dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis and statistics To detect a potential effect of pollen diet or parasite on resource collection, reproductive success, stress response, and individual health, mixed models were fitted for each parameter using diet, parasite and their interaction as fixed factors, and colony as random factor. Pollen collection, pollen efficacy, and pollen dilution (log-transformed data) were analysed using a Gaussian distribution (i. e. , normality of residuals; shapiro. test function from the stats R-package v. 4. 1. 0; R Core Team, 2021) (lmer function from the nlme R package v. 3. 1. 160; Pinheiro et al. , 2022). Total hatched offspring mass, emerged male mass, and fat body content (i. e. , proportion data) were analysed using a Gamma distribution and a log link function (glmmTMB function from the glmmTMB R-package v.
1. 1. 4; Brooks et al. , 2017). For fat body content, values were square root-transformed and sex was added as crossed-fixed effect. For emerged male mass and fat body content, microcolony was nested within colony in the random factor to deal with pseudo-replication (i. e. , several measures per microcolony). For larval ejection, a binomial distribution (ejected larvae and total number of living offspring produced as bivariate response) was used after checking for overdispersion and zero inflation (testDispersion and testZeroInflation functions from DHARMa R-package v. 0. 4. 6; Hartig, 2022). For worker mortality, a Cox proportional hazard (mixed-effect) model was run with individuals alive at the end of the 35-day treatment assigned as censored, and those who died as uncensored (coxme function from the coxme R-package v.
1. 8. 2;  Lenth, 2022). For fat body content, data were analysed separately for workers and males as a sex-significant effect was detected. Graphs and plots were all performed using the R-package ggplot2 v. 3. 4. 0  (Wickham, 2016)  except the one referring to the survival probability of the workers performed with the ggsurvplot function of the survminer R-package v. 0. 4. 9  (Kassambara et al. , 2021). All the statistical analyses were done using the R software v. 4. 1. 0 (R Core Team, 2021). For all statistical analyses, p < 0. 05 was used as a threshold for significance.
Acknowledgements We would like to thank  D. Evrard  and  L. Marin  for their help in colony and microcolony maintenance, as well as the numerous people that helped with microcolony dissection. We are very grateful to the  Laboratory of Cellular Biology (UMons)  and the  Laboratory of Therapeutic Chemistry and Pharmacognosy (UMons)  for their advice and the access to the device needed for the microscope analyses and the diet preparation. We also thank  François Dittlo  for providing us with heather pollen, as well as the family of the first author (I. , C. , C. , and  C. Tourbez ) for their help.
Funding This study was funded by the ' METAFLORE , 2019-2023′  project, one  ARC 'Actions de Recherche Concertées ' project. C. T. PhD grant is funded by the  University of Mons (UMONS). A. G. is supported by a  F. R. S. -FNRS PhD  grant ' Aspirant'. The PhD grant of I. S. is supported by the  ARC  project  METAFLORE.
13. TwoSampleMR (v0. 5. 6) R package was used for Mendelian randomisation (MR) analyses. 14. RIMBANET (version 26th June 2019, https://labs. icahn. mssm. edu/zhulab/?s=rimbanet) and Enrichr webserver (Version of 29th March 2021, https://maayanlab. cloud/Enrichr/) were used for Bayesian network queries for prioritized genes. 15. The code for the druggability analysis can be found in GitLab (https://cfinan. gitlab. io/biomisc/scripts/drug_lookups. html). For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e. g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.
We identified 16 risk loci, including 11 new association signals, demonstrating a substantial polygenic heritability for this disease. Importantly, we show that several common genetic risk loci for SCAD are shared with CAD but have a directionally opposite effect and a different genetic contribution of established Article https://doi. org/10. 1038/s41588-023-01410-1 (for example, the colon, small intestine and uterus) (Supplementary Fig. 5 ). Based on recently published analyses of single-cell open chromatin in 30 adult tissues  24  , we determined that vascular smooth muscle cells (VSMCs) and fibroblasts were the top enriched cell types for SCAD-associated loci among clusters represented in aorta and tibial artery datasets (Fig. 2a  and Supplementary Fig. 6 ). Consistently, all but one SCAD locus included at least one variant that overlapped with enhancer marks or open chromatin peaks in coronary artery tissue, VSMCs or fibroblasts (Supplementary Fig.
