dataesr/scientific-paragraphs-categorization huggingface dataset:
3 ). For TMEM198, increased expression in the aorta was associated with an increase in spQRSTa, but an inverse relationship was observed in LV tissue. All other genes with significant findings in multiple tissues had concordant directions of effect. Non-coding variants may influence cardiac electrophysiology through effects on regulatory elements and chromatin folding. We used 40 kb and ~4 kb-resolution long-range chromatin interaction (Hi-C) datasets to identify potential target genes of regulatory variants  18, 19. Promoter interactions were identified at 17 (27. 9%) multi-ancestry loci in LV or RV tissues (Supplementary Data 11a, b). GWAS Analysis of Regulatory and Functional Information Enrichment with LD correction (GARFIELD) was used to test for enrichment of variants at DNase 1 hypersensitivity sites in specific tissues using European ancestry summary statistics.
The strongest enrichment was in fetal heart tissue (P < 7. 5 × 10 -36 ); however, additional tissues were identified, including fetal renal pelvis, adult heart and brain (Supplementary Fig. 4 ). With single nucleus Assay for Transposase-Accessible Chromatin using sequencing (snATAC-seq) data, we tested for enrichment of noncoding variants at open chromatin regions, to identify cell-type specific functional effects in adult heart, by utilizing Chromatin Element Enrichment Ranking by Specificity (CHEERS)  20, 21. Significant enrichment was observed across all variants in atrial and ventricular cardiomyocytes (Supplementary Fig. 5 ). Reconstituted gene-sets in Data-driven Expression-Prioritisation Integration for Complex Traits (DEPICT) software were used to prioritize potential candidate genes based on overlapping functional pathways  22.
Genetic correlation and overlap of GWS loci with other ECG measures LD Score Regression (LDSC) software was used to estimate genetic correlations (r g ) of spQRSTa and fQRSTa with ECG markers of cardiac conduction (PR interval), ventricular depolarization (QRS duration) and repolarization (QT and JT intervals)  28, 29. There was a high positive genetic correlation between spQRSTa and fQRSTa (r g = 0. 61). Weak positive correlations wereobserved with PR interval (r g = 0. 12, P = 6 × 10 -4 for spQRSTa; r g = 0. 19, P = 2. 2 × 10 -5 for fQRSTa). However, no statistically significant correlation was observed with the other ECG traits (Supplementary Fig. 6 ). We used additional approaches to interrogate genetic overlaps. First, lead variants reported for other resting ECG traits were extracted and overlap was reported if they mapped within spQRSTa locus boundaries (within r 2 > 0.
8%), 7 (11. 5%), 14 (22. 9%) and 12 (19. 7%) loci involving PR, QRS, HR, QT and JT, respectively (PP > 0. 9). Of the loci that shared effects with QT and JT, 8/14 (57. 1%) and 6/12 (50%) loci, respectively, also influenced QRS duration (Supplementary Data 15). The smallest P-value for variants at the NOS1AP locus in the spQRSTa multi-ancestry meta-analysis was 7. 3 × 10 -5. NOS1AP is the locus consistently reported with the strongest QT and JT associations. We performed a sensitivity (ventricular repolarization) and heart rate (HR). Overlap was declared if a lead variant for these ECG measures mapped to within ±500 kb or r 2 > 0. 1 of a lead variant at a spQRSTa locus. Some loci overlap with other ECG traits (not visualised here but presented in Supplementary Data 15). At seven spQRSTa loci, no overlap was observed with any ECG trait (blue circle bottom right).
Pleiotropy of genetic variants with other phenotypes We performed a phenome-wide association study (PheWAS) to identify associations of European ancestry lead and conditionally independent spQRSTa variants with 1301 clinical conditions in 395,758 unrelated individuals European-ancestry individuals. Data on clinical conditions were from hospital episode statistics. Significantly associated conditions included atrial fibrillation, bundle branch block (BBB), atrioventricular block (AVB), arterial embolism and thrombosis, and hypertension (Fig. 6 ). We also performed lookups of all multiancestry lead spQRSTa variants (and proxies) in Phenoscanner (v2), to determine if they appeared in GWAS reports for non-ECG phenotypes and diseases (Supplementary Data 16). Lead variants or proxies at 19 spQRSTa loci (31.
