dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, genome-wide cis-expression quantitative trait locus (eQTL) mapping studies in different tissues have identified more than 3,000 genes associated with nearby genetic variants  [49, 50]. Through combining GWA and gene expression data from SS patients, we sought to identify and characterize SS-associated variants that influence the expression of genes within the IFN signature by utilizing a genomic convergence approach (Fig  1 ). Through cis-eQTL analyses we identified an association of a SNP rs10774671, located within the OAS1 gene locus, with SS. Functional studies were performed to assess biological consequence of the OAS1 variants.
05 and FC > 2 or < -2. We found 73 differentially expressed genes in our dataset, among which 57 genes are regulated by type I IFNs (S1 Table ). The majority of dysregulated genes (66 out of 73) were overexpressed in SS patients and formed the IFN signature in cases after unsupervised hierarchical clustering (Fig  2A ). Of note, the IFN signature was observed in most but not all anti-Ro/SSA positive SS cases, in accordance with our previous work  [34] , and the intensity of this feature was heterogeneous among patients (Fig  2B ). These results suggest that the expression of IFN signature genes might be influenced by genetic variants, which could be identified through cis-eQTL analysis.
Identification of SNP-SS association with rs10774671 in the locus OAS1 We hypothesized that variants near the differentially expressed IFN signature genes may potentially influence disease susceptibility through cis-regulatory mechanisms. We would, however, expect to identify many cis-eQTLs in these regions regardless of whether they associate with disease susceptibility or not. Therefore, instead of performing cis-eQTL analyses for all of the 73 dysregulated IFN signature genes, we first sought to identify variants that showed a disease association of P assoc <0. 05 for subsequent evaluation of their role in altering gene expression. Genetic associations with SS susceptibility for 2,163 SNPs in regions of the 73 differentially expressed genes were tested using a combined dataset (Dataset 1; Table  1 ) from genome-wide genotyping arrays consisting of 765 SS cases and 3,825 population controls of European ancestry.
This genomic convergence approach resulted in increased power to identify and prioritize disease susceptibility genes for further genetic replication and functional studies. https://doi. org/10. 1371/journal. pgen. 1006820. g001 (S1 Table ) and observed nominal associations (1×10 -4 P assoc <0. 05) with SS susceptibility in 42 additional regions (S1 Table ). To determine whether these disease-associated genetic variants (P assoc <0. 05) were related to the altered expression levels of their nearby differentially expressed IFN signature genes, we performed cis-and trans-eQTL analyses for all SNPs with P assoc <0. 05 (173 SNPs in 44 regions; S1 Table) using a linear model by integrating the transcriptome and genotype datasets in 178 European individuals (108 anti-Ro/SSA positive SS cases, 55 anti-Ro/SSA negative SS cases,  ).
Also, we performed a co-localization analysis using eCAVIAR  [53]  to identify the potential causal variant in the OAS1 region. We estimated colocalization posterior probability (CLPP) scores for all the tested 453 variants, and rs10774671 has the highest CLPP score among all the variants (S2 Table ). Therefore, we prioritized this SS-associated cis-eQTL variant, rs10774671, for further replication and functional studies. We replicated the genetic association of rs10774671 with SS susceptibility in an independent dataset (Dataset 2; Table  1 ) consisting of 514 European SS cases and 3,466 European population controls (genotyped using TaqMan assays, P rep = 5. 16×10 -6 ; odds ratio = 0. 71; 95% confidence interval = 0. 63-0. 83). Meta-analysis was performed to combine the results between  the initial genetic association study (Dataset 1) and the replication cohorts (Dataset 2) and established the association of rs10774671 with SS risk (P meta = 2.
59×10 -9 ; odds ratio = 0. 75; 95% confidence interval = 0. 66-0. 86; risk allele [the A allele] frequency: case = 0. 70, control = 0. 64; with no heterogeneity between the two datasets as determined by I 2 = 0). We also performed a stratified analysis and a permutation analysis using merged samples from Dataset 1 and Dataset 2 to determine whether the observed genetic association was restricted to anti-Ro/SSA positive or negative patients. We did not find any evidence to support the genetic effect to be specific to any sub-group of the patients (S1 Fig). In summary, we identified a potential causal variant, rs10774671, that was associated with SS susceptilibty, likely through its impact on the expression of a key IFN signature gene, OAS1.