Tissue coagulation as a novel mechanism in SCAD We applied a multi-source strategy to identify candidate genes located in risk or GWAS loci, or loci at risk for SCAD. We prioritized: (1) genes that were targets of eQTLs colocalizing with a GWAS signal (Supplementary Fig. 8a  and Supplementary Table  6 ) or transcriptome-wide association study (TWAS) hits in at least one tissue relevant to arterial dissection (aorta, coronary or tibial artery, fibroblasts or whole blood from the Genotype Tissue Expression (GTEx) database) (Supplementary Fig. 8b  and Supplementary Table  7 ); (2) genes with a biological function linked to the cardiovascular system in humans or mice; (3) genes involved in significant long-range chromatin conformation interactions from Hi-C data with SCAD-associated variants in the aorta  25  ; and (4) those genes closest to or overlapping with the top associated variants.
70 (h 2 SNP = 0. 71 ± 0. 11 on the liability scale using linkage disequilibrium score regression  21  and h 2 SNP = 0. 70 ± 0. 12 using SumHer  22  ; Supplementary Table  3 ). The ECM1/ADAMTSL4 locus on chromosome 1 accounted for the largest proportion of heritability for SCAD in our dataset (h 2 = 0. 028), followed by the COL4A1/COL4A2 locus, which contained two independent GWAS signals (h 2 = 0. 022; Supplementary Table  4  and Supplementary Fig. 4 ). Overall, we estimate that the 16 loci explain ∼24% of the total SNP-based heritability of SCAD (Supplementary Table  4 ).
Consideration of genes encoding druggable targets, as derived by Finan et al. 26  , indicated that tissue factor is a clinical phase drug candidate (tier 1 druggable target), with target reference numbers CHEMBL4081 (factor III) and CHEMBL2095194 (factor III/factor VII complex) (Supplementary Table  8 ). To globally assess the biological mechanisms involving prioritized genes, we applied a network query based on Bayesian gene regulatory networks constructed from expression and genetics data from arterial tissues and fibroblasts  [27] [28] [29]. We found extracellular matrix organization to be the biological function at which most prioritized genes and their respective immediate subnetworks clustered (Supplementary Fig. 9 ). Among the genes we prioritized in novel loci, a number encode proteins involved in extracellular matrix formation, including integrin alpha 1 (ITGA1), basement membrane constituent collagen type IV alpha 1 chain (COL4A1) and alpha 2 chain (COL4A2), serine protease HtrA serine peptidase 1 (HTRA1), metallopeptidase thrombospondin type 1 domain containing 4 (THSD4, encoding a partner of fibrillin 1, whose gene is located in a previously reported SCAD locus (FBN1)) and TIM metallopeptidase inhibitor 3 gene (TIMP3).
3a  and Supplementary Table  7 ). Genetic correlation confirmed a genome-wide negative   24. The SCAD 95% credible set of causal SNPs and their linkage disequilibrium proxies were matched to random pools of neighboring SNPs using the GREGOR package  43. Enrichment represents the ratio of the number of SCAD SNPs overlapping open chromatin regions over the average number of matched SNPs overlapping the same regions. P values were evaluated by binomial one-sided test, with greater enrichment as the alternative hypothesis  43. The bottom dashed line represents significance (P < 0. 05) after adjustment for 105 subclusters. Higher opacity is used to identify significant associations (adjusted P < 0. 05). Bottom, composition of artery tissues relative to 105 single-cell subclusters, as determined by snATAC-seq in 30 adult tissues  24.
17 ± 0. 03; P = 2. 6 × 10 -7 ), indicating a shared genetic basis with SCAD (Fig. 3b  and Supplementary Table  10 ). To assess the extent to which blood pressure and main cardiovascular risk factors may contribute to the risk of SCAD, we leveraged existing GWAS datasets to identify instrumental variables and conducted comparative Mendelian randomization associations with SCAD or CAD. We found robust significant associations estimated by inverse variance-weighted (IVW), MR-Egger and weighted median methods between genetically predicted blood pressure traits and increased risk of SCAD (β IVW/SBP = 0. 05 ± 0. 01 (P = 7. 6 × 10 -6 ); β IVW/DBP = 0. 10 ± 0. 02 (P = 1. 9 × 10 -8 )) and CAD (β IVW/SBP = 0. 04 ± 0. 002 (P = 8. 6 × 10 -49 ); β IVW/DBP = 0. 06 ± 0. 004 (P = 1. 6 × 10 -44 )) (Fig. 4  and Supplementary Table  13 ).
