dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, even after narrowing the MAGENTA analysis to gene sets in curated databases with names suggestive of roles in glucose, insulin or broader metabolic pathways, we did not identify any pathways that met our Bonferroni-corrected threshold for significance of Po2 Â 10 À 4 (Supplementary Table  19 ). Testing nonsynonomous variants for association in known loci. Owing to the expected functional effects of protein-altering variants, we tested SNVs (4,513 for FG and 1,281 for FI) annotated as nonsynonymous, splice-site or stop gain/loss by dbNSFP  31  in genes within 500 kb of known glycaemic variants  1, 27, 32  for association with FG and FI to identify associated coding variants, which may implicate causal genes at these loci (Supplementary Table  20 ). At the DNLZ-GPSM1 locus, a common nsSNV (rs60980157; S391L) in the GPSM1 gene was significantly associated with FG (Bonferroni corrected P value o1.
Using this approach, we implicate causal genes at six loci associated with fasting glucose and/or FI (G6PC2, GPSM1, SLC2A2, SLC30A8, RREB1 and COBLL1) and five with T2D (ARAP1, GIPR, KCNJ11, SLC30A8 and WFS1). For example, via gene-based analyses, we identified 15 rare variants in G6PC2 (p SKAT ¼ 8. 2 Â 10 À 18 ), which are independent of the common non-coding signals at this locus and implicate this gene as underlying previously identified associations. We also revealed non-coding variants whose putative functions in epigenetic and post-transcriptional regulation of ABO and G6PC2 are supported by experimental ENCODE Consortium, GTEx and transcriptome data from islets and for which future focused investigations using human cell culture and animal models will be needed to clarify their functional influence on glycaemic regulation.
Methods Study cohorts. The CHARGE consortium was created to facilitate large-scale genomic meta-analyses and replication opportunities among multiple large population-based cohort studies  12. The CHARGE T2D-Glycemia Exome Consortium was formed by cohorts within the CHARGE consortium as well as collaborating non-CHARGE studies to examine rare and common functional variation contributing to glycaemic traits and T2D susceptibility (Supplementary Note 1). Up to 23 cohorts participated in this effort representing a maximum total sample size of 60,564 (FG) and 48,118 (FI) participants without T2D for quantitative trait analyses. Individuals were of European (84%) and African (16%) ancestry. Full study characteristics are shown in Supplementary Data 1. Of the 23 studies contributing to quantitative trait analysis, 16 also contributed data on T2D status.
These studies were combined with six additional cohorts with T2D casecontrol status for follow-up analyses of the variants observed to influence FG and FI and analysis of known T2D loci in up to 16,491 T2D cases and 81,877 controls across 4 ancestries combined (African, Asian, European and Hispanic; see Supplementary Data 2 for T2D case-control sample sizes by cohort and ancestry). All studies were approved by their local institutional review boards and written informed consent was obtained from all study participants. Quantitative traits and phenotypes. FG (mmol l À 1 ) and FI (pmol l À 1 ) were analysed in individuals free of T2D. FI was log transformed for genetic association tests. Study-specific sample exclusions and detailed descriptions of glycaemic measurements are given in Supplementary Data 1.
The quality control criteria performed by each study for filtering of poorly genotyped individuals and of low-quality SNVs included a call rate of o0. 95, gender mismatch, excess autosomal heterozygosity, and SNV effect estimate s. e. 410 À 6. Concordance rates of genotyping across the exome chip and GWAS platforms were checked in ARIC and FHS and was 499%. After SNV-level and sample-level quality control, 197,481 variants were available for analyses. The minor allele frequency spectrums of the exome chip SNVs by annotation category are depicted in Supplementary Table  22. Cluster plots of GLP1R and ABO variants are shown in Supplementary Fig. 9. Whole-exome sequencing. For exome sequencing analyses we had data from up to 14,118 individuals of European ancestry from seven studies, including four studies contributing exome sequence samples that also participated in the exome chip analyses (Atherosclerosis Risk in Communities Study (ARIC, N ¼ 2,905), Cardiovascular Health Study (CHS, N ¼ 645), Framingham Heart Study (FHS, N ¼ 666) and Rotterdam Study (RS, N ¼ 702)) and three additional studies, Erasmus Rucphen Family Study (ERF, N ¼ 1,196), the Exome Sequencing Project (ESP, N ¼ 1,338) and the GlaxoSmithKline discovery sequence project 3 (GSK, N ¼ 6,666).
