dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion A map of hundreds of thousands of genetic associations has enabled new biological insights related to DNAm variation. Using a rigorous analytical framework enabled us to minimize heterogeneity and expand sample sizes for large -omic data. This revealed a genetic architecture of DNAm that is polygenic. Given the diverse ranges of age, sex proportions and geographical origins between the cohorts in this analysis, the minimal extent of heterogeneity across datasets indicates that genetic effects on DNAm are relatively stable across contexts, at least when restricted to European ancestries. We show that cis-and trans-mQTLs operate through distinct mechanisms, because their genomic properties are distinct. A driver of long-range associations may be co-regulated through TF binding and nuclear organization.
Third, DNAm might be on the causal pathway in a disease-relevant cell type or context. Fourth, if the path from genotype to complex traits is nonlinear, for example, involving the statistical interactions between different regulatory features  16  , then our results indicate that large individual-level multi-omic datasets will be required to dissect such mechanisms. Higher-density DNAm microarrays  12  or low-cost sequencing technologies  58  will expedite detailed interrogations of enhancer and other regulatory regions. Given our projection of mQTL yields expected for future studies, pleiotropy involving mQTLs is likely to be increasingly important to model when interpreting genotype-trait pathways. Overall, our data and results present a comprehensive atlas of genetic effects on DNAm. We expect that this atlas will be of use to the scientific community for studies of genome regulation and causality analysis, and that it will contribute to the control of confounding in EWAS.
59  which includes probes with potential cross-reaction and probes that could not be mapped to a genome. Second, we removed an additional 14,882 probes, including multi-mapping probes (bisulfite-converted sequences allowing two mismatches at any position mapped to the hg19 primary assembly) and probes with variants (minor allele frequency (MAF) >5%, UK10K) at the CpG dinucleotide or the extension base (for type I probes). All candidate mQTL associations at P < 1 × 10 -5 were combined to create a unique 'candidate list' of mQTL associations. In total we identified 102,965,711 candidate mQTL associations in cis (P < 1 × 10 -5 , ±1 Mb from DNAm site) and 710,638,230 candidate mQTL associations in trans (>1 Mb from DNAm site) in at least one dataset. In at least two datasets, 59% of the candidate mQTL associations in cis (n = 61,103,065) and 2.
4% of the associations in trans (n = 17,246,702) were found (Supplementary Fig. 1 ). To reduce the computational burden, we included cis associations found in at least one dataset and trans associations in at least two datasets. The candidate list (n = 120,212,413) was then sent back to all studies, and the association estimates were obtained for every mQTL association on the candidate list. In phase 2 of our study, we performed association tests for each of the candidate mQTL associations in 20 studies from phase 1 and 16 additional studies with European ancestry (total n = 27,750; Supplementary Table  1 ). The estimates for the candidate list were meta-analyzed to obtain the final results (Fig. 1a ). This two-phase approach had a single objective: to minimize the computational burdens of storing summary data from the complete analysis of every study.
In the first module, the data format of the genotype data, DNAm and co-variate data was checked. In addition, the number of individuals with DNAm and genotype data (requirement of n > 100), the number of SNPs, the number of sites, co-variates including cell counts, genotype build and strand, and the number of DNAm outliers were recorded. We also generated matrices with mean and s. d. by DNAm site and study descriptives. The entire pipeline can be viewed at https://github. com/ MRCIEU/godmc, and the following text describes the procedures used.
2) without long-range LD regions before we calculated the first 20 genetic PCs on LD-pruned SNPs and excluded regions of high LD from the analysis. We used PLINK2. 0 (ref. 61 ) for unrelated participants and GENESIS  62  for related participants to identify ancestry outliers. Ancestry outliers that deviated by 7 s. d. from the mean were removed. After outlier removal we recalculated genetic PCs for use in subsequent analyses. To identify relatedness in unrelated datasets, we pruned the genotype data to a set of independent HapMap3 SNPs with MAF > 0. 01 and calculated genome-wide average identity by state using PLINK2. 0. Participants with identity by state >0. 125 were removed. DNAm data normalization and quality control. DNAm was measured in whole blood or cord blood using HumanMethylation450 or EPIC arrays in at least 100 European individuals.
