dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, genes in the expanded gene list that have reported eQTLs, but lack cell-specific chromatin interactions, should be further examined in the context of specific stimuli or environments. In conclusion, we performed a GWAS of Sjögren's of European ancestry, then leveraged fine mapping and bioinformatic databases to determine the functional potential of GWS Sjögren-SNPs. Evaluating the local regulatory networks of each region in the context of immune cell type-specific eQTLs revealed that most of the regions have broad regulatory networks collectively involving >40 up-and downstream of the index gene. Further, pathway analyses of genes within these expanded local regulatory networks have diverse functional potential and may influence the pathogenesis of disease by altering cellular functions that might not have been otherwise considered (Supplementary Fig.
Methods Subjects. We obtained genotype data from a total of 3,885 Sjögren's cases and 23,725 population controls of European ancestry collected from the United States of America, Australia, Austria, France, Germany, Hungary, Italy, Norway, Spain, Sweden, Switzerland, and United Kingdom (Supplementary Data 2). Genotype data were organized into six datasets: Dataset 1 (DS1; Phase 1)  16  , DS2 (Scandinavian-1), DS3 (Non-Scandinavian), DS4 (SICCA; phs000672. v1. p1 [https://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi?study_id=phs000672. v1. p1])  19  , DS5 (Scandinavian-2), and DS6 (PRECISESADS)  82  (Fig. 1a ; Supplementary Data 2). In addition, we performed meta-analysis using case-control ImmunoChip 1. 0 data in DS7  16  (Supplementary Data 2). All datasets were subjected to quality control measures described below.
Then, this study was conducted in accordance with the OMRF Institutional Review Board approval. Genotyping and QC. Ilumina Infinium Omni1 or Omni2. 5 Genome-Wide Genotyping Array kits were used to genotype DS1  16  and DS4  19. Illumina OmniExpress kit was used to genotype DS2, DS3, and DS5. DS6 was genotyped using the Illumina Global Screening Array (GSA) kit following Infinium chemistry. DS7 was genotyped using the ImmunoChip 1. 0 array  16. Strict quality control procedures were applied to each dataset: (i) subjects within well-defined cluster scatter plots; (ii) SNP having MAF > 1%; (iii) SNPs and samples each with call rate >95%; (iv) controls with Hardy-Weinberg proportion test with P > 0. 001; (v) cases and controls with differential missingness (P > 0. 001) were selected for downstream analysis.
PLINK (v1. 9) was used to merge the quality controlled DS1, DS2, DS3, DS4, DS5, and DS6 into a single merged dataset, then quality control procedures were applied to the merged genotyped dataset  85. Individual genotyped data were excluded from the merged genotyped dataset and DS7 if it had <95% call rate and excessive heterozygosity (>5 s. d. from the mean). PLINK (v1. 9) was used to determine relatedness within the remaining samples using identity-by-descent (IBD) estimates  85. One individual from each pair was removed if the proportion of the alleles that shared IBD was >0. 4. Base pair positions were assigned according to the GRCh37/hg19 version of the human genome reference sequence. After quality control, 3,232 cases and 17,481 population controls in the merged genotyped dataset, and 619 cases and 6171 controls in DS7 were available for subsequent analyses (Fig.
1a ; Supplementary Data 2). Assessment of population stratification. Population substructure within the PI1 and DS7 datasets was determined using EIGENSTRAT [https://www. hsph. harvard. edu/alkes-price/software/] with 53,108 (merged genotyped dataset) or 16,596 (DS7) independent markers (r 2 < 0. 20 between variants), respectively  86. Eigenvectors distinguish five continental ancestral populations from the 1000 Genomes Project samples: East Asian (CHB, JPT, CHS, CDx, KHV), Admixed American (MXL, PUR, CLM, PEL), European (CEU, TSI, FIN, GBR, IBS), African (YRI, LWK, GWD, MSL, ESN, ASW), and South Asian (GIH, PJL, BEB, STU, ITU)  87. Principal component analysis (PCA) was used to identify and remove outliers defined as having standard deviations greater than 6 (s. d. > 6) from the mean. Case and control samples used in the merged genotyped dataset and DS7 were plotted by principal components (PC) 1 and PC2, consistent with a European ancestral population.
