dataesr/scientific-paragraphs-categorization huggingface dataset:
05/27,941 = 1. 79 x 10 -6 for the P S-MultiXcan ). Novel associations were called when >1Mb from both a GWAS-significant SNP and a TWAS locus. As expected, all these loci showed evidence of a risk association in the full TWAS (FDR < 0. 05, P < 2. 86 x 10 -3 ). Transcripts with boundaries < 1 Mb apart were considered to be in the same cluster. This resulted in seven CRC associations. One further association (*) was identified based on conditional TIsWAS analysis (Supplementary Table  8 ). Other annotations are as per Table  5. Colorectal cancer risk associations identified by methylome-wide association study. SMultiXcan uses a two-sided F-test to quantify the significance of the joint fit of the linear regression of the phenotype on predicted expression from multiple tissue models jointly. All associations shown were methylome-wide significant after Bonferroni correction for 88,888 CpGs with an S-PrediXcan model (P = 0.
Criteria for declaring new CRC risk associations Multi-omic studies present inherent difficulties for deciding on what constitutes a novel GWAS, TWAS or MWAS association. To declare statistically significant associations, for GWAS we have used the established threshold of P = 5 x 10 -8. We applied this to both loci >1Mbp from a previously known SNP and analyses conditioned on the most significant SNP within 1Mb region. For TWAS or MWAS we also followed convention and used a Bonferroni correction P = 0. 05/N, where N is the number of gene models successfully derived from the reference tissue. Furthermore, for TIsWAS and cross-tissue TWAS, we used Bonferroni-corrected P-value thresholds for significance in each of the reference tissue data sets separately, owing to the overlap in between tissue groups and the fact that many eQTLs are present across tissues.
GWAS data analysis Meta-analysis: Within each of the 31 analytical units, we conducted logistic regression under a log-additive model to examine the association between allelic dosage for each genetic variant and the risk of CRC, adjusted for unit-specific covariates. Meta-analysis under a fixed-effects inverse-variance weighted model was performed using META v1. 7  25. Variants in the metaanalysis only included those with an imputation quality score (info/R 2 ) > 0. 4, MAF > 0. 005, and seen in at least 15 analytical units. The I 2 statistic was calculated to quantify between study heterogeneity and variants with I 2 > 65% were excluded. A total of 8,782,440 variants were taken forward in the meta-analysis. Meta-analysis of risk estimates was conducted under an inverse To estimate the sample size required to detect a given proportion of the GWAS heritability, we made use of GENESIS software (GENetic Effect-Size distribution Inference from Summary-level data)  31  , which implements a likelihood-based approach to model the effect-size distribution in conjunction with LD information, using the three-component model (mixture of two normal distributions).
TWAS analysis Gene expression models for the six in-house expression datasets were generated using the PredictDB v7 pipeline for a total of 1,077 participants  9, 10. Elastic net model building with 10-fold cross-validation was performed independently for each dataset. The elastic net models for GTEx v8 Colon Transverse were obtained from the PredictDB data repository (http://predictdb. org/) and had been generated using the same pipeline. Models were computed using HapMap2 SNPs ±1Mb from each gene, together with covariate factors estimated using PEER  32  , clinical covariates when appropriate (age, sex and, where appropriate, case-control status, type of polyp and anatomic location in the colorectum), and three PCs from the individual dataset's SNP genotype data. Transcriptome-wide association tests were then performed for each dataset with the S-PrediXcan feature using summary statistics from the GWAS meta-analysis.
We used individual level GWAS data from GECCO (n=8,725) to derive the LD reference covariance matrix. S-MultiXcan analysis was then undertaken across datasets. Significant associations were declared using Bonferroni correction (0. 05/number of gene models from S-MultiXcan). As recommended  33  , an additional filter of a TWAS association statistic, P S-PrediXcan ≤ 10 -4 , in at least one individual reference data set was implemented to minimize potential errors due to LD mismatches. Genes localizing to the HLA/MHC region (chr6:28,477,797-33,448,354bp) were excluded. Transcript-based TWAS analyses (TIsWAS) were likewise performed by using transcript-level data from the SOCCS, BarcUVa-Seq and GTEx Colon Transverse datasets. 0. 02 in >5% of samples, probes with <3 reads in >5% of samples per probe and all non-CpG probes.
