dataesr/scientific-paragraphs-categorization huggingface dataset:
05 and FDR less than 0. 25 for each pathway (Table  3 ). The KEGG pathway was the neuroactive ligand receptor interaction pathway (i-GSEA P = 0. 001 and FDR = 0. 006). The 22 GO terms included substrate-specific channel activity (i-GSEA P < 0. 001 and FDR = 0. 005), ion channel activity (i-GSEA P < 0. 001 and FDR = 0. 005), gated channel activity (i-GSEA P = 0. 002 and FDR = 0. 006), and several similar terms. Susceptibility pathways and GO terms in replication. We also examined whether our findings of chromosome 15q25. 1-related pathways could be validated as involved in lung cancer pathogenesis and conducted an independent GWAS with a populationbased case-control study among 18,439 lung cancer cases and 14,026 healthy controls in the replication phase using i-GSEA4GWAS. Of the eight index SNPs and the 3409 candidate SNPs in the discovery phase, 3411 SNPs were found in the replication cohort and, after pruned, 2525 SNPs were applied for enrichment (Supplementary Data 2).
088) (Supplementary Table  4 ). To aid interpretation of the relationship of the sharing pathways in both GWAS analysis and eQTL studies, we calculated the overlapping genes in the KEGG neuroactive ligand receptor interaction pathway and the GO terms and clarified the parent and child terms of the GO terms (Fig. 3 ). In addition, we investigated the gene expression level in normal lung tissue from Genecards database and found that all the genes in the neuroactive ligand receptor interaction pathway and the gated channel activity term, as well as the four transporter activity terms whose functional annotation for eQTL study had borderline significant association with lung cancer, are normally expressed in normal lung tissue and may play roles in cell growth, differentiation, or function of normal lung cell.
Combined effect of genes on lung cancer risk. In order to explore whether genes from our susceptibility KEGG pathways and GO terms could jointly affect lung cancer risk, we calculated the individual and combined effects of multiple functionallyrelated genes from our susceptibility pathways and GO terms on lung cancer risk. Supplementary Data 5 shows the results from our study of all selected genes, which were identified by our GWAS enrichment analysis as significant genes constituting the susceptibility pathways/GO terms, and the reference SNP, as well as its P value associated with lung cancer risk. Since the combined effect of weaker SNPs/genes might have minor influence and lead to difficulties in exploring the systems view, only those genes whose reference SNPs were associated with lung cancer risk with border-line significance (association test P < 0.
Second, only GWAS data without genomewide expression data were applied to identify the SNPs and their related genes that interact with the chromosome 15q25. 1 locus. However, the susceptibility pathways and GO terms from our GWA study can share pathways with the functional annotation of genes in chromosome 15q25. 1 and its related genes identified by eQTL studies, which supports the interpretation of some of our findings. A concern in this study is the large number of tests that were performed to identify epistatically acting SNPs. However, the purpose of conducting SNP-SNP interaction test in the current study is to select a group of candidate SNPs which are the most associated with the index SNPs in chromosome 15q25. 1. On the other hand, majority of SNPs/genes in the pathways have weak and minor influence on the pathogenesis of complex disorder  12.
The Oncoarray consortium is a network created to increase understanding of the genetic architecture of common cancers and included GWAS data of a total of 57,776 samples, obtained from 29 studies across North America and Europe, as well as Asia  56. The participants who lacked imputed data, disease status, were close relatives (second-degree relatives or closer) or had low-quality DNA, or were non-European, were excluded from the current study. Therefore, a total of 18,439 cases and 14,026 healthy controls were included in the current casecontrol study. Noncancerous lung tissue from Laval University was obtained from 420 patients undergoing surgical resection for lung cancer. Through quality controls, 409 samples were used for whole-genome gene expression profiling in the lung and eQTL analysis.
All patients in the Laval cohort were from a French Canadian population and underwent lung cancer surgery between April 2004 and December 2008. Samples were stored at the Institut universitaire de cardiologie et de pneumologie de Quebec (IUCPQ) site of the Respiratory Health Network Tissue Bank of the Fonds de la recherche en sante du Quebec (www. tissuebank. ca)  57. Lung tissue samples were obtained in accordance with Institutional Review Board guidelines. Genotype data and a detailed pathology reports were available for all patients. Human participant approval was obtained from the Institutional Review Board of each participating Hospital and University and by the National Cancer Institute, Bethesda, MD, USA. Written informed consent was obtained from each participant. Genotyping. A total of 561,466 SNPs in EAGLE samples were genotyped using Illumina HumanHap550v3_B BeadChips (Illumina, San Diego, CA, USA) at the Center for Inherited Disease Research, part of the Gene Environment Association Studies Initiative (GENEVA) funded through the National Human Genome Research Institute.