Each study performed normalization and quality control on the DNAm data independently, with most studies using functional normalization through the R package meffil v. 0. 1. 0 (ref. 63 ) (Supplementary Table  1 ). Briefly, meffil has been designed to preprocess raw idat files to a normalization matrix for large sample sizes without large computational memory requirements, and to perform quality control in an automated way where the analyst can adjust default parameters easily. Sample quality control included removal of participants in whom >10% of the DNAm sites failed the detection P value of 0. 1 and/or threshold of three beads. In addition, mismatched samples were identified by comparing the 65 SNPs on the DNAm array with the genotype array and a sex check. Additional DNAm quality was checked by the methylated versus the unmethylated ratio, dye bias using the normalization control probes and bisulfite control probes.
Protocols can be found at the following website: https://github. com/perishky/meffil/wiki. For each DNAm site, we replaced outliers that were 10 s. d. from the mean (three iterations) with the DNAm site being the mean. Co-variates. We used sex, age at measurement, batch variables (slide, plate, row if available), smoking (if available) and recorded cell counts to adjust for possible confounding and to reduce residual variation. Additional confounders (genetic PCs, nongenetic DNAm PCs and, where necessary, predicted smoking and cell counts) were calculated using the GoDMC pipeline. After quality control and normalization of the DNAm data, we predicted smoking status by using previously reported DNAm associations with smoking  64. In addition, we predicted cell counts using the Houseman algorithm  46  implemented in meffil v.
0. 1. 0 (ref. 63 ). We performed a principal component analysis on the 20,000 most variable autosomal DNAm sites and kept all PCs that cumulatively explained 80% of the variance. We performed GWASs on the DNAm PCs and retained the PCs that were not associated with a genotype (P > 1 × 10 -7 ). We kept a maximum of 20 nongenetic PCs for subsequent adjustment. DNAm data adjustment. We attempted to minimize nongenetic variation in the DNAm data to improve the power for mQTL detection. We adjusted datasets with predominant family structures (pedigrees, twin studies) and population-based studies in slightly different ways. For unrelated participants we regressed out age, sex, predicted cell counts, predicted smoking and genetic PCs (adjustment 1). For related participants, we did the same, except also fitting the genetic kinship matrix using the method described in GRAMMAR  65.
We took the residuals from the first adjustment forward to regress out the nongenetic DNAm PCs on the adjusted DNAm β values (adjustment 2). The residuals from these analyses were rank transformed and centered to have mean 0 and variance 1. Positive and negative controls. Before we performed the meta-analysis, we checked the number of SNPs and indels, sites and individuals analyzed, and the average mean and s. d. for each DNAm site to identify possible inconsistencies. Each of the 38 studies conducted a GWAS of cg07959070. We chose this DNAm site as a positive control because it showed a strong cis-mQTL in several datasets on chr22 and there hasn't been a proposal to exclude it from the analyses by probe annotation efforts  59, [66] [67] [68]. To identify possible errors, we checked the cis association on chromosome 22 (P < 0.
001) for this DNAm site. In addition, we checked quantile-quantile and Manhattan plots for this DNAm site. We also used this control to identify studies with deflated or inflated λ values (λ > 1. 1 or λ < 0. 9). We noticed deflation of the genomic λ after adjustment of the index cis-SNP in datasets with relatedness. However, λ values were around 1 when not adjusted. After inspection one study was removed from the analysis due to deflation and one study due to a lack of the positive control association signal, leaving 36 studies for the final meta-analysis. Association analyses. Phase 1: creating the candidate list of associations. We performed a fast, comprehensive analysis of all cis and trans associations on NAtuRe GeNetics | www. nature. com/naturegenetics 420,509 reliable  59  residualized DNAm sites separately in 22 studies (n = 16,907) using the R package Matrix eQTL v.
2. 1. 0 (ref. 69 ). For each DNAm site, j, the residual value, y ji , was regressed against each SNP, k: yji = α jk + β jk x ki + e jki where genotype values x ki were coded as allele counts {0, 1, 2}, α jk was the intercept term and β jk was the effect estimate of each SNP k on each residualized DNAm site j. Phase 2: obtaining summary data from all studies for meta-analysis. This candidate list was sent to 36 studies (n = 27,750), in which effect sizes for all putative associations were recalculated by fitting linear models. For putative cis-mQTLs we performed linear regression as in phase 1. To improve statistical power to estimate the trans-mQTL effects we recorded the top cis-SNP x c , for each DNAm site (based on the lowest P value within that study) and fit this as a co-variate in the trans-mQTL regressions: yji = α jk + β jc xci + β jk x ki + e jki Evaluation of DNAm data adjustment.
