <li>pval_nominal - nominal P-value from linear regression</li> <li>slope - slope of the linear regression</li> <li>slope_se - standard error of the slope</li> <li>pval_perm - first permutation P-value directly obtained from the permutations with the direct method</li> <li>pval_beta - second permutation P-value obtained via beta approximation. This is the one to use for downstream analysis</li> <li>qval - Storey q-value derived from pval_beta (FDR adjusted)</li> <li>pval_nominal_threshold - nominal P-value threshold for calling a variant-gene pair significant for the gene</li> </ul> Allele Information for each variant: <ul> <li>CHROM - chromosome position of the variant</li> <li>POS - position of the variant in the chromosome</li> <li>REF - reference allele (GRCh38)</li> <li>ALT - alternative allele (this is the effect allele in the eQTL and sQTL analysis)</li> <li>ID - variant id (rsid or chr:position:ref:alt)</li> </ul>

opencc-by-4.0Aug 2021View details →

zenodo40/100

QTL mapping and transcriptome analysis of Sclerotinia-resistance in the wild cabbage species Brassica oleracea var. villosa [Main code]

This is the main code supplement for my computational analysis for the manuscript: "QTL mapping and transcriptome analysis of Sclerotinia-resistance in the wild cabbage species Brassica oleracea var. villosa". The main code is availabe in separate html-files. DOI will be added if available.

opencc-by-4.0Feb 2021View details →

zenodo40/100

Pooled DNA sequencing to identify SNPs associated with a major QTL for bacterial wilt resistance in Italian ryegrass (Lolium multiflorum Lam.)

We used pooled DNA sequencing to characterize a major QTL for bacterial wilt resistance of Italian ryegrass and to develop inexpensive sequence-based markers to efficiently target resistance alleles for marker-assisted recurrent selection. From the mapping population segregating for the QTL, DNA of 44 of the most resistant and 44 of the most susceptible F1 individuals were pooled and sequenced using the Illumina HiSeq2000 platform. Allele frequencies of 18 x 106 single nucleotide polymorphisms (SNP) were determined in the resistant and susceptible pool. A total of 271 SNPs on 140 scaffold sequences of the reference parental genome showed significantly different allele frequencies in both pools. We converted 44 selected SNPs to KASP markers, genetically mapped these proximal to the major QTL and thus validated their association with bacterial wilt resistance.

opencc-by-4.0Sep 2018View details →

dryad40/100

Epistatic QTL for yield heterosis in tomato

Controlled population development and genome-wide association studies have proven powerful in uncovering genes and alleles underlying complex traits. An underexplored dimension of such studies is the phenotypic contribution of non-additive interactions between quantitative trait loci (QTL). Capturing such epistasis in a genome-wide manner requires very large populations to represent replicated combinations of loci whose interactions determine phenotypic outcomes. Here, we dissect epistasis using a densely genotyped population of 1400 backcross-inbred lines (BILs) between a modern processing tomato inbred (Solanum lycopersicum) and the Lost Accession of a distant, green-fruited, drought-tolerant wild species, Solanum pennellii. The homozygous BILs, each representing an average of 11 introgressions, and their hybrids with the recurrent parents were phenotyped for tomato yield components. Population-wide mean yield of the BILs was less than 50% of that of their hybrids (BILH), and correspondingly, homozygous introgressions across the genome reduced yield relative to recurrent parent while several QTL of the BILHs independently improved productivity. Analysis of two QTL scans showed 32 cases of less than additive and 48 cases of more than additive interactions. Strikingly, one epistatic interaction involving S. pennellii QTL on chromosomes 1 and 7, which independently did not affect yield, increased fruit yield by 20-50% in the double introgression hybrid grown in irrigated and dry fields over a period of three years and varied genetic backgrounds. Our work demonstrates the power of large, interspecific controlled population development to uncover hidden QTL phenotypes, and how rare epistatic interactions can improve crop productivity via heterosis.

opencc-zeroNov 2022View details →

zenodo40/100

Molecular mapping of quantitative trait loci (QTL) for resistance to early blight in tomato