The GlaxoSmithKline (GSK) discovery sequence project provided summary level statistics combining data from GEMS, CoLaus and LOLIPOP collections that added additional exome sequence data at GLP1R, including N ¼ 3,602 samples with imputed genotypes. In all studies sequencing was performed using the Illumina HiSeq 2000 platform. The reads were mapped to the GRCh37 Human reference genome (http://www. ncbi. nlm. nih. gov/projects/ genome/assembly/grc/human/) using the Burrows-Wheeler aligner (BWA  49  , http://bio-bwa. sourceforge. net/), producing a BAM  50  (binary alignment/map) file. In ERF, the NARWHAL pipeline  51  was used for this purpose as well. In GSK paired-end short reads were aligned with SOAP  52. GATK  53  (http:// www. broadinstitute. org/gatk/) and Picard (http://picard. sourceforge.
SNVs with 420% missingness, 42 observed alleles, monomorphic, mean depth at the site of 4500-fold or HWE Po5 Â 10 À 6 were removed. After variant-level QC, a quality assessment of the final sequence data was performed in ARIC, CHS and FHS based on a number of measures, and all samples with a missingness rate of 420% were removed. In RS, samples with low concordance to genotyping array (o 95%), low transition/transversion ratio (o2. 3) and high heterozygote to homozygote ratio (42. 0) were removed from the data. In ERF, low-quality variants were removed using a QUALo150 filter. Details of variant and sample exclusion criteria in ESP and GSK have been described before  3, 57. In brief, in ESP these were based on allelic balance (the proportional representation of each allele in likely heterozygotes), base quality distribution for sites supporting the reference and alternate alleles, relatedness between individuals and mismatch between called and phenotypic gender.
We collected results for 2-h insulin levels in a total of 19,362 individuals and for 30 min-insulin levels in 16,601 individuals. Analyses of 2-h glucose, 2-h insulin and 30 min-insulin were adjusted using three models: (1) age, sex and centre; (2) age, sex, centre and BMI; and (3) age, sex, centre, BMI and FG. The main results in the manuscript are presented using model 3. We opted for the model that included FG because these traits are dependent on baseline FG  1, 58. Adjusting for baseline FG assures the effect of a variant on these glycaemic physiologic traits are independent of FG. We calculated the insulinogenic index using the standard formula: [insulin 30 min À insulin baseline]/[glucose 30 min À glucose baseline] and collected data from five cohorts with appropriate samples (total N ¼ 16,203 individuals).
All analyses were adjusted for age, sex, principal components calculated from genome-wide or exome chip genotypes and study-specific covariates (when applicable) (Supplementary Data 1). Models testing FI were further adjusted for BMI  32. Each study analysed ancestral groups separately. At the meta-analysis level ancestral groups were analysed both separately and combined. Meta-analyses were performed by two independent analysts and compared for consistency. Overall quantile-quantile plots are shown in Supplementary Fig. 10. Bonferroni correction was used to determine the threshold of significance. In single-variant analyses, for FG and FI, all variants with a MAF40. 02% (equivalent to a MACZ20; N SNVs ¼ 150,558) were included in single-variant association tests; the significance threshold was set to Pr3 Â 10 À 7 (P ¼ 0.