Before meta-analysis, we removed SNPs with low minor allele frequencies (<0. 01), low imputation quality (r 2 < 0. 8) and deviations from Hardy-Weinberg equilibrium (P < 10 -5 ). A total of 6,691,677 variants met these criteria and were kept in the final results. Results from individual GWASs were combined using an inverse variance-weighted fixed-effects meta-analysis in METAL software  48  , with correction for genomic control. Heterogeneity was assessed using the I 2 metric from the complete study-level meta-analysis. Between-study heterogeneity was tested using Cochran's Q statistic and considered significant at P ≤ 10 -3. The genome-wide significance threshold was set at the level of P = 5. 0 × 10 -8. LocusZoom (http://locuszoom. org/) was used to provide regional visualization of the results.
Enrichment of SCAD variants in regulatory regions. To calculate the enrichment of SCAD-associated SNPs among functionally annotated genomic regions, we retrieved available H3K27ac chromatin immunoprecipitation followed by sequencing (ChIP-seq) datasets (narrowPeak beds) in any tissue from ENCODE (https://www. encodeproject. org/ (ref. 50)) and single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) peak files (bed format) from the Human Enhancer Atlas (http://catlas. org/humanenhancer (ref. 24)). A complete list of datasets is available in Supplementary Table  16. For H3K27ac marks, bed files corresponding to the same tissue were concatenated and sorted before combining overlapping peaks using the bedtools (version 2. 29. 0) merge command. Variant enrichment was calculated using the GREGOR package (version 1.
Identification of variants with potential regulatory function. We used H3K27ac peaks in coronary arteries (as described above), open chromatin regions in healthy coronary arteries (obtained as previously described  35, 51  ) and open chromatin regions from merged snATAC-seq clusters, which were mapped fragments from snATAC-seq in 25 adult tissues that we retrieved from the Gene Expression Omnibus (GSE184462)  24  in bed format. Mapped fragments from all clusters representing >1% of cells in at least one arterial tissue (T lymphocyte 1, CD8 + , endothelial general 2, endothelial general 1, macrophage general, fibroblast general, vascular smooth muscle 2 or vascular smooth muscle 1) were extracted and grouped by annotated cell type as T lymphocytes, macrophages, fibroblasts, endothelial cells and VSMCs, respectively.
Genome coverage was calculated using the bedtools (version 2. 29. 0) coverage function. We detected peaks from bedGraph output using the MACS2 bdgpeakcall function (Galaxy Version 2. 1. 1. 20160309. 0) on the Galaxy webserver  52, 53. All peak files were extended 100 base pairs upstream and downstream using the bedtools (version 2. 29. 0) slop function. We detected overlaps of SCAD potential functional variants with relevant genomic regions using the findOverlap function from the rtracklayer package (version 1. 52. 1)  54. We used the Integrated Genome Browser (version 9. 1. 8) to visualize read density profiles and peak positions in the context of the human genome  55. Gene prioritization. Genes located within 500 kb of lead variants were annotated to prioritize the most likely causal genes.
To find the closest gene(s) from lead SNPs and genes overlapping with variants in the credible set of causal SNPs, gene coordinates were retrieved from Gencode release 38 and aligned to hg19 genomic coordinates (gencode. v38lift37. annotation. gff3. gz). Significant eQTL associations and all SNP-gene eQTL associations in the version 8 release of the GTEx database were retrieved from the GTEx website (www. gtexportal. org/ home/datasets). Colocalization of association with SCAD and eQTLs was evaluated using the R coloc package (version 5. 1. 0) with default values as priors. We considered that there was evidence for colocalization if H4 coefficients were >75% or if eQTL association was significant for SCAD lead SNPs and H4 was over 25%. TWASs were performed using the FUSION R/Python package  44.
Gene expression models were pre-computed from GTEx data (version 8 release) and were provided by the authors. Only genes with a heritability P < 0. 01 were used in the analysis. Both tools used linkage disequilibrium information from the European panel of phase 3 of the 1000 Genomes Project. Bonferroni multiple testing correction was applied using the p. adjust function in R (version 4. 1. 0). Significant capture Hi-C hits in aorta tissue were provided as supplementary data by Jung et al. 25. Genes associated with mouse cardiovascular phenotypes (code MP:0005385) were retrieved from the Mouse Genome Informatics database (www. informatics. jax. org)  56. We also queried the DisGeNET database, using the disge-net2r package (version 0. 99. 2), for genes with reported evidence in human cardiovascular disease (code C14) with a score of >0.
