Dataset Information

Broad Institute

ABSTRACT: Organisation EGAO00000000633

PROVIDER: EGAO00000000633 | EGA |

REPOSITORIES: EGA

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Publications

The genetic architecture of type 2 diabetes.

Fuchsberger Christian C Flannick Jason J Teslovich Tanya M TM Mahajan Anubha A Agarwala Vineeta V Gaulton Kyle J KJ Ma Clement C Fontanillas Pierre P Moutsianas Loukas L McCarthy Davis J DJ Rivas Manuel A MA Perry John R B JRB Sim Xueling X Blackwell Thomas W TW Robertson Neil R NR Rayner N William NW Cingolani Pablo P Locke Adam E AE Tajes Juan Fernandez JF Highland Heather M HM Dupuis Josee J Chines Peter S PS Lindgren Cecilia M CM Hartl Christopher C Jackson Anne U AU Chen Han H Huyghe Jeroen R JR van de Bunt Martijn M Pearson Richard D RD Kumar Ashish A Müller-Nurasyid Martina M Grarup Niels N Stringham Heather M HM Gamazon Eric R ER Lee Jaehoon J Chen Yuhui Y Scott Robert A RA Below Jennifer E JE Chen Peng P Huang Jinyan J Go Min Jin MJ Stitzel Michael L ML Pasko Dorota D Parker Stephen C J SCJ Varga Tibor V TV Green Todd T Beer Nicola L NL Day-Williams Aaron G AG Ferreira Teresa T Fingerlin Tasha T Horikoshi Momoko M Hu Cheng C Huh Iksoo I Ikram Mohammad Kamran MK Kim Bong-Jo BJ Kim Yongkang Y Kim Young Jin YJ Kwon Min-Seok MS Lee Juyoung J Lee Selyeong S Lin Keng-Han KH Maxwell Taylor J TJ Nagai Yoshihiko Y Wang Xu X Welch Ryan P RP Yoon Joon J Zhang Weihua W Barzilai Nir N Voight Benjamin F BF Han Bok-Ghee BG Jenkinson Christopher P CP Kuulasmaa Teemu T Kuusisto Johanna J Manning Alisa A Ng Maggie C Y MCY Palmer Nicholette D ND Balkau Beverley B Stančáková Alena A Abboud Hanna E HE Boeing Heiner H Giedraitis Vilmantas V Prabhakaran Dorairaj D Gottesman Omri O Scott James J Carey Jason J Kwan Phoenix P Grant George G Smith Joshua D JD Neale Benjamin M BM Purcell Shaun S Butterworth Adam S AS Howson Joanna M M JMM Lee Heung Man HM Lu Yingchang Y Kwak Soo-Heon SH Zhao Wei W Danesh John J Lam Vincent K L VKL Park Kyong Soo KS Saleheen Danish D So Wing Yee WY Tam Claudia H T CHT Afzal Uzma U Aguilar David D Arya Rector R Aung Tin T Chan Edmund E Navarro Carmen C Cheng Ching-Yu CY Palli Domenico D Correa Adolfo A Curran Joanne E JE Rybin Denis D Farook Vidya S VS Fowler Sharon P SP Freedman Barry I BI Griswold Michael M Hale Daniel Esten DE Hicks Pamela J PJ Khor Chiea-Chuen CC Kumar Satish S Lehne Benjamin B Thuillier Dorothée D Lim Wei Yen WY Liu Jianjun J van der Schouw Yvonne T YT Loh Marie M Musani Solomon K SK Puppala Sobha S Scott William R WR Yengo Loïc L Tan Sian-Tsung ST Taylor Herman A HA Thameem Farook F Wilson Gregory G Wong Tien Yin TY Njølstad Pål Rasmus PR Levy Jonathan C JC Mangino Massimo M Bonnycastle Lori L LL Schwarzmayr Thomas T Fadista João J Surdulescu Gabriela L GL Herder Christian C Groves Christopher J CJ Wieland Thomas T Bork-Jensen Jette J Brandslund Ivan I Christensen Cramer C Koistinen Heikki A HA Doney Alex S F ASF Kinnunen Leena L Esko Tõnu T Farmer Andrew J AJ Hakaste Liisa L Hodgkiss Dylan D Kravic Jasmina J Lyssenko Valeriya V Hollensted Mette M Jørgensen Marit E ME Jørgensen Torben T Ladenvall Claes C Justesen Johanne Marie JM Käräjämäki Annemari A Kriebel Jennifer J Rathmann Wolfgang W Lannfelt Lars L Lauritzen Torsten T Narisu Narisu N Linneberg Allan A Melander Olle O Milani Lili L Neville Matt M Orho-Melander Marju M Qi Lu L Qi Qibin Q Roden Michael M Rolandsson Olov O Swift Amy A Rosengren Anders H AH Stirrups Kathleen K Wood Andrew R AR Mihailov Evelin E Blancher Christine C Carneiro Mauricio O MO Maguire Jared J Poplin Ryan R Shakir Khalid K Fennell Timothy T DePristo Mark M de Angelis Martin Hrabé MH Deloukas Panos P Gjesing Anette P AP Jun Goo G Nilsson Peter P Murphy Jacquelyn J Onofrio Robert R Thorand Barbara B Hansen Torben T Meisinger Christa C Hu Frank B FB Isomaa Bo B Karpe Fredrik F Liang Liming L Peters Annette A Huth Cornelia C O'Rahilly Stephen P SP Palmer Colin N A CNA Pedersen Oluf O Rauramaa Rainer R Tuomilehto Jaakko J Salomaa Veikko V Watanabe Richard M RM Syvänen Ann-Christine AC Bergman Richard N RN Bharadwaj Dwaipayan D Bottinger Erwin P EP Cho Yoon Shin YS Chandak Giriraj R GR Chan Juliana C N JCN Chia Kee Seng KS Daly Mark J MJ Ebrahim Shah B SB Langenberg Claudia C Elliott Paul P Jablonski Kathleen A KA Lehman Donna M DM Jia Weiping W Ma Ronald C W RCW Pollin Toni I TI Sandhu Manjinder M Tandon Nikhil N Froguel Philippe P Barroso Inês I Teo Yik Ying YY Zeggini Eleftheria E Loos Ruth J F RJF Small Kerrin S KS Ried Janina S JS DeFronzo Ralph A RA Grallert Harald H Glaser Benjamin B Metspalu Andres A Wareham Nicholas J NJ Walker Mark M Banks Eric E Gieger Christian C Ingelsson Erik E Im Hae Kyung HK Illig Thomas T Franks Paul W PW Buck Gemma G Trakalo Joseph J Buck David D Prokopenko Inga I Mägi Reedik R Lind Lars L Farjoun Yossi Y Owen Katharine R KR Gloyn Anna L AL Strauch Konstantin K Tuomi Tiinamaija T Kooner Jaspal Singh JS Lee Jong-Young JY Park Taesung T Donnelly Peter P Morris Andrew D AD Hattersley Andrew T AT Bowden Donald W DW Collins Francis S FS Atzmon Gil G Chambers John C JC Spector Timothy D TD Laakso Markku M Strom Tim M TM Bell Graeme I GI Blangero John J Duggirala Ravindranath R Tai E Shyong ES McVean Gilean G Hanis Craig L CL Wilson James G JG Seielstad Mark M Frayling Timothy M TM Meigs James B JB Cox Nancy J NJ Sladek Rob R Lander Eric S ES Gabriel Stacey S Burtt Noël P NP Mohlke Karen L KL Meitinger Thomas T Groop Leif L Abecasis Goncalo G Florez Jose C JC Scott Laura J LJ Morris Andrew P AP Kang Hyun Min HM Boehnke Michael M Altshuler David D McCarthy Mark I MI