7 Â 10 À 6 (0. 05/29,732 tests (14,866 genes Â 2 gene-based tests)) in the analysis of T2D. Owing to the association of multiple rare variants with FG at G6PC2 from both single and gene-based analyses, we removed one variant at a time and repeated the SKAT test to determine the impact of each variant on the gene-based association effects (Wu weight) and statistical significance. We performed conditional analyses to control for the effects of known or newly discovered loci. The adjustment command in seqMeta was used to perform conditional analysis on SNVs within 500 kb of the most significant SNV. For ABO we used the most significant SNV, rs651007. For G6PC2 we used the previously reported GWAS variants, rs563694 and rs560887, which were also the most significant SNV(s) in the data analysed here.
Maximum sample sizes were 64,965 in adiposity analyses, 56,538 in lipid analyses and 92,615 in blood pressure analyses. Threshold of significance was P ¼ 6. 2 Â 10 À 3 (P ¼ 0. 05/8, where eight is the number of traits tested). Pathway analyses of GLP1R. To examine whether biological pathways curated into gene sets in several publicly available databases harboured exome chip signals below the threshold of exome-wide significance for FG or FI, we applied the MAGENTA gene-set enrichment analysis (GSEA) software as previously described using all pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), Reactome, Panther, BioCarta and Ingenuity pathway databases  67. Genes in each pathway were scored based on unconditional meta-analysis P values for SNVs falling within 40 kb upstream and 110 kb downstream of gene boundaries; we used a 95th percentile enrichment cutoff in MAGENTA, meaning pathways (gene sets) were evaluated for enrichment with genes harbouring signals exceeding the 95th percentile of all genes.
As we tested a total of 3,216 pathways in the analysis, we used a Bonferroni-corrected significance threshold of Po1. 6 Â 10 À 5 in this unbiased examination of pathways. To limit the GSEA analysis to pathways that might be implicated in glucose or insulin metabolism, we selected gene sets from the above databases whose names contained the terms 'gluco,' 'glycol,' 'insulin' or 'metabo. ' We ran MAGENTA with FG and FI data sets on these 'glucometabolic' gene sets using the same gene boundary definitions and 95th percentile enrichment cutoff as described above; as this analysis involved 250 gene sets, we specified a Bonferroni-corrected significance threshold of Po2. 0 Â 10 À 4. Similarly, to examine whether genes associated with incretin signalling harboured exome chip signals, we applied MAGENTA software to a gene set that we defined comprised genes with putative biologic functions in pathways common to GLP1R activation and insulin secretion, using the same gene boundaries and 95th percentile enrichment cutoff described above (Supplementary Table  4 ).
3 ) used snapshots every 100 ps. The final snapshot is shown in all the structural figures. Annotation and functional prediction of variants. Variants were annotated using dbNSFP v2. 0 (ref. 31). GTEx (Genotype-Tissue Expression Project) results were used to identify variants associated with gene expression levels using all available tissue types  16. The Encyclopedia of DNA Elements (ENCODE) Consortium results  14  were used to identify non-coding regulatory regions, including but not limited to transcription factor binding sites (ChIP-seq), chromatin state signatures, DNAse I hypersensitive sites and specific histone modifications (ChIP-seq) across the human cell lines and tissues profiled by ENCODE. We used the UCSC Genome Browser  15, 70  to visualize these data sets, along with the public transcriptome data contained in the browser's 'Genbank mRNA' (cDNA) and 'Human ESTs' (Expressed Sequence Tags) tracks, on the hg19 human genome assembly.
Acknowledgements CHARGE: Funding support for 'Building on GWAS for NHLBI-diseases: the  U. S. CHARGE consortium'  was provided by the  NIH  through the  American Recovery and Reinvestment Act of 2009 (ARRA)  ( 5RC2HL102419 ). Sequence data for 'Building on GWAS for NHLBI-diseases: the  U. S. CHARGE consortium'  was provided by  Eric Boerwinkle on behalf of the Atherosclerosis Risk in Communities (ARIC) Study ,  L. Adrienne Cupples , principal investigator for the  Framingham Heart Study , and  Bruce Psaty , principal investigator for the Cardiovascular Health Study. Sequencing was carried out at the  Baylor Genome Center  ( U54 HG003273 ). Further support came from  HL120393 , 'Rare variants and NHLBI traits in deeply phenotyped cohorts' ( Bruce Psaty , principal investigator). Supporting funding was also provided by  NHLBI  with the  CHARGE infrastructure grant   HL105756.