Further analysis was performed as per the TWAS. CpGs were annotated to a known GWAS signal if within 1Mb of a genome-wide significant GWAS risk SNP and otherwise considered novel. For the MWAS models the number of CpG probes successfully predicted with R 2 > 0. 01 (equivalent of R>0. 1) varied from 24325 for INTERMPHEN rectum to 30385 for COLONOMICS. The mean CV-based prediction R 2 for all genes varied from 0. 14 (25th-7th percentile 0. 07-0. 16) for INTERMPHEN proximal dataset to 0. 19 for SOCCS (0. 07-0. 25).
Conditional analysis using sMiST for TWAS and MWAS findings S-MultiXcan is a powerful method for assessing predicted gene expression across multiple tissues and samples, but cannot readily undertake conditional analysis to determine independence of a TWAS or MWAS association from other GWAS, TWAS or MWAS associations. We therefore used the summary statistics-based Mixed effects Score Test (sMiST)  36  method to perform conditional analysis of TWAS, TIsWAS and MWAS data adjusting for GWAS risk SNPs. sMiST can assess the total effect, including both predicted molecular features (gene expression or methylation) and the residual direct effects of SNPs that are not explained by predicted molecular features, on CRC risk. To be consistent with S-MultiXcan, we only assessed the association of predicted molecular features.
Funding and acknowledgements At the Institute of Cancer Research, this work was supported by Cancer Research UK (C1298/A25514 -RSH). Additional support was provided by the National Cancer Research Network. In Edinburgh, the work was supported by Programme Grant funding from Cancer Research UK (C348/A12076 to MGD, C6199/A16459 to IT), EU ERC Advanced Grant EVOCAN, and the infrastructure and staffing of the Edinburgh CRUK Cancer Research Centre. CFR was supported by a Marie Sklodowska-Curie Intra-European Fellowship Action (IEF-301077) for the INTERMPHEN project and received considerable help from many staff in the Department of Endoscopy at the John Radcliffe Hospital in Oxford. Support from the European Union [FP7/207-2013, grant 258236], FP7 collaborative project SYSCOL, and COST Actions EuColonGene and TransColonCan are also acknowledged [BM1206 and CA17118] (IT).
Data availability Summary level data for the full set of Asian and European GWAS are available through GWAS catalog (accession number GCST90129505). For individual-level data, CCFR, CORECT, CORSA_2 and GECCO are deposited in dbGaP (phs001415. v1. p1, phs001315. v1. p1, phs001078. v1. p1, phs001903. v1. p1, phs001856. v1. p1 and phs001045. v1. p1). NSCCG and COIN are available in the European Genome-phenome Archive under accession numbers EGAS00001005412 (NSCCG), EGAS00001005421 (COIN). UK Biobank data are available through http://www. ukbiobank. ac. uk/ and Finnish data through THL Biobank. Access to individual-level data for the remaining studies is controlled through oversight committees. CCFR 1 and CCFR 2 data can be requested by submitting an application for collaboration to the CCFR (forms, instructions and contact information can be located at (www.
coloncfr/collaboration. org). Applications for individual level data from the QUASAR2 and SCOT clinical trials will be assessed by the Translational Research Steering Committees that oversee those studies. Individual level data from the CORGI (UK1) study will be made available subject to standard institutional agreements. Application forms for these three studies, and for Scotland Phase 1, Scotland Phase 2, SOCCS, DACHS4 and Croatia, will be provided by emailing a request to access. crc. gwas. data@outlook. com. For access to CORSA_1, please contact gecco@fredhutch. org. For Generation Scotland (GS) access is through the GS Access Committee (GSAC) (access@generationscotland. org). Applications for The Lothian Birth Cohort data should be made through https://www. ed. ac. uk/lothian-birth-cohorts/data-accesscollaboration.
For details of the application process for Aichi1, Aichi2, BBJ, Guanzhou1, HCES, HCES2, Korea and Shanghai cohorts, please go to https://swhs-smhs. app. vumc. org/ or contact Dr. Zheng at wei. zheng@vanderbilt. edu. CRC-relevant epigenome data were obtained from the NCBI Gene Expression Omnibus (GEO) database under accession number GSE77737 and GSE36401. variance weighted, fixed-effects model  3. None of the analytical units showed strong evidence of genomic inflation ( ranged from 0. 95 to 1. 28), and the value for the meta-analysis was 1. 30 ( 1000 = 1. 01) Supplementary figure  3 ). To account for any -ancestral differences between analytical units, we implemented MR-MEGA v0. 1. 5  26 , including 10 principal components (PCs) in the analysis. To measure the probability of associations being false positives, the Bayesian False-Discovery Probability (BFDP)  3  was calculated based on a plausible odds ratio (OR) of 1.