Imputation. Whole-genome imputation was performed using the Haplotype Reference Consortium panel version 1. 1 (n = 32,611; human haplotypes = 64,976; SNPs = 39,235,157) on the Michigan Imputation Server, a free service for largescale population studies [https://imputationserver. sph. umich. edu/]  88. Each dataset (DS1-DS7) was imputed individually, using only the variants that passed quality control. SHAPEIT [https://odelaneau. github. io/shapeit4/] was used for prephasing and Minimac3 [https://github. com/Santy-8128/Minimac3] for the imputation  89, 90. To be included in analyses, imputed variants had to meet or exceed the imputation quality score (INFO) > 0. 5 and quality control criteria described above. Imputed variants from datasets DS1-DS6 were merged to create dataset PI1 (Fig. 1a ) and subjected to quality control criteria described above.
Whole-genome imputation increased the number of SNPs tested for single-marker trait association from 101,574 to 6,257,359. Statistical analysis. Logistical regression models were computed using PLINK to test for single marker SNP-Sjögren's association in the post-imputation PI1 dataset  85. The additive genetic model was calculated for the 22 autosomal chromosomes, while adjusting for the first four principal components. PC1-PC4 accounted for >80% of the variation in the dataset after quality control. To analyze imputed SNPs on the X chromosome, two separate logistical regression analyses were performed, one for each sex, using the same qualitycontrolled samples from the analysis of the autosomal genome and adjusting for the first four principal components. A dosage compensation model was used to account for inactivation of chromosome X, thereby assigning the SNPs as 0 or 2 (i.
org/haploview/ haploview], and a threshold of r 2 > 0. 8  94. Polygenic risk score calculation. Genotyped individuals were randomly separated into a training dataset (2/3rd of the individuals) and testing dataset (1/3rd of the individuals) to calculate individual polygenic risk scores (PRS). PRSs were calculated for both the Sjögren-All and Sjögren-Ro + dataset (Fig. 1a ). Anti-Ro autoantibody positivity was determined and reported by each respective cohort as described above. To correct for population stratification, PCA analyses were performed for each of the datasets separately, then the first three PCs were used as covariates in the PRS analysis. Data was pruned to remove highly correlated SNPs using independent pairwise analysis with a window size of 50 kb, step size or variant count of 5, and r 2 > 0.
2 in PLINK (v1. 9)  85, 94  , then PRSice-2 v2. 3. 3 [https:// www. prsice. info/] 95 was used to calculate PRSs. After pruning, the Sjögren-All training dataset included 2166 cases and 11,638 population controls and testing dataset included 1076 cases and 5826 population controls. The Sjögren-Ro + testing dataset included 1100 cases and 11,544 population controls and training dataset included 618 cases and 5,894 population controls. In subsequent analyses, the HLA region (24-37 Mb) was dropped and PRSs were recalculated using PRSice-2. PRSs were generated at multiple P value thresholds (P T ) ranging from P = 0. 001 to P = 1. The best-fit thresholds were used to predict Sjögren's status under logistic regression, while adjusting for the three PCs using the general linear model (glm) function in R 3.
6. 0. Distribution of PRSs among cases and controls were plotted using R. Area under the receiver operating characteristic curves (AUROC) were used to evaluate the accuracy of the two PRS models to distinguish the case from control status using the pROC package [http://web. expasy. org/pROC/] in R v4. 0. 4 [http://www. r-project. org/]. Genetic correlation with autoimmune diseases. Cross-trait linkage disequilibrium score regression (LDSC) was performed on the GWAS summary statistics using LDSC software [https://github. com/bulik/ldsc]  96 to estimate the degree of genetic correlation between Sjögren's and 19 other immune-mediated diseases and other common traits. Publicly available summary statistics were obtained for 19 other diseases and traits [https://alkesgroup. broadinstitute. org/ sumstats_formatted/?C=S;O=A].
The LD Score computed using European data from the 1000 Genomes Project was used as the reference panel [https:// alkesgroup. broadinstitute. org/LDSCORE]  97. Analysis was limited to the Sjögrenassociated GWS SNPs imputed using HapMap3 as implemented in the LDSC software. Traits with a genetic correlation (rg) of p < 0. 05 were considered as genetically correlated with Sjögren's. Cells and tissue-specific epigenetic enrichment and Sjögren's heritability. Partitioned LD Regression (LDSC)  96  was performed to estimate enrichment of Sjögren's risk loci in cell-type-specific enhancer peaks (Fig. 1B ). Cell type-and tissue-specific annotations were downloaded from EnhancerAtlas2 database [http://www. enhanceratlas. org/]  35. LD scores were calculated based on Enhancer-Atlas2 custom annotation, linkage pattern derived from European samples from 1000 Genomes Project and the baseline model suggested by Finucane et al.