Impact of rs10774671 on OAS1 splicing Following establishment of the association between rs10774671 and SS susceptibility, we further determined the influence of different genotypes on the alternative splicing of OAS1. Four In order to determine the influence of rs10774671 on the expression of each alternatively spliced isoform of OAS1 and compare OAS1 isoform composition, we performed RNA-seq on whole blood from 57 SS cases and 27 healthy controls. After QC, the reads were aligned to the human genome using TopHat  [57]  without gene annotation to facilitate the detection of potentially novel isoforms of OAS1. The transcript level of each isoform was compared across samples with different genotypes of rs10774671 based on the measurement of fragments per kilobase of transcript per million mapped reads (FPKM) using Cufflinks  [58].
However, protein expression of the p42 isoform remained unchanged upon IFN stimulation (Fig 7A ), even though its transcript level significantly increased after stimulation (Fig  7B ). The protein expressions of p48 and p44 were low in all of the samples, and not responsive to type I IFN stimulation (Fig  7A ). We then cloned and transfected each isoform into human embryonic kidney (HEK) cell line 293T cells and observed similar protein expression results as in EBV cells: lower protein levels for p48 and p44 The expression level of each isoform was determined using RNA-seq. The abundance of each isoform was measured by FPKM in Cufflinks and was compared between individuals with different genotypes of rs10774671. The risk allele A of the SS-associated variant rs10774671 was correlated with higher transcript expression of (A) p42, (B) p48, and (C) p44 isoforms, but lower levels of the (D) p46 isoform.
However, our data suggested that the alternatively spliced 3'-terminus (A) Protein expression of OAS1 isoforms was evaluated in EBVtransformed B cells from SS patients (four independent samples from each genotype group) using anti-OAS1 antibody targeting the shared epitope of all the isoforms. The stimulated cells were treated with universal type I IFN (1500U/ml) for 24hrs. The p44 isoform was not detectable using western-blot due to its low expression. The right panel shows quantified band intensity normalized to the GAPDH in each sample. (B) The transcript levels of each OAS1 isoform from the same sets of cells described above were determined using real-time PCR. Consistent with the RNA-seq results, the SS-associated risk allele A of rs10774671 was correlated with decreased levels of p46 and increased expression of the p42, p48, and p44 isoforms (significance levels are shown at the bottom).
Lack of response to IFNs has also been seen in multiple sclerosis (MS), in which patients carrying the homozygous rs10774671 GG genotype, a protective genotype in MS associated with less active disease, were more responsive to IFN-β treatment than AA and AG patients, as measured by time to first relapse  [79]. We searched the Genotype-Tissue Expression (GTEx) database and confirmed the association between rs10774671 and OAS1 expression in whole blood  [80]. We also searched eQTL for all our top variants in the 43 SS-associated genes (besides OAS1) as well as any variants in LD with those top variants (r 2 >0. 8). Out of the 614 variants we checked, two variants were also eQTLs for their corresponding genes: the top SS-associated variant in ANKRD22 (rs1147601, P assoc = 2. 38×10 -3 , P eQTL-GTEx = 6.
Through searching the GTEx database for rs10774671 eQTLs, we found rs10774671 is a significant eQTL of OAS1 in 5 tissues (S6A Fig). Interestingly, the eQTL effect in the Esophagus-Mucosa tissue is in the opposite direction compared to other tissues. In whole blood, the p46 isoform is predominant, thus the A allele caused reduced expression of OAS1 as a whole (S6B Fig); however, in Esophagus Mucosa, the isoform is p42 (S6C Fig)  and  results in an opposite effect of rs10774671 on the total OAS1 expression. We propose that the ambiguous genetic effects of rs10774671 on different diseases might be due to different functional isoforms in disease-relevant tissues (not necessarily Esophagus-Mucosa). While reduced expression of the functional isoform p46 in whole blood increases risk of SS and MS, it protects individuals from T1D.
A total of 178 European subjects (108 anti-Ro/SSA positive SS cases, 55 anti-Ro/SSA negative SS cases, and 15 healthy controls) for whom genotype data were also available were used in the cis-eQTL analysis. Genetic association study. All SS cases used in the genetic association analyses were collected through the Sjo ¨gren's Genetics Network and organized at the OMRF. Two datasets (Dataset 1A and 1B) were combined for the initial genetic association analysis (Table  1 ). All subjects in Dataset 1A have been previously described in a genome-wide association study  [15]. In Dataset 1B, 384 SS cases of European descent were genotyped and subjected to QC measurements outlined below. The genotype data of 3,315 population controls in Dataset 1B were obtained from the database of Genotypes and Phenotypes (dbGaP; http://www.