2, including "ALL" databases  57. In the absence of a missense variant, colocalization and TWAS criteria were given a tenfold weight compared with other criteria. At each locus, we prioritized genes fulfilling the largest number of criteria. In cases where several candidates were retained, we prioritized genes that were most likely to have a function in arterial disease (for example, expression in arterial tissues or exclusion of pseudo-genes). https://doi. org/10. 1038/s41588-023-01410-1 Druggability of prioritized genes. The druggability of the gene products identified through the GWAS was assessed by reference to the set of genes encoding druggable targets derived by Finan et al. 26  using ChEMBL version 17. Targets in this set are subclassified into: (1) the efficacy targets of approved agents and clinical phase drug candidates (tier 1); (2) genes encoding targets with known bioactive drug-like small molecule binding partners and those with substantial sequence with approved drug targets (tier 2); and (3) genes encoding secreted or extracellular proteins, proteins with more distant similarity to approved drug targets and members of key druggable gene families not already included in tiers 1 or 2.
Further lookups of approved and clinical phase targets were performed against ChEMBL 58 version 30 and the British National Formulary (accessed 9 April 2021). Note that identified drug targets can either be: (1) a single protein providing a 1:1 link with the causal gene nominated in a GWAS and post-GWAS analysis; (2) a protein complex where the causal gene can encode a member of the complex; or (3) a protein family with the causal gene being a member of the family. Bayesian network query of SCAD candidate genes. Gene expression data from the aorta artery, coronary artery, tibial artery and cultured fibroblasts were curated from version 8 of the GTEx database (ref. 28). Gene expression data from the mouse aorta was curated from the Hybrid Mouse Diversity Panel (HMDP)  27. Tissue-specific gene regulatory Bayesian networks were constructed from the GTEx and HMDP gene expression data using RIMBANET  29.
The Bayesian network from each dataset included only network edges that passed a probability of >30% across 1,000 generated Bayesian networks starting from different random genes. Bayesian networks were combined for the top GWAS hits query, and mouse gene symbols were converted to their human orthologs. Bayesian networks were queried for the identified top GWAS hits to identify their first-degree network connections and to determine connections between their surrounding subnetwork nodes. The directions of edges were informed by prior knowledge, such as eQTLs and previously known regulatory relationships between genes. Subnetworks were annotated by top biological pathways representative of the subnetwork genes using Enrichr with a false discovery rate of <0. 05.
Colocalization with other traits and diseases Summary statistics were retrieved from individual studies, as indicated in Supplementary Table  17. At each locus, we selected variants found in both SCAD and the other studies with a high quality of imputation (r 2 > 0. 9) and located within 500 kb from the SCAD lead SNP. COL4A1 and COL4A2 loci were separated by placing an equidistant border from SCAD lead SNPs for the inclusion of SNPs in the analysis. Signal colocalization was evaluated using the R coloc package (version 5. 1. 0) with default values as priors. We reported H4 coefficients indicating the probability of two signals sharing a common causal variant at each locus.
Heritability estimates and genetic correlation We used linkage disequilibrium score regression  21  implemented in the ldsc package (version 1. 0. 1; https://github. com/bulik/ldsc/) and SumHer  22  implemented in the LDAK software (www. ldak. org) to quantify the heritability explained by common variants or SNP-based heritability (h 2 SNP ) for SCAD and the degree of genetic correlation between SCAD and other diseases and traits. We also used SumHer to estimate the SNP-based heritability attributable to loci associated with SCAD at genome-wide statistical significance. Loci were defined as the 1 megabase region around lead SNPs in the GWAS meta-analysis. SNPs belonging to each locus were used as annotations to calculate the partitioned heritability. Two analyses were performed: one that considered separated loci and a second that aggregated all SNPs as one annotation.
6 within a 10,000 kb window). We used the multiplicative random-effects IVW method  59  implemented in the TwoSampleMR R package to estimate the associations of genetically predicted cardiovascular risk factors, including blood pressure (SBP and DBP), lipids (HDL, LDL and triglycerides), BMI, smoking liability and type 2 diabetes, with each of the outcomes of interest (SCAD or CAD). Estimates were scaled to a doubling in genetically predicted smoking risk, or to a one-unit increase in the genetically predicted trait for the continuous traits. We performed sensitivity analyses using the weighted median and MR-Egger methods to assess the consistency of estimates under alternative assumptions about genetic pleiotropy, as recommended  59. We also performed Cochran's Q test to assess the heterogeneity between estimates obtained using different variants.
