Dataset Information

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease.

ABSTRACT: Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

SUBMITTER: Zhou W

PROVIDER: S-EPMC9903716 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease.

Zhou Wei W Kanai Masahiro M Wu Kuan-Han H KH Rasheed Humaira H Tsuo Kristin K Hirbo Jibril B JB Wang Ying Y Bhattacharya Arjun A Zhao Huiling H Namba Shinichi S Surakka Ida I Wolford Brooke N BN Lo Faro Valeria V Lopera-Maya Esteban A EA Läll Kristi K Favé Marie-Julie MJ Partanen Juulia J JJ Chapman Sinéad B SB Karjalainen Juha J Kurki Mitja M Maasha Mutaamba M Brumpton Ben M BM Chavan Sameer S Chen Tzu-Ting TT Daya Michelle M Ding Yi Y Feng Yen-Chen A YA Guare Lindsay A LA Gignoux Christopher R CR Graham Sarah E SE Hornsby Whitney E WE Ingold Nathan N Ismail Said I SI Johnson Ruth R Laisk Triin T Lin Kuang K Lv Jun J Millwood Iona Y IY Moreno-Grau Sonia S Nam Kisung K Palta Priit P Pandit Anita A Preuss Michael H MH Saad Chadi C Setia-Verma Shefali S Thorsteinsdottir Unnur U Uzunovic Jasmina J Verma Anurag A Zawistowski Matthew M Zhong Xue X Afifi Nahla N Al-Dabhani Kawthar M KM Al Thani Asma A Bradford Yuki Y Campbell Archie A Crooks Kristy K de Bock Geertruida H GH Damrauer Scott M SM Douville Nicholas J NJ Finer Sarah S Fritsche Lars G LG Fthenou Eleni E Gonzalez-Arroyo Gilberto G Griffiths Christopher J CJ Guo Yu Y Hunt Karen A KA Ioannidis Alexander A Jansonius Nomdo M NM Konuma Takahiro T Lee Ming Ta Michael MTM Lopez-Pineda Arturo A Matsuda Yuta Y Marioni Riccardo E RE Moatamed Babak B Nava-Aguilar Marco A MA Numakura Kensuke K Patil Snehal S Rafaels Nicholas N Richmond Anne A Rojas-Muñoz Agustin A Shortt Jonathan A JA Straub Peter P Tao Ran R Vanderwerff Brett B Vernekar Manvi M Veturi Yogasudha Y Barnes Kathleen C KC Boezen Marike M Chen Zhengming Z Chen Chia-Yen CY Cho Judy J Smith George Davey GD Finucane Hilary K HK Franke Lude L Gamazon Eric R ER Ganna Andrea A Gaunt Tom R TR Ge Tian T Huang Hailiang H Huffman Jennifer J Katsanis Nicholas N Koskela Jukka T JT Lajonchere Clara C Law Matthew H MH Li Liming L Lindgren Cecilia M CM Loos Ruth J F RJF MacGregor Stuart S Matsuda Koichi K Olsen Catherine M CM Porteous David J DJ Shavit Jordan A JA Snieder Harold H Takano Tomohiro T Trembath Richard C RC Vonk Judith M JM Whiteman David C DC Wicks Stephen J SJ Wijmenga Cisca C Wright John J Zheng Jie J Zhou Xiang X Awadalla Philip P Boehnke Michael M Bustamante Carlos D CD Cox Nancy J NJ Fatumo Segun S Geschwind Daniel H DH Hayward Caroline C Hveem Kristian K Kenny Eimear E EE Lee Seunggeun S Lin Yen-Feng YF Mbarek Hamdi H Mägi Reedik R Martin Hilary C HC Medland Sarah E SE Okada Yukinori Y Palotie Aarno V AV Pasaniuc Bogdan B Rader Daniel J DJ Ritchie Marylyn D MD Sanna Serena S Smoller Jordan W JW Stefansson Kari K van Heel David A DA Walters Robin G RG Zöllner Sebastian S Martin Alicia R AR Willer Cristen J CJ Daly Mark J MJ Neale Benjamin M BM

Cell genomics 20221012 10

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWAS ...[more]