As adjustment for nongenetic DNAm PCs might have substantial benefits on power or an adverse effect by inducing collider bias  70  , we explored the impact by comparing mQTLs not adjusted for nongenetic PCs with mQTLs adjusted for nongenetic PCs in ARIES. Specifically, we found 80,890 clumped mQTL associations in the PC-adjusted dataset and 74,402 clumped mQTL associations in the PC-unadjusted dataset. Pearson's correlation between effect sizes of the PC-unadjusted clumped mQTLs versus PC-adjusted mQTLs (cis r = 0. 998; trans r = 0. 998) and PC-adjusted clumped mQTLs (cis r = 0. 997; trans r = 0. 997) versus PC-unadjusted mQTLs was very high (Supplementary Fig. 36 ). These results suggest that, if collider bias is impacting the results, it is extremely small. The simplest explanation for the minimal difference in effect sizes and slightly higher mQTL yield among the PC-adjusted mQTLs is that reduced residual variance has improved the power.
We found that we have no loss of power (<1%) for loci that explain >1. 2% or <0. 1% of the variance. Within these bounds >80% of power is lost for cis-mQTLs with r 2 = 0. 16-0. 38%. For trans-mQTLs, power suffers slightly more because of requiring detection by at least two studies in the first stage (r 2 = 0. 27-0. 64%; Extended Data Fig. 4a ). Meta-analyses. We used the SNP effect estimates and s. e. for each SNP-DNAm site pair in the candidate list in the meta-analyses. Inverse-variance fixed effect (FE) meta-analyses of the 36 studies were performed using METAL  71. We modified METAL (https://github. com/explodecomputer/random-metal) to incorporate the DerSimonian and Laird random effect (RE) models  72  and multiplicative random effect (MRE) models  73. These results are available at: http://mqtldb.
Data availability A database of our results is available as a resource to the community at http:// mqtldb. godmc. org. uk. The individual-level genotype and DNAm data are available by request from each individual study or can be downloaded from Gene Expression Omnibus (GEO, https://www. ncbi. nlm. nih. gov/geo), European Genome-Phenome Archive (EGA, https://ega-archive. org) or Array Express (https://www. ebi. ac. uk/ arrayexpress). As the consent for most studies requires the data to be under managed access, the individual-level genotype and DNAm data are not available from a public repository unless stated. ALS BATCH1 and -2 data are available to researchers by request as outlined in the Project MinE access policy. ARIES data are available to researchers by request from the Avon Longitudinal Study of Parents and Children Executive Committee (http://www.
bristol. ac. uk/alspac/researchers/access) as outlined in the study's access policy http://www. bristol. ac. uk/media-library/sites/alspac/documents/ researchers/data-access/ALSPAC_Access_Policy. pdf. BAMSE data are available from the GABRIEL consortium as well as on request in EGA, under accession no. EGAC00001000786. BASICMAR DNAm data are available under accession no. GSE69138. Born-in-Bradford data are available to researchers who submit an expression of interest to the Born-in-Bradford Executive Group (https:// borninbradford. nhs. uk/research). BSGS DNAm data are available under accession no. GSE56105. GOYA data are available by request from DNBC: https://www. dnbc. dk. Dunedin data are available via a managed access system (contact: ac115@duke. edu). E-Risk DNAm data are available under accession no.
GSE105018. Estonian biobank (ECGUT) data can be accessed on ethical approval by submitting a data release request to the Estonian Genome Center, University of Tartu (http://www. geenivaramu. ee/en/access-biopank/data-access). EPIC-Norfolk data can be accessed by contacting the study management committee: http://www. srl. cam. ac. uk/epic/contact. Requests for EPICOR data accession may be sent to Professor Giuseppe Matullo (giuseppe. matullo@unito. it). FTC data can be accessed on approval from the Data Access Committee of the Institute for Molecular Medicine Finland FIMM (fimm-dac@helsinki. fi). Requests for Generation R data access are evaluated by the Generation R Management Team. Researchers can obtain a de-identified GLAKU dataset after having obtained an approval from the GLAKU Study Board.
