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8 results for “sQTL”
Results for eQTL and sQTL meta-analysis and colocalization
<p>This dataset is part of the manuscript: "<em>Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies</em>", by Lopes KP, Snijders GJL, Humphrey J, et al.</p> <p> </p> <p>Description of files:</p> <p><em>COLOC_supp_table_all_results.tsv.gz - </em>Table with results from <strong>COLOC</strong><em> </em>(gzip-compressed). Table columns are formatted as follows:</p> <ol> <li>disease - disease name (Alzheimer’s disease - AD, Bipolar Disorder - BPD, Multiple sclerosis - MS, Parkinson’s disease - PD, Schizohphrenia - SCZ)</li> <li>GWAS - GWAS study (IMSGC_2019, Jansen_2018, Kunkle_2019, Lambert_2013, Marioni_2018, Nalls23andMe_2019, Ripke_2014, Stahl_2019)</li> <li>locus - locus id according to each GWAS study</li> <li>GWAS_SNP - SNP reported in the GWAS study</li> <li>GWAS_P - <em>P</em>-value of the GWAS_SNP reported in the GWAS study</li> <li>GWAS_chr - chromosome of the GWAS_SNP (hg38)</li> <li>GWAS_pos - genomic position in the chromosome of the GWAS_SNP (hg38)</li> <li>QTL - id for the QTL study</li> <li>type - the type of QTL (eQTL or sQTL)</li> <li>QTL_SNP - SNP id from the QTL association</li> <li>QTL_P - <em>P</em>-value for the QTL association </li> <li>QTL_Beta - Slope (beta) for the QTL association</li> <li>QTL_MAF - minor allele frequency for the QTL_SNP in each QTL study. If not available, values were obtained from the European superpopulation of 1000 Genomes phase 3</li> <li>QTL_chr - chromosome for the QTL_SNP (hg38)</li> <li>QTL_pos - genomic position in the chromosome of the QTL_SNP (hg38)</li> <li>QTL_junction - splicing junction tested in the association (for sQTLs only)</li> <li>QTL_Gene - gene name for the QTL association</li> <li>QTL_Ensembl - Ensembl gene id for the QTL_gene (GENCODE v30)</li> <li>nsnps - number of SNPs tested </li> <li>PP.H0.abf - posterior probability for H0 (no causal variant)</li> <li>PP.H1.abf - posterior probability for H1 (causal variant for trait 1 only)</li> <li>PP.H2.abf - posterior probability for H2 (causal variant for trait 2 only)</li> <li>PP.H3.abf - posterior probability for H3 (two distinct causal variants)</li> <li>PP.H4.abf - posterior probability for H4 (one common causal variant)</li> <li>cell_type - cell type of the QTL study</li> <li>SNP_distance - the absolute distance between GWAS_SNP and QTL_SNP</li> <li>LD - linkage disequilibrium between the GWAS_SNP and the QTL_SNP according to 1000 genomes phase 3 European reference panel 3 (only for PP4>0.5, -Inf otherwise)</li> </ol> <p><em>mashR_lfsr_eQTL.txt.gz - </em><strong>mashR </strong>results for <strong>eQTL</strong><em> </em>(gzip-compressed). Table columns are formatted as follows:</p> <ol> <li>ensembl_snp - Ensembl ID and the SNP prioritized by mashR (best SNP per gene)</li> <li>MFG_eur_expression_peer10.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the MFG region</li> <li>STG_eur_expression_peer10.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the STG region</li> <li>SVZ_eur_expression_peer5.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the SVZ region</li> <li>THA_eur_expression_peer10.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the THA region</li> </ol> <p><em>mashR_lfsr_eQTL.txt.gz - </em><strong>mashR </strong>results for <strong>sQTL</strong><em> </em>(gzip-compressed). Table columns are formatted as follows:</p> <ol> <li>pos_ensembl_rsnp - splicing junction coordinates, Ensembl ID, and SNP ID prioritized by mashR (best SNP per junction)</li> <li>MFG_eur_rsplicing_peer5_gene.