Index SNP selection. SNPs within the 15q25. 1 locus spanning 203 kb, which were associated with lung cancer risk with P values of less than 5 × 10 -8 in the 1st discovery cohort and in the meta-analysis of the 1st and 2nd discovery cohort, were selected as index SNPs. A Bonferroni correction was applied to adjust the association of the index SNP for multiple comparisons with using PLINK version 1. 9 and R package of adjust P-values for multiple comparisons. Because 310,276 SNPs in the discovery phase were performed for association analyses and meta-analyses and 3411 SNPs in the replication phase were conducted association analyses, adjustments for 310,276 tests in discovery and 3411 tests in replication were used. After selection, eight SNPs met the criterion for index SNP selection and were used for further selection of the candidate SNPs in the whole genome which had potential functional connections with the index SNPs in the chromosome 15q25.
After selection, 3409 candidate SNPs met the criterion for candidate SNP selection and were identified to have potential connections with the eight index SNPs in the chromosome 15q25. 1 locus. Pathway analysis with GWAS data. We included curated pathways from the Canonical pathways, Reactome, BioCarta, KEGG databases  59  , and GO  60. The Reactome database is based on reactions between diverse molecular species rather than limiting the pathways to protein-protein interactions. The KEGG database represents experimentally-validated pathways of metabolic processes and gene sets of human diseases. GO is a major framework for the model of biology that defines classes used to describe gene function, and relationships between these concepts. Gene set enrichment analysis was performed by i-GSEA4GWAS.
SNPs were retained for analysis that were within 20 kb upstream or downstream of a gene. We used gene set databases of canonical pathways, GO biological process, GO molecular function, and GO cellular component, separately, and applied the standard input gene set file of KEGG, BioCarta and, Reactome, which were downloaded from the Molecular Signatures Database (MSigDB) in GSEA (http:// software. broadinstitute. org/gsea/msigdb/collections. jsp), we selected gene sets whose number of genes were between 21 and 200, and without limiting gene sets by keyword (e. g. immune) and without masking the MHC region. In order to reduce the possibility of biased results due to LD patterns from SNP arrays  13  , we pruned the set of SNPs, including the index SNPs and the candidate SNPs, for LD and only inputted SNPs not in LD (r 2 < 0.
2) to enrich pathways. After pruning, we performed gene set enrichment analysis with associated P values of the SNPs that using the option of "-logarithm transformation", as required by the software, in the meta-analysis of discovery cohorts and the replication cohort, respectively. i-GSEA4GWAS performs gene set enrichment to identify pathways that show a higher proportion of statistically significant genes than randomly expected and, with some modifications, is based on the GSEA algorithm  61, 62. i-GSEA4GWAS implements SNP label permutation to analyze SNP P values and to correct gene and gene set variation and multiplies a significance proportion ratio factor to the enrichment score (ES) to yield the significant proportion-based enrichment score (SPES). SPES multiplies by the proportion of significant SNPs in the pathway so that i-GSEA4GWAS identifies pathways/gene sets including a high proportion of significant genes.
It is, therefore, more appropriate for study of the combined effects of possibly modest SNPs/genes and gives i-GSEA improved sensitivity for complex diseases  13. Pathways/gene sets with FDR < 0. 25 were regarded as possibly associated with traits; FDR < 0. 05 were regarded as high confidence or with statistical significance. Gene set enrichment analysis was also performed by GSA-SNP2, which is a successor of GSA-SNP  14, 15  , using same SNPs and P value and to retain SNPs with 20 kb upstream or downstream of a gene. We used gene set databases which were downloaded from the Molecular Signatures Database (MSigDB) in GSEA and selected gene sets whose number of genes was between 21 and 200. Genome-wide gene expression levels and eQTL study. All lung samples were reviewed by an experienced pathologist for clinical diagnosis and staging.