GSK DNAm data are available under accession no. GSE125105. INMA data are available by request from the INfancia y Medio Ambiente Executive Committee for researchers who meet the criteria for access to confidential data. IOW F2 data are available by request from Isle of Wight Third Generation Study. Please contact Mr Stephen Potter (stephen. potter@iow. nhs. uk). LLS DNAm data were submitted to the EGA under accession no. EGAS00001001077. LBC1921 and LBC1936 data are available on request from the Lothian Birth Cohort Study, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh (I. Deary@ed. ac. uk). DNAm from MARTHA participants are available under accession no. E-MTAB-3127. NTR DNAm data are available on request in EGA, under the accession no. EGAD00010000887. PIAMA data are available on request.
Requests can be submitted to the PIAMA Principal Investigators (https://piama. iras. uu. nl/english). PRECISESADS data are available through ELIXIR at https://doi. org/10. 17881/ th9v-xt85. Collaboration in data analysis of PREDO is possible through specific research proposals sent to the PREDO Study Board (predo. study@helsinki. fi) or primary investigators Katri Räikkönen (katri. raikkonen@helsinki. fi) or Hannele Laivuori (hannele. laivuori@helsinki. fi). Data are available on request at Project MinE (https://www. projectmine. com). Raine data are available on request (https:// ross. rainestudy. org. au). Requests for the data accession of the Rotterdam Study may be sent to Frank van Rooij (f. vanrooij@erasmusmc. nl). SABRE data are available by request from SABRE (https://www. sabrestudy.
org). SCZ1 DNAm data are NAtuRe GeNetics | www. nature. com/naturegenetics Extended Data Fig. 7  | correspondence of MR estimates amongst multiple independent instruments. a, to evaluate if a site having a shared causal variant with a trait was potentially due to the site being on the causal pathway to the trait, we reasoned that independent instruments for the site should exhibit consistent effects on the outcome consistent with the original co-localizing variant. b, Amongst the putative co-localizing signals, 440 involved a DNAm site that had at least one other independent mQtL. the plot shows the causal effect estimate estimated from the original co-localizing signal against the causal effect estimates obtained from the independent variants (n=440). Grey regions represent the 95% confidence of the slope.
Acknowledgements C. L. R. , G. D. S. , G. S. , J. L. M. , K. B. ,  M. Suderman , T. G. R. and T. R. G. are supported by the  UK Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol  ( MC_UU_00011/1 ,  MC_UU_00011/4 ,  MC_UU_00011/5 ). C. L. R. receives support from a  Cancer Research UK   Programme grant  (no. C18281/A191169 ). G. H. is funded by the  Wellcome Trust  and the  Royal Society  ( 208806/Z/17/Z ). E. H. and J. M. were supported by  MRC  project grants (nos. MR/K013807/1  and  MR/R005176/1  to J. M. ) and an  MRC Clinical Infrastructure award  (no. MR/M008924/1  to J. M. ). B. T. H. is supported by the  Netherlands CardioVascular Research Initiative  (the  Dutch Heart Foundation ,  Dutch Federation of University Medical Centres , the  Netherlands Organisation for Health Research and Development , and the  Royal Netherlands Academy of Sciences)  for the  GENIUS  project 'Generating the best evidence-based pharmaceutical targets for atherosclerosis' ( CVON2011-19 ,  CVON2017-20 ).
code availability Datasets were processed using https://github. com/perishky/meffil unless stated otherwise. Individual study analysts used a github pipeline https://github. com/ MRCIEU/godmc to conduct the mQTL analysis. We used https://github. com/ MRCIEU/godmc_phase1_analysis for the phase 1 analysis, https://github. com/ explodecomputer/random-metal for the meta-analyses and https://github. com/ MRCIEU/godmc_phase2_analysis for the follow-up analyses.
0 ST chips. The chips were washed and stained by the Affymetrix Fluidics Station using the standard format and protocols from Affymetrix. The probe arrays were stained with streptavidin phycoerythrin solution (Molecular Probes, Carlsbad, CA, USA) and enhanced by using an antibody solution containing 0. 5 mg/ml biotinylated anti-streptavidin (Vector Laboratories, Burlingame, CA, USA). The probe arrays were scanned using an Affymetrix Gene Chip Scanner 3000. Gene expression intensities were calculated using the Gene Chip Operating software 1. 2 (Affymetrix). A GC-corrected robust multichip analysis (RMA) normalization model was used to correct for background, and nonspecific binding. All analyses were performed using Partek Genomics Suite. The raw data was submitted to Gene Expression Omnibus (GEO) under accession number GSE151808.