Molecular mapping of quantitative trait loci (QTL) for resistance to early blight in tomato (1135884)

opencc-by-4.0Jun 2023View details →

zenodo40/100

isoMIGA: Expression and Splicing QTL Summary Statistics in Human Microglia

https://github.com/RajLabMSSM/isoMiGA  Full associations and top association summary statistics for QTLs mapped in a multi-ethnic meta-analysis of human microglia, as part of the isoform-centric Microglia Genomic Atlas (isoMiGA) project. Sample size = 555 samples from 391 unique donors 6 cohorts (Raj MFG, Raj STG, Raj, SVZ, Raj, THA, Roussos, Gaffney) meta-analysed using the linear mixed model random-effects meta-analysis software mmQTL (PMID: 35058635). Each file has the following naming convention: {REFERENCE}_{PHENOTYPE}_{ASSOC}.tsv.gz References The following two transcript references were used for mapping phenotypes: 1. GENCODE - GENCODE v38 comprehensive transcripts (https://www.gencodegenes.org/human/release_38.html) 2. Union - a union of all GENCODE v38 transcripts with an additional 35,879 novel transcripts identified from long-read RNA-seq in 30 human microglia samples. GTF to be uploaded in Zenodo. Phenotypes The following phenotypes were tested for genetic association: <ul> <li>expression: total gene expression of each gene following voom normalization of read counts.</li> <li>transcript: transcript usage - each transcript expression normalized by TPM divided by the total TPM for that gene</li> <li>leafcutter: junction usage using the Leafcutter framework to count and cluster intron-splicing junction reads. Each phenotype is the relative usage of the intron against the total count of the introns within that cluster.</li> <li>SUPPA_A3: alternate 3' splice site usage, as identified by SUPPA2 from transcript TPMs</li> <li>SUPPA_A5: alternate 5' splice site usage</li> <li>SUPPA_AF: alternate first exon usage</li> <li>SUPPA_AL: alternate last exon usage</li> <li>SUPPA_RI: intron retention usage</li> <li>SUPPA_SE: exon skipping usage</li> </ul> All phenotype matrices were scaled and centred and then quantile normalized. Associations Top associations (top_assoc.tsv.gz) list the SNP-feature pair with the lowest adjusted P-value (qval) for that feature. Full associations (full_assoc.tsv.gz) list all tested SNP-feature pairs. Data dictionary The columns of the two association files only differ by the presence of the qval column in the top associations. feature: the phenotype being tested variant_id: the genetic variant being tested chr: chromosome pos: position (hg38) ref: reference allele alt: alternate allele Allele: the effect allele that the beta is relative to beta_tissue_0: Gaffney cohort beta sd_tissue_0: Gaffney cohort standard error z_tissue_0: Gafney cohort Z-score beta_tissue_1: Roussos cohort beta sd_tissue_1: Roussos cohort standard error z_tissue_1: Roussos cohort Z-core beta_tissue_2: Raj MFG cohort beta sd_tissue_2: Raj MFG cohort standard error z_tissue_2: Raj MFG cohort Z-score beta_tissue_3: Raj STG cohort beta sd_tissue_3: Raj STG cohort standard error z_tissue_3: Raj STG cohort Z-score beta_tissue_4: Raj SVZ cohort beta sd_tissue_4: Raj SVZ cohort standard error z_tissue_4: Raj SVZ cohort Z-score beta_tissue_5: Raj THA cohort beta sd_tissue_5: Raj THA cohort standard error z_tissue_5: Raj THA cohort Z-score fixed_beta: Fixed effect meta-analysis estimate of the beta fixed_sd: Fixed effect meta-analysis standard error of the beta fixed_z: Fixed effect meta-analysis Z-score Random_Z: Random effect meta-analysis Z-score Fixed_P: Fixed effect meta-analysis P-value Random_P: Random effect meta-analysis P-value Fixed_bonf:  Fixed effect meta-analysis P-value adjusted for the number of variants tested in that feature (Bonferroni) Random_bonf: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (Bonferroni) Fixed_FDR: Fixed effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR) Random_FDR: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR) qval: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR) and for the number of features tested in the dataset (Storey's q value)

opencc-by-4.0Aug 2023View details →

zenodo40/100

Integrative QTL mapping and selection signatures in Groningen White Headed cattle inferred from whole-genome sequences

