dataesr/scientific-paragraphs-categorization huggingface dataset:
Results The genetic contribution to EGPA. We performed genome-wide association testing at 9. 2 million genetic variants in 534 cases and 6688 controls. The EGPA patients' clinical features are summarised in Table  1. Further details of the cohorts are presented in Supplementary Tables  1 2 3. Consistent with previous reports  2  , 176 (33%) were positive for ANCA, and of the 164 who were positive for specific ANCA by ELISA, 159 (97%) had MPO+ ANCA and five PR3 + ANCA. Genotyping was performed using the Affymetrix Axiom UK Biobank array and high-density genotype data was generated through imputation against the 1000 Genomes phase 3 reference panel (see the 'Methods' section). Despite attempting to control for population stratification by the inclusion of 20 genetic principal components (PCs) as covariates in the logistic regression model, the genomic inflation factor lambda remained elevated at 1.
7, 8 , and four exemplars are shown in Fig. 2. We cross-referenced the lead EGPA-associated variant at each locus with disease-associated variants in linkage disequilibrium from the NHGRI GWAS Catalogue (Supplementary Data 1). It is interesting that 7 of the 8 alleles associated with increased EGPA risk are also associated with increased physiological eosinophil count at genome-wide significance (Fig. 4a , Supplementary Table  4 ). Given that some associations with EGPA were detected by cFDR analysis using eosinophil count GWAS data, we considered the possibility that this result was a consequence of our analytical approach. We therefore performed an unbiased assessment of all eosinophilassociated variants for their effect on EGPA risk in our primary GWAS analysis (i. e. , agnostic of cFDR), and observed that the relationship between genetic effect on eosinophil count and EGPA risk held true (Supplementary Fig.
9 ). In addition, 5 non-MHC EGPA risk alleles also confer increased risk of asthma (Supplementary Table  4 ), and 2 confer increased risk of nasal polyps (Supplementary Table  10 , Supplementary Data 1), consistent with the association between EGPA and these traits. To identify potential candidate genes, we sought long-range interactions between the EGPA-associated variants and gene promoters and regulatory regions in promoter capture Hi-C datasets using the CHiCP browser (see the 'Methods' section). We emphasise that whilst Hi-C data can suggest a link between a disease-associated variant and a candidate gene, it does not provide conclusive evidence of causality. In addition, we identified genes for which the sentinel EGPA-associated variants (or their proxies in linkage disequilibrium (LD)) are expression quantitative loci (eQTLs) (Supplementary Data 2).
We sought external evidence from genomic databases (Supplementary Data 1-3) and the experimental literature (Supplementary Table  10 ) to provide corroboration for the candidate genes that we identified. For the majority of loci, we identified strong experimental evidence to implicate candidate genes in the pathogenesis of EGPA. Individual loci are discussed below. BCL2L11 and MORRBID. The sentinel EGPA-associated variant lies in an intron in ACOXL, near BCL2L11, that encodes BIM, a Bcl2 family member essential for controlling apoptosis, immune homeostasis and autoimmune disease  [17] [18] [19]  , and mast cell survival  20. The EGPA-associated variant also lies within MIR4435-2HG (MORRBID), that encodes a long non-coding RNA that regulates Bim transcription, controls eosinophil apoptosis and may be dysregulated in hypereosinophilic syndrome  21.
4, 5   Shaded cells show data from external sources: MPO+ AAV clinical data was derived from the EVGC AAV GWAS  15  , and rituximab response rates for MPO+ AAV from the RAVE study  53  and for EGPA from ref. 32. n. Points represent genome-wide significant conditionally independent variants associated with blood eosinophil count in the GWAS by Astle et al. 13  (where typed or reliably imputed in the EGPA dataset). Coloured lines represent estimated causal effect of eosinophil count on risk of EGPA from Mendelian randomisation (MR) methods. IVW inverse-variance weighted eosinophilia and eosinophilic inflammatory disease. Th s inference is possible because the genetic effects on eosinophil count were derived in an independent dataset of healthy individuals (and thus the eosinophil count in this cohort is not confounded by the presence of disease).