Each lung tissue sample was snap-frozen in liquid nitrogen and stored at -80 °C until further processing. The SV96 Total RNA Isolation System (Promega) was used to extract RNA. Expression profiling was carried out with an Affymetrix custom array (GEO platform GPL10379). The robust multichip average method  63  as implemented in the Affymetrix Power Tools software was used to examine expression values. Standard quality control parameters  64  were applied to check the quality of the arrays. Through quality controls, 409 patients were available for eQTL analyses with both genotypes and gene expression levels. R statistical software was used to perform tests for robust multichip average expression. Association tests between the expression traits, which were adjusted for age, sex, and smoking status, and the most significant SNPs in each genes within chromosome 15q25.
8) to cluster functionally similar genes into groups of GO terms, with Species and background being set up as Homo sapiens. We used the candidate genes in the whole genome with and without the genes in the chromosome 15q25. 1 locus to perform the analyses, by choosing 'Homo sapiens' selection to limit annotations by species. Relationship of the susceptibility pathways and GO terms. We clarified the relationship of sharing pathways identified by GWAS analysis of both discovery and replication phase and validated by eQTL studies with information from the European Bioinformatics Institute (http://www. ebi. ac. uk/QuickGO/) and applied Cytoscape (version 3. 4. 0) with EnrichmentMap plugin (version 2. 1). The standard gene sets for EnrichmentMap plugin were downloaded from MSigDB Collections (http://software.
broadinstitute. org/gsea/msigdb/collections. jsp). For the selected genes in our susceptibility pathways from current GWAS pathway analyses, we achieved the gene expression level in normal lung tissue from Genecards database (http://www. genecards. org/). Accumulating risk of lung cancer. We calculated the individual and combined effect of genes in the pathways/gene set on lung cancer risk using the SNPs that were identified by i-GSEA4GWAS as reference SNPs for the selected genes in each pathway. The genes whose reference SNPs were associated with lung cancer risk with borderline significance (P < 0. 1) in the meta-analysis of discovery cohorts and in the replication cohort, were selected to assess the individual effect and joint effects on lung cancer risk. Genotype frequencies between the cases and controls were evaluated using a chi-square test.
Univariate and multivariate logistic regression models were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) of each genotype to estimate its effect on lung cancer risk with or without adjustment for age, sex and smoking status (never and ever). Statistical analyses were performed with Statistical Analysis System (SAS) software (version 9. 1; SAS Institute, Cary, NC, USA) and P value < 0. 05 was considered significant. Stratified analyses. We determined whether there were different pathways among the overall group, and in the group when stratified by smoking status (never and ever). In the subgroups of smokers and non-smokers, we used a similar process to select index SNPs and candidate SNPs, and to carry out pathway analyses. Among smokers, eight index SNPs and the 3401 candidate SNPs were selected for further gene set enrichment analysis in the 1st and 2nd discovery cohort, the meta-analysis of discovery cohorts and the replication cohort.
Among non-smokers, no SNPs in chromosome 15q25. 1 reached the criteria for index SNP selection, and therefore no subsequent steps for pathway analyses were conducted. Genomics of Heart and Lung Diseases. The research undertaken by M. T. , L. W. , and M. A. was partly funded by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. M. D. T. holds a Medical Research Council Senior Clinical Fellowship (G0902313).
Acknowledgments This work was supported by  National Institutes of Health (NIH)  for the research of lung cancer (grants  P30CA023108 ,  P20GM103534 , and  R01LM012012 );  Transdisciplinary Research in Cancer of the Lung (TRICL)  (grant  U19CA148127 );  UICC American Cancer Society   Beginning Investigators Fellowship  funded by the  Union for International Cancer Control (UICC)  (to X. J. ). CAPUA study was supported by  FIS-FEDER/Spain  grant numbers  FIS-01/310 ,  FIS-PI03-0365 , and  FIS-07-BI060604 ,  FICYT/Asturias grant  numbers  FICYT PB02-67  and  FICYT IB09-133 , and the  University Institute of Oncology (IUOPA)  of the  University of Oviedo  and the  Ciber de Epidemiologia y Salud Pública. CIBERESP, SPAIN. CARET study was supported by  NIH  awards  UM1 CA167462 ,  UO1-CA6367307 ,  CA111703 , and  R01 CA151989.
The Liverpool Lung project is supported by the  Roy Castle Lung Cancer Foundation. The  Harvard Lung Cancer Study  was supported by  NIH  grants  CA092824 ,  CA090578 , and  CA074386. The Multiethnic Cohort Study was partially supported by  NIH  grants  CA164973 ,  CA033619 ,  CA63464 , and  CA148127. The MSH-PMH study was supported by The  Canadian Cancer Society Research Institute  ( 020214 ),  Ontario Institute of Cancer and Cancer Care Ontario Chair Award  to R. H. and G. L. and the  Alan Brown Chair  and  Lusi Wong Programs  at the  Princess Margaret Hospital Foundation. R. T is supported by a  Canada Research Chair in Pharmacogenomics, CIHR  grant ( FDN-154294 ), and  CAMH. NJLCS was funded by the  State Key Program of National Natural Science of China  ( 81230067 ), the  National Key Basic Research Program  Grant ( 2011CB503805 ), the  Major Program of the National Natural Science Foundation of China  ( 81390543 ).
The latter was also shown to be associated with POAG (using N = 2979 cases and 7399 controls), although not in the direction that was expected (i. e. , the CCT-decreasing allele was associated with decreased risk of POAG)  17. Over 26 loci have been associated with CCT to date, explaining around 8% of the CCT heritability  17. Increased knowledge of the genetic basis of the variation in CCT in the general population promises to help in prioritising future research in corneal disease. To identify new CCT-associated loci, we performed a larger cross-ancestry genome-wide association study (GWAS) including over 25,000 individuals of European and Asian descent, with genotypes imputed to the 1000 genomes reference panel. Further, we assess the relevance of CCT influencing loci to the risk of keratoconus and POAG using slightly larger (keratoconus) and substantially larger (POAG) ocular disease datasets than those previously described  17.
Independent signals. In our previous CCT GWAS we identified loci harbouring multiple independent variants 17. To identify additional independently associated variants in European population, we performed conditional and joint multiple-SNP (CoJo) analysis implemented in the program GCTA. We used genotype data of 2582 unrelated Australians from the BMES cohort  19. The CoJo analysis resulted in 16 independent SNPs, of which seven have not been previously associated with CCT (Table  3 ). Thus, in total, we identified 44 loci associated with CCT, harbouring 54 independent association signals (i. e. , 28 previously published + 19 from the cross-ancestry meta-analysis + 7 from CoJo analyses). Gene-based association analysis. To further identify loci not implicated in the single marker association tests, we performed gene-based tests using the software VEGAS2 20 using a '-10 kb' window (see Methods section).
In total, 974 variants (i. e. , 54 lead SNPs + 920 SNPs in LD) were examined. Of these, 118 were prioritized including the 54 lead SNPs and another 64 SNPs which were selected based on their RegulomeDB score  22  (i. e. ,1a-1f, 2a-2c or 3a). SNPs with a score from 1a-1f to 2a-2c were classified as showing maximum evidence for being located in regulatory regions, while SNPs with a score of 3a were classified as showing medium evidence (Supplementary Data 3). In total, 63% (75/118) of the prioritized SNPs overlap with at least two regulatory elements of the ENCODE data (i. e. , promoter or enhancer histone marks, DNase I hypersensitive sites, transcription factor or other protein-binding sites and eQTLs). Strong enrichment for histone modifications, particularly, H4K20me1 (which indicates transcriptional activation), was also found when results from the European-specific metaanalysis were assessed using GARFIELD (http://www.
High expression levels (>200 PLIER) were observed for SMAD3, COL12A1 and DCN, LUM, KERA (Supplementary Table  7 ), the latter three being at the same "DCN" locus. Pathway analysis. We tested enrichment of the genes defined by VEGAS2 in 9981 pathways or gene-sets derived from the Biosystem's database. Using a 10 kb window in the VEGAS2 computation, we identified 23 pathways that were significantly enriched after correcting for multiple testing (P gene-set < 5. 01 × 10 -6 ),   1  and 2. New loci are in bold Supplementary Data 4. The majority of these gene-sets are involved in the metabolic activities associated with collagen and extracellular matrix (ECM). We confirmed the previously identified significant association of the collagen trimer pathway (GO:0005581) with CCT  17. Additional pathways involved in basement membrane (GO:0005604), TGF-β regulation (GO:0071636) and skeletal system development (GO:0001501), were also identified as associated with CCT.