A genome-wide significant risk variant was considered novel if >1Mb from a known risk variant. GWAS conditional analysis: To identify independent association signals at the discovered CRC risk associations, we performed conditional analyses using GCTA-COJO  24  on the meta-analysis summary statistics. Analyses were performed separately for European and East Asian ancestry populations, to account for LD structure differences. The conditioned data were meta-analyzed together as described above, and associations with P conditional < 5 × 10 -8 were considered novel secondary associations. As reference for LD estimation, we made use of genotyping data from 6,684 unrelated samples of East Asian ancestry, and 4,284 samples from combined UK10K and European samples in 1000 Genomes.
Heritability analysis Fernandez-Rozadilla et al 7 We used the LDSC regression package with default parameters as implemented in LD Hub 27 to estimate the SNP heritability from the GWAS meta-analysis summary statistics data  3. SNPs were filtered to HapMap3 SNPS with 1000 Genomes EUR MAF above 5%. SNPs with imputation info score < 0. 9, MAF < 0. 01 and within the major histocompatibility complex (MHC) region (i. e. SNPs between 26Mb and 34Mb on chromosome six were excluded. Precalculated LD scores files computed using 1000 Genome European data were used. The contribution of risk SNPs to the familial risk of CRC was calculated as , where λ 0 is the familial risk to first-degree relatives of CRC cases, assumed to be 2. 2  28  , and λ k is the familial relative risk associated with SNP k, calculated as , where p k is the risk allele frequency for SNP k, q k = 1-p k , and r k is the estimated per-allele OR from the meta-analysis  3, 29.
Pleiotropy analysis We explored cross-trait pleiotropic effects using the LDSC regression package with default parameters  30  as implemented in LD Hub. The summary statistics for 252 phenotypes were extracted from LD Hub. For comparability of results across the traits we limited our analysis to the CRC GWAS of European ancestry. After excluding GWAS performed on non-European cohorts, traits where the LD Hub output came with the following warning messages: "Caution: using this data may yield results outside bounds due to relative low Z score of the SNP heritability of the trait" and "Caution: using this data may yield less robust results due to minor departure of the LD structure", as well as highly correlated traits, 171 phenotypes were included in the analysis. The departure of the LD structure means departure from the assumption of equal LD structure between two datasets, e.
Sample size prediction Additional TWAS analyses were similarly performed using the non-colonic mucosa tissue data available from GTEx. These correspond to S-PrediXCan elastic net models from 48 additional GTEx tissues with eQTL data and the DGN whole blood cohort. Five tissue groupings were tested: "Sigmoid colon", corresponding to muscle and other sub-epithelial tissues; "Immune", comprising DGN + GTEx Cells_EBV-transformed_lymphocytes + GTEx Whole_Blood + GTEx_Spleen (n=1,966 samples); "Mesenchymal", comprising GTEx Adipose_Subcutaneous + GTEx Adipose_Visceral_Omentum + GTEx Cells_Cultured_fibroblasts (n=1,533 samples); "Gastrointestinal", comprising six in-house datasets + GTEx Pancreas + GTEx Liver + GTEx Stomach + GTEx Terminal_Ileum + GTEx Oesophageal_Mucosa + GTEx Colon_Transverse; n=2,615 samples); and "All", comprising the six in-house datasets + all 49 GTEx tissues + DGN (n=16,832 samples).
The predictive performance of the models for TWAS and TisWAS across the datasets was similar. For the TWAS models the number of genes successfully predicted with R 2 > 0. 01 (equivalent of R>0. 1) varied between 3308 for the BarcUVa data set and 5092 for SOCCS rectum, while GTEx Colon Transverse models were available for 6295 genes. The mean CV-based prediction R 2 for all genes varied between 0. 09 (25-75th percentile 0. 04-0. 12) for BarcUVa to 0. 19 for INTERMPHEN (0. 07-0. 24), compared with 0. 12 (0. 04-0. 16) for GTEx Colon Transverse model. The numbers were slightly higher when comparing the overlapping 736 genes only. The in-house TisWAS models were constructed for a lesser number of transcripts (n=4632 for BarcUVa dataset and n=11262 for SOCCS rectum dataset) compared to GTEx Colon Transverse (n=15500), owing to greater read depth and larger sample size for GTEx.