The raw phenotype was also analysed to calculate clinically meaningful effect sizes (on the degree [°] scale). Study level GWASs were performed using an additive genetic model, accounting for relatedness with appropriate software (e. g. BOLT linear mixed model [BOLT-LMM])  15  or by including a kinship matrix or pedigree  [64] [65] [66]. Poorly imputed genotypes were excluded (Rsq > 0. 3 or similar for IMPUTE) and a MAF > 0. 01 was applied, so that only high-quality variants were included in the study. Summary statistics for cohort level distributions of each ECG trait and covariates included in the GWAS model, are provided in Supplementary Data 3. Age, sex, RR interval, height, and body-mass index (BMI) were mandatory covariates in the GWAS model. In addition, as the QT interval is associated with the QRS-T angle and we wished to identify associations that were not primarily driven by this marker of ventricular depolarization and repolarization, the QT interval was also included as a covariate.
If pedigree data was not available, or if the chosen GWAS software did not correct for underlying population stratification, genetic principal components (PCs) were also included as covariates. Cohorts could also select additional covariates if relevant to their study, such as genotyping method or recruitment site. Cohorts comprising multiple ancestries performed separate analyses for each ancestry. Individuals were not included in the study-level GWAS if they had a prior diagnosis of heart failure, myocardial infarction, pacemaker or implantable cardiac defibrillator; were prescribed class I or III antiarrhythmics, QT-prolonging or digitalis medication; or were pregnant at the time of ECG acquisition. In addition, individuals were excluded if atrial fibrillation, BBB or a QRS duration greater than 120 ms, was present on their ECG.
Additional quality control of study-level data After submissions of results in a standardized format, quality control was performed using EasyQC (R package v9. 2)  67. Allele frequencies of all variants were compared to those reported in the reference panel used by the study for imputation. To identify analytical errors, QQ and P-Z-score plots were inspected, and summary statistics for β estimates and SE were compared across all studies. To identify potential uncorrected population stratification, the genomic-control inflation factor was calculated to identify test statistic inflation.
Association with tissue-specific gene expression To identify relationships between lead and conditionally independent variants (and their proxies), with tissue-specific gene expression, cis-eQTL data was extracted from the GTEx portal (v8)  17, 76, 77. Tissues included in these analyses were cardiac (LV and RAA) and vascular (coronary and aorta artery), for their known influence on cardiovascular disease. If a variant was also a lead cis-eQTL variant, colocalization analysis were performed at the locus using the R package COLOC, to determine whether the variant was causal in both the GWAS metaanalysis and the eQTL study  78. These colocalization analyses use Bayesian statistical methods to calculate a posterior probability (PP) for the variant being causal in both analyses (PP > 75%). To predict the effects of gene expression levels on spQRSTa and fQRSTa, we performed a TWAS using S-PrediXcan.
S-PrediXcan is an extension of the original software PrediXcan and infers results using GWAS summary statistics, thus removing the need for individual-level genotype and phenotype data  79. S-PrediXcan provides a precalculated transcriptome model database from GTEX-based tissues and covariance matrices of SNPs within each gene model (https://github. com/ hakyimlab/MetaXcan). We used European meta-analysis summary statistics for these analyses and tested for association in a total of 16,097 genes across LV, RAA and vascular tissues. A Bonferroni corrected threshold (0. 05/number of genes tested [16,097] = 3. 1 × 10 -6 ) was used to declare significance and results are only reported when more than one SNP was included in the model. Tissue-and cell-type specific regulatory elements GARFIELD (v2) was used to identify tissue-specific enrichment of variants at DNase I hypersensitivity sites  80.
GARFIELD annotates variants with data from the ENCODE, GENCODE and Roadmap Epigenomics projects and calculated odd ratio using a generalised linear model framework  80. Chromatin interaction data was used to identify target genes of regulatory variants (RegulomeDB score ≤3b) in LV and RV tissues. First, using FUMA GWAS (Functional Mapping and Annotation of Genome-Wide Association Studies) software (v1. 3. 6), overlap was identified between lead and conditionally independent variant, and pre-processed loops determined by Fit-Hi-C pipelines  18, 81. An FDR threshold <0. 05 was used to report results. In addition, we performed the same analysis using loops called from recently published Knight-Ruiz normalised 5 kb, 10 kb and 25 kb resolution promotor capture Hi-C data  19. To identify cardiac cell-type specific enrichment of non-coding variants, we utilized accessible chromatin information from snATACseq data, for atrial and ventricular cardiomyocyte, smooth muscle, endothelial, adipocyte, macrophage, fibroblast, lymphocyte and nervous cells  21.
Candidate gene prioritisation and pathway enrichment To identify additional candidate genes at each locus, DEPICT (v3) software was used, that prioritizes genes according to common functional pathways. DEPCT calculates a membership probability for each gene within 14,461 reconstituted gene-sets  22. Additional analyses were performed using DEPICT to identify pathway enrichment of these genes using Gene-Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG), REACTOME and the Mouse genetics initiative (MGI) data. DEPICT also performs gene-set tissue enrichment analyses using annotations from human Affymetrix microarray probes. For all analyses, an FDR < 0. 01 was used to identify significant results. To visualise GO biological processes from the DEPICT spQRSTa multi-ancestry meta-analysis output were analysed using the REVIGO web application to remove redundant terms and cluster related nodes  23.
They were subsequently visualised using Cytoscape (v3. 8. 2)  82. The output of all bioinformatic analyses were pooled and supplemented with trait relevant information from Online Mendelian Inheritance in Man (OMIM)  24  and International Mouse Phenotyping Consortium  25  (IMP, www. mousephenotype. org) databases, the Human Protein Atlas 26 (www. proteinatlas. org) and a PubMed literature review (Supplementary Data 14). We also performed a look up of each lead variant in the Open Targets Genetics "Locus to Gene" machine learning pipeline, which uses supervised learning to weight evidence from different sources and prioritize genes at a locus  27. This database does not include trait-specific information in the pipeline and therefore it is used to supplement the analyses performed for this work.
Association between genetically determined spQRSTa and fQRSTa with cardiovascular diseases A PRS was calculated for each trait using lead variants from the European meta-analysis, to test for association with atrial fibrillation, stroke, coronary artery disease, conduction disease, heart failure, nonischaemic cardiomyopathy and ventricular arrhythmia. Analyses were performed in individuals of European ancestry in UKB (N = 395,758). Participants included in the GWAS meta-analysis were excluded, along with related pairs up to the 2nd-degree (kinship coefficient <0. 0884). To take advantage of genotype probability data in BGEN format, PRSice-2  91  was used. The PRSs were calculated by summing the dosage of lead variants weighted by the effect size from the corresponding raw-phenotype meta-analysis.
Relationship between spQRSTa and HCM and DCM The TwoSampleMR R package (v0. 5. 6), was performed to test for association of spQRSTa with cardiomyopathies, using data from cohorts with HCM and DCM  92. First, summary statistics from a previously reported multi-ancestry (2780 cases, 47,486 controls) and European (2,244 cases, 42,668 controls) HCM GWAS were retrieved  32. Summary statistics for multi-ancestry sarcomere positive (871 cases, 20,142 controls) and sarcomere negative (1874 cases, 27,344 controls) HCM GWAS were also extracted. The HCM GWAS included UKB participants as controls; however, there was no overlap of individuals included in the spQRSTa meta-analyses. Multi-ancestry (61 variants) and European (51 variants) IVs were constructed from GWS variants in the rank-based inverse normal transformed spQRSTa meta-analysis, with the corresponding β, SE and P retrieved from the untransformed meta-analysis to facilitate clinical interpretation.
Acknowledgements All study acknowledgements can be found in Supplementary Note 3, and study funding information in Supplementary Note 4. W. J. Y acknowledges support by an  MRC  grant  MR/R017468/1. A. A is supported by an  NHLBI  award  K24HL148521. A. L. P. R is supported in part by  CNPq  ( 310679/2016-8  and  465518/2014-1 ) and by  FAPEMIG  ( PPM-00428-17  and  RED-00081-16 ). E. T-S is supported by  Brazilian Ministry of Health (National Program of Genomics and Precision Health) ,  Brazilian Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq) ,  Fundação de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG ,  RED00314-16 ). M. F. L-C is supported by  Brazilian Ministry of Health  ( DECIT/MS, EPIGEN-Brazil Project ),  Brazilian Ministry of Science and Technology (Financiadora de Estudos e Projetos (FINEP) ,  Brazilian Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq) ,  Fundação de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG) ).