PMID: 36777996

Similar Datasets

Project description:BackgroundLarge medical centers in urban areas, like Los Angeles, care for a diverse patient population and offer the potential to study the interplay between genetic ancestry and social determinants of health. Here, we explore the implications of genetic ancestry within the University of California, Los Angeles (UCLA) ATLAS Community Health Initiative-an ancestrally diverse biobank of genomic data linked with de-identified electronic health records (EHRs) of UCLA Health patients (N=36,736).MethodsWe quantify the extensive continental and subcontinental genetic diversity within the ATLAS data through principal component analysis, identity-by-descent, and genetic admixture. We assess the relationship between genetically inferred ancestry (GIA) and >1500 EHR-derived phenotypes (phecodes). Finally, we demonstrate the utility of genetic data linked with EHR to perform ancestry-specific and multi-ancestry genome and phenome-wide scans across a broad set of disease phenotypes.ResultsWe identify 5 continental-scale GIA clusters including European American (EA), African American (AA), Hispanic Latino American (HL), South Asian American (SAA) and East Asian American (EAA) individuals and 7 subcontinental GIA clusters within the EAA GIA corresponding to Chinese American, Vietnamese American, and Japanese American individuals. Although we broadly find that self-identified race/ethnicity (SIRE) is highly correlated with GIA, we still observe marked differences between the two, emphasizing that the populations defined by these two criteria are not analogous. We find a total of 259 significant associations between continental GIA and phecodes even after accounting for individuals' SIRE, demonstrating that for some phenotypes, GIA provides information not already captured by SIRE. GWAS identifies significant associations for liver disease in the 22q13.31 locus across the HL and EAA GIA groups (HL p-value=2.32×10-16, EAA p-value=6.73×10-11). A subsequent PheWAS at the top SNP reveals significant associations with neurologic and neoplastic phenotypes specifically within the HL GIA group.ConclusionsOverall, our results explore the interplay between SIRE and GIA within a disease context and underscore the utility of studying the genomes of diverse individuals through biobank-scale genotyping linked with EHR-based phenotyping.

Project description:Heart failure (HF) is a morbid and heritable disorder for which the biological mechanisms are incompletely understood. We therefore examined genetic associations with HF in a large national biobank, and assessed whether refined phenotypic classification would facilitate genetic discovery. We defined all-cause HF among 488010 participants from the UK Biobank and performed a genome-wide association analysis. We refined the HF phenotype by classifying individuals with left ventricular dysfunction and without coronary artery disease as having nonischemic cardiomyopathy (NICM), and repeated a genetic association analysis. We then pursued replication of lead HF and NICM variants in independent cohorts, and performed adjusted association analyses to assess whether identified genetic associations were mediated through clinical HF risk factors. In addition, we tested rare, loss-of-function mutations in 24 known dilated cardiomyopathy genes for association with HF and NICM. Finally, we examined associations between lead variants and left ventricular structure and function among individuals without HF using cardiac magnetic resonance imaging (n=4158) and echocardiographic data (n=30201). We identified 7382 participants with all-cause HF in the UK Biobank. Genome-wide association analysis of all-cause HF identified several suggestive loci ( P<1×10-6), the majority linked to upstream HF risk factors, ie, coronary artery disease ( CDKN2B-AS1 and MAP3K7CL) and atrial fibrillation ( PITX2). Refining the HF phenotype yielded a subset of 2038 NICM cases. In contrast to all-cause HF, genetic analysis of NICM revealed suggestive loci that have been implicated in dilated cardiomyopathy ( BAG3, CLCNKA-ZBTB17). Dilated cardiomyopathy signals arising from our NICM analysis replicated in independent cohorts, persisted after HF risk factor adjustment, and were associated with indices of left ventricular dysfunction in individuals without clinical HF. In addition, analyses of loss-of-function variants implicated BAG3 as a disease susceptibility gene for NICM (loss-offunction variant carrier frequency=0.01%; odds ratio,12.03; P=3.62×10-5). We found several distinct genetic mechanisms of all-cause HF in a national biobank that reflect well-known HF risk factors. Phenotypic refinement to a NICM subtype appeared to facilitate the discovery of genetic signals that act independently of clinical HF risk factors and that are associated with subclinical left ventricular dysfunction.

Dataset Information

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease.

Publications

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets