dataesr/scientific-paragraphs-categorization huggingface dataset:
Our TWAS analyses using both ancestry-matched and ancestry-discordant lung eQTL datasets identified both common and unique genes from each dataset, highlighting potential benefits of an eQTL dataset of larger sample size and the importance of an ancestry-matched eQTL dataset, even at a smaller sample size, in detecting susceptibility genes. We evaluated the presence of a gene-environment interaction with tobacco smoking in our EA data. We found that the association between a PRS (constructed by the lead variants at the 25 loci with genome-wide significance in EA) and LUAD in never-smokers was statistically significantly stronger than in individuals with a history of smoking (Fig. 4 ). This finding, together with our recent paper showing a stronger association of PRS for LUAD risk in non-coal users than in coal users  71  , provides evidence that genetic susceptibility may vary by exposure patterns in EA populations.
Our genetic architecture analysis suggested that LUAD is a highly polygenic disease. Expanding GWAS of LUAD will continue to identify many risk variants albeit with smaller effect sizes. Moreover, our analysis predicts that the AUC of PRS for EA never-smokers could be improved to 66. 9% for a GWAS training dataset with 70,000 cases and 70,000 controls that could be further increased with a greater number of controls. Thus, an expanded GWAS in the future can lead to the substantial improvement in knowledge about the underlying genetic architecture of LUAD; increased understanding of how known or suspected lung cancer environmental risk factors interact with genetic susceptibility; and assessment of the potential clinical utility of risk models integrating both genetic and non-genetic risk factors  74, 75.
There are several limitations in the current study. First, the discovery phase included subjects of diverse EA populations (Mainland China 38. 2%, Japan 45. 9%) and the replication phase only included subjects from Japan. However, our data did not show evidence of heterogeneity in effect sizes for susceptibility variants between Han Chinese and Japanese populations or across geographic locations (Supplementary Table  5 ), suggesting a minimal impact for using a single EA population for replication. Second, we were underpowered to conduct formal heritability correlation analyses to compare the genetic architecture in EA and EUR populations stratified by smoking status; larger studies will be needed to conclusively characterize differences. Furthermore, completely elucidating the genetic basis of ancestry differences requires detailed information about age of onset, family history and exposures.
For "1 million controls", the x-coordinate represents the number of cases, assuming the study has 1 million controls. For "Equal number of cases and controls", the x-coordinate represents the numbers of cases, assuming the same number of cases and controls. analysis of EA and EUR populations. Colocalization and TWAS analyses using an ancestry-matched lung tissue eQTL dataset identified candidate susceptibility genes with suggested roles in alveolar lineage. At the same time, a large majority of variants identified in the EA GWAS showed no evidence of association in EUR populations. Larger samples sizes with data on environmental risk factors will be needed to further characterize the etiologic differences between these populations. Finally, our genetic architecture analysis suggests that the performance and the clinical utility of the PRS will be substantially improved by larger GWAS in the future.
Quality control, imputation and association analysis in EA populations For each study, SNPs with minor allele frequency (MAF) < 0. 01, Hardy-Weinberg Equilibrium (HWE) p value < 10 -6 in controls were removed; subjects with missing rate >3%, sex discrepancy, or displaying non-East Asian ancestry based on principal component analysis scores were removed. Moreover, for any pairs of subjects estimated to be related with identity by descent pihat >0. 10 using PLINK (V2. 0), we removed one subject. Imputation was performed using IMPUTE2 and the 1000 Genomes Project East Asian samples (Phase 3) as reference. After imputation, SNPs with imputation quality score ≥ 0. 5 were used for association analysis in each study. Logistic regression under an additive model was performed using SNPTest (V2) or PLINK2 based on imputed genotypic dosage data adjusting for smoking (if both smokers and never smokers were present) and PCA scores to control for population stratification.
Conditional analysis and fine mapping To identify independently associated SNPs at an established susceptibility locus, we performed conditional analysis using software Genome-wide Complex Trait Analysis (GCTA)  79  based on the GWAS meta-analysis summary results of EA populations. LD for the conditional analysis was calculated using a reference population of 4544 controls from the FLCCA study to achieve a desirable accuracy. Here, genotypes for FLCCA were imputed using IMPUTE2 and the 1000 Genomes Project (Phase 3) reference samples with EA ancestry. SNPs with imputation quality <0. 5 were excluded from the reference set for conditional analysis. Conditional analysis was restricted to 14 loci with lead SNPs achieving genome-wide significance in the discovery-phase meta-analysis. We did not perform conditional analyses for other new SNPs that did not achieve genome-wide significance in the discoveryphase meta-analysis because secondary SNPs would not survive multiple testing correction.
In addition to the functional annotations provided by the sLDSC package, we also analyzed the gene sets defined by smoking studies: differentially expressed genes in peripheral blood mononuclear cells upon nicotine treatment ("PBMC nicotine" gene set) from Moyerbrailean et al. 81  , those in non-tumorous lungs between current-and never-smokers ("Lung smoking" gene set) from Bosse et al. 82  , and those in normal bronchial airway epithelial cells between current-and neversmokers ("Airway smoking" gene set) from Beane et al. 83. An annotation was considered to be significantly enriched for LUAD heritability if FDR < 0. 05. We then performed sLDSC to prioritize relevant tissue types (lung, blood/immune, and brain/CNS) using tissue-specific expressed genes from GTEx v6p (53 tissue types) and other public expression datasets (152 tissue types), as well as tissue-specific chromatin annotations from EnTEX (111 annotations in 26 tissue types) and Roadmap dataset (378 annotations in 85 tissue types) as described by Finucane and colleagues  36.
We used GTEx v6p expression data based on a comparison with v8 data, where a median of 83% of tissue-specific differentially expressed genes were shared between two versions. In general, we did not find significant enrichment for individual annotations after adjusting for the multiple testing. To increase the power of prioritizing relevant tissues (lung, blood/immune, and brain/CNS), we performed an aggregated analysis to test if p values from one tissue (e. g. , lung) tended to be smaller than those from the other two tissue groups (blood/immune, and brain/CNS) using the Wilcoxon rank test.
eQTL colocalization analysis and TWAS EA lung eQTL dataset is based on a cohort of 115 never-smoking LUAD patients from Taiwan, referred to as LCTCNS (Lung cancer tissue cohort of never-smokers). Expression array data was obtained for nontumor lung tissues of these patients using the Illumina WG-DASL HumanRef-8 v3 or HumanHT-12 v4 BeadChip (Illumina Inc. ) (Gene Expression Omnibus accession number GSE46539)  84. Genotype data from buffy coat DNA was obtained using the Illumina Human660W-Quad BeadChip. A systematic quality control for the genotype data was performed as previously described  12  (SNPs were excluded if call rate <90%, MAF < 5%, or P < 0. 0001 based on the Hardy-Weinberg equilibrium test. Samples were excluded if call rate <90%, sex discrepancies based on the X chromosome heterozygosity, contaminated samples with high heterozygosity scores, or first or second-degree relatives), and imputation was carried out using Minimac4 (V4.
eQTL analysis was conducted for 29 GWAS lead SNPs (all EA loci including discovery, replication, and conditional signals plus new loci from the multi-ancestry GWAS). In LCTCNS, all these SNPs have a MAF of >0. 01. For each GWAS lead SNP, its association with each probe located within ±500 kb of the SNP was tested using an additive linear model where the dependent variable was the expression level as described above and the independent variable was the effect allele count. Based on the resulting p values of these eQTL analyses for all 29 SNPs, the corresponding Benjamini-Hochberg FDR was calculated. Colocalization analysis was performed using eCAVIAR  37  and HyPrColoc 38 via ezQTL platform for eight GWAS lead SNP-eQTL gene pairs displaying FDR < 0. 05 in LCTCNS (Supplementary Data 5). For each of these eight SNP-probe pairs, we further examined the association between the probe and SNPs within ±100 kb of the lead SNP using Matrix eQTL to obtain the summary statistics as an input to ezQTL for colocalization analysis using HyPrColoc and eCAVIAR.
For loci on MHC regions, ±10 kb window was used for computational efficiency of colocalization analyses. LD matrix was obtained from 1000 Genomes EA populations. For HyPrColoc, posterior probability of >0. 7 was used as a cutoff for colocalization. For eCAVIAR analysis, colocalization posterior probability (CLPP) score > 0. 01 was used as a cutoff for colocalization. For TWAS, we adopted FUSION  85  using LCTCNS or GTEx v8 lung eQTL data and summary statistics of EA discovery GWAS metaanalysis. We computed weights using the elastic-net regression model for 24,216 expression probes (LCTCNS) or 24,687 genes (GTEx v8 lung) and cis-SNPs within 500 kb of the gene for each probe. LD matrix was obtained from 1000 Genomes EA populations. We performed association analysis for 1875 expression probes (LCTCNS) or 5534 genes (GTEx v8 lung) with cross-validation cutoff of R 2 > 0.
Data availability All data supporting the findings described in this paper are available in the paper and in the Supplementary Information and from the corresponding author or as otherwise indicated upon request. Full TWAS results are included in Supplementary Data 6. The summary statistics for the meta-analysis of the 4 GWAS datasets in East Asian populations for SNPs with p ≤ 0. 01 are in Supplementary Data 10. The results of the replication study for the 38 SNPs tested and the meta-analysis with the GWAS data are in Supplementary Data 11. For the FLCCA study, the GWAS summary data for SNPs with p < 0. 01 in the study and all SNPs with genome-wide significance in the meta-analysis of East Asian samples are in Supplementary Data 12. The individual genotype data for the FLCCA data are in dbGaP phs000716.
cgi?study_id=phs001273. v3. p2). To gain access to all data in dbGaP cited in this paper, please apply for dbGaP Authorized Access. The expression data of the lung cancer tissue cohort of never-smokers in Taiwan are publicly available at Gene Expression Omnibus under accession number GSE46539. The expression and eQTL data from GTEx (v6 and v8) are available from https://gtexportal. org/home/ datasets.
Acknowledgements This work utilized the computational resources of the  NIH HPC Biowulf cluster. (http://hpc. nih. gov). Female  Lung Cancer Consortium in Asia (NCI ): This study was supported by a  Grant-in-Aid for Scientific Research on Priority Areas  from the  Ministry of Education, Science, Sports, Culture and Technology of Japan , a  Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control  from the  Ministry Health, Labor and Welfare of Japan , by  Health and Labor Sciences Research Grants for Research on Applying Health Technology  from the  Ministry of Health, Labor and Welfare of Japan , by the  National Cancer Center Research and Development Fund , the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MEST)  (grant No. 2011-0016106 ), a grant of the  National Project for Personalized Genomic Medicine, Ministry for Health & Welfare, Republic of Korea  ( A111218-11-GM04 ), the  Program for Changjiang Scholars and Innovative Research Team in University in China  ( IRT_14R40  to K.
This work was also supported by the  Jinan Science Research Project Foundation  ( 201102051 ), the  National Key Scientific and Technological Project  ( 2011ZX09307-001-04 ), the  National Natural Science Foundation of China  (No. 81272293 ), the  State Key Program of National Natural Science of China  ( 81230067 ), the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIP)  (No. NRF-2014R1A2A2A05003665 ),  Sookmyung Women's University Research Grants ,  Korea  ( 1-1603-2048 ),  Agency for Science, Technology and Research (A*STAR), Singapore  and the  US National Institute of Health  Grant ( 1U19CA148127-01 ). The overall  GWAS  project was supported by the intramural program of the  US National Institutes of Health/National Cancer Institute. The following is a list of grants by study center: SKLCS (Y.
T. K. )- National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MEST)  ( 2011-0016106 ). (J. C. ) -This work was supported by a grant from the  National R&D Program for Cancer Control, Ministry of Health &Welfare, Republic of Korea  (grant no. 0720550-2 ). (J. S. S)-grant number is A010250. WLCS (T. W. )- National Key Basic Research and Development Program  ( 2011CB503800 ). SLCS (B. Z. )- National Nature Science Foundation of China  ( 81102194 ). Liaoning  Provincial Department of Education  ( LS2010168 ). China Medical Board  ( 00726 ). GDS (Y. L. W. )-Foundation of  Guangdong Science and Technology Department  ( 2006B60101010 ,  2007A032000002 ,  2011A030400010 ). Guangzhou Science and Information Technology Bureau  ( 2011Y2-00014 ). Chinese Lung Cancer Research Foundation ,  National Natural Science Foundation of China  ( 81101549 ).
JLCS (K. M. , T. K. )- Grants-in-Aid  from the  Ministry of Health, Labor, and Welfare for Research on Applying Health Technology  and for the 3rd-term Comprehensive 10-year Strategy for Cancer Control; by the  National Cancer Center Research and Development Fund ; by  Grant-in-Aid for Scientific Research on Priority Areas and on Innovative Area  from the  Ministry of Education, Science, Sports, Culture and-Technology of Japan. (W. P. )-NCI  R01-CA121210. HKS (J. W. )- General Research Fund of Research Grant Council ,  Hong Kong  (781511 M). The  Environment and Genetics in Lung Cancer Etiology (EAGLE) ,  Prostate, Lung, Colon, Ovary Screening Trial (PLCO) , and  Alpha-Tocopherol ,  Beta-Carotene Cancer Prevention  (ATBC) studies were supported by the  Intramural Research Program  of the  National Institutes of Health, National Cancer Institute (NCI), Division of Cancer Epidemiology and Genetics.
The  Carotene and Retinol Efficacy Trial (CARET)  is funded by the  National Cancer Institute, National Institutes of Health  through grants  U01-CA063673 ,  UM1-CA167462 , and  U01-CA167462. The  Cancer Prevention Study-II (CPS-II) Nutrition Cohort  was supported by the  American Cancer Society. The  NIH Genes, Environment and Health Initiative (GEI)  partly funded DNA extraction and statis-tical analyses ( HG-06-033-NCI-01  and  RO1HL091172-01 ), genotyping at the  Johns Hopkins University Center for Inherited Disease Research. This research was supported by the National.
Materials and Methods Microfluidic experimental setup: Droplets production (two entries chip): Droplets were produced in two-entries chips. Flow rates were controlled by Fluigent software. Flow rates of 1000 μL/h for aqueous phase and of 1200-1400 μL/h for fluorinated oil phase (3M HFE 7500) were used to create droplets of 40-50 pL. Emulsion was collected in an Eppendorf filled with oil and closed with a PDMS plug to prevent coalescence due to contact with air. For control experiments, 2. 5% w/w of non-functionalized surfactant (008-FluoroSurfactant, RAN Biotechnologies) was used in oil phase. For SPAAC reaction, the functionalized surfactant were used at 2. 5% w/w in oil phase. Biomolecules were introduced via the aqueous phase and dissolved in CHAPS (1 mM in PBS 1X). Droplets production for cell encapsulation (three entries chip): Droplets were produced in three-entries chips.
Flow rates were controlled by Fluigent software. Flow rates of 500 μL/h for aqueous phase 1 and 2 and of 1000-1200 μL/h for fluorinated oil phase (3M HFE 7500) were used to create droplets of 40-50 pL. Emulsion was collected in an Eppendorf filled with oil and closed with a PDMS plug to prevent coalescence due to contact with air. For SPAAC reaction, the functionalized surfactant were used at 2. 5% w/w in oil phase. Biomolecules were introduced via one aqueous phase and dissolved in CHAPS (1 mM in PBS 1X). E. Coli were brought via the second aqueous phase in PBS 1X. Aqueous phases had identical flow rates in order for the ratio of the two aqueous phases in the droplet to be 1:1. Picoinjection: Picoinjection were done in three entries chips. Flow rates were controlled by Fluigent software. Flow rates of 1700 μL/h for fluorinated oil phase (3M HFE 7500), 1000 μL/h for emulsion sample, and 180 μL/h for picoinjected solution were used to picoinject droplets.
ACKNOWLEDGMENT Performed within  Interdisciplinary Thematic Institute "IMCBio " ( ITI 2021-2028 program  of  University of Strasbourg ,  CNRS  and  Inserm ), this work was also supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ),  SFRI-STRAT'US  project ( ANR-20-SFRI0012 ),  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program , the previous  LabEx NetRNA  ( ANR-10-LABX-0036 ), and also supported by  Centre National de la Recherche Scientifique (CNRS) ,  Université de Strasbourg , and its  Initiative of Excellence (IdEx).
Ca (100) surface is used in this study. DFT calculations are performed using the Vienna ab initio simulation package (VASP)  [28, 29]  within the framework of the Perdew-Burke-Ernzerhof  [30]  exchange-correlation functional with the Grimme DFT-D3  [31]  correction to include van der Waals interactions in the system. The influence of outer solvent molecules on the first solvation sphere is taken into account using the polarizable continuum model (PCM) provided in VASPSOL. [32, 33] The PCM is parameterized with the solvent dielectric constant, which is  r = 7. 20 and 7. 50 for G1 and G4, respectively. All calculations are performed with a cut-off energy of 550 eV and a k-points grid of 3 × 3 × 1. Structural relaxation is performed on all atoms except the central Ca layer of the slab, and the residual forces after structural relaxation are lower than 0.
These data suggested that AeORM1 and AeORM2 might act in concert and that AeORM1, in addition to nodulation, may have other roles in the development of A. evenia plants. To analyze the spatial expression of the AeORM1 gene, we fused the ~ 2. 1 kb promoter region to the GUS reporter gene. This pAeORM1-GUS construct was used to transform WT A. evenia roots with A. rhizogenes and GUS activity was monitored in non-and inoculated roots. In non-inoculated roots, GUS staining was observed in lateral root primordia as found, in particular, in the younger part of the primary root (Figure  3A  to C). Once emerged from the primary root, GUS staining was observed both at the base and tip of lateral roots (Figure  3D  to F). After inoculation with Bradyrhizobium ORS278, enhanced GUS staining was observed at nodule primordium initiation sites located at lateral root bases and where both rhizobial infection and cell divisions occur (Figure  3G  to K).
In silico gene analysis Genes homologous to AeORM1 were identified in legume species by mining the orthogroups database generated with OrthoFinder  (Quilbé et al. , 2021). The dataset was completed by searching for other legume genes as well as Arabidopsis and rice ORM genes in the Legume Information System (https://www. legumeinfo. org), The Arabidopsis Information Resource (https://www. arabidopsis. org) and the Rice Genome Annotation Project (http://rice. uga. edu) databases, respectively. A ML phylogenetic tree reconstruction was obtained by aligning nucleotide sequences with the MUSCLE program that is incorporated in the MEGA X (v10. 1. 8) software. Aligned sequences were further processed in MEGA X using the maximum likelihood approach and the Kimura 2-parameter model with a 1000x bootstrap (BS).
The data are presented as rooted tree using rice as outgroup. For the AeORM1 and AeORM2 genes, microsynteny analysis was performed using the Legume Information System with the Genome Context Viewer (https://legumeinfo. org/lis_context_viewer) to visualize the gene collinearity in syntenic regions and their expression patterns were obtained using the A. evenia gene atlas available at the AeschynomeneBase (http://aeschynomenebase. fr/content/gene-expression). Protein sequences were aligned using the MUSCLE program in the MEGA X software and sequence alignments were visualized with Jalview v2. 11. 0. AeORM1 protein domains were identified, annotated using InterProscan (http://www. ebi. ac. uk/interpro/) and DeepTMHMM (https://dtu. biolib. com/DeepTMHMM), and refined by comparative structural analysis with other Arabidopsis ORM proteins.
Analysis of promoter-GUS expressing plants The pCambia2200DsRED vector containing the proAeORM1-GUS fusion was transformed into Agrobacterium rhizogenes strain ARqua1, and cells harboring this plasmid were used to transform WT A. evenia as described  (Quilbé et al. , 2021). Plants developing transformed roots were selected as described above. The AeORM1 promoter activity was analyzed in noninoculated roots and at different times after inoculation with the Bradyrhizobium strain ORS278. Untransformed WT seedlings were used as negative control. Whole roots and 70µm-thick nodule sections (obtained with a Leica VT1000S vibratome (Nanterre, France) were stained with X-gluc  (Fabre et al. , 2015). Images of GUS-analyzed tissues were taken with a stereo-macroscope (Niko AZ100, Champigny-sur-Marne, France) using the Nikon Advanced software.
Statistical analyses To evaluate statistically the monogenic determinism of the three orm1 mutants a Student's ttest was performed t. A Mann-Whitney test was used to compare gene expression levels in the WT and orm1-1 lines. One-way Kruskal-Wallis analysis was performed to compare root parameters between the WT line and the three orm1 mutants. Values of P < 0. 05 were considered statistically significant. A Welch's t-test was used to compare sphingolipid levels between orm1 mutants and the WT line. The statistical analyses were done using the R package or GraphPad Prism 8. 3. 0. For each analysed gene, no statistically significant differences were observed between WT and orm1-1 mutant plnats (Mann-Whitney test ; P < 0,05).
ATP hydrolysis activity of Srs2 898 point mutants Srs2 exhibits robust DNA-dependent ATP hydrolysis activity in vitro  40, 41, 73. The Srs2 mutant K41A has a lysine to alanine substation within the Walker A nucleotide-binding motif and is deficient for ATP hydrolysis activity  40. ATP hydrolysis is intimately coupled to Srs2 function and as such the K41A mutant has no detectable helicase activity and cannot dismantle Rad51-ssDNA filaments  40, 43, 46. All of the Srs2 898 mutants were expressed in E. coli and purified to near homogeneity (Supplementary Fig. 2a ). ATP hydrolysis assays were conducted using 40 nM of each specified Srs2 898 protein and 0. 5 to 8. 0 mM ATP (as indicated). Reaction products were resolved by thin layer chromatography (Supplementary Fig. 2b ), data quantitated using phosphor imaging and graphed as Michaelis-Menten plots for analysis (Fig.