In addition, M. J. P. was supported through the 2014  CHARGE Visiting Fellow grant -HL105756 ,  Dr Bruce Psaty , PI. ENCODE: ENCODE collaborators  Ben Brown  and  Marcus Stoiber  were supported by the LDRD#  14-200  (B. B. and M. S. ) and  4R00HG006698-03  (B. B. ) grants. AGES: This study has been funded by  NIA  contract  N01-AG-12100  with contributions from  NEI ,  NIDCD  and  NHLBI , the  NIA   Intramural Research Program ,  Hjartavernd  (the  Icelandic Heart Association ) and the  Althingi (the Icelandic Parliament ). ARIC: The  Atherosclerosis Risk in Communities (ARIC) Study  is carried out as a collaborative study supported by  National Heart, Lung, and Blood Institute (NHLBI)  contracts ( HHSN268201100005C ,  HHSN268201100006C ,  HHSN268201100007C ,  HHSN268201100008C ,  HHSN268201100009C ,  HHSN268201100010C ,  HHSN268201100011C  and  HHSN268201100012C ),  R01HL087641 ,  R01HL59367  and  R01HL086694 ;  National Human Genome Research Institute  contract  U01HG004402 ; and  National Institutes of Health  contract  HHSN268200625226C.
The CoLaus study was supported by research grants from  GlaxoSmithKline  and from the  Faculty of Biology and Medicine of Lausanne, Switzerland. The PsyCoLaus study was supported by grants from the  Swiss National Science Foundation  (# 3200B0-105993 ) and from  GlaxoSmithKline  ( Drug Discovery-Verona , R&D). CROATIA-Korcula: The  CROATIA-Korcula study would like  to acknowledge the invaluable contributions of the recruitment team in Korcula, the administrative teams in Croatia and Edinburgh and the people of Korcula. Exome array genotyping was performed at the  Wellcome Trust Clinical Research Facility Genetics Core at Western General Hospital, Edinburgh, UK. The  CROATIA-Korcula study on the Croatian island of Korucla  was supported through grants from the  Medical Research Council UK  and the  Ministry of Science, Education and Sport in the Republic of Croatia  (number  108-1080315-0302 ).
EFSOCH: We are extremely grateful to the  EFSOCH  study participants and the EFSOCH study team. The opinions given in this paper do not necessarily represent those of  NIHR , the  NHS  or the  Department of Health. The EFSOCH study was supported by  South West NHS Research and Development, Exeter NHS Research and Development , the  Darlington Trust , and the  Peninsula NIHR Clinical Research Facility at the University of Exeter. Timothy Frayling , PI, is supported by the  European Research Council  grant:  SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC.
016. 009 , 047. 017. 043 ), Erasmus MC , and the  Centre for Medical Systems Biology  ( CMSB; National Genomics Initiative ). Exome sequencing analysis in ERF was supported by the  ZonMw grant  ( 91111025 ). For the ERF Study, we are grateful to all participants and their relatives, to general practitioners and neurologists for their contributions, to  P. Veraart  for her help in genealogy and to  P. Snijders  for his help in data collection. FamHS: The  Family Heart Study (FamHS)  was supported by  NIH  grants  R01-HL-087700  and  R01-HL-088215  (Michael A. Province, PI) from  NHLBI ; and  R01-DK-8925601  and  R01-DK-075681  ( Ingrid B. Borecki , PI) from  NIDDK. FENLAND: The  Fenland Study  is funded by the  Medical Research Council  ( MC_U106179471 ) and  Wellcome Trust. We are grateful to all the volunteers for their time and help, and to the General Practitioners and practice staff for assistance with recruitment.