ncbi. nlm. nih. gov/gap). Each SS case was genetically matched to five population controls in Dataset 1A and 1B, respectively, using the identity-by-state (IBS) to assess allele sharing as implemented in PLINK v1. 07  [91]. The remaining controls were used in the replication study (Dataset 2). A total of 622 SS cases and 3,502 population controls were subjected to QC in the replication phase (Dataset 2; Table  1 ). RNA-seq study. For RNA-seq experiments, a total of 90 European subjects were evaluated, including 27 anti-Ro/SSA positive SS cases, 33 anti-Ro/SSA negative SS cases, and 30 healthy controls.
Microarrays were washed under high stringency and labeled with streptavidin-Cy3, and fluorescent intensity-based gene expression data were collected using Illumina's BeadStation 500 scanner or iScan. QC and statistical analyses. Unless otherwise stated, all statistical analyses were performed in the R Bioconductor suite. Microarray experiments were performed in two batches, with 93 SS cases and 34 healthy controls in Batch 1 and 89 cases and 42 controls in Batch 2. Raw intensity values for the two datasets were background subtracted separately using Illumina BeadStudio software. Identification of outlier and poor-performing samples was accomplished by applying the package arrayQualityMetrics (AQM)  [92]  to log 2 -transformed microarray data from each experiment. QC measures were applied to each dataset to filter out transcripts expressed in <10% of the subjects (detection call threshold P<0.
05) and probes with differential missingness rates (P<0. 001 by Fisher's exact test) between the two datasets. The remaining probes were then compared against data tables from the Re-annotation and Mapping of Oligonucleotide Array Technologies (ReMOAT)  [93] , in which Illumina BeadArray probe quality was extensively assessed and re-annotated. Each dataset was then independently normalized using Robust Multiarray Average (RMA)  [94] , followed by log 2 transformation and quantile normalization. The ComBat program was subsequently applied to the combined dataset to adjust for non-biological experimental variation (i. e. batch effects)  [95]. Final re-annotation of un-annotated probes was performed manually using the NCBI probe database (http://www. ncbi. nlm. nih. gov/probe) and UCSC Genome Browser alignment (https://genome.
ucsc. edu) [52]. Probes mapping to un-annotated genes or having inconsistent results from the two databases were removed from the analysis. The results and pipeline of the QC and normalization procedures applied to microarray data are summarized in (S8 Fig). Gene expression comparisons were performed using Welch's t-test between the mean expression values in SS cases and controls. The clustering of genes and samples was performed in Cluster 3. 0  [96]  and visualized in Java TreeView  [97]. Unless otherwise noted, unsupervised hierarchical clustering was performed using centroid linkage with uncentered correlation for both genes and samples.
Genetic association experiments and analyses Genotyping and imputation. Genotyping for Dataset 1A was described previously  [15]. Genotypes for Dataset 1B were obtained using the Illumina OmniExpress arrays at OMRF following the manufacturer's protocol. To increase informativeness, imputation was conducted in subjects from the merged datasets (Dataset 1) for the OAS1 region (chr12: 113294739-113507712; hg19) meeting the criteria for suggestive association with SS (P assoc <1×10 -4 ). Imputation was performed using IMPUTE2 and the European Impute2 1000 Genomes Phase 1 April 2012 reference panel  [98] [99] [100]. A probability threshold of 0. 9 and information score of >0. 5 were applied to the imputed genotypes in addition to the QC criteria described below for the association analyses. Genotypes for the replication study (Dataset 2) were determined using TaqMan probes and reagents (Life Technologies) following the manufacturer's protocol.
QC process. Variants and samples in the genetic association analysis were subjected to a strict QC procedure as described previously  [15] : single-nucleotide polymorphism (SNP) call rate >95% in all individuals; minor allele frequency >1%; Hardy-Weinberg proportion test with a P>0. 001 in controls; and P>0. 001 for differential missingness between cases and controls. Samples from Dataset 1A and Dataset 1B passing QC were retained if results showed: >95% call rate for all variants; no excessive increased heterozygosity (>5 standard deviations from the mean); and no relatedness determined by identity-by-descent (IBD) >0. 4 using PLINK v1. 07  [91]. Population substructure was identified using EIGENSTRAT  [101]  with independent genetic markers (r 2 <0. 2 between variants). The resulting Eigenvectors were used to distinguish the four continental ancestral populations with the following HapMap samples: Africans (ASW, LWK, MKK, and YRI), Europeans (CEU and TSI), Hispanic and East Indians (MEX and GIH), and Asians (CHB, CHD, and JPT)  [102, 103].
