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opencc-by-4.0Feb 2022View details →

Data for article: A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils

Supplementary information for: A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils Torsten Hauffe, Mathias M. Pires, Tiago B. Quental, Thomas Wilke, and Daniele Silvestro   <ul> <li> Simulations <ul> <li>Scripts <ul> <li>Scenario1_SamplingHeterogeneity.R: Script to simulate biogeographic histories with sampling heterogeneity</li> <li>Scenario3_SealevelInvasion.R: Script to simulate biogeographic histories where sea level facilitates dispersal and invasion induces extinction</li> <li>Scenario3_DiversityDependence.R: Script to simulate diversity-dependent biogeographic histories</li> <li>Scenario4_TraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> <li>Scenario5_CategoricalTraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> </ul> </li> <li>Results <ul> <li>Scenario1_SamplingHeterogenetiy_alpha05.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 0.5</li> <li>Scenario1_SamplingHeterogenetiy_alpha1.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 1</li> <li>Scenario1_SamplingHeterogenetiy_alpha2.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 2</li> <li>Scenario1_SamplingHeterogenetiy_alpha10.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 10</li> <li>Scenario2_Independent_dispersal_and_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_independent_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_independent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and invasion induced extinction</li> <li>Scenario3_Independent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-independent dispersal and extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_independent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Independent_dispersal_and_Diversity_dependent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and extinction</li> <li>Scenario4_Independent_dispersal_and_extinction.txt: Results of scenario 4 with trait-independent dispersal and extinction</li> <li>Scenario4_Trait_dependent_dispersal_and_independent_extinction.txt: Results of scenario 4 with trait-dependent dispersal and independent extinction</li> <li>Scenario4_Independent_dispersal_and_trait_dependent_extinction.txt: Results of scenario 4 with independent dispersal and trait-dependent extinction</li> <li>Scenario4_trait_dependent_dispersal_and_extinction.txt: Results of scenario 4 with trait-dependent dispersal and extinction</li> <li>Scenario5_CatTrait_dependent_dispersal_and_independent_extinction.txt: Results of model 2 with categorical traits (e.g family) influence dispersal but no influence of a category-specific continuous traits</li> </ul> </li> </ul> </li> <li>Carnivora <ul> <li>BinnedOccurrence: Folder with 100 replicates of binned occurrences of max. 330 carnivoran genera throughout the Neogene</li> <li>BodyMass: Folder with 100 replicates of body mass for 330 carnivoran genera</li> <li>Sealevel: Folder with sea level through the Neogene</li> <li>Temperature: Folder with the temperature record of the Neogene</li> <li>Families: Folder with families as taxonomic proxy for phylogeny. FamilyGeneraNumeric.txt is the numeric coding used for the Bayesian analyses of carnivoran biogeography</li> </ul> </li> </ul>

opencc-by-4.0Jun 2022View details →

UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits

Summary-level GWAS data for 53 traits generated by <a href="https://www.genomicsplc.com/">Genomics plc</a> as presented in: Thompson D. et al. UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits (<a href="https://doi.org/10.1101/2022.06.16.22276246">https://doi.org/10.1101/2022.06.16.22276246</a>) If you have any questions or comments regarding these files, please contact Genomics plc at <a href="mailto:research@genomicsplc.com">research@genomicsplc.com</a> NOTES These analyses were carried out using the full UK Biobank (UKB) imputation data release (v3b). After removal of exclusions and withdrawals, a subset of 337,151 UKB individuals, the White British Unrelated (WBU) subgroup, was defined as the intersection of two sample groups created by Bycroft et al 2018 (Nature 562, 203-209): the ‘White British ancestry’ group (UKB Data Field 22006) and the ‘used in genetic principal components’ group (UKB Data Field 22020), the latter being high quality samples that were filtered to avoid closely related individuals. All GWAS analyses were performed on the WBU subgroup. Phenotypes were defined as described in Supplementary Table 1 ‘Phenotype definitions’ using a combination of Hospital Episode Statistics, Cancer Registry reports (where applicable) and self-report responses, with the exception of coronary artery disease (CAD). GWAS data was generated for both a “narrow” and a “broad” definition of CAD. The former was used as part of the training data for the Enhanced CAD PRS, the latter was used as part of the training data for the Enhanced CVD PRS. The phenotype definitions for “narrow” and a “broad” CAD are as follows: <table> <tbody> <tr> <td>Narrow CAD (includes angina)</td> <td>ICD10 codes (where .X indicates all subcodes) from both hospital and death records: I21, I22, I23, I24.1, I25.2, I20.X. ICD9 codes: 410-412, 42979, 413.X. OPCS-4 codes (K40.1–40.4, K41.1–41.4, K45.1–45.5,K49.1–49.2, K49.8–49.9, K50.2, K75.1–75.4, K75.8–75.9), self-reported heart attack (UKB codes 1075 in field 20002; code 1 in field 6150), self-reported coronary angioplasty (ptca) or coronary artery bypass graft (UKB codes 1070 and 1095  in field 20004), self-reported angina.</td> </tr> <tr> <td>Broad CAD (includes angina and all ischaemic heart disease)</td> <td>As for Narrow CAD, plus ICD10 codes I24.X, I25X, and ICD9 codes 414.X (where .X indicates all subcodes).</td> </tr> </tbody> </table> Note that there is no GWAS for cardiovascular disease (CVD) per se. This is because the UKB training data for the Enhanced CVD PRS consisted of separate GWASs for “narrow” CAD and ischaemic stroke. All analyses included Age at assessment, sex (for non-sex specific traits), genotyping chip, and 10 principal components as covariates. GWAS summary statistics for each trait were generated by applying PLINK 2.0 to the WBU subgroup, using a logistic regression for disease traits, and a linear regression model for quantitative traits. For chromosome X variants males were treated as having 0 or 2 alternative alleles. The results are not adjusted for genomic control. DATA FILE CONTENT DESCRIPTION (DISEASE TRAITS) <table> <tbody> <tr> <td>cpra</td> <td>Variant ID in ‘CPRA’ format. Position reflects position in b37</td> </tr> <tr> <td>chrom</td> <td>Chromosome</td> </tr> <tr> <td>pos</td> <td>Position in base pairs (b37, 1-based)</td> </tr> <tr> <td>alt</td> <td>Alternative allele (effect allele)</td> </tr> <tr> <td>beta</td> <td>Effect size (log odds ratio)</td> </tr> <tr> <td>standard_error</td> <td>Standard error of beta</td> </tr> <tr> <td>minus_log10_p</td> <td>Minus log(base 10) of P-value</td> </tr> <tr> <td>ref</td> <td>Reference allele (non-effect allele)</td> </tr> <tr> <td>ncase</td> <td>Number of cases</td> </tr> <tr> <td>ncontrol</td> <td>Number of controls</td> </tr> </tbody> </table> DATA FILE CONTENT DESCRIPTION (QUANTITATIVE TRAITS) <table> <tbody> <tr> <td>cpra</td> <td>Variant ID in ‘CPRA’ format. Position reflects position in b37</td> </tr> <tr> <td>chrom</td> <td>Chromosome</td> </tr> <tr> <td>pos</td> <td>Position in base pairs (b37, 1-based)</td> </tr> <tr> <td>alt</td> <td>Alternative allele (effect allele)</td> </tr> <tr> <td>beta</td> <td>Effect size</td> </tr> <tr> <td>standard_error</td> <td>Standard error of beta</td> </tr> <tr> <td>minus_log10_p</td> <td>Minus log(base 10) of P-value</td> </tr> <tr> <td>ref</td> <td>Reference allele (non-effect allele)</td> </tr> <tr> <td>ntotal</td> <td>Total sample size</td> </tr> </tbody> </table> FILE NAMES The following is a list of traits and their corresponding file names. DISEASE TRAITS <table> <tbody> <tr> <td>Age-related macular degeneration</td> <td>amd_strict_UKB_WBU.csv.gz</td> </tr> <tr> <td>Alzheimer's disease</td> <td>alzheimers_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Asthma</td> <td>asthma_UKB_WBU.csv.gz</td> </tr> <tr> <td>Atrial fibrillation</td> <td>atrial_fibrillation_UKB_WBU.csv.gz</td> </tr> <tr> <td>Bipolar disorder</td> <td>bipolar_disorder_UKB_WBU.csv.gz</td> </tr> <tr> <td>Bowel cancer</td> <td>CRC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Breast cancer</td> <td>BC_UKB_WBU_women.csv.gz</td> </tr> <tr> <td>Coeliac disease</td> <td>celiac_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Narrow coronary artery disease</td> <td>NARROW_CAD_UKB_WBU.csv.gz</td> </tr> <tr> <td>Broad coronary artery disease</td> <td>BROAD_CAD_UKB_WBU.csv.gz</td> </tr> <tr> <td>Crohn's disease</td> <td>crohns_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Epithelial ovarian cancer</td> <td>OC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Hypertension</td> <td>HT_UKB_WBU.csv.gz</td> </tr> <tr> <td>Ischaemic stroke</td> <td>IS_stroke_UKB_WBU.csv.gz</td> </tr> <tr> <td>Melanoma</td> <td>melanoma_UKB_WBU.csv.gz</td> </tr> <tr> <td>Multiple sclerosis</td> <td>multiple_sclerosis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Osteoporosis</td> <td>OP_WBU_training.csv.gz</td> </tr> <tr> <td>Prostate cancer</td> <td>PC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Parkinson's disease</td> <td>parkinsons_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Primary open angle glaucoma</td> <td>POAG_WBU_training.csv.gz</td> </tr> <tr> <td>Psoriasis</td> <td>psoriasis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Rheumatoid arthritis</td> <td>rheumatoid_arthritis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Schizophrenia</td> <td>schizophrenia_UKB_WBU.csv.gz</td> </tr> <tr> <td>Systemic lupus erythematosus</td> <td>lupus_UKB_WBU.csv.gz</td> </tr> <tr> <td>Type 1 diabetes</td> <td>t1d_UKB_WBU.csv.gz</td> </tr> <tr> <td>Type 2 diabetes</td> <td>T2D_UKB_WBU.csv.gz</td> </tr> <tr> <td>Ulcerative colitis</td> <td>ulcerative_colitis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Venous thromboembolic disease</td> <td>VTE_UKB_WBU.csv.gz</td> </tr> </tbody> </table> QUANTITATIVE TRAITS <table> <tbody> <tr> <td>Age at menopause</td> <td>age_at_menopause_UKB_WBU.csv.gz</td> </tr> <tr> <td>Apolipoprotein A1</td> <td>apolipoprotein_a1_UKB_WBU.csv.gz</td> </tr> <tr> <td>Apolipoprotein B</td> <td>apolipoprotein_b_UKB_WBU.csv.gz</td> </tr> <tr> <td>Body mass index</td> <td>bmi_UKB_WBU.csv.gz</td> </tr> <tr> <td>Calcium</td> <td>calcium_UKB_WBU.csv.gz</td> </tr> <tr> <td>Docosahexaenoic acid</td> <td>docosahexaenoic_acid_UKB_WBU.csv.gz</td> </tr> <tr> <td>Estimated bone mineral density T-score</td> <td>BMD_WBU_training.csv.gz</td> </tr> <tr> <td>Estimated glomerular filtration rate (creatinine based)</td> <td>egfr_UKB_WBU.csv.gz</td> </tr> <tr> <td>Estimated glomerular filtration rate (cystatin based)</td> <td>egfr_cys_UKB_WBU.csv.gz</td> </tr> <tr> <td>Glycated haemoglobin</td> <td>hba1c_UKB_WBU_nodiabetes.csv.gz</td> </tr> <tr> <td>High density lipoprotein cholesterol</td> <td>hdl_cholesterol_UKB_WBU.csv.gz</td> </tr> <tr> <td>Height</td> <td>height_UKB_WBU.csv.gz</td> </tr> <tr> <td>Intraocular pressure</td> <td>iop_WBU_training.csv.gz</td> </tr> <tr> <td>Low density lipoprotein cholesterol</td> <td>ldl_UKB_WBU_nostatins.csv.gz</td> </tr> <tr> <td>Omega-6 fatty acids</td> <td>omega_6_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Omega-3 fatty acids</td> <td>omega_3_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Phosphatidylcholines</td> <td>phosphatidylcholines_UKB_WBU.csv.gz</td> </tr> <tr> <td>Phosphoglycerides</td> <td>phosphoglycerides_UKB_WBU.csv.gz</td> </tr> <tr> <td>Polyunsaturated fatty acids</td> <td>polyunsaturated_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Resting heart rate</td> <td>resting_heart_rate_UKB_WBU.csv.gz</td> </tr> <tr> <td>Remnant cholesterol (Non-HDL, Non-LDL cholesterol)</td> <td>remnant_cholesterol__UKB_WBU.csv.gz</td> </tr> <tr> <td>Sphingomyelins</td> <td>sphingomyelins_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total cholesterol</td> <td>total_cholesterol_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total fatty acids</td> <td>total_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total triglycerides</td> <td>total_triglycerides_UKB_WBU.csv.gz</td> </tr> </tbody> </table>

opencc-by-4.0May 2022View details →

Phenotypic variation and quantitative trait loci for resistance to southern anthracnose and clover rot in red clover

Red clover (Trifolium pratense L.) is an important forage legume of temperate regions, particularly valued for its high yield potential and its high forage quality. Despite substantial breeding progress during the last decades, continuous improvement of cultivars is crucial to ensure yield stability in view of newly emerging diseases or changing climatic conditions. The high amount of genetic diversity present in red clover ecotypes, landraces and cultivars provides an invaluable, but often unexploited resource for the improvement of key traits such as yield, quality, and resistance to biotic and abiotic stresses. A collection of 397 red clover accessions was genotyped using a pooled genotyping-by-sequencing approach with 200 plants per accession. Resistance to the two most pertinent diseases in red clover production, southern anthracnose caused by Colletotrichum trifolii, and clover rot caused by Sclerotinia trifoliorum, was assessed using spray inoculation. The mean survival rate for southern anthracnose was 22.9% and the mean resistance index for clover rot was 34.0%. Genome-wide association analysis revealed several loci significantly associated with resistance to southern anthracnose and clover rot. Most of these loci are in coding regions. One quantitative trait locus (QTL) on chromosome 1 explained 16.8% of the variation in resistance to southern anthracnose. For clover rot resistance we found eight QTL, explaining together 80.2% of the total phenotypic variation. The SNPs associated with these QTL provide, once validated, a promising resource for marker-assisted selection in existing breeding programs, facilitating the development of novel cultivars with increased resistance against two devastating fungal diseases of red clover.

opencc-by-4.0Jul 2019View details →

Contemporary phenotypic change in plant quantitative traits

This is a new version of the Gorné & Díaz 2017 database (doi:10.5281/zenodo.580095). We cheked the categorization of each case, fixed of some mistakes. Also, we disambiguated the trait type moderator and add a new (mean based) measure of change. This database included studies that provide data of changes in quantitative traits of angiosperms within a known temporal framework (<300 years). The search was performed by Scopus (www.scopus.com), up to 22 December 2015 (search strings in Gorné and Díaz 2017). The database includes studies that measured intraspecific change in a quantitative trait and which report the elapsed time when the phenotypic change occurred. The studies recorded a single population before and after a change in the environment or compared two (or more) populations by measuring a quantitative trait across two situations, where one of them was a new condition of known age. Both, by measuring change directly in the field or by performing common condition experiments (e.g. common garden experiments or reciprocal transplants). Studies reporting results from artificial selection or interspecific hybridization were excluded. The environmental changes included expansions of distributional range, soil or air pollution, exposure to herbicides, changes in salinity, pH, climate, disturbance or irrigation regime, and addition or loss of species in the local community. All data available in each study were recorded, including several observations of the same species. These procedures resulted in a database containing 1716 observations from 128 studies, with changes in populations of 152 species from 34 families, in elapsed times of < 260 years, and covering a wide range of traits, lifespan, growth forms and environmental situations. All data points were categorized according to biological properties of the study system (lifespan, growth form, trait type) and methodological ones. The amount and rate of phenotypic change is expresed as the standardized mean difference Hedges g (Hedges 1981, 1982), a rate of change which is the Hedges g over the elapsed time in years, and the log-transformation of both of them. The standardized mean difference is equal to the haldane numerator, which is a standard rate of evolution (Haldane 1949; Gingerich 1993). In addition, we upgraded the Díaz and Gorné (2017) database, computing the response ratio effect size (logRR) (Hedges et al. 1999) whenever possible. The response ratio is a mean-scaled metric equal to the darwins numerator (Haldane 1949). So that we compute a rate of change similar to darwins (time expressed as years instead of million years).   contact email address: gorneld@gmail.com

opencc-by-4.0Dec 2022View details →

Hypergraph Factorisation Expression Quantitative Trait Loci

Please cite: <pre><code>Hypergraph factorisation for multi-tissue gene expression imputation. Vinas Torne, Ramon and Joshi, Chaitanya K. and Georgiev, Dobrik and Lin, Phillip and Dumitrascu, Bianca* and Gamazon, Eric* and Lio, Pietro*. *Co-corresponding authors. </code></pre>

opencc-by-4.0Dec 2022View details →

Placental Expression Quantitative Trait Loci In An East Asian Population

Analysis script, full eQTL summary statistics, and fine-mapping statistics of article "Placental Expression Quantitative Trait Loci In An East Asian Population". This data contains workflow and result of 102 East  Asian placental expression quantitative trait loci analysis.Genotype from 102 cord blood used in the analysis is also included. Variants with minor allele frequencies less than 0.01 was filtered out to prevent personnel identification.

opencc-by-4.0Apr 2024View details →

QR GWAS summary statistics for 39 quantitative traits in the UK Biobank

Quantile regression (QR) GWAS summary statistics from the study "Genome-wide discovery for biomarkers using quantile regression at biobank scale". The preprint is available at <a href="https://doi.org/10.1101/2023.06.05.543699" target="_blank" rel="noopener">https://doi.org/10.1101/2023.06.05.543699</a>.  List of traits A comma-delimited text file, QRGWAS.Traits_n39.csv, includes the list of 39 quantitative traits from the UK Biobank reported in the QR GWAS analyses above. Summary statistics The tab-delimited text files are QR GWAS summary statistics, which are bgzip compressed (.tsv.gz files) and tabix indexed (.tbi files). <ul> <li>Column "CHR": chromosome</li> <li>Column "POS": based pair position</li> <li>Column "ID": variant ID</li> <li>Column "REF": non-effect allele</li> <li>Column "ALT": effect allele tested in GWAS</li> <li>Column "EAF": frequency of the effect allele</li> <li>Column "N": sample size</li> <li>Column "P_QR": integrated p-value of the quantile regression (QR) model across multiple quantile levels.</li> <li>Column "P_LR": p-value of the linear regression (LR) association statistic</li> <li>Columns from "P_Q10" to "P_Q90": quantile-specific QR p-value for the quantile levels 0.1, 0.2, ..., 0.9 (10th, 20th, ..., 90th quantiles).</li> </ul>

opencc-by-4.0Sep 2018View details →

Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits

Introduction These are the Summary Level-data as presented in: "Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits". Eur J Hum Genet. 2019 Jan 24. doi: 10.1038/s41431-018-0295-z. [Epub ahead of print] If you use these data please cite the corresponding manuscript, which can be downloaded here: <a href="http://em.rdcu.be/wf/click?upn=lMZy1lernSJ7apc5DgYM8eFz0euOx0-2B13Abimi4Sb0A-3D_2NNavOiAD9A7CPFnsa04dGla3sU002fLfkDtL-2FhGlad0GuoM-2B3OlDb0C5GiEhwIvtH7ba4KKF45ipTOFodx6CqvVvoP2GQ992sPGoV9ZPWIe04tUd8-2BGWey0In0TXPII5zK-2Bfp8Wk9TpEqEcSd-2BEmywqZc8o5TW4xGPXZqmchfUH8chy3P4SEtpzHXMG1LwsIYrKfwegqTXG85RAJPr-2B21Tk9SobtpvFs0frMkJ4ekKsl33ryoZfFPk1byjQunJYn4-2BB0iqMgGs6cXv0AOgAxg-3D-3D">https://rdcu.be/bh8mu</a>. When you have any questions or comments regarding this study or these files, please contact me via: Jessica van Setten, PhD | Department of Cardiology, University Medical Center Utrecht, Utrecht University | j.vansetten [at] umcutrecht [dot] nl   Files and description There are four files available: <ol> <li>RR_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for RR interval</li> <li>PR_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for PR interval</li> <li>QT_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for QT interval</li> <li>QRS_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for QRS duration</li> </ol> All these files have the same lay-out and are gzipped. The reference used for meta-analysis of GWAS was Genome of the Netherlands v4.  <ul> <li>SNP - variantID (rsID), please note that few hundred variants do not have an rsID, but are NA instead. These can still be identified by chromosome and position.</li> <li>CHR - chromosome numbers [1-22 and X].</li> <li>POS - base pair position, hg19 / build37.</li> <li>CODED_ALLELE - coded allele, i.e. the effect allele, as represented (and harmonized) across cohorts. Note that this is not necessarily the minor allele.</li> <li>NON_CODED_ALLELE - the other allele, i.e. the non-effect allele.</li> <li>CODED_ALLELE_FREQ - coded allele frequency, i.e. the effect allele frequency. Note that this is not necessarily the minor allele frequency.</li> <li>BETA - beta from the fixed-effects model.</li> <li>SE - standard error from the fixed-effects model.</li> <li>P - P-value from the fixed-effects model.</li> <li>NEAREST_GENE - the gene closest to the respective variant.</li> </ul>

opencc-by-4.0Dec 2017View details →

Quantitative Trait Loci of Solanaceae species

This archive contains experimental data on Quantitative Trait Loci (QTLs) mapped in Solanacea species (tomato and potato). QTLs were extracted from scientific literature using the QTLTableMiner++ tool. The resulting data are distributed in: <ul> <li><a href="https://sqlite.org/">SQLite</a> database files (.db)</li> <li>CSV files (.csv)</li> <li><a href="https://www.w3.org/TR/turtle/">RDF/</a><a href="https://www.w3.org/TR/turtle/">Turle</a> files (gzip-ed .ttl)</li> </ul>

opencc-by-4.0Oct 2024View details →

Expression quantitative trait loci influence DNA damage-induced apoptosis in cancer

Exposure expression quantitative trait loci (e2QTL) The analysis of e2QTL allows for the identification of context-specific eQTL effects (Kim-Hellmuth et al. (2017), PMID: 28814792). To evaluate how inter-individual genetic variability influences the regulation of DNA damage-induced apoptosis, we performed e2QTL analysis of CD8+ T cells from 461 healthy European participants stimulated with high doses of 5 different carcinogens. These include Methyl-methanesulfonate (MMS), tert-butyl-hydroperoxide (TBOOH), benzo(a)pyrene-7,8-diol-9,10-epoxide (BPDE), 4-hydroxycyclophosphamide (HC) and UVC radiation. <code>eQTL_DNA_damage_induced_apoptosis.csv:</code> eQTL data. FastQTL was used to analyze cis-eQTL within a 1 MB window of a gene’s transcription start site. Filtering and normalization of expression data was performed as described by the Genotype Tissue Expression (GTEx) project (The GTEx Consortium (2015), PMID: 25954001) including 60 PEER factors, top 3 genotype PCs and sex as covariates. Genotypes were filtered by PHWE > 10-6 and MAF > 5 %. Adjusted p-values were generated using 1,000 to 10,000 permutations. Variant IDs (CHR:POS:REF:ALT) are based on GRCh38. <code>e2QTL_DNA_damage_induced_apoptosis.csv:</code> e2QTL data. The most significant variant for each analyzed gene was determined based on eQTL data to calculate e2QTL in a z-test that were corrected for multiple testing using Bonferroni correction as described by Kim-Hellmuth et al. (2017). Variant IDs (CHR:POS:REF:ALT) are based on GRCh38.

opencc-by-4.0Jun 2023View details →

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-zeroJul 2020View details →

Seaweed functional diversity revisited: confronting traditional groups with quantitative traits

1. Macroalgal (seaweed) beds and forests fuel coastal ecosystems and are rapidly reorganising under global change, but quantifying their functional structure still relies on binning species into coarse groups on the assumption that they adequately capture relevant underlying traits. 2. To interrogate this 'group gambit', we measured 12 traits relating to competitive dominance and resource economics across 95 macroalgal species collected from the UK and widespread on North-East Atlantic rocky shores. We assessed the amount of trait variation explained by commonly-used traditional groups – (i) two schemes based on gross morphology and anatomy and (ii) two categorisations of vertical space use – and examined species reclassification into post hoc, so-called emergent groups arising from the functional trait dataset. We then offer an alternative, emergent grouping scheme of macroalgal functional diversity. 3. (i) Morphology and anatomy-based groups explained slightly more than a third of multivariate trait expression with considerable group overlap (i.e. low precision) and extensive mismatch with underlying trait expression (i.e. low accuracy). (ii) Categorisations of vertical space use accounted for about a quarter of multivariate trait expression with considerable group overlap. Nonetheless, turf species tended to display attributes of opportunistic forms. (iii) A nine-group emergent scheme provided a highly explanatory and parsimonious alternative to traditional functional groupings. 4. Synthesis: Our analysis using a comprehensive dataset of directly measured functional traits revealed a general mismatch between traditional groups and underlying traits, highlighting the deficiencies of the group gambit in macroalgae. While existing grouping schemes may allow first order approximations, they risk considerable loss of information at the trait and, potentially, ecosystem levels. Instead, we call for further development of a trait-based approach to macroalgal functional ecology to capture unfolding community and ecosystem changes with greater accuracy and generality.

opencc-zeroApr 2020View details →

Data from: Müllerian mimicry of a quantitative trait despite contrasting levels of genomic divergence and selection

Hybrid zones, where distinct populations meet and interbreed, give insight into how differences between populations are maintained despite gene flow. Studying clines in genetic loci and adaptive traits across hybrid zones is a powerful method for understanding how selection drives differentiation within a single species, but can also be used to compare parallel divergence in different species responding to a common selective pressure. Here, we study parallel divergence of wing colouration in the butterflies Heliconius erato and H. melpomene, which are distantly related Müllerian mimics that show parallel geographic variation in both discrete variation in pigmentation, and quantitative variation in structural colour. Using geographic cline analysis, we show that clines in these traits are positioned in the roughly the same geographic region for both species, which is consistent with direct selection for mimicry. However, the width of the clines varies markedly between species. This difference is explained in part by variation in the strength of selection acting on colour traits within each species, but may also be influenced by differences in the dispersal rate and total strength of selection against hybrids between the species. Genotyping-by-sequencing also revealed weaker population structure in H. melpomene, suggesting the hybrid zones may have evolved differently in each species; which may also contribute to the patterns of phenotypic divergence in this system Overall, we conclude that multiple factors are needed to explain patterns of clinal variation within and between these species, although mimicry has probably played a central role.

zenodo36/100

Dataset for Quantitative Trait Loci Associated with Lodging, Stem Strength, Yield, and Other Important Agronomic Traits in Dry Field Peas, 330 markers, sequences included

Dataset for Quantitative Trait Loci Associated with Lodging, Stem Strength, Yield, and Other Important Agronomic Traits in Dry Field Peas, 330 markers, sequences included.

opencc-by-4.0Jul 2017View details →

opencc-zeroFeb 2024View details →

Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing

Additive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short-term responses to selection, but they are notoriously challenging to estimate in most non-model wild populations. Specifically, large-sized or panmictic populations may be characterized by low variance in genetic relatedness among individuals which in turn, can prevent accurate estimation of quantitative genetic parameters. We used estimates of genome-wide identity-by-descent (IBD) sharing from autosomal SNP loci to estimate quantitative genetic parameters for ecologically important traits in nine-spined sticklebacks (Pungitius pungitius) from a large, outbred population. Using empirical and simulated datasets, with varying sample sizes and pedigree complexity, we assessed the performance of different crossing schemes in estimating additive genetic variance and heritability for all traits. We found that low variance in relatedness characteristic of wild outbred populations with high migration rate can impair the estimation of quantitative genetic parameters and bias heritability estimates downwards. On the other hand, the use of a half-sib/full-sib design allowed precise estimation of genetic variance components, and revealed significant additive variance and heritability for all measured traits, with negligible dominance contributions. Genome-partitioning and QTL mapping analyses revealed that most traits had a polygenic basis and were controlled by genes at multiple chromosomes. Furthermore, different QTL contributed to variation in the same traits in different populations suggesting heterogenous underpinnings of parallel evolution at the phenotypic level. Our results provide important guidelines for future studies aimed at estimating adaptive potential in the wild, particularly for those conducted in outbred large-sized populations.

zenodo36/100

Minimal dataset for the manuscript "Better together against genetic heterogeneity: a sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank".