Here, we aimed to identify and characterize genomic regions that differ between Groningen White Headed (GWH) breed and other cattle, and in particular to identify candidate genes associated with coat color and/or eye-protective phenotypes. Firstly, whole genome sequences of 170 animals from eight breeds were used to evaluate the genetic structure of the GWH in relation to other cattle breeds by carrying out principal components and model-based clustering analyses. Secondly, the candidate genomic regions were identified by integrating the findings from: a) a genome-wide association study using GWH, other white headed breeds (Hereford and Simmental), and breeds with a non-white headed phenotype (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel, Dutch Belted, and Holstein Friesian); b) scans for specific signatures of selection in GWH cattle by comparison with four other Dutch traditional breeds (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel and Dutch Belted) and the commercial Holstein Friesian; and c) detection of candidate genes identified via these approaches. The alignment of the filtered reads to the reference genome (ARS-UCD1.2) resulted in a mean depth of coverage of 8.7X. After variant calling, the lowest number of breed-specific variants was detected in Holstein Friesian (148,213), and the largest in Deep Red (558,909). By integrating the results, we identified five genomic regions under selection on BTA4 (70.2–71.3 Mb), BTA5 (10.0–19.7 Mb), BTA20 (10.0–19.9 and 20.0–22.7 Mb), and BTA25 (0.5–9.2 Mb). These regions contain positional and functional candidate genes associated with retinal degeneration (e.g., CWC27 and CLUAP1), ultraviolet protection (e.g., ERCC8), and pigmentation (e.g. PDE4D) which are probably associated with the GWH specific pigmentation and/or eye-protective phenotypes, e.g. Ambilateral Circumocular Pigmentation (ACOP). Our results will assist in characterizing the molecular basis of GWH phenotypes and the biological implications of its adaptation.

opencc-by-4.0Oct 2022View details →

dryad40/100

Epistatic QTL for yield heterosis in tomato

Open the record for dataset details and reuse information.

publicNov 2022View details →

dryad36/100

Data from: Major QTL controls adaptation to serpentine soils in Mimulus guttatus

Spatially varying selection is a critical driver of adaptive differentiation. Yet, there are few examples where the fitness effects of naturally segregating variants that contribute to local adaptation have been measured in the field. Plant adaptation to harsh soil habitats provides an ideal study system for investigating the genetic basis of local adaptation. The work presented here identifies a major locus underlying adaptation to serpentine soils in Mimulus guttatus and estimates the strength of selection on this locus in native field sites. Reciprocal transplant and common garden studies show that serpentine and non-serpentine populations of M. guttatus differ in their ability to survive on serpentine soils. We directly mapped these field survival differences by performing a bulk segregant analysis with F2 survivors from a field transplant study and identify a single QTL where individuals that are homozygous for the non-serpentine allele do not survive on serpentine soils. Genotyping the survivors from an independent mapping population reveals that this same QTL controls serpentine tolerance in a second, geographically distant population. Finally, we show that this QTL controls tolerance to soil properties, as opposed to some other aspect of the field sites that may differ, by performing a lab-based common-garden experiment in native serpentine soils that replicates the survival differences observed in the field. These results indicate that despite the myriad chemical and physical challenges that plants face in serpentine habitats, adaptation to these soils in M. guttatus has a simple genetic basis.

opencc-zeroDec 2017View details →

dryad36/100

Dissecting the genetic basis of variation in Drosophila sleep using a multiparental QTL mapping resource

There is considerable variation in sleep duration, timing and quality in human populations, and sleep dysregulation has been implicated as a risk factor for a range of health problems. Human sleep traits are known to be regulated by genetic factors, but also by an array of environmental and social factors. These uncontrolled, non-genetic effects complicate powerful identification of the loci contributing to sleep directly in humans. The model system, Drosophila melanogaster, exhibits a behavior that shows the hallmarks of mammalian sleep, and here we use a multitiered approach, encompassing high-resolution QTL mapping, expression QTL data, and functional validation with RNAi to investigate the genetic basis of sleep under highly controlled environmental conditions. We measured a battery of sleep phenotypes in >750 genotypes derived from a multiparental mapping panel and identified several, modest-effect QTL contributing to natural variation for sleep. Merging sleep QTL data with a large head transcriptome eQTL mapping dataset from the same population allowed us to refine the list of plausible candidate causative sleep loci. This set includes genes with previously characterized effects on sleep and circadian rhythms, in addition to novel candidates. Finally, we employed adult, nervous system-specific RNAi on the Dopa decarboxylase, dyschronic, and timeless genes, finding significant effects on sleep phenotypes for all three. The genes we resolve are strong candidates to harbor causative, regulatory variation contributing to sleep.