We thank the  Fenland Study Investigators, Fenland Study Co-ordination team  and the  Epidemiology Field, Data and Laboratory teams. The  Fenland Study  is funded by the  Medical Research Council  ( MC_U106179471 ) and  Wellcome Trust. FHS: Genotyping, quality control and calling of the  Illumina HumanExome BeadChip in the Framingham Heart Study  was supported by funding from the  National Heart, Lung and Blood Institute Division of Intramural Research  ( Daniel Levy  and  Christopher J. O'Donnell ,  Principle Investigators). A portion of this research was conducted using the Linux Clusters for Genetic Analysis (LinGA) computing resources at  Boston University Medical Campus. Also supported by  National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK)   R01 DK078616 ,  NIDDK   K24 DK080140  and  American Diabetes Association Mentor-Based Postdoctoral Fellowship Award  # 7-09-MN-32 , all to  Dr Meigs , a  Canadian Diabetes Association   Research Fellowship Award  to  Dr Leong , a research grant from the  University of Verona, Italy  to  Dr Dauriz , and  NIDDK   Research Career Award   K23 DK65978 , a  Massachusetts General Hospital Physician Scientist Development Award  and a  Doris Duke Charitable Foundation Clinical Scientist Development Award  to  Dr Florez.
This particular project was supported by project grants from the  Swedish Heart-Lung Foundation , the  Swedish Research Council , the  Swedish Diabetes Association, Påhlssons Foundation and Novo nordisk  (all grants to P. W. Franks). Danish Heart Foundation , the  Danish Pharmaceutical Association , the  Augustinus Foundation , the  Ib Henriksen Foundation , the  Becket Foundation  and the  Danish Diabetes Association. Genetic studies of both Inter99 and Health 2008 cohorts were funded by the  Lundbeck Foundation  and produced by The  Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention and Care  (LuCamp, www. lucamp. org). The  Novo Nordisk Foundation Center for Basic Metabolic Research  is an independent Research Center at the  University of Copenhagen  partially funded by an unrestricted donation from the  Novo Nordisk Foundation  (www.
Abbas Dehghan is supported by  NWO  grant veni (veni,  916. 12. 154 ) and the  EUR Fellowship. We are grateful to the study participants, the staff from the  Rotterdam Study  and the participating general practitioners and pharmacists. SCARF: We thank all participants in this study. The study was funded by the  Foundation for Strategic Research , the  Swedish Heart-Lung Foundation , the  Swedish Research Council  ( 8691 ,  12660 ,  20653 ), the  European Commission  ( LSHM-CT-2007-037273 ), the  Knut and Alice Wallenberg Foundation , the  Torsten and Ragnar So ¨derberg Foundation , the  Strategic Cardiovascular and Diabetes Programmes of Karolinska Institutet  and the  Stockholm County Council , and the  Stockholm County Council  ( 560183 ). Bengt Sennblad acknowledges funding from the  Magnus Bergvall Foundation  and the  Foundation for Old Servants.
This work was funded by the  Wellcome Trust  ( 098051 ). We thank all study participants and their families as well as all volunteers for their contribution in this study. We thank the Sample Management and  Genotyping Facilities  staff at the  Wellcome Trust Sanger Institute  for sample preparation, quality control and genotyping. Uppsala Longitudinal Study of Adult Men (ULSAM ): The exome chip genotyping and data analyses were supported by  Uppsala University, Knut och Alice Wallenberg Foundation ,  European Research Council ,  Swedish Diabetes Foundation  (grant no. 2013-024 ),  Swedish Research Council  (grant no. 2012-1397 ), and  Swedish Heart-Lung Foundation  ( 20120197 ). C. M. L. is supported by a  Wellcome Trust   Research Career Development Fellowship  ( 086596/Z/08/Z ). INGI-VB: The  Val Borbera study (INGI-VB ) thanks the inhabitants of the  Val Borbera  for participating in the study, the local administrations and the  ASL-Novi Ligure  for support and  Fiammetta Vigano  `for technical help.