C. H is supported by an  MRC University Unit Programme  Grant  MC_UU_00007/10  (QTL in Health and Disease). C. A and A. B were supported by  NIH  grants  R01HL142825 , and  U01HG007416. D. D was supported by  NIH  grants  R01HL138737  and  T32HL139439. N. G, C. N and T. H are supported by the  Novo Nordisk Foundation  (Grant number  NNF18CC0034900 ). H. M is supported by the  CHARGE infrastructure grant  ( HL105756 ). J-W. B is funded by the  Research Project CVON-AI  ( 2018B017 ) financed by the  PPP Allowance made available by Top Sector Life Sciences & Health  to the  Dutch Heart Foundation  to stimulate public-private partnerships. D. M-K is supported by  Dutch Science Organization (ZonMW-VENI  Grant  916. 14. 023 ). J. F. W acknowledges support from the  MRC Human Genetics Unit programme  grant, "Quantitative traits in health and disease" ( U.
MC_UU_00007/10 ). L. R is funded by a  University of Edinburgh  studentship. P. N is supported by the  MRC Human Genetics Unit programme  grant, "Quantitative traits in health and disease" ( U. MC_UU_00007/10 ). X. S was in receipt of a  Starting Grant  ( 2017-02543 ) from the  Swedish Research Council (Vetenskaprådet). This research has been conducted using the UK Biobank Resource under Application Number  8256. This research used data assets made available by  National Safe Haven  as part of the Data and Connectivity National Core Study, led by  Health Data Research UK  in partnership with the  Office for National Statistics  and funded by  UK Research and Innovation  (grant ref  MC_PC_20029 ). Copyright © (2022),  NHS Digital. Re-used with the permission of the  NHS Digital  [and/or UK Biobank].
Data availability Summary statistics from each genome-wide association study metaanalysis have been uploaded to the NHGRI-EBI Catalog of human genome-wide association studies website, https://www. ebi. ac. uk/ gwas/. (Study accession numbers GCST90246318, GCST90246320, GCST90246322, GCST90246324 for Multi-ancestry, European, African and Hispanic ancestry meta-analyses for the spatial QRS-T angle, respectively. Study accession numbers GCST90246319, GCST90246321, GCST90246323, GCST90246325 for Multi-ancestry, European, African and Hispanic ancestry meta-analyses for the frontal QRS-T angle, respectively). Data relating to UK Biobank will be return to the study. The UK Biobank will make these data available to all bona fide researchers for all types of health-related research that is in the public interest, without preferential or exclusive access for any person.
5. With the availability of an expanded monitoring network of PM 2. 5 and its growing importance in the Global Burden of Disease study, additional investigations are required to understand better the association between PM 2. 5 , O 3 , and mortality. In this study, we used multiple statistical approaches to systematically examine whether PM 2. 5 and O 3 have an interactive effect on total, cardiovascular, and respiratory mortality, and we investigated potential factors that might influence this association at a global level, such as region and season. We used data from the Multi-Country Multi-City (MCC) Collaborative Research Network (https://mccstudy. lshtm. ac. uk/), which collects vast time series data in the specialty of environmental health. Using this dataset, the research group has previously estimated significant mortality hazards of short term exposure to PM 2.
Data collection We collected daily air pollution data and mortality records from the MCC database, as described previously. 18 19  Data on daily PM 2. 5 and O 3 concentrations were available for 372 cities across 19 countries and regions. Supplementary eFigure 1 and eFigure 2 show the geographical locations of the cities, along with the corresponding average annual concentrations of PM 2. 5 and O 3. Mortality data were obtained from the national or regional death registries of each country and region. Causes of death were classified using ICD-10 (international classification of diseases, 10th revision) codes. In each location, mortality is represented by daily counts due to either non-external causes (A00-R99) or, when not available, all causes. We also collected mortality data on two major causes from 14 countries: cardiovascular disease (I00-I99) and respiratory disease (J00-J99).