The first two principal components (PCs) output by EIGENSTRAT were plotted and used to identify samples outside the European cluster  [101, 102]  (S9 Fig). Outliers from the European population were removed from further analysis. Population stratification analysis was performed for the replication cohort using genotype data from a previous study for the 622 SS cases  [15]  and genotype data from the respective dbGaP studies. Statistical analysis. A logistic regression model was used to test the association of genetic variants with SS susceptibility in PLINK v1. 07  [91]. The additive genetic model was calculated for variants within and 50kb flanking the 73 differentially expressed transcripts while adjusting for gender and the first three PCs as determined by the Scree test to evaluate the loading of each PC for the amount of variance explained  [104].
Meta-analysis of rs10774671 between the initial genetic association analysis (Dataset 1) and the replication study (Dataset 2) were calculated using a Z-score weighted by the sample size of each dataset in METAL  [105]. We tested our logistic regression model for deviation from additivity using PLINK in both the discovery dataset and replication dataset, and neither model shows significant deviation (p = 0. 40 and p = 0. 31, respectively). Logistic regression adjusting for the most significant variants (conditional analysis) was performed to determine independence of the association. LD and probable haplotypes were determined using Haploview  [106].
Cis-and trans-eQTL analysis Quantitative levels of the differentially expressed transcripts from the microarray analysis were used as phenotypic traits in 178 European subjects described above. Variants showing nominal association with SS (P assoc <0. 05) in the genetic association analysis were selected to test for cis-eQTLs, defined by variant-transcript pairs within 50kb of the target genes or trans-eQTLs for variants at least 1Mb away. Association of genotype with transcript expression was evaluated using both linear regression (adjusted for gender and disease status) and analysis of variance (ANOVA) in Matrix-eQTL  [107]. FDR-adjusted P values were calculated to determine the significance of the eQTL. The results of the cis-eQTL analyses were plotted in Prism 6. We also used a tool, PEER, based on a Bayesian framework to adjust for unknown non-genetic factors in gene expression  [108].
We transformed our expression values using all the genes that passed QC by running PEER for 15 factors. We then used the PEER residuals from the 44 SS-associated (P assoc <0. 05) IFN signature genes as quantitative traits to determine eQTL while adjusting for other known potentially confounding factors: sex, disease status, anti-Ro/SSA status, and age. In addition to additive genetic models, we also performed eQTL analyses using linear regression by three other models: recessive (recode genotype from 0,1,2 [where 2 equals to AA] to 0,0,1), dominant (0,1,1), and overdominant (0,1,0). We used the coefficient of determination (R 2 ) to evaluate the goodness-of-fit in each of these models. As shown in S4 Table, both the p42 and p48 isoforms fit the additive model best (highest R 2 ), whereas the recessive model outperformed in the p46 isoform regression (R 2 rec = 0.
RNA-seq experiments and analyses Peripheral blood mRNA transcripts from 27 anti-Ro/SSA positive SS cases, 33 anti-Ro/SSA negative SS cases, and 30 healthy controls were isolated and measured as described above. RNA-seq was performed using the Illumina HiSeq 2000 employing standard procedures. Multiplexing of 6 samples per lane was utilized. Post sequence data were processed with Illumina Pipeline software v. 1. 7. Quality of raw sequence data was assessed using FASTQC. We assessed the quality of each sample using AQM  [92]  as described above. A total of 6 samples were removed from analysis due to significantly different expression patterns revealed by PC analysis. Raw FASTQ files were aligned to the human reference genome (hg19) using TopHat  [57]  that aligns the reads across splicing junctions independent of gene annotations, which benefits de novo detection of alternative splicing events.
The total gene transcript level was determined by normalized read counts (raw read counts divided by estimated size factor) in DESeq  [109]. To determine alternative splicing events, the reference-independent construction of the transcripts was performed using Cufflinks  [58]  to identify transcripts >1% of the most abundant isoform in each sample. We only kept the transcripts that were detected in more than 10% of the samples for further analysis. The previously annotated isoforms (p46, p42 and p48) and an un-annotated isoform identified across multiple samples (p44) were used as reference to reconstruct the isoforms of OAS1. The novel identified isoforms of OAS1 were also checked manually in the Integrative Genomics Viewer (IGV)  [110]  to confirm the transcripts and cross-exon reads. The FPKM values calculated by Cufflinks were used to determine the expression levels of each isoform of OAS1.