opencc-zeroMar 2020View details →

dryad36/100

Data from: Validation of grain yield QTL from soft winter wheat using a CIMMYT spring wheat panel

Validation of quantitative trait loci (QTLs) is an essential step in marker-assisted breeding. The objectives of this study were to validate grain yield (GY) QTLs previously identified in soft red winter wheat (Triticum aestivum L.) through biparental and association mapping using the spring wheat association mapping initiative (WAMI) panel from CIMMYT, Mexico, and to identify allele combinations of the validated QTLs that resulted to the highest GY. Linked single-nucleotide polymorphisms for IWA3560 (3A), IWA1818 (4B), and IWA755 (6B) were significantly associated (P < 0.001) with GY, grain number, and thousand-grain weight in the WAMI. Lines possessing the favorable allele for the QTL at the 3A, 4B, and 6B loci (ACG allele combination) validated on the WAMI had the highest mean GY at 4.55 t ha−1, but three other haplotypes (ACA, GCA, and GCG) differing by one or two alleles in the validated QTL regions were not significantly different. These results validate GY QTLs across winter and spring wheat through genome-wide association analysis and further demonstrate the potential for pyramiding favorable alleles for the genetic improvement of wheat breeding populations.

opencc-zeroDec 2017View details →

zenodo36/100

QTL script and VCF files - GENETIC ARCHITECTURE OF FLOWERING-TIME VARIATION IN BRACHYPODIUM DISTACHYON

Supplemental data for the article: GENETIC ARCHITECTURE OF FLOWERING-TIME VARIATION IN BRACHYPODIUM DISTACHYON (Woods et al., 2016). Supplemental data includes: <ul> <li> A folder named “Data”, which contains: <ol> <li> Raw genotypic data. Data showing the parental genotype in a RIL population (F7; Bd21 X Bd1-1) </li> <li> Raw phenotypic data. Flowering time (leaves and days to flowering) of the RIL population in different growth environments. </li> <li> The genetic map (see material and methods section of Woods et al., 2016 for additional information). </li> <li> Gene positions. A file containing the position of annotated genes on the Brachypodium distachyon genome V2.1. Data from Phytozome (https://phytozome.jgi.doe.gov/pz/portal.html). </li> </ol> </li> <li> The R script used for the QTL analysis (Final_script.R). </li> <li> VCF files of the loci of interest. A folder called “VCF” includes the VCF files of the genes presented in Fig. 6  (VRN1, PHYC, VRN2, and FD).  </li> </ul>

opencc-zeroSep 2016View details →

dryad36/100

QTL mapping for seedling and adult plant resistance to stripe and leaf rust in two winter wheat populations

The two recombinant inbred lines (RIL) populations developed by crossing Almaly × Avocet S (206 RILs) and Almaly × Anza (162 RILs) were used to detect the novel genomic regions associated with adult plant resistance (APR) and seedling or all-stage resistance (ASR) to yellow rust (YR) and leaf rust (LR). Both the populations were evaluated for YR APR in two environments (2018 and 2019) and LR APR in three environments (2018, 2019, and 2020) in the Anza population and two environments (2018 and 2019) in the Avocet population; both the populations were phenotyped for one environment during 2020 for LR and YR ASR and genotyped using high throughput DArTseq technology. A set of 51 QTLs including 22 for YR APR, nine for LR APR, nine for YR ASR, and 11 for LR ASR were identified. Also, a set of 13 stable QTLs including nine QTLs (QYR-APR-2A.1, QYR-APR-2A.2, QYR-APR-4D.2, QYR-APR-1B, QYR-APR-2B.1, QYR-APR-2B.2, QYR-APR-3D, QYR-APR-4D.1, and QYR-APR-4D.2) for YR APR and four QTLs (QLR-APR-4A, QLR-APR-2B, QLR-APR-3B, and QLR-APR-5A.2) for LR APR were identified. In silico analysis revealed that the key putative candidate genes such as Cytochrome P450, Protein kinase-like domain superfamily, Zinc-binding ribosomal protein, SANT/Myb domain, WRKY transcription factor, Nucleotide-sugar transporter, and NAC domain superfamily were in the QTL regions and involved in the regulation of host response towards the pathogen infection. The stable QTLs identified in this study are useful for developing rust-resistant varieties through marker-assisted selection (MAS).

opencc-zeroNov 2023View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record