GSK (CoLaus, GEMS, Lolipop): We thank the  GEMS Study Investigators :  Philip Barter, PhD ;  Y. Antero Kesa ¨niemi, PhD ;  Robert W. Mahley, PhD ;  Ruth McPherson , FRCP; and  Scott M. Grundy, PhD. Dr Waeber MD , the CoLaus PI's  Peter Vollenweider MD  and  Gerard Waeber MD , the LOLIPOP PI's,  Jaspal Kooner MD  and  John Chambers MD , as well as the participants in all the studies. The GEMS study was sponsored in part by  GlaxoSmithKline. The CoLaus study was supported by grants from  GlaxoSmithKline , the  Swiss National Science Foundation  (Grant  33CSCO-122661 ) and the  Faculty of Biology and Medicine of Lausanne. Health ABC: The  Health, Aging and Body Composition (HABC) Study  is supported by  NIA  contracts  N01AG62101 ,  N01AG62103  and  N01AG62106. The exome-wide association study was funded by  NIA  grant  1R01AG032098-01A1  to Wake Forest University Health Sciences and was supported in part by the  Intramural Research Program  of the  NIH, National Institute on Aging  ( Z01 AG000949-02  and  Z01 AG007390-07 , Human subjects protocol  UCSF IRB  is  H5254-12688-11 ).
Portions of this study utilized the high-performance computational capabilities of the Biowulf Linux cluster at the  National Institutes of Health, Bethesda, MD. (http:/biowulf. nih. gov). Health2008: The Health2008 cohort was supported by the  Timber Merchant Vilhelm Bang's Foundation , the  Danish Heart Foundation  (Grant number  07-10-R61-A1754-B838-22392F ), and the  Health Insurance Foundation (Helsefonden)  (Grant number  2012B233 ). HELIC: This work was funded by the  Wellcome Trust  ( 098051 ) and the  European Research Council  ( ERC-2011-StG 280559-SEPI ). The MANOLIS cohort is named in honour of  Manolis Giannakakis ,  1978-2010. We thank the residents of Anogia and surrounding Mylopotamos villages, and of the Pomak villages, for taking part. The HELIC study has been supported by many individuals who have contributed to sample collection (including  Antonis Athanasiadis ,  Olina Balafouti ,  Christina Batzaki ,  Georgios Daskalakis ,  Eleni Emmanouil ,  Chrisoula Giannakaki ,  Margarita Giannakopoulou ,  Anastasia Kaparou ,  Vasiliki Kariakli ,  Stella Koinaki ,  Dimitra Kokori ,  Maria Konidari ,  Hara Koundouraki ,  Dimitris Koutoukidis ,  Vasiliki Mamakou ,  Eirini Mamalaki ,  Eirini Mpamiaki ,  Maria Tsoukana ,  Dimitra Tzakou ,  Katerina Vosdogianni ,  Niovi Xenaki ,  Eleni Zengini ), data entry ( Thanos Antonos ,  Dimitra Papagrigoriou ,  Betty Spiliopoulou ), sample logistics ( Sarah Edkins ,  Emma Gray ), genotyping ( Robert Andrews ,  Hannah Blackburn ,  Doug Simpkin ,  Siobhan Whitehead ), research administration ( Anja Kolb-Kokocinski ,  Carol Smee ,  Danielle Walker ) and informatics ( Martin Pollard ,  Josh Randall ).
INCIPE:  NIcole Soranzo 's research is supported by the  Wellcome Trust  (Grant Codes  WT098051  and  WT091310 ), the  EU   FP7  ( EPIGENESYS Grant Code 257082  and  BLUEPRINT Grant Code HEALTH-F5-2011-282510 ). Inter99: The Inter99 was initiated by  Torben Jørgensen  (PI),  Knut Borch-Johnsen  (co-PI),  Hans Ibsen  and  Troels F. Thomsen. The steering committee comprises the former two and  Charlotta Pisinger. The study was financially supported by research grants from the  Danish Research Council , the  Danish Centre for Health Technology Assessment ,  Novo Nordisk Inc. ,  Research Foundation of Copenhagen County, Ministry of Internal Affairs and Health , the.