The Gene, Environment Association Studies consortium (GENEVA): maximizing the knowledge obtained from GWAS by collaboration across studies of multiple conditions.
paper
Primary
Public
- Authors
- Cornelis, Marilyn C; Agrawal, Arpana; Cole, John W; Hansel, Nadia N; Barnes, Kathleen C; Beaty, Terri H; Bennett, Siiri N; Bierut, Laura J; Boerwinkle, Eric; Doheny, Kimberly F; Feenstra, Bjarke; Feingold, Eleanor; Fornage, Myriam; Haiman, Christopher A; Harris, Emily L; Hayes, M Geoffrey; Heit, John A; Hu, Frank B; Kang, Jae H; Laurie, Cathy