Dataset Information

GoT2D WGS analysis files

ABSTRACT: This data set includes the following summary level data files used for the GoT2D WGS analysis: wgs.assoc.samples.list: list of samples to keep for association analysis wgs.assoc.variants.list: list of variants to keep for association analysis wgs.sv.assoc.txt: single variant association results

PROVIDER: EGAD00010001185 | 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 repository contains all the FASTQ files for the five data modalities (scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics) used in the article \\"An Atlas of Cells in The Human Tonsil,\\" published in Immunity in 2024. Inspired by the TCGA barcodes, we have named each fastq file with the following convention: [TECHNOLOGY].[DONOR_ID].[SUBPROJECT].[GEM_ID].[LIBRARY_ID].[LIBRARY_TYPE].[LANE].[READ].fastq.gz which allows to retrieve all metadata from the name itself. Here is a full description of each field: - TECHNOLOGY: scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics (Visium). We also include the fastq files associated with the multiome experiments performed on two mantle cell lymphoma patients (MCL). - DONOR_ID: identifier for each of the 17 patients included in the cohort. We provide the donor-level metadata in the file \\"tonsil_atlas_donor_metadata.csv\\", including the hospital, sex, age, age group, cause for tonsillectomy and cohort type for every donor. - SUBPROJECT: each subproject corresponds to one run of the 10x Genomics Chromium™ Chip. - GEM_ID: each run of the 10x Genomics Chromium™ Chip consists of up to 8 \\"GEM wells\\" (see https://www.10xgenomics.com/support/software/cell-ranger/getting-started/cr-glossary): a set of partitioned cells (Gel Beads-in-emulsion) from a single 10x Genomics Chromium™ Chip channel. We give a unique identifier to each of these channels. - LIBRARY_ID: one or more sequencing libraries can be derived from a GEM well. For instance, multiome yields two libraries (ATAC and RNA) and CITE-seq+scVDJ yields 4 libraries (RNA, ADT, BCR, TCR). - LIBRARY_TYPE: the type of library for each library_id. Note that we used cell hashing () for a subset of the scRNA-seq libraries, and thus the library_type can be \\"not_hashed\\", \\"hashed_cdna\\" (RNA expression) or \\"hashed_hto\\" (the hashtag oligonucleotides). - LANE: to increase sequencing depth, each library was sequenced in more than one lane. Important: all lanes corresponding to the same sequencing library need to be inputed together to cellranger, because they come from the same set of cells. - READ: for scATAC-seq we have three reads (R1, R2 or R3), see cellranger-atac's documentation. While we find these names to be the most useful, they need to be changed to follow cellranger's conventions. We provide a code snippet in the README file of the GitHub repository associated with the tonsil atlas to convert between both formats (https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/TonsilAtlas/). Besides the fastq files, cellranger (and other mappers) require additional files, which we also provide in this repository: - cell_hashing_metadata.csv: as mentioned above, we ran cell hashing (10.1186/s13059-018-1603-1) to detect doublets and reduce cost per cell. This file provides the sequence of the hashtag oligonucleotides in cellranger convention to allow demultiplexing. - cite_seq_feature_reference.csv: similar to the previous file, this one links each protein surface marker to the hashtag oligonucleotide that identified it in the CITE-seq experiment. - V10M16-059.gpr and V19S23-039.gpr: these correspond to the two slides of the two Visium experiments performed in the tonsil atlas. They are needed to run spaceranger. - [GEM_ID]_[SLIDE]_[CAPTURE_AREA].jpg: 8 images associated with the Visium experiments. Here, GEM_ID refers to each of the 4 capture areas in each slide. - [TECHNOLOGY]_sequencing_metadata.csv: the GEM-level metadata for each technology. It includes the relationship between subproject, gem_id, library_id, library_type and donor_id. These are the other repositories associated with the tonsil atlas: - Expression and accessibility matrices: https://zenodo.org/records/10373041 - Seurat objects: https://zenodo.org/records/8373756 - HCATonsilData package: https://bioconductor.org/packages/release/data/experiment/html/HCATonsilData.html - Azimuth: https://azimuth.hubmapconsortium.org/ - Github: https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/TonsilAtlas

Dataset Information

GoT2D WGS analysis files

Publications

The genetic architecture of type 2 diabetes.

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