97. Epigenomic enrichments of genetic variants were tested using GREGOR [http:// csg. sph. umich. edu/GREGOR/] 98. Sjögren's variants with P < 5 × 10 -8 were tested for the enrichment in 4035 genomic features. The saddle-point approximation was used to estimate the enrichment P value by comparing it to the distribution of permuted statistics  97. The enrichment was considered significant if the enrichment P value was less than the Bonferroni-corrected threshold of 1. 23 × 10 -5 (nominal P = 0. 05 of 4035 tested genomic features). Imputed narrow peak data from the Roadmap Epigenomics Project was used for this purpose  33, 34. Transcription factor enrichment in Sjögren's risk loci. Regulatory Element Locus Intersection (RELI) algorithm  37  was used to identify transcription factors intersecting with Sjögren's risk loci (Fig.
org/genform. php?type=yourdata] was used to plot logistic regression results for each independent Sjögren-associated region  99. Bayesian analyses were used to further refine the association signal in each GWS region. Posterior probability (PP) estimates were calculated for each SNP in the GWS region as if the SNP was a true putative casual variant. Then, a credible set of SNPs (PP > 95%) was defined using the Trinculo software package [https://sourceforge. net/projects/ trinculo/files/] 100,101. Fine-mapping was also performed separately using EcholocatoR R package [https://github. com/RajLabMSSM/echolocatoR] 44 that uses SuSiE and PolyFun +SuSiE and summary statistics to analyse SNPs from a 1 Mb window centered on each designated index SNP (i. e. , ±0. 5 Mb flanking the SNP). Both approaches used the Bayesian model and provided posterior probability (PP) to each SNP on a scale from 0 to 1 and defined the credible sets based on PP ≥ 0.
6). Then, it provides a list of all known SNPs, regardless of presence in the GWAS input, that are in LD (r 2 ≥ 0. 6) with the independent SNPs for further annotation. Index SNPs were defined as independent SNPs with an r 2 < 0. 1. Independent Sjögren-associated genomic risk loci were defined as regions that are both in LD with an index SNP and separated by >250 kb from another region of association. Hi-C data of GM12878 and eQTL from DICE database in FUMA was utilized for chromatin interaction mapping with a false discovery rate (FDR) cutoff of <1 × 10 -5 to define the significant interactions. FUMA performs chromatin interaction mapping, overlapping independent significant SNPs and SNPs in LD with one end of significantly interacting regions in user-defined tissues/cell types. Predicted enhancer regions were selected in any of the 111 tissues/ cell types from the Roadmap Epigenomics Project  34  and the promotor region (250 bp up and 500 bp downstream of transcription start site) as predicted by Roadmap Epigenomics Project  34.
Circos software in FUMA was used to visualize associated regional plots, genomic risk loci, chromatin interactions, and eQTLs mappings. Candidate functional SNPs from the 95% credible sets were further prioritized based on data mining from HaploReg [https://pubs. broadinstitute. org/mammals/ haploreg/haploreg. php]  43 , RegulomeDB [https://www. regulomedb. org/regulomesearch/] 41,42 , and Open Targets [https://www. opentargets. org]  103 databases. Publicly available repositories, including eQTL Catalogue 104 , single-cell RNA sequencing eQTLs  105  , Database of Immune Cell eQTLs and Epigenomics (DICE)  39, 40  , and minor salivary gland (GTEx v8)  106  were mined, then FUMA was used to map all tested SNPs to genes up to 1 Mb apart that show a significant (P < 0. 05) cis-eQTL association.
We also used QTLbase [http://mulinlab. tmu. edu. cn/qtlbase] to retrieve significant (P < 0. 05) cis-eQTL information on SNPs and mapped genes. Pre-processed capture Hi-C data for the candidate functional variants from 14 immune cell types and the GM12878 cell line were obtained from the 3D Genome Browser [http://3dgenome. fsm. northwestern. edu/chic. php]  107. Promotor and enhancer sites located 5 kb on either side of each SNP were queried and the start and end coordinates for each loop were obtained. Consensus loops for each SNP were defined, then the start and end coordinates of each consensus loop were visualized in the UCSC Genome Browser [http://genome. ucsc. edu/] to find nearby or overlapping genes. Risk loci and IMPACT annotation. To have a better understanding of the link between the Sjögren's risk loci and transcription factor binding sites, the risk loci were annotated against ~700 cell-type-specific active transcription factor binding sites from the IMPACT model (Fig.