Similar pathways were observed using single variants with sub-threshold association Pvalues < 1 × 10 -4 as input for the interval enrichment analysis (INRICH) method, Supplementary Table  8. Pathway analysis using a 200 kb window in VEGAS2, showed comparable pathways and additionally revealed the endoplasmic-reticulum-associated protein degradation (ERAD) pathway (GO:0036503) (Supplementary Data 5). The ERAD pathway also emerged as an overrepresented canonical pathway in the Ingenuity Pathway Analysis (IPA) (Supplementary Fig. 7 ), along with pathways related to connective tissue disorders and metabolism (Supplementary Tables  9, 10 ). The FUMA platform (https://ctg. cncr. nl/software/fuma_gwas) highlighted that eight of the closest genes to the 44 CCT cross ethnic meta-analysis lead variants are amongst the 64 fibroblastic signature genes overexpressed in cancer cells that have undergone epithelial to mesenchymal transition 24 : THBS2, COL5A1, FBN1, LOXL2, DCN, LUM, COL6A2 and GLT8D2.
Mutations in DCN have been identified in congenital stromal corneal dystrophy  35, 39  ; which produces characteristic corneal opacity and increased corneal thickness  35  , making it an excellent candidate CCT gene. However, rs7308752 also shows a significant cis-eQTL effect (P = 3. 1 × 10 -6 , in adipose tissue), modifying the expression of KERA, a keratan sulfate proteoglycan vital for maintaining corneal transparency. Mutations in KERA cause cornea plana-2 (OMIM:217300)  40, 41  , a recessive corneal disorder characterised by flattening of the normally convex corneal surface. Additionally, LUM, another gene in the region (30 kb apart from rs7308752), is a member of the small interstitial proteoglycan gene family and has been implicated in high myopia  32, 42. In the ocular tissue database  43  , all three genes showed high expression levels in the cornea, with KERA showing the highest expression.
The individual cohorts are as described in detail in previous publications  18, 52  , with summary statistics and imputation details in Supplementary Table  1. To investigate the role of the identified CCT loci in keratoconus and POAG, we then tested the implicated CCT loci in disease case-control sets. The keratoconus datasets comprised cases and controls from Australia (711 keratoconus cases and 2622 controls from the Blue Mountains Eye Study) and the United States (222 keratoconus cases and 3324 controls). The POAG cases and controls were drawn from studies in Australia (1155 cases and 1992 controls) and the United States (3853 cases and 33480 controls). Detailed information of the keratoconus and POAG cohorts can be found in the Supplementary Note 1. The local research and medical ethics committees approved the individual studies.
Written informed consent was obtained from all participants (or parents in case of minors) in accordance with the Declaration of Helsinki. Ancestry-specific and cross-ancestry GWAS meta-analyses. All participating studies performed association testing under an additive model for the effect of the risk allele while adjusting for age, sex and at least the first five principle components for the population-based studies. In samples with related individuals association testing accounting for family structure was conducted using the -fastAssoc option in MERLIN  53  or the -mmscore 54 option implemented in GenABEL  55. Before meta-analysis, we removed variants with MAF < 0. 01, and with imputation quality scores less than 0. 3. Ancestry specific meta-analyses (European-specific and Asian-specific) and joint meta-analysis were performed using the inverse variance fixed effect scheme implemented in the software METAL  56.
The 'genomic inflation' correction option was used in METAL  56  and applied to all input files. We also computed the test statistics for heterogeneity of effect among studies for each variant using Cochran's Q-test. We removed variants with heterogeneity P-value < 0. 001 from both European-specific and Asian-specific meta-analyses. Moreover, we focused on variants that were present in more than 25% of participating studies in the European-specific meta-analysis (at least four studies) and the Asian-specific metaanalysis (at least two studies). Finally, to detect additional loci associated with CCT, we performed a fixed-effect cross ancestry meta-analysis. Selecting independent variants. We applied the conditional and joint (CoJo) analysis approach  57  implemented in the software Genome-wide Complex Trait Analysis  58  (GCTA) on European-specific meta-analysis results in order to identify potentially independent signals within the same genomic regions.