Dataset "lin2024-sex_combined_interaction-association_signifincant_in_one_or_more_tests-summary.txt" is a minimal dataset to reproduce the figures and tables in the manuscript "Better together against genetic heterogeneity: a sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank".  To generate this dataset, see "https://github.com/BoxiLin/t2meta" Steps 0, 1. This dataset is the input for Steps 2, 3, 4, 5 to generate Figures 1-3 and Table 2-3.   ##### Column information ######################## The following columns are annotations on each variant in the GWAS, calculated across the analysis subset of 361,194 samples by the Neale lab: code: Phenotype identifier in the form of "[UKB Data field]_raw" variant: Unique variant identifier in the form "chr:pos:ref:alt", where "ref" is aligned to the forward strand. chr: Chromosome of the variant. pos: Position of the variant in GRCh37 coordinates. rsid: rs ID ref: Reference allele on the forward strand. alt: Alternate allele (not necessarily minor allele). p_hwe: Hardy-Weinberg p-value. info: Imputation INFO score as provided by UK Biobank.   The following columns are sex-stratified test statistics calculated by the Neale lab: minor_allele.x: Minor allele (AF < 0.5) in the female GWAS  minor_AF.x: Minor allele frequency in the female GWAS  beta.x: Estimated effect size of alt allele in the female GWAS  se.x: Estimated standard error of beta in the female GWAS tstat.x: t-statistic of beta estimate (= beta/se) in the female GWAS  pval.x: p-value of beta significance test in the female GWAS  minor_allele.y: Minor allele (AF < 0.5) in the male GWAS  minor_AF.y: Minor allele frequency in the male GWAS  beta.y: Estimated effect size of alt allele in the male GWAS  se.y: Estimated standard error of beta in the male GWAS  tstat.y: t-statistic of beta estimate (= beta/se) in the male GWAS  pval.y: p-value of beta significance test in the male GWAS    The following columns are sex-combined test statistics calculated in our analysis: T.I: test statsitic for interaction effect-only  p.T.I:  p-value of the interaction effect-only test  TSG.L:  test statsitic for inverse variance weighted meta-analysis p.TSG.L: p-value of the inverse variance weighted meta-analysis TSG.Q: test statsitic for the omnibus meta-analysis p.TSG.Q: p-value for the omnibus meta-analysis

opencc-by-4.0Mar 2024View details →

zenodo36/100

Pleiotropic Expression Quantitative Trait Loci Are Enriched in Enhancers and Transcription Factor Binding Sites and Impact More Genes

This dataset comprises two files that accompany the article (link to be added upon publication). <h2>1. gwas2eqtl_colocalization_full.tar.gz</h2> This file contains the complete colocalization dataset generated using the code from the following GitHub repository: gwas2eqtl. This dataset is used as input for the pleiotropic eQTL analysis available at gwas2eqtl_pleiotropy, which produces the figures in the article. File structure: <blockquote> . └── gwas417     └── coloc         ├── ebi-a-GCST000998         │   └── pval_5e-08         │       └── r2_0.1         │           └── kb_1000         │               └── window_1000000         │                   ├── Alasoo_2018_ge_macrophage_IFNg+Salmonella.tsv         │                   ├── Alasoo_2018_ge_macrophage_IFNg.tsv ... </blockquote> Each TSV file contains the following columns: <blockquote> chrom    pos    rsid    ref    alt    eqtl_gene_id    gwas_beta    gwas_pval    gwas_id    eqtl_beta    eqtl_pval    eqtl_id    PP.H4.abf    SNP.PP.H4    nsnps    PP.H3.abf    PP.H2.abf    PP.H1.abf    PP.H0.abf    coloc_variant_id    coloc_region 1    109272258    rs4970834    C    T    ENSG00000168765    -0.12874.25001047052626e-09    ebi-a-GCST000998    -0.250697    0.0893351    Alasoo_2018_ge_macrophage_IFNg+Salmonella    0.108283426725895    0.0520205502224409    6    0.000552728832012655    2.09397277427793e-07    0.890881419183881    0.000282215860934432    1_109279544_G_A    1:108779544-109779543 1    109274968    rs12740374    G    T    ENSG00000168765    -0.103341    1.63998546891446e-09    ebi-a-GCST000998    -0.197397    0.172673Alasoo_2018_ge_macrophage_IFNg+Salmonella    0.108283426725895    0.0857585178966856    6    0.000552728832012655    2.09397277427793e-07    0.890881419183881    0.000282215860934432    1_109279544_G_A    1:108779544-109779543 1    109275216    rs660240    T    C    ENSG00000168765    0.1044492.78997299740827e-09    ebi-a-GCST000998    0.214165    0.139318    Alasoo_2018_ge_macrophage_IFNg+Salmonella    0.108283426725895    0.0557749486050279    6    0.000552728832012655    2.09397277427793e-07    0.890881419183881    0.000282215860934432    1_109279544_G_A    1:108779544-109779543 1    109275684    rs629301    G    T    ENSG00000168765    0.1054716.129993302249e-10    ebi-a-GCST000998    0.197397    0.172673    Alasoo_2018_ge_macrophage_IFNg+Salmonella    0.108283426725895    0.22229240584331    6    0.000552728832012655    2.09397277427793e-07    0.890881419183881    0.000282215860934432    1_109279544_G_A    1:108779544-109779543 1    109278889    rs602633    T    G    ENSG00000168765    0.1034352.15998134341707e-09    ebi-a-GCST000998    0.226329    0.102673    Alasoo_2018_ge_macrophage_IFNg+Salmonella    0.108283426725895    0.0782482718431504    6    0.000552728832012655    2.09397277427793e-07    0.890881419183881    0.000282215860934432    1_109279544_G_A    1:108779544-109779543 ... </blockquote>   The dataset provides colocalization statistics for GWAS-eQTL pairs, including posterior probabilities and variant annotations. <h2>2. gwas2eqtl0.1.3.tsv.gz</h2> This file is a filtered version of the colocalization dataset, refined based on cutoffs of PP.H4.abf ≥ 0.75 and SNP.PP.H4 ≥ 0. This subset is utilized in the gwas2eqtl web application for data visualization. Sample Columns: <blockquote> chrom   pos19   pos38   cytoband        rsid    ref     alt     gwas_trait      gwas_class      gwas_beta       eqtl_gene_symbol        eqtl_beta       eqtl_id eqtl_gene_id    gwas_id gwas_pval       eqtl_pval          pp_h4_abf       snp_pp_h4       tophits_variant_id      nsnps 1       1163804 1228424 1p36.33 rs7515488       C       T       Inflammatory bowel disease      Autoimmune dis. 0.0874308       ANKRD65 -0.175816       BrainSeq_ge_brain       ENSG00000235098 ebi-a-GCST003043           2.85292266979231e-10    0.000841046     0.978425254116226       6.18454060493069e-12    1_1312114_T_C   3 1       1163804 1228424 1p36.33 rs7515488       C       T       Inflammatory bowel disease      Autoimmune dis. 0.0874308       ANKRD65 -0.175816       BrainSeq_ge_brain       ENSG00000235098 ieu-a-294       2.85292266979231e-10       0.000841046     0.974019788384412       7.52286530905187e-12    1_1312114_T_C   4 1       1163804 1228424 1p36.33 rs7515488       C       T       Inflammatory bowel disease      Autoimmune dis. 0.0874308       ANKRD65 -0.293529       CommonMind_ge_DLPFC_naive       ENSG00000235098 ebi-a-GCST003043   2.85292266979231e-10    2.30452e-06     0.953333690803618       6.9758581004380506e-15  1_1312114_T_C   6 1       1163804 1228424 1p36.33 rs7515488       C       T       Inflammatory bowel disease      Autoimmune dis. 0.0874308       ANKRD65 -0.293529       CommonMind_ge_DLPFC_naive       ENSG00000235098 ieu-a-294          2.85292266979231e-10    2.30452e-06     0.951092499048109       7.0876793540547e-15     1_1312114_T_C   7 1       1163804 1228424 1p36.33 rs7515488       C       T       Inflammatory bowel disease      Autoimmune dis. 0.0874308       ANKRD65 -0.510549       FUSION_ge_adipose_naive ENSG00000235098 ebi-a-GCST003043           2.85292266979231e-10    1.2212e-06      0.974412793352836       1.70246427912903e-11    1_1312114_T_C   6 </blockquote>   This filtered dataset focuses on high-confidence colocalization events for functional exploration of genetic associations and regulatory mechanisms.

opencc-by-4.0Nov 2024View details →

opencc-zeroMay 2022View details →

Root penetration index 3, a major quantitative trait locus (QTL) associated with root system penetrability in Arabidopsis.

Soil mechanical impedance precludes root penetration, confining root system development to shallow soil horizons where mobile nutrients are scarce. Using a two-phase-agar system, we characterized Arabidopsis thaliana responses to low and high mechanical impedance at three root penetration stages. We found that seedlings whose roots fail to penetrate agar barriers show a significant reduction in leaf area, root length and elongation zone and an increment in root diameter, while those capable of penetrating show only minor morphological effects. Analyses using different auxin-responsive reporter lines, exogenous auxins and inhibitor treatments suggest that auxin responsiveness and PIN-mediated auxin distribution play an important role in regulating root responses to mechanical impedance. The assessment of 21 Arabidopsis accessions revealed that primary root penetrability (PRP) varies widely among accessions. To search for quantitative trait loci (QTLs) associated to root system penetrability, we evaluated a recombinant inbred population (RIL) derived from Landsberg erecta (Ler-0, with a high PRP) and Shahdara (Sha, with a low PRP) accessions. QTL analysis revealed a major-effect QTL localized in chromosome 3 (q-RPI3), which accounted for 29.98% (LOD = 8.82) of the total phenotypic variation. Employing an introgression line (IL-321), with a homozygous q-RPI3region from Sha in the Ler-0 genetic background, we demonstrated that q-RPI3 plays a crucial role in root penetrability. This multiscale study revels new insights into root plasticity during the penetration process in hard agar layers, natural variation and genetic architecture behind primary root penetrability in Arabidopsis.