1b )  108. First, the total number of active transcription factor binding sites was calculated for each genomic position. A genomic position with an IMPACT score >0. 5 was reported as an active transcription factor binding site. The candidate SNPs in Sjögren's risk loci against the total active binding sites were then annotated. Functional pathway analysis. Candidate functional genes that coalesced with eQTLs and TAD interactions were selected for canonical pathway analysis and disease and function analysis using the Ingenuity Pathway Analysis Program (version: 60467501, Ingenuity System). through a federal contract from the National Institutes of Health (NIH) to Johns Hopkins University (contract numbers HHSN268200782096C, HHSN268201100011I, HHSN268201200008I). Funds for genotyping were provided by the NIDCR through CIDR's NIH contract.
Assistance with data cleaning and imputation was provided by the University of Washington. We thank investigators from the following studies that provided DNA samples for genotyping: the Genetic Architecture of Smoking and Smoking Cessation, Collaborative Genetic Study of Nicotine Dependence (phs000404. v1. p1); Age-Related Eye Disease Study (AREDS) -Genetic Variation in Refractive Error Substudy (phs000429. v1. p1); and National Institute of Mental Health's Human Genetics Initiative (phs000021. v3. p2, phs000167. v1. p1). We thank the many clinical collaborators and research participants who contributed to this research. Phs000196. v3. p1: Investigators and Parkinson Disease patients that contributed to this Genome-wide Association Study of Parkinson Disease. phs000187. v1. p1: Research support to collect data and develop an application to support the High Density SNP Association Analysis of Melanoma project was provided by 3P50CA093459, 5P50CA097007, 5R01ES011740, and 5R01CA133996.
Acknowledgements We thank all the research and clinical staff, consortium investigators, and study participants (detailed in Supplementary Information), and funding agencies who made this study possible. The content of this publication is solely the responsibility of the authors and does not represent the official views of the funding agencies listed below. Research reported in this publication was supported by the  National Institutes of Health (NIH) :  R01AR073855  (C. J. L. ),  R01AR065953  (C. J. L. ),  R01AR074310  (A. D. F. ),  P50AR060804  (K. L. S. ),  R01AR050782  (K. L. S),  R01DE018209  (K. L. S. ), R33AR076803 (I. A. ),  R21AR079089  (I. A. );  NIDCR Sjögren's Syndrome Clinic  and Salivary Disorders Unit were supported by  NIDCR Division of Intramural Research at the National Institutes of Health  funds -Z01-DE000704 (B.
We also thank  Rezvan Kiani Dehkordi ,  Karolina Tandre ,  Käth Nilsson ,  Marianne Eidsheim ,  Kjerstin Jacobsen ,  Ingeborg Kvivik  and  Kjetil Bårdsen  for collecting patient blood samples. We acknowledge the  SNP&SEQ Technology Platform, Uppsala , part of  National Genomics Infrastructure (NGI) Sweden , for genotyping of Scandinavian samples, and the  Swedish Twin Registry  for access to data. The  SNP&SEQ Technology Platform  was supported by  Science for Life Laboratory, Uppsala University , the  Knut and Alice Wallenberg Foundation  and the  Swedish Research Council. Last, we thank the investigators for the following dbGaP studies: Phs000428. v2. p2:This study used control data from the Health and Retirement Study in dbGaP (phs000428. v2. p2) submitted by  David Weir, PhD  at the  University of Michigan  and funded by the  National Institute of Aging   RC2 AG036495  and  RC4 AG039029.
Phs000672. v1. p1:Genotype data from the  Sjögren's International Collaborative Clinical Alliance (SICCA) Registry  was obtained through dbGAP accession number phs000672. v1. p1. This study was supported by the  National Institute of Dental and Craniofacial Research (NIDCR) , the  National Eye Institute , and the  Office of Research on Women's Health  through contract number  N01-DE-32636. Genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded.
Data availability The genome-wide association summary statistics generated in this study have been deposited in the Databases of Genotypes and Phenotypes (dbGaP) under accession number: phs002723. v1. p1. Individual-level genotype data for 1199 subjects included in this study are available via dbGAP controlled access under accession number: phs002723. v1. p1. Individual-level genotype data for 10,850 subjects are available via dbGAP controlled access under accession numbers: phs000672. v1. p1 (n = 735), phs000428. v2. p2 (n = 8519), phs000196. v3. p1 (n = 995), phs000187. v1. p1 (n = 602). Remaining pre-existing individual-level genotype data were generated by coauthors and provided for specific use in this study and cannot be publicly shared per data use agreements. Contact the corresponding author (chris-lessard@omrf.