Dataset description and microarray data mining Published microarray datasets in GEO (Gene Expression Omnibus) (http://www. ncbi. nlm. nih. gov/geo/) were mined to reveal phenotypes that may correlate with the present dataset using the broad institute Gene Set Enrichment Analysis program (GSEA) (http://broadinstitute. org/GSEA) according to the methods previously described  [32] [33] [34]. Raw transcriptomic data was downloaded from the National Center for Biotechnology Information (NCBI) GEO database and imported into Partek Genomics Suite software (Partek, Inc. , St. Louis, MI). After RMA normalization, log2 transformation and gene summarization, differentially expressed gene sets were generated based on ANOVA (p < 0. 05 and 2-fold change). The GSEA algorithm computes a ranked list of all genes from our microarray and compare them to biologically well-defined and published gene signatures to determine whether individual genes of an a priori functionally defined gene set are enriched or randomly distributed across the whole ranked gene list, using a modified Kolmogorov-Smirnov statistic and generating an enrichment score.
7E ). In a search of a master gene whose dysregulation by oxidative distress might trigger a widespread transcriptional response mitigated by treatment with PSELT, we found that genes described in the NCBI database as associated/interacting with the EZH2 protein, a master transcription regulator acting epigenetically, are significantly correlated with PSELT treatment (Fig. 7F  and Fig. S1D  for more correlative analyses), and that genes up-regulated after EZH2 knockout with a H3K27me3 mark  [38]  correlate with genes up-regulated by MPP + and down-regulated by PSELT (Fig. 7G ). These gene expression correlation analyses support the notion that PSELT may promote redox homeostasis and cell survival through epigenetic regulation.
Cell viability measurements revealed that EZH2  A: Unsupervised clustering based on gene expression profiling of the different experimental conditions indicated under the dendrogram (n = 3 per condition). Genes with a relatively higher level of expression are shown in red, and those with a lower level are shown in blue according to the color scale at the bottom. B: Gene set enrichment analysis of a ranked list of all genes comparing MPP + and PSELT treatment versus MPP + with those up-regulated in Parkinson's disease (p value = 0, enrichment score = 1. 15). Genes up-regulated in Parkinson's disease positively correlate with genes up-regulated by MPP + and down-regulated by PSELT. Data were taken from GSE68719_Parkinson disease  [36]  or from GSE78757_Gaucher disease, GSE20292_Parkinson substantia nigra and GSE9397_Parkinson MSN  [36, 61, 62]  in supplementary Fig.
S1A. C: Gene set enrichment analysis of a ranked list of all genes comparing MPP + and PSELT treatment versus MPP + with those down-regulated in cortical neurogenesis (p value = 0, enrichment score = -1. 29). Genes down-regulated during impaired corticogenesis correlate with genes up-regulated by PSELT. Data were taken from GSE74683_-Cortex_Neurogenesis (ELP3 knockout)  [63]  or from GSE 42904_HIPPO  [64] , GSE8425_RASGRF1 KO  [65]  and the orphan nuclear receptor TLX targets  [66]  in supplementary Fig. S1B. D: Gene set enrichment analysis of a ranked list of all genes comparing MPP + and PSELT treatment versus MPP + with those up-regulated during caloric restriction (p value = 0, FDR = 0, enrichment score = 1. 30). Genes that are up-regulated by caloric restriction are also up-regulated by PSELT.
Data were taken from GSE75569  [67]. E: Gene set enrichment analysis of a ranked list of all genes comparing MPP + and PSELT treatment versus MPP + with those up-regulated by resveratrol (p value = 0, enrichment score = 1. 23). Genes that are up-regulated by resveratrol are also up-regulated by PSELT and inversely, genes down-regulated by resveratrol are also down-regulated by PSELT. Data were taken from GSE36930  [68]  or from GSE36930  [68]  and GSE42432  [69]  in supplementary Fig. S1C. F: Gene set enrichment analysis of a ranked list of all genes comparing MPP + and PSELT treatment versus MPP + with those associated/interacting with the transcription regulator EZH2 (p value = 0, enrichment score = -1. 25). The EZH2-interacting genes are mostly correlated with PSELT treatment. Data were taken from the NCBI database (https://www.