27E-07 All -1. 160 -0. 167 2 2 rs7530780 93,130,268 4. 18E-05 2 LINGO4 1 151,772,740 151,778,546 2. 73E-08 All 1. 666 0. 034 27 6 rs9826 151,778,899 3. 81E-06 3 FAM98A 2 33,808,725 33,824,429 2. 98E-06 Immune 4. 672 0. 166 1 1 rs1448561 33,854,344 5. 92E-07 4 FBLN7 2 112,895,962 112,945,793 1. 28E-06 All -0. 711 -0. 023 28 10 rs7580507 112,879,209 2. 71E-07 5 ARHGEF4 2 131,671,559 131,804,836 2. 33E-08 All -0. 243 -0. 026 14 8 rs73960398 131,795,345 4. 86E-06 6 GBE1 3 81,538,850 81,811,312 1. 95E-12 All -0. 557 -0. 032 8 7 rs554330436 81. 039,172 1. 69E-04 7 DIRC2 3 122,513,642 122,599,986 1. 25E-06 All 0. 812 0. 003 16 13 rs6774610 122,521,477 6. 85E-07 8 GAB1 4 144,258,304 144,395,721 1. 11E-07 All 1. 756 0. 040 10 6 rs72726477 143,517,452 2. 91E-05 "Gastrointestinal": the 6 in-house colorectal mucosa datasets + GTEx Pancreas + GTEx Liver + GTEx Stomach + GTEx Terminal_Ileum + GTEx Oesophageal_Mucosa + GTEx Colon_Transverse (n=2,615 samples; P Bonferroni = 3.
| EXPERIMENTAL DETAIL Raman spectra were recorded on both ventral and dorsal sides of each artefact focusing on obtaining differences between the hafted and working halves of the tools. A WITec 3000 Alpha spectrometer was used for the Raman analyses, with a 532-nm solid state laser (WITec Focus Innovation) as excitation source. A long distance 50 Zeiss objective focused the laser on the samples to a spot size of $2 μm 2 for recording single spectra, and the 20Â objective was used to map small surface areas. The number of acquisitions and acquisition time depended on the crystallinity of the material, but in general, an acquisition time of 12 Â 6 s with a spectral resolution of 2 cm À1 was used. In some instances, spectra were baseline corrected using the WiTec software. Laser power was kept to a minimum and in most instances was below the measuring threshold of the handheld Coherent laser power meter (10 μW -10 mW), as laser heating had an influence on the relative intensity and position of Raman bands; see Figure  S5  (Supporting Information).
ACKNOWLEDGEMENTS We thank  Prof. Bruce Cairncross  for the graphite sample from  Umzimzulu Quarry in KwaZulu-Natal. LW thanks  Russell Thompson  for advice and for cutting the wood strips and fixing them with the experimental adhesives for stress testing. Scrooby's Laboratory in Benoni performed the stress tests with the  Universal Testing Machine. The research was carried out with a permit obtained from the  South African Heritage Agency.
71, 95% CI=0. 57-0. 90). In another study  (37, 688  cases with some overlap with the previous study, 18,618 UK Biobank proxy-cases, 1. 4 million controls), there was a trend towards an inverse association between ever-smoking and PD that was not statistically significant (OR per log odds of ever-smoking=0. 94, 95% CI=0. 88-1. 01). [28]  Recently, another MR study used the same dataset to assess the role of several tobacco behaviours (smoking initiation, continuation, heaviness, and age at initiation). They showed that smoking continuation (current versus former smokers) was inversely associated with PD (OR per doubling of odds for smoking continuation=0. 64, 95%CI=0. 46-0. 89, p=0. 008) but smoking initiation, heaviness, or age at initiation were not. However, they used only 87 SNPs for smoking initiation and analyses were not stratified by smoking status for analyses of smoking characteristics among smokers.
[35]  The iPDGC PD MR Portal, a platform which offers MR analyses for a total of 5,839 GWAS versus the largest iPDGC GWAS available with some overlap with the two previous studies, also reports some evidence to support a protective effect of current smoking using a genetic instrument that included 12 SNPs. [36]  These studies are not independent as they relied on overlapping datasets and did not examine associations in younger subjects or in those with shorter disease duration to assess whether survival or incidence-prevalence bias may have distorted MR estimates. Our study replicates these findings and add further evidence in favour of an association between smoking initiation and lower PD risk; in addition, our findings show that genetic liability toward PD is not associated with smoking initiation which does not support the hypothesis of reverse causation for smoking.
Alcohol drinking A meta-analysis of the relation between alcohol and PD reported an inverse association in case-control studies but not in cohort studies, with marked heterogeneity across studies. [46]  Two of the cohort studies reported positive associations, while three reported inverse associations, but none of them was statistically significant. A previous MR study that examined several risky behaviours in relation with PD using iPDGC data (9,581 cases, 33,245 controls) did not provide evidence in favour of an association for the number of drinks per week (70 SNPs; OR=1. 15, 95% CI=0. 87-1. 53) or alcohol consumption (7 SNPs; OR=1. 39, 95% CI=0. 11-17. 56). [34]  In contrast, another MR study that used the most recent iPDGC dataset showed an inverse and significant association with the number of drinks per weeks (OR=0.