org) with inquiries about accessing this pre-existing genotyping data. All other data presented in this study was previously published and can be accessed by: Haplotype Reference Consortium panel version 1. 1 (Michigan Imputation Server) [https://imputationserver. sph. umich. edu/], 1000 Genomes Project reference data [http://ftp. 1000genomes. ebi. ac. uk/vol1/ftp/release/ 20130502/], EnhancerAtlas 2. 0 database [http://www. enhanceratlas. org/], FUMA [http://fuma. ctglab. nl/], RegulomeDB [https://www. regulomedb. org/regulome-search/], Open Targets [https://www. opentargets. org], Haploreg [https://pubs. broadinstitute. org/mammals/haploreg/ haploreg. php], GWAS Summary used in LDSC analysis [https://alkesgroup. broadinstitute. org/ sumstats_formatted/?C=S;O=A], LD Score European data from the 1000 Genome Project [https://alkesgroup.
Accuracy of user performance likely differs from one laboratory or experimentalist to another, especially when analyzing large datasets. Additionally, cognition of each user may fluctuate depending on instrumentation, physical and cognitive biases  [19]. Consequently, reducing the need for human decision at each step of the image analysis process is a critical challenge for experimental reproducibility and accuracy  [20]. Recently, deep learning (DL) tools have opened new perspectives for image analysis. DL is a category of machine learning, using training datasets (i. e. , images) that will feed an artificial neural network, allowing for a task of interest to be automatedly performed. DL techniques are typically based on manually segmented datasets (training and validation datasets) that are also prone to follow human biases.
Taken together, these results show that iCRAQ tends to reduce the inter-user variability, a benefit that remains dependent on each user tendency for manual readjustment of the segmentation. Hence, while iCRAQ necessitates significant manual intervention to define H-watershed thresholds and include or remove individual objects, it provides a robust and accurate semi-automated tool for nucleus and chromocenter quantification enabling both individual analyses and the production of training datasets. Reproducibility also suffers from the objectiveness of the discriminative features of the object of interest. Accordingly, while all users agreed on the definition of a chromocenter as distinct bright foci inside the nucleus (Figure  2A ), they may differ on their definition of "distinct" and "bright", thus leading to discordant segmentation.
Furthermore, iCRAQ-assisted segmentation guides segmentation toward objects that meet criteria measurable by the software but which only approximate the objects' distinctive features visually recognized by the users. To enable fast and high-throughput image analysis and to overcome low segmentation reproducibility due to intra-and inter-user variability, we set up a fully automated DL-based tool for nucleus and chromocenter segmentation: Nucl. Eye. D (  [41] ; Figure  5 ). Importantly, this tool was developed to reproduce realistic average lab conditions, such as the availability of limited training image datasets displaying inter-user diversity in sample preparation and image acquisition. The script includes all necessary codes and explanations for any user with basic programming skills to easily train his/her own model with his/her own images in case the provided pre-trained model is not fitted for the intended use  [41].
5 by default. In this case, as soon as the model reaches a higher probability for a pixel to be defined as part of the object rather than the background, the pixel is kept within the segmentation. Training of the three successive models takes around 12 h (about 192 000 images, after data augmentation). Once trained, models can be used to predict nuclear and chromocenter structures on any unannotated images. Upon the prediction process, input images are also refined into image fragments, with one nucleus per image, and a full-image prediction mask is automatically reconstructed from the different image fragments. This user-friendly output format allows masks to be overlaid to the original input images for calculating desired parameters (areas, signal intensities and shapes; Figure  5 ). In contrast to the training process, prediction of large datasets using the trained model can be performed within a few minutes (as an example, one image per second).
Nucl. Eye. D-Based Analysis of Nucleus and Chromocenters We first used a Light/Dark dataset (Figure  5 ) to evaluate the accuracy of nucleus and chromocenter segmentation using Nucl. Eye. D. This Light/Dark set was not part of the training set and was newly produced independent using biological replicates, sample preparation, and image acquisition protocols to reflect variable laboratory conditions wherein one can use a given version of trained Nucl. Eye. D for analyzing its own data. As shown in Figure  6A , segmentation performed by Nucl. Eye. D shows a coherent overlay among different training sets. Whereas the One_User and Ten_Users models lead to relatively similar results, the Ten_Users_iCRAQ model recognized a few more chromocenters in Dark nuclei (Figures  6B  and S3 ). When calculating HF, RHI and RHF using the segmentation masks produced by Nucl.