Acknowledgments We are grateful to all the individuals with Sjo ¨gren's syndrome and those serving as healthy controls who participated in this study. We would like to thank the following individuals for their help in the collection and ascertainment of the samples used in this study:  Erin Rothrock ,  Judy Harris ,  Sharon Johnson ,  Sara Cioli ,  Nicole Weber ,  Dominique Williams ,  Wes Daniels ,  Cherilyn Pritchett-Frazee ,  Kylia Crouch ,  Laura Battiest ,  Abu N. M. Nazmul-Hossain ,  Justin Rodgers ,  James Robertson ,  Thuan Nguyen ,  Amanda Crosbie ,  Ellen James ,  Carolyn Meyer ,  Amber McElroy ,  Eshrat Emamian ,  Julie Ermer ,  Kristine Rohlf ,  Joanlise Leon ,  Anita Petersen ,  Danielle Hartle ,  Jill Novizke ,  Ward Ortman ,  Carl Espy ,  Beth Cobb ,  Rezvan Kiani ,  Marianne Eidsheim  and  Joelle Benessiano Centre de Ressources Biologiques, Hopital Bichat, Paris, France.
We would like to thank the following funding agencies: this publication was made possible by grants from  NIH :  P50AR0608040  (KLS, CJL,  RHS , and ADF),  1R01AR065953  (CJL), 5R01DE015223 (KLS and JBH)CJL),  U19AI056363  (ADFThe contents are the sole responsibility of the authors and do not necessarily represent the official views of the  NIH. Additional funding was obtained from  Intramural Research Program  of the  National Institute of Dental and Craniofacial Research (GGI) ,  US Department  of Veterans Affairs IMMA 9 (JBH),  USA Department of Defense   PR094002  (JBH),  American College of Rheumatology Research and Education Foundation/Abbott Health   Professional Graduate Student Preceptorship Award 2009  (CJL and KLS),  Oklahoma Medical Research Foundation  (CJL and KLS),  Sjo ¨gren's Syndrome Foundation  (HL, CJL, and KLS),  Phileona Foundation (KLS) ,  French  ministry of health:  PHRC N˚2006-AOM06133  and French ministry of research:  ANR-2010-BLAN-1133  (XM and CM),  The Strategic Research Program at Helse Bergen ,  Western Norway Regional Health Authority  (LGG, JGB, and RJ), The Broegelmann Foundation (JGB and RJ),  Norwegian Foundation for Health and Rehabilitation (EH) ,  KFO 250 TP03 ,  WI 1031/6-1  (TW), KFO 250, Z1 (TW),  Medical Research Council ,  UK G0800629  (WN and SB),  Northumberland ,  Tyne & Wear CLRN  (WN), The  Swedish Research Council  (MW and LR), The King Gustaf the V-th 80-year Foundation (MW and LR),  Knut and Alice Wallenberg Foundation (LR) , and The  Swedish Rheumatism Association  (MW, GN, LR, and PE).
Systematic ratios were then applied to predicted physician densities to estimate the number of nurses/midwives and AOWs. The economic model specifies physician density (dependent variable) as a function of GDP per capita, OOP per capita, and the size of the population over 65 years. Given data availability constraints, we included a vector of dummy variables by country (i. e. , fixed effects) to account for constant differences in characteristics across countries. All independent variables were lagged up to 5 years to ensure that historical levels of these measures predict future worker density numbers, and which allows time for such factors to work through the economy and affect the labor market. A stepwise approach was used to select the combination of year lags of the different predictors that maximized the predictive power of each variable.
Alternative model specifications were explored, which included different ways in which input parameters could be calculated (e. g. , percentage of the population aged 65+, OOP health spending as a percentage of total health expenditures). To select the appropriate model, the data were split into an "initializing" dataset (data years 1995-2004) and an "attesting" dataset (data years 2005-2013) with which to assess the precision of predicted values resulting from different specifications  [26]. The model in Eq. 1 yielded predictions with the lowest mean errors. We conducted two additional sensitivity analyses of the projections of physician demand resulting from alternative input parameters for the optimal demand model chosen. First, we assessed the stability of the predictions to alternative estimated future values of GDP per capita (US$2010) obtained from the Economic Research Service International Macroeconomic Data Set published by the United States Department of Agriculture (USDA)  [27].