Nature 20160711 7614

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed ...[more]

PMID: 27398621

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Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (mailto:nshoresh@broad.mit.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track displays maps of chromatin state generated by the Broad/MGH ENCODE group using ChIP-seq. Chemical modifications (methylation, acetylation) to the histone proteins present in chromatin influence gene expression by changing how accessible the chromatin is to transcription. The ChIP-seq method involves first using formaldehyde to cross-link histones and other DNA-associated proteins to genomic DNA within cells. The cross-linked chromatin is subsequently extracted, mechanically sheared, and immunoprecipitated using specific antibodies. After reversal of cross-links, the immunoprecipitated DNA is sequenced and mapped to the human reference genome. The relative enrichment of each antibody-target (epitope) across the genome is inferred from the density of mapped fragments. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf ChIP-seq: Cells were grown according to the approved ENCODE cell culture protocols. Cells were fixed in 1% formaldehyde and resuspended in lysis buffer. Chromatin was sheared to 200-700 bp using a Diagenode Bioruptor. Solubilized chromatin was immunoprecipitated with antibodies against each of the histone antibodies listed above. Antibody-chromatin complexes were pulled-down using protein A-sepharose (or anti-IgM-conjugated agarose for RNA polymerase II), washed and then eluted. After cross-link reversal and proteinase K treatment, immunoprecipitated DNA was extracted with phenol-chloroform, ethanol precipitated, treated with RNAse and purified. One to ten nanograms of DNA were end-repaired, adapter-ligated and sequenced by Illumina Genome Analyzers as recommended by the manufacturer. Alignment: Sequence reads from each IP experiment were aligned to the human reference genome (GRCh37/hg19) using MAQ with default parameters, except '-C 11' and '-H output_file', which outputs up to 11 additional best matches for each read (if any are found) to a file. This information was used to filter out any read that had more than 10 best matches on the genome. Note: It is likely that instances where multiple reads align to the same position and with the same orientation are due to enhanced PCR amplification of a single DNA fragment. No attempt has been made, however, to remove such artifacts from the data, following ENCODE practices. Signal: Fragment densities were computed by counting the number of reads overlapping each 25 bp bin along the genome. Densities were computed using igvtools count with default parameters (in particular, '-w 25' to set window size of 25 bp and '-f mean' to report the mean value across the window), except for '-e' set to extend the reads to 200 bp, and the .wig output was converted to bigWig using wigToBigWig from the UCSC Kent software package. Peaks: Discrete intervals of ChIP-seq fragment enrichment were identified using Scripture, a scan statistics approach, under the assumption of uniform background signal. All data sets where processed with '-task chip', and with '-windows 100,200,500,1000,5000,10000,100000'. (No mask file nor the '-trim' option have been used.) The resulting called segments were then further filtered to remove intervals that are significantly enriched only because they contain smaller enriched intervals within them. This post-processing step has been implemented using Matlab. The use of the post-processing step allowed very large enriched intervals (of the order of Mbps for H3K27me3, for instance) to be detected, as well as much smaller intervals, without the need to tailor the parameters of Scripture based on prior expectations.

Dataset Information

Broad Institute

Publications

The genetic architecture of type 2 diabetes.

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