Strengths and weaknesses of the study Strengths of this study include the assessment of several lifestyle behaviours and use of data from a large consortium in which PD cases were carefully assessed by experienced movement disorders specialists. The MR design represents another strength as it avoids bias from reverse causation and confounding. [8]  This approach, however, relies on a number of assumptions. We cannot exclude that our findings might have been affected by weak instrument bias. However, in MR analyses, the F-statistic was >10 for all exposures and bias from weak instruments is expected to be towards the null in a two-sample setting. [48]  Pleiotropy is an important concern for MR analyses, and recent guidelines recommend using multiple methods that make different assumptions to assess the robustness of findings.
Conclusions and future research Using an independent dataset, our study confirms previous MR findings adds further evidence in favour of a protective effect of smoking on PD and shows that this association is not explained by survival or incidence-prevalence bias. For alcohol and coffee drinking, larger studies and stronger genetic instruments are needed. The number of PD cases is predicted to double between 2015-2040,  [50]  and the identification of neuroprotective strategies is a major goal. Our findings may help prioritise neuroprotective approaches for PD. Further research is necessary to improve genetic instruments for some of the exposures examined here, to understand the pathways involved in these associations, to determine whether these findings are corroborated in non-European populations, whether PD patients subgroups should be more specifically targeted, and whether there are critical periods of exposure.
1-3   All studies were approved by local ethical committees following the procedures of each country, and material transfer agreements were set up between participating sites and the University of Tübingen (Germany). According to the study's consortium agreement, participating sites contributed DNA and demographic/environmental data. DNAs (25μl of DNA at a concentration of 50 to 100 ng/μl) were shipped for quality control to University of Tübingen (Germany) and genotyped in a central laboratory in Munich (Institute of Human Genetics, Helmholtz Zentrum, Germany). The samples from two sites (Gasser, Morris/Wood) were genotyped at the Laboratory of Neurogenetics (National Institute on Aging, National Institutes of Health, Bethesda, USA). Demographic data were harmonized and collected using a standardized form and cleaned at Inserm U1018 (Villejuif, France).
Genotyping The Neurochip chip was used to genotype all the samples. 4 Briefly, this chip is a custom-designed array containing a tagging variant backbone with good genome-wide resolution of about 306,670, complemented with a manually curated custom content comprised of 179,467 variants implicated in diverse neurological diseases, including PD. Genotyping was performed with an automated protocol according to the manufacturer's instructions (Illumina, San Diego, CA, USA). All arrays were scanned with an Illumina iScan and raw data were analyzed with the Illumina Beeline and GenomeStudio software packages using the manifest file Neuro_Consortium_20013217_A1. bpm. Clustering was performed in GenomeStudio with the GenTrain cluster 2. 0 algorithm. Genotypes were post-processed with zCall (DOI: 10.
Quality control Genotyped data exported from Genome Studio to PLINK format were used for quality control and downstream analysis.  Identification of Individuals with discordant sex information: the homozygosity rate was calculated for X-linked SNPs for each individual and compared to the expected rate. Participants for whom phenotypic and genotypic sex were discordant were removed.  Identification of duplicated or related Individuals: pairs of individuals with an identity by descent (IBD) greater than 0. 185 were removed.  Eigensoft software was used to compute principal components in order to correct for population stratification by merging our dataset with HapMap. 5 6 A scatter plot of the first two principal components was used to identify outliers in each study.
9). 7   Summary statistics from the GWAS of the 23 studies were meta-analysed using the GWAMA software. 8 To assess heterogeneity of genetic associations with each SNP in Courage-PD, we used the I² statistic; I² values of 0 to 24% suggest little heterogeneity, 25 to 49% reflect moderate heterogeneity, 50 to 74% reflect large heterogeneity, and >75% reflect very large heterogeneity. Effects size and standard errors of SNPs with little heterogeneity across studies (I²≤25%) were combined using a fixed-effect model, while we used a random-effects model for SNPs with higher heterogeneity (I²>25%). Of 368 SNPs used in our MR analyses, 324 (88%) displayed little heterogeneity and 44 (12%) had moderate heterogeneity; there were no SNPs with I² above 50%. Analyses were first performed overall. We then repeated our analyses: (i) in two groups defined by median age at study (≤67 years, >67 years; Bold coefficients and corresponding SE and p-values are significant at p≤0.