Of these, 294 (55%) were female and 240 (45%) male. Clinical characteristics are shown in Table  1. Three hundred and fifty two patients were ANCA-negative, 159 patients were MPO-ANCA+ve, and 5 were PR3-ANCA+ve. For 5 patients, there was no data on ANCA status. Thirteen patients were ANCA positive with either no data on specific antibodies to PR3 or MPO, or with positive ANCA immunofluorescence without detectable antibodies to PR3 or MPO. Genotype data for 6000 UK controls was obtained from the European Prospective Investigation of Cancer (EPIC) Consortium. Four hundred and ninety eight individuals with a history of asthma were excluded. After QC, 5465 individuals remained. In addition, we recruited and genotyped controls from 6 European countries (Supplementary Table  2 ). A further 150 patients with a clinical diagnosis of EGPA that fulfilled the MIRRA criteria were recruited from Germany and Italy for replication purposes, along with 125 controls from these countries.
In total, 142 cases were included in the study following the removal of poor quality samples, of these 43 were MPO ANCA+ ve. All individuals provided written informed consent. Genotyping and quality control (QC). Genomic DNA was extracted from whole blood using magnetic bead technologies at the Centre for Integrated Genomic Medical Research (Manchester, UK) according to manufacturer's instructions. Patients with EGPA and healthy controls were genotyped using the Affymetrix UK Biobank Axiom array according to the manufacturer's protocol. Genotyping of cases and non-UK controls was performed by AROS Applied Biotechnology (Aarhus, Denmark). Genotyping of UK controls had been performed previously by the EPIC-Norfolk consortium  39  , also using the Affymetrix Axiom UK Biobank array. Genotype calling was performed using the Affymetrix Powertools software.
Calling was performed in batches of contemporaneously run plates as per the manufacturer's advice. After genotype calling, genetic data was processed in the following sequence using PLINK v1. 9  40. Samples with a sex mismatch, abnormal heterozygosity, or proportion of missing variants > 5%, were removed. Variants with missing calls >2%, deviation from Hardy-Weinberg Equilibrium (p-value < 1 × 10 -6 ), or which were monomorphic were removed. The QC process was performed separately for each batch. The post-QC genotype data from each batch was then merged. Following this merger, duplicated or related samples (identified using Identity by State) were removed, and the genetic variant QC was performed again so that variants with missing calls >2% or deviation from Hardy-Weinberg Equilibrium (p-value < 1 × 10 -6 ) in the combined data were excluded.
Genotype calling and QC was performed as outlined above except that samples were processed as a single batch. Following these steps, 142 cases, 121 controls and 626,229 autosomal genetic variants passed QC (Supplementary Table  5 ). Association testing on the directly genotyped data. Case-control association testing on the 543,639 directly genotyped autosomal variants was initially performed using logistic regression with the SNPTEST software. Principal components (PCs) were included as covariates to adjust for confounding factors. We calculated PCs on the genotype matrix containing both cases and controls. Whilst this carries a small risk of masking true disease associations, we felt that this more conservative approach was appropriate since (i) the UK controls were genotyped in a different facility thereby potentially leading to confounding from a batch effect, and (ii) we did not have French controls.
QQ plots of expected test statistics under the null hypothesis of no genotype-disease association vs. the observed test statistics are shown in Supplementary Fig. 15. The genomic inflation factor (lambda, the ratio of the median of the observed chi-squared statistics to the median expected chi-squared statistic under the null hypothesis) was calculated. We assessed the effect of using increasing numbers of PCs as covariates on lambda. Using the first 20 PCs produced a reduction in lambda to 1. 09, with little benefit from the inclusion of further PCs. Pre-phasing and imputation. Imputation was performed using the 1000 genomes phase 3 individuals as a reference panel (data download date 7th July 2015). Prephasing was first performed with SHAPEIT  41  , and then imputation was performed using IMPUTE2  42, 43.
For the IMPUTE2 Monte Carlo Markov Chain algorithm we used the default settings (30 iterations, with the first 10 discarded as the 'burn-in' period). The 'k_hap' parameter was set to 500. IMPUTE2 was provided with all available reference haplotypes from the 1000 Genomes individuals, as the software chooses a custom reference panel for each individual to be imputed. Association testing following imputation. Association testing on the imputed data was initially carried out using the SNPTEST software. We used an additive genetic model (option '--frequentist 1'). Uncertainty in the imputed genotypes was taken into account in the association testing by using a missing data likelihood score test. Despite inclusion of the first 20 PCs as covariates, the genomic inflation factor for the association tests using the imputed data was 1.
Clearly, the cFDR method requires that genotype data for a given variant is available for both traits. The cFDR analysis was limited to variants that were directly typed in each GWAS. The cFDR analysis for EGPA conditional on asthma examined 74,776 variants and that for EGPA conditional on eosinophil count 513,801. Replication cohort analysis. Significant variants from the primary cohort analysis (identified either through the linear mixed model analysis or the cFDR method) were tested in the replication cohort using SNPTEST and including 2 principal components as covariates to control for population stratification. The results of the two cohorts were meta-analysed using the META software using an inversevariance method based on a fixed-effects model. Fine-mapping. To identify the most likely causal variant we performed finemapping as follows.
We computed approximate Bayes factors for each variant using the Wakefield approximation  45  , with a prior parameter W = 0. 09, indicating our prior expectation that true effect sizes (relative risks) exceed 2 only 1% of the time. Thus, we calculated posterior probabilities that each variant is causal, assuming a single causal variant per LD-defined region as previously proposed  46. Code to perform these steps is available at https://github. com/chr1swallacw/lyonset-al. HLA imputation. Two thousand seven hundred and seventy seven SNPs, 343 classical HLA alleles to 2 or 4 digit resolution and 1438 amino acid variants were imputed at 8 HLA loci (HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DPA1 and HLA-DPB1) from phased genotype data using the HLA*IMP:03 software  47. Association testing for HLA variants was performed with BOLT-LMM.
Narrow-sense heritability estimation. The narrow-sense heritability (h 2 ) of EGPA was estimated using LD-score regression  48. LD scores were calculated using the European 1000 Genomes Project reference panel. To estimate the contribution of the MHC to heritable phenotypic variance we performed a variance-components analysis using BOLT-REML  30. Promoter enhancer interaction mining. Long-range interactions between genetic variants associated with EGPA and gene promoter and regulatory regions were identified in promoter capture Hi-C datasets from a range of primary cell types  49  and cell lines 50 using the CHiCP browser  51. Data mining. Other traits associated with EGPA-associated loci were identified using PhenoScanner  52. Phenoscanner searches GWAS data from multiple sources including the NHGRI-EBI (National Human Genome Research Institute-European Bioinformatics Institute) GWAS Catalogue and NHLBI GRASP (Genome-Wide Repository of Associations Between SNPs and Phenotype) Catalogue, and accounts for LD between queried genetic variants and those in the catalogues of trait-associated variants.
MR analysis was performed using the Men-delianRandomization R package. Summary statistics were obtained for 209 conditionally independent variants associated with peripheral blood eosinophil count in a population study by Astle et al. 13. One hundred and ninety three of these were typed or imputed in the EGPA dataset. MR analysis was performed using the beta coefficients and standard errors for these 193 variants in the eosinophil count GWAS and in our EGPA GWAS (all EGPA vs. controls using BOLT-LMM). The beta coefficients from BOLT-LMM are distinct from the log(OR) obtained from logistic regression. Therefore beta coefficients from BOLT-LMM and their standard errors were transformed by dividing them by (μ × (1 -μ)), where μ is the case fraction, to provide estimates comparable to traditional log(ORs).
Acknowledgements This work was funded primarily by Project Grants from  Arthritis Research UK  ( 20593  to  Drs. Smith  and Lyons) and the  British Heart Foundation  ( PG/ 13/64/30435  to  Drs. Smith  and Lyons). Additional support was provided by the  NIHR Cambridge Biomedical Research Centre , the  West Anglia Comprehensive Research Network , a  Medical Research Council Programme Grant  ( MR/L019027/1  to  Dr. Smith ), a  Wellcome Trust   Investigator Award  ( 200871/Z/16/Z  to  Dr. Smith ), a  NIHR Senior Investigator Award  (to Dr. Smith), a  Wellcome Trust Senior Research Fellowship  ( WT107881  to  Dr. Wallace ), a  Medical Research Council  grant ( MC_UU_00002/4  to  Dr. Wallace ), a  Wellcome Trust Mathematical Genomics and Medicine Programme Studentship  (to  Dr. Liley ), a  Career Development Award  from the  Cambridge British Heart Foundation Centre for Research Excellence  and a  UK Research Innovation Fellowship  ( RE/13/6/30180  and  MR/S004068/ 1  to  Dr.
