Dataset Information

Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

ABSTRACT: To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82?K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44?K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

SUBMITTER: Flannick J

PROVIDER: S-EPMC5735917 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Publications

Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Flannick Jason J Fuchsberger Christian C Mahajan Anubha A Teslovich Tanya M TM Agarwala Vineeta V Gaulton Kyle J KJ Caulkins Lizz L Koesterer Ryan R Ma Clement C 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 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 Dennis 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 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 Valeri 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 Cn JC 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 Mohlke Karen L KL Meitinger Thomas T Groop Leif L Abecasis Goncalo G Scott Laura J LJ Morris Andrew P AP Kang Hyun Min HM Altshuler David D Burtt Noël P NP Florez Jose C JC Boehnke Michael M McCarthy Mark I MI

Scientific data 20171219

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in ...[more]

PMID: 29257133

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Project description:Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93-100%), while drug-based components were the main contributors in RLDs (81-100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies.

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Project description:Association tests that pool minor alleles into a measure of burden at a locus have been proposed for case-control studies using sequence data containing rare variants. However, such pooling tests are not robust to the inclusion of neutral and protective variants, which can mask the association signal from risk variants. Early studies proposing pooling tests dismissed methods for locus-wide inference using nonnegative single-variant test statistics based on unrealistic comparisons. However, such methods are robust to the inclusion of neutral and protective variants and therefore may be more useful than previously appreciated. In fact, some recently proposed methods derived within different frameworks are equivalent to performing inference on weighted sums of squared single-variant score statistics. In this study, we compared two existing methods for locus-wide inference using nonnegative single-variant test statistics to two widely cited pooling tests under more realistic conditions. We established analytic results for a simple model with one rare risk and one rare neutral variant, which demonstrated that pooling tests were less powerful than even Bonferroni-corrected single-variant tests in most realistic situations. We also performed simulations using variants with realistic minor allele frequency and linkage disequilibrium spectra, disease models with multiple rare risk variants and extensive neutral variation, and varying rates of missing genotypes. In all scenarios considered, existing methods using nonnegative single-variant test statistics had power comparable to or greater than two widely cited pooling tests. Moreover, in disease models with only rare risk variants, an existing method based on the maximum single-variant Cochran-Armitage trend chi-square statistic in the locus had power comparable to or greater than another existing method closely related to some recently proposed methods. We conclude that efficient locus-wide inference using single-variant test statistics should be reconsidered as a useful framework for devising powerful association tests in sequence data with rare variants.

Dataset Information

Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Publications

Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Similar Datasets

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