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the MFG region</li> <li>STG_eur_rsplicing_peer5_gene.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the STG region</li> <li>SVZ_eur_rsplicing_peer0_gene.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the SVZ region</li> <li>THA_eur_rsplicing_peer5_gene.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the THA region</li> </ol> <p><em>out_mfg_stg_svz_tha.metasoft.gz - </em><strong>METASOFT</strong> results<strong> </strong>for <strong>eQTLs</strong> meta-analysis from MiGA four brain regions<em> </em>(gzip-compressed). Table columns are formatted as follows:</p> <ol> <li>RSID - Id composed by gene Ensembl and SNP ID separated by an underscore for each gene-SNP pair tested in the eQTL study</li> <li>#STUDY - number of studies included in the meta-analysis</li> <li>PVALUE_FE - <em>P</em>-value of the fixed-effects model (FE) according to METASOFT</li> <li>BETA_FE - Estimated Beta under the fixed-effects model according to METASOFT</li> <li>STD_FE - Standard error of BETA_FE</li> <li>PVALUE_RE - <em>P</em>-value of the random effects model (RE) according to METASOFT</li> <li>BETA_RE - Estimated Beta under the random-effects model (RE) according to METASOFT</li> <li>STD_RE - Standard error of BETA_RE</li> <li>PVALUE_RE2 - <em>P</em>-value of the Han and Eskin's Random Effects model (RE2) according to METASOFT</li> <li>STAT1_RE2 - RE2 statistic mean effect part</li> <li>STAT2_RE2 - RE2 statistic heterogeneity part</li> <li>PVALUE_BE - BE P-value (“NA” in all row, -binary_effects option is not used)</li> <li>I_SQUARE - I-square heterogeneity statistic</li> <li>Q - Cochran's Q statistic</li> <li>PVALUE_Q - Cochran's Q statistic's <em>P</em>-value</li> <li>TAU_SQUARE - Tau-square heterogeneity estimator of DerSimonian-Laird</li> <li>PVALUES_OF_STUDIES(Tab_delimitered) - <em>P</em>-values of each study in the respective order 1-MFG, 2-STG, 3-SVZ, 4-THA</li> <li>MVALUES_OF_STUDIES(Tab_delimitered) - M-values of each study in the respective order 1-MFG, 2-STG, 3-SVZ, 4-THA</li> </ol> <p><em>out_miga_young_mynd_fairfax.metasoft.gz - </em><strong>METASOFT</strong> results<strong> </strong>for <strong>eQTL</strong> meta-analysis from MiGA four brain regions plus microglia eQTL from Young et al. (2019), and monocytes eQTL from Navarro et al. (2020) and Fairfax et al. (2014)<em> </em>(gzip-compressed). Table columns are formatted as follows:</p> <ol> <li>RSID - Id composed by gene Ensembl and SNP ID separated by an underscore for each gene-SNP pair tested in the eQTL study</li> <li>#STUDY - number of studies included in the meta-analysis</li> <li>PVALUE_FE - <em>P</em>-value of the fixed-effects model (FE) according to METASOFT</li> <li>BETA_FE - Estimated Beta under the fixed-effects model according to METASOFT</li> <li>STD_FE - Standard error of BETA_FE</li> <li>PVALUE_RE - <em>P</em>-value of the random effects model (RE) according to METASOFT</li> <li>BETA_RE - Estimated Beta under the random-effects model (RE) according to METASOFT</li> <li>STD_RE - Standard error of BETA_RE</li> <li>PVALUE_RE2 - <em>P</em>-value of the Han and Eskin's Random Effects model (RE2) according to METASOFT</li> <li>STAT1_RE2 - RE2 statistic mean effect part</li> <li>STAT2_RE2 - RE2 statistic heterogeneity part</li> <li>PVALUE_BE - BE P-value (“NA” in all row, -binary_effects option is not used)</li> <li>I_SQUARE - I-square heterogeneity statistic</li> <li>Q - Cochran's Q statistic</li> <li>PVALUE_Q - Cochran's Q statistic's <em>P</em>-value</li> <li>TAU_SQUARE - Tau-square heterogeneity estimator of DerSimonian-Laird</li> <li>PVALUES_OF_STUDIES(Tab_delimitered) - <em>P</em>-values of each study in the respective order 1-MFG, 2-STG, 3-SVZ, 4-THA, 5-Young et al., 6-Navarro et al., 7-Fairfax et al.