20  Mortality data on cardiovascular and respiratory diseases were not available in Australia, Chile, Germany, Israel, and Romania. We obtained daily concentrations of PM 2. 5 (24 hour average) and O 3 (eight hours maximum) from fixed site monitoring stations and averaged these at city level. Daily mean temperature and relative humidity were obtained from the local meteorological bureau or other government sectors. 18  Overall, the percentages of missing daily observations for all cause mortality, PM 2. 5 , O 3 , and temperature were 0. 20%, 10. 02%, 8. 04%, and 2. 10%, which were omitted from subsequent analyses.
Secondly, we tested the influence of longer lags of temperature modelling (using a natural spline function with six degrees of freedom), including the present day and the moving average of the present and the previous 7, 14, and 21 days. We conducted all statistical analyses in R software (version 3. 3. 1), using the mgcv and dlnm packages for fitting first stage models, and the mixmeta package for performing multilevel meta-analyses. Results are presented as percentage change in (central estimates) or relative risk of mortality, with respective 95%.
Data sharing: Data have been collected within the MCC (Multi-Country Multi-City) Collaborative Research Network (https://mccstudy. lshtm. ac. uk/) under a data sharing agreement and cannot be made publicly available. Researchers can refer to MCC participants listed as coauthors for information on accessing the data for each country. The lead authors (HK and AG) affirm that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, www. isimip. org) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include highresolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems.
Experiments and underlying rationale All ISIMIP experiments are described by specifying the underlying set of climate-related forcings and 160 direct human forcings. In the following the individual experiments are introduced by defining the combination of both types of forcing data sets, where the associated specifiers to be used in the file naming are indicated in brackets (CRF specifier + DHF specifier) in the subheadings naming the experiments. The different combinations of the default sets of ISIMIP3a CRFs  ('obsclim', 'counterclim')  and DHFs  ('histsoc', '2015soc', '1901soc', '1850soc', 'nat')  are sketched in Figure  1  and described in 165 more detail below. Some of the forcing data sets are mandatory: i. e. if impact models account for the forcing, the specified dataset must be used; if an alternative input data set is used instead, the run cannot be considered an ISIMIP simulation.
, 2022; Messager et al. , 2016)  and HydroLAKES v1. 0  (Khazaei et al. , 2022; Messager et al. , 2016)  datasets  (Golub et al. , 2022). Each hypsographic curve consists of 11 data pairs. Level refers to the depth of the lake taking the lake bottom as the reference. Volume is the volume at the corresponding level.
Lake area at different depths optional The gridded data set describes the lake area at different depths of one hypothetical lake representing the typical characteristics of all real lakes in the grid cell according to the GLOBathy  (Khazaei et al. , 2022; Messager et al. , 2016) and HydroLAKES  (Khazaei et al. , 2022; Messager et al. , 2016)  datasets  (Golub et al. , 2022). Each hypsographic curve consists of 11 data pairs. Level refers to the depth of the lake taking the lake bottom as the reference.
5°(~60 km) to determine which resolution is required to improve the 290 impact model simulations compared to observed impact indicators. The evaluation of these historical sensitivity experiments will inform future downscaling activities for the GCM climate forcing data including future projections. The '1800arcsec' experiment is included as a reference, as the aggregated CHELSA-W5E5 data differ from the standard W5E5 data at the same resolution (see section 3. 1). So far the experiments have been added to the agriculture, lakes, global and regional water, regional 295 forests, terrestrial biodiversity, and labour protocol. However, they may be added to other sectors, too. The inclusion of the experiment is only constrained by the restricted set of variables included in CHELSA-W5E5. We do not provide spin-up data for the experiments.
As a first step, it has to be demonstrated that the processes represented in the impact model can explain the observed changes in the affected system, i. of the Global Soil Wetness Project phase 3 (GSWP3) dataset  (Kim, 2017) , bias-adjusted to W5E5 v2. 0 in order to reduce discontinuities at the 1978-1979 transition. The method used for this bias adjustment was ISIMIP3BASD v2. 5  (Lange, 2019 (Lange, , 2021)). The GSWP3 dataset is a dynamically downscaled and bias-adjusted version of the Twentieth Century Reanalysis version 2 (20CRv2;  Compo et al. , 2011).