Data availability Gene reference names and coordinates were retrieved from the GENCODE project through the European Bioinformatics Institute FTP server. gencode. v38. annotation. gff3 and gencode. v38lift37. annotation. gff3 files were used. eQTL data were retrieved from version 8 of the GTEx database (https://gtexportal. org/home/datasets). H3K27ac ChIP-seq datasets (narrowPeak beds) in any tissue were retrieved from ENCODE (https://www. encodeproject. org/). Single-nucleus ATAC-seq peak files (bed format) were retrieved from the Human Enhancer Atlas (http://catlas. org/humanenhancer). Open chromatin regions in healthy coronary arteries were generated from raw reads retrieved from the Sequence Read Archive (SRR2378591, SRR2378592 and SRR2378593). Raw snATAC-seq data in 25 adult tissues were retrieved from the Gene Expression Omnibus (GSE184462).
Acknowledgements We acknowledge clinical colleagues who referred SCAD cases, patients who supported this research and healthy volunteers included in this study. This study was funded by a  European Research Council  grant ( ERC-Stg-ROSALIND-716628  to N. B. -N. ); the  French Society of Cardiology , through  Fondation Coeur et Recherche  (to N. B. -N. ); La Fédération Française de Cardiologie  (to N. B. -N. ); the  British Heart Foundation  ( PG/13/96/30608  to D. A. and  SP/16/4/32697  to T. R. W. ); the  Leicester NIHR Biomedical Research Centre and BeatSCAD charity  (to D. A. ); the  National Health and Medical Research Council  (to R. M. G. ); the  Cardiac Society of Australia and New Zealand Cardiovascular Research Innovation Grant Australia  ( APP1161200  to R. M. G. , D. W. M. M. , D.
org/10. 1038/s41588-023-01410-1supported by grant  PT17/0019 , of the PE I + D + i 2013-2016, funded by  Instituto de Salud Carlos III  and the  European Regional Development Fund. Fondation Alzheimer  supported genotyping of the 3C study (Paris, France) to P. A. We thank  AstraZeneca's Centre for Genomics Research (Discovery Sciences, BioPharmaceuticals R&D)  for funding the sequencing of participants in cohort SCAD-UK I and providing bioinformatics support. We acknowledge the leadership of the  ESC-ACVC SCAD Study Group. The DISCO investigators thank the  French Society of Cardiology and French Coronary Atheroma and Interventional Cardiology Group  for support, as well as clinical research associates of the  Clermont-Ferrand University Hospital :  E. Chazot ,  C. Bellanger ,  L. Cubizolles ,  A.
Code availability Publicly available software and packages were used throughout this study according to each developer's instructions. No custom algorithms were generated for the study. The following software was used and can be downloaded or accessed online: Michigan Imputation Server (https://imputationserver. sph. umich. edu/index. html#), PLINK version 2. 0 (https://www. cog-genomics. org/plink/2. 0/), METAL (March 2011 version; http://csg. sph. umich. edu/abecasis/Metal/), LocusZoom (version 0. 12. 0; http://locuszoom. org/), FUSION (version released 15 November 2021; https://github. com/gusevlab/fusion_twas), R (version 4. 1. 0; https://cran. r-project. org/), RStudio (version 1. 2. 335; https:// posit. co/download/rstudio-desktop/), R packages (colorspace_2. 0-3, ggnewscale_0. 4. 7, corrcoverage_1.
2. 1, locuscomparer_1. 0. 0, coloc_5. 1. 0, dplyr_1. 0. 8, tidyr_1. 2. 0, ggrepel_0. 9. 1, RColorBrewer_1. 1-3, shades_1. 4. 0, rtracklayer_1. 52. 1, LDlinkR_1. 1. 2, ggplot2_3. 3. 5, GenomicRanges_1. 44. 0, GenomeInfoDb_1. 28. 4, IRanges_2. 26. 0, S4Vectors_0. 30. 2, BiocGe-nerics_0. 38. 0 and readr_2. 1. 2; available from https://cran. r-project. org/ and/or https://www. bioconductor. org/), disgenet2r_0. 99. 2 (https://www. disgenet. org/disgenet2r), bedtools (version 2. 29. 0; https://github. com/arq5x/bedtools2), Galaxy (https://usegalaxy. org/), the Integrated Genome Browser (version 9. 1. 8; https://www. bioviz. org/), GREGOR (version 1. 4. 0; http://csg. sph. umich. edu/ GREGOR/), the ldsc package (version 1. 0. 1; https://github. com/ bulik/ldsc/), SumHer implemented in the LDAK software (version 5.