Metabolite Profiling Metabolite analysis was performed on a Waters Acquity UltraPerformance Liquid Chromatography machine with diode array detection (Waters ACQUITY UPLC-DAD) using methods and software described in the Waters Corporation user manual. The manual was adapted for OSR tissue  (Albert et al. , 2012; Deleu et al. , 2013). The AccQtag method was used to quantify amino acids, and the integration software Empower (Waters Corporation, Milford, USA) was used for analysis. Samples were resuspended in 100 ml distilled water. Subsequently, 5 ml were derivatized using AccQTag Ultra Derivatization Kit, according to the manufacturer's recommendations. An external standard of 100 mmol/L of each amino acid was run every 10 samples. Quantification of sugars was performed using a Gas Chromatography-Flame Ionization Detector (GC-FID) System from Agilent Technologies (Santa Clara, CA, USA) according to  (Lugan et al.
, 2009). The integrated Agilent software ChemStation Rev. B. 04. 02 was used for data analysis. Samples were resuspended in 50 ml pyridine (100%) with methoxamine hydrochloride (240 mmol/L), then derivatized with 50 ml MSTFA (N-methyl-N-(trimethylsilyl)trifluoro acetamide) (100%). An external Although most accessions started to germinate, defined as the radicle breaking through the testa, at 12 hai, main differences arise at T50 (required time to reach 50% of germination) with values that differ from 27 to 42 hai. The graph represents germination rates obtained for WOSR accessions C166, C129, C124, C033, and C032 for each time point tested  (2, 12, 36, and 72 hai). (B) WOS accessions seeds with representative germination rates (high, medium, or low; C129, C033, and C032, respectively) were photographed during the time course to show visual differences in germination stages: seed imbibition (12 h), radicle breaking through the testa (24 h), radicle growth and cotyledon expansion (36 h), emerged seedling (48 h), and green seedling (72 h).
Mean germination rates for each time point are written at the top right corner of each picture. (C) Dendrogram plot of WOSR accessions groups them in three clusters according to their germination kinetics. We used hierarchical clustering where Y-axes represent similarity based on Pearson correlation coefficients. (D) Biplot of principal component analysis (PCA) based on seed and germination traits shows that major contributors to variability in WOSR accessions (labeled in bold black) are seed germination kinetic parameters (blue boxes), whereas seed and seed imbibition traits show less contribution. Values were obtained from the GEVES automated phenotyping platform and measured for the C166, C129, C124, C110, C033, and C032 WOSR accessions. All traits are labeled in blue. All data were obtained using four biological replicates (25 seeds per replicate).
Calibration curves were created for all compounds of interest. Quality control samples (QCs) were run along with the tissue samples. Analysis was performed on a UPLC/ESI-MS/MS utilizing a Waters ACQUITY UPLC system, equipped with a binary solvent delivery manager and a sample manager coupled to a Waters Micromass Quattro Premier XE quadrupole tandem mass spectrometer via a Z-spray interface. MassLynx™ and QuanLynx™ (Micromass, Manchester, UK) were used for data acquisition and data analysis.
Hormone Quantification by HPLC-ESI-MS/MS The procedure for quantification of ABA and ABA catabolites, cytokinin, auxin, and gibberellins in plant tissue was performed as described in detail in  Lulsdorf et al. (2013). Samples were injected onto an ACQUITY UPLC ® HSS C18 SB column (2. 1 × 100 mm, 1. 8 µm) with an in-line filter and separated by a gradient elution of water containing 0. 02% formic acid against an increasing percentage of a mixture of acetonitrile and methanol (50:50, v/v). Briefly, the analysis utilizes the Multiple Reaction Monitoring (MRM) function of the MassLynx v4. 1 (Waters Inc. ) control software. The resulting chromatographic traces are quantified off-line by the QuanLynx v4. 1 software (Waters Inc. ) wherein each trace is integrated and the resulting ratio of signals (nondeuterated/internal standard) is compared with a previously constructed calibration curve to yield the amount of analyte present (ng per sample).
Sequencing Data Analysis Reads obtained from the sequencers were filtered to remove adapters. Reads were mapped to the reference genome (Genome assembly, AST_PRJEB5043_v1,  Chalhoub et al. , 2014)  using HISAT. Assembly and quantification of transcripts in each sample were performed using StringTie. From all sets of probes obtained from each WOSR variety, we selected all probes that were seen in at least one time point (74,019 coding genes remained out of 101,040). Differentially expressed genes lists were obtained by pairwise comparison of all transcripts across samples and conditions [cutoff -1 > log 2 fold change (FC) > 1]. For pipeline details, see  Pertea et al. , 2016.