</li> <li>MVALUES_OF_STUDIES(Tab_delimitered) - M-values of each study in the respective order 1-MFG, 2-STG, 3-SVZ, 4-THA, 5-Young et al., 6-Navarro et al., 7-Fairfax et al.</li> </ol> <p><em>out_mfg_stg_svz_tha_sClusters.metasoft.gz - </em><strong>METASOFT</strong> results<strong> </strong>for <strong>sQTLs</strong> meta-analysis from MiGA four brain regions (gzip-compressed). Table columns are formatted as follows:</p> <ol> <li>RSID - Id composed by splicing junction coordinates, gene Ensembl ID, and SNP ID separated by underscores for each junction-SNP pair tested in the sQTL study (e.g. chr1_962047_962355_ENSG00000187961.14_1:11008:C:G)</li> <li>#STUDY - number of studies included in the meta-analysis</li> <li>PVALUE_FE - <em>P</em>-value of the fixed-effects model (FE) according to METASOFT</li> <li>BETA_FE - Estimated Beta under the fixed-effects model according to METASOFT</li> <li>STD_FE - Standard error of BETA_FE</li> <li>PVALUE_RE - <em>P</em>-value of the random effects model (RE) according to METASOFT</li> <li>BETA_RE - Estimated Beta under the random-effects model (RE) according to METASOFT</li> <li>STD_RE - Standard error of BETA_RE</li> <li>PVALUE_RE2 - <em>P</em>-value of the Han and Eskin's Random Effects model (RE2) according to METASOFT</li> <li>STAT1_RE2 - RE2 statistic mean effect part</li> <li>STAT2_RE2 - RE2 statistic heterogeneity part</li> <li>PVALUE_BE - BE P-value (“NA” in all row, -binary_effects option is not used)</li> <li>I_SQUARE - I-square heterogeneity statistic</li> <li>Q - Cochran's Q statistic</li> <li>PVALUE_Q - Cochran's Q statistic's <em>P</em>-value</li> <li>TAU_SQUARE - Tau-square heterogeneity estimator of DerSimonian-Laird</li> <li>PVALUES_OF_STUDIES(Tab_delimitered) - <em>P</em>-values of each study in the respective order 1-MFG, 2-STG, 3-SVZ, 4-THA</li> <li>MVALUES_OF_STUDIES(Tab_delimitered) - M-values of each study in the respective order 1-MFG, 2-STG, 3-SVZ, 4-THA</li> </ol> <p><strong>NOTE:</strong> The effect sizes of eQTLs and sQTL are defined as the effect of the alternative allele (ALT) relative to the reference (REF) allele in the human genome reference (GRCh38). A file containing that information for all alleles tested is available at 10.5281/zenodo.4301005</p>
GTEx v8 fine mapping on eQTL and sQTL
<p># Data usage policy</p> <p>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).</p> <p># GTEx-GWAS integration: Finemapping</p> <p>This package contains DAP-G results on GTEx v8 eQTL and sQTL data.<br> See ([DAP-G software](https://github.com/xqwen/dap)) for details.<br> We used only European individuals and variants with MAF>0.01, on genes that are annotated as `protein_coding` or `lncRNA`. <br> DAP-G `ld_control` parameter was 0.75.</p> <p>The results were analyzed in [this preprint](https://www.biorxiv.org/content/10.1101/814350v1)</p> <p>## Contents</p> <p>```<br> finemapping/<br> |-- README_finemapping.md<br> |-- dapg_eqtl.tar<br> `-- dapg_sqtl.tar<br> ```<br> Unpack each tarball with a command like `tar -xvpf dapg_sqtl.tar`</p> <p>For every tissue:</p> <p>* `{tissue}.variants_pip.txt.gz` contains the variants' posterior inclusion probabilities at being causal for every gene.<br> * gene: gene id (or intron id)<br> * rank: ranking of the variant according to its PIP (see below)<br> * variant_id: gtex variant id<br> * pip: posterior inclusion probability of the variant in the causal models<br> * log10_abf: approximate Bayes factor (-log10)<br> * cluster_id: id of cluster to which the variant belongs <br> * `{tissue}.models_variants.txt.gz` contains, for every model contemplated by DAPG, the list of variants involved. Most of them have single variant.<br> * `{tissue}.model_summary.txt.gz` contains, for every analized gene, a summary of the modes such as expected number of causal variants<br> * gene: gene id (or intron id)<br> * pes: posterior expected model size (i.e. number of causal variants)<br> * pse_se: standard error of the above<br> * log_nc: dapg undocumented statistic<br> * log10_nc: dapg undocumented statistic<br> * `{tissue}.models.txt.gz` for every analyzed gene:<br> * gene: gene id (or intron id)<br> * model: number (serving as a model name)<br> * n: number of variants (0 for null model)<br> * pp: posterior inclusion probability of the model<br> * ps: posterior score<br> * `{tissue}.clusters.txt.gz` for every analyzed gene:<br> * gene: gene id (or intron id)<br> * cluster: number (serving as cluster name)<br> * n_snps: number of variants in the cluster<br> * pip: posterior inclusion probability<br> * average_r2: average correlation within the cluster<br> * `{tissue}.cluster_correlations.txt.gz`: upper triangular matrix of correlations among clusters </p> <p> </p> <p> </p> <p># Disclaimer</p> <p>The data is provided "as is", and the authors assume no responsibility for errors or omissions. <br> The User assumes the entire risk associated with its use of these data. <br> The authors shall not be held liable for any use or misuse of the data described and/or contained herein. <br> The User bears all responsibility in determining whether these data are fit for the User's intended use. </p> <p>The information contained in these data is not better than the original sources from which they were derived,<br> and both scale and accuracy may vary across the data set. <br> These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics<br> appropriate for applications that potential users of the data may contemplate. <br> <br> The user is responsible to comply with any data usage policy from the original GWAS studies;<br> refer to the list of traits described [here](https://www.biorxiv.org/content/10.1101/814350v1)<br> to identify their respective Consortia's requirements.</p> <p><br> THE DATA IS PROVIDED WITHOUT WARRANTY OF ANY KIND,<br> EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,<br> FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.<br> IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,<br> WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,<br> OUT OF OR IN CONNECTION WITH THE DATA OR THE USE OR OTHER DEALINGS IN THE DATA.</p>
Population-biased sQTL catalog
<p>Population-biased sQTL catalog generated by <a href="https://github.com/guigolab/sqtlseeker2-nf/tree/interaction">sqtlseeker2.int-nf</a> in the <a href="https://www.gtexportal.org/home/">GTEx</a> dataset, as described in the publication<i> A fast non-parametric test of association for multiple traits</i> by Garrido-Martín et al. in Genome Biology (<a href="https://doi.org/10.1186/s13059‑023‑03076‑8">https://doi.org/10.1186/s13059‑023‑03076‑8</a>).</p>
INTERVAL eQTL & sQTL summary statistics
<p>Summary statistics for eQTL & sQTL traits computed from the INTERVAL RNA-seq samples (n=4,372) as detailed in the following preprint: <a href="https://www.medrxiv.org/content/10.1101/2023.11.25.23299014v1">https://www.medrxiv.org/content/10.1101/2023.11.25.23299014v1</a></p>
GTEx v8 SMR sQTL results
<p>SMR results using sQTLs from GTEx (release 8)</p> <p># Data usage policy</p> <p>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).</p> <p># Disclaimer</p> <p>The data is provided "as is", and the authors assume no responsibility for errors or omissions. <br> The User assumes the entire risk associated with its use of these data. <br> The authors shall not be held liable for any use or misuse of the data described and/or contained herein. <br> The User bears all responsibility in determining whether these data are fit for the User's intended use. </p> <p>The information contained in these data is not better than the original sources from which they were derived,<br> and both scale and accuracy may vary across the data set. <br> These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics<br> appropriate for applications that potential users of the data may contemplate. <br> <br> The user is responsible to comply with any data usage policy from the original GWAS studies;<br> refer to the list of traits described [here](https://www.biorxiv.org/content/10.1101/814350v1)<br> to identify their respective Consortia's requirements.</p> <p><br> THE DATA IS PROVIDED WITHOUT WARRANTY OF ANY KIND,<br> EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,<br> FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.<br> IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,<br> WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,<br> OUT OF OR IN CONNECTION WITH THE DATA OR THE USE OR OTHER DEALINGS IN THE DATA.</p>
AIDA PBMC sQTL
<p>This is AIDA PBMC cis-sQTL dataset including 19 cell types.</p>
Results for sQTL analysis for each brain region
<p>This dataset is part of the manuscript: "<em>Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies</em>", by Lopes KP, Snijders GJL, Humphrey J, et al.</p> <p> </p> <p>Description of files:</p> <p><em>MFG_eur_splicing_peer5_gene.cis_qtl_nominal_tabixed.tsv.gz - </em>Full <strong>nominal sQTL</strong> summary statistics from medial frontal gyrus (<strong>MFG</strong>)<em> </em>(gzip-compressed)</p> <ol> </ol> <p><em>STG_eur_splicing_peer5_gene.cis_qtl_nominal_tabixed.tsv.gz </em>- Full <strong>nominal sQTL</strong> summary statistics from superior temporal gyrus (<strong>STG</strong>) (gzip-compressed)</p> <p><em>SVZ_eur_splicing_peer0_gene.cis_qtl_nominal_tabixed.tsv.gz -</em> Full <strong>nominal sQTL</strong> summary statistics from the subventricular zone (<strong>SVZ</strong>) (gzip-compressed)</p> <p><em>THA_eur_splicing_peer5_gene.cis_qtl_nominal_tabixed.tsv.gz - </em>Full <strong>nominal sQTL</strong> summary statistics from the thalamus (<strong>THA</strong>) (gzip-compressed)</p> <p> </p> <ul> </ul> <p><em>MFG_eur_splicing_peer5_cluster.cis_qtl.txt.gz - </em>Full <strong>sQTL</strong> summary statistics <strong>permuted by intron cluster </strong>from medial frontal gyrus (<strong>MFG</strong>) (gzip-compressed)</p> <p><em>STG_eur_splicing_peer5_cluster.cis_qtl.txt.gz - </em>Full <strong>sQTL</strong> summary statistics <strong>permuted by intron cluster</strong> from superior temporal gyrus (<strong>STG</strong>)<em> </em>(gzip-compressed)</p> <p><em>SVZ_eur_splicing_peer0_cluster.cis_qtl.txt.gz - </em>Full <strong>sQTL</strong> summary statistics <strong>permuted by intron cluster</strong> from the subventricular zone (<strong>SVZ</strong>)<em> </em>(gzip-compressed)</p> <p><em>THA_eur_splicing_peer5_cluster.cis_qtl.txt.gz - </em>Full <strong>sQTL</strong> summary statistics <strong>permuted by intron cluster</strong> from the thalamus (<strong>THA</strong>)<em> </em>(gzip-compressed)</p> <p> </p> <p><em>MFG_eur_splicing_peer5_gene.cis_qtl.txt.gz - </em>Full <strong>sQTL</strong> summary statistics <strong>permuted by gene </strong>from medial frontal gyrus (<strong>MFG</strong>)<em> </em>(gzip-compressed)</p> <p><em>STG_eur_splicing_peer5_gene.cis_qtl.txt.gz </em> - Full <strong>sQTL</strong> summary statistics <strong>permuted by gene</strong> from superior temporal gyrus (<strong>STG</strong>) (gzip-compressed)</p> <p><em>SVZ_eur_splicing_peer0_gene.cis_qtl.txt.gz - </em>Full <strong>sQTL</strong> summary statistics <strong>permuted by gene</strong> from the subventricular zone (<strong>SVZ</strong>)<em> </em>(gzip-compressed)</p> <p><em>THA_eur_splicing_peer5_gene.cis_qtl.txt.gz - </em>Full <strong>sQTL</strong> summary statistics <strong>permuted by gene</strong> from the thalamus (<strong>THA</strong>) (gzip-compressed)</p> <p> </p> <p>Nominal QTL results include all SNP-junction pairs tested (using a 100kb window from the center of each intron cluster). Table columns are formatted as follows:</p> <ol> <li>phenotype_id - id composed by splicing junction position, splicing cluster id, and gene Ensembl id (GENCODE v30) each 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 MiGA 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 <em>P</em>-value from linear regression</li> <li>slope - slope of the linear regression</li> <li>slope_se - standard error of the slope</li> <li>chr - chromosome of the SNP (hg38)</li> <li>pos - position for the SNP in the chromosome (hg38)</li> </ol> <p>Permuted QTL results include only the top SNP-junction association (by cluster or gene level). Table columns are formatted as follows:</p> <ol> <li>phenotype_id - id composed by splicing junction position, splicing cluster id, and gene Ensembl id (GENCODE v30), each separated by a colon</li> <li>num_var - total number of variants tested in <em>cis </em>per group (cluster or gene, depending on the file)</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 <em>P</em>-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 top 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 <em>P</em>-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 <em>P</em>-value directly obtained from the permutations with the direct method</li> <li>pval_beta - second permutation <em>P</em>-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 <em>P</em>-value threshold for calling a variant-gene pair significant for the group (cluster or gene, depending on the file)</li> </ol> <p><strong>NOTE: </strong>The effect sizes of eQTLs and sQTL are defined as the effect of the alternative allele (ALT) relative to the reference (REF) allele in the human genome reference (GRCh38). A file containing that information for all alleles tested is available at 10.5281/zenodo.4301005</p>
sQTL catalog generated by sqtlseeker2-nf in the GTEx dataset
<p>sQTL catalog generated by <a href="https://github.com/guigolab/sqtlseeker2-nf">sqtlseeker2-nf</a> in the <a href="https://www.gtexportal.org/home/">GTEx</a> dataset, as described in the publication<em> Identification and analysis of splicing quantitative trait loci across multiple tissues in the human genome</em> by Garrido-Martín et al. in Nat. Commun. (<a href="https://doi.org/10.1038/s41467-020-20578-2">https://doi.org/10.1038/s41467-020-20578-2</a>). It contains the results of the sqtlseeker2-nf pipeline, run using different input data: i) RSEM transcript quantifications and genotype data from GTEx V7, ii) LeafCutter intron excision ratios and genotype data from GTEx V7 and iii) RSEM transcript quantifications and genotype data from GTEx V8. For each GTEx tissue, it includes summary statistics corresponding to all the tests performed (both nominal and permutation passes) and the significant sQTLs identified.</p> <p> </p>
ScienceDex guides
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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.