Statistics and Data Analysis Hierarchical clustering was performed using the germination data shown in Figure  1A  by the R software package with the HCLUST method using Pearson correlation distance as a metric. Correlation matrices were calculated for seed trait data from the automatic phenotyping platform and for hormone profile data (Supplementary Table  S1 , Datasheet 1, 3) using R software and Pearson's correlation coefficient as the statistical metric. To find positive and negative correlations between hormones and germination, the germination data shown in Figure  1  was added as a continuous variable together with the hormone levels data. Principal component analysis (PCA) was performed using FactoMiner and factoextra R packages. Correspondence analysis shown in Figure  6C  was performed using COA tool in MeV software  (Chu et al.
, 2008). For k-means clustering of differentially expressed genes, we used Fuzzy K-means algorithm from XLSTAT software (Addinsoft) using a fuzziness coefficient of 1. Gene ontology analysis was performed using SeqEnrich software  (Becker et al. , 2017). Comparison of GOs obtained for each cluster was performed using SEACOMPARE tool included in AgriGO toolkit  (Du et al. , 2010).
The higher number of clusters with dynamics involving mainly transcriptional activation compared to transcriptional repression (seven out of nine) is consistent with our previous observation (Figure  6B ). After identifying the major patterns of gene expression during seed germination, we performed a hierarchical clustering on each k-means cluster. Later, we represented the corresponding heatmaps in a chronological order based on their expression patterns (Figure  7A ). The results of these analyses highlighted 12 hai again as a key All data were obtained using three pooled biological replicates (50 seeds per replicate). For complete value data set, see Supplementary Table  S1  (Datasheet 3). point for a major switch in transcriptome dynamics. Accordingly, no major transcriptional changes take place before this time point, with most genes showing low expression levels, except the ones categorized in clusters 8 and 9 (repression clusters).
(A) Correlation plot of seed traits showing a negative correlation of TSW, Lt0, Vt0, IS, MC and ER with seed germination traits. Data was obtained from the GEVES automated phenotyping platform. Positive and negative correlations are shown in blue and red, respectively. Size and color intensity are indicative of correlation coefficient values and when the p-value<0. 05 is indicated with an asterisk (*). All data were obtained using four biological replicates (25 seeds per replicate). (B) Quantification of TSW, (C) Vt0, (D) area and (E) Lt0 from C129 (medium GR36), C033 (medium GR36) and C032 (low GR36) WOSR accessions showed significant differences in these seed parameters (C129>C032>C033) but no correlation with germination rates. Statistic analyses of the different mean values of weight, volume, area and length, were done by one-way ANOVA analysis (nonparametric).
All data were obtained using three pooled biological replicates (50 seeds per replicate). For complete value data set see Supplementary Table  S1  (datasheet 3). S3  | Kinetics of RNA levels of genes related to ABA and GA metabolism and signaling. RNA levels according to the RNA-seq data generated in this paper for B. napus accessions C129, C33 and C32 (left to right in each graph). The five time points per accession were: dry seed, 2, 12, 36, 72 hai. Pooled samples of 30 seeds each were used with three biological replicates for each time point. B. napus gene codes and names are indicated in each graph. S1  | Seed trait analysis and metabolic and hormonal profiles of WOSR accessions during germination. S2  | Data of DEGs between C129, C033 and C032; k-means cluster and corresponding representative GOs.
ACKNOWLEDGMENTS We thank  Marie Héléne Wagner  and  Sylvie Ducournau  from  GEVES  ( Station Nationale d'Essais de Semences, Groupe d'Etude et de contrôle des Variétés et des Semences, Angers, France ) for the PHENOTIC platform data, the  Metabolic Profiling and Metabolomic Platform (P2M2)  for metabolic data (IGEPP, Rennes, France),  Frank Breuer  from  KWS SAAT AG (Einbeck, Germany ) for hormonal profiling analysis, and  Zeljko Micic  from  DSV Deutsche Saatveredelung AG (Salzkotten, Nordrhein-Westfalen, Germany ) for the multiplication of the different seedlots in the field. We also thank  Genomics4All (Madrid, Spain)  for the re-annotation of Brassica napus genome and for the differential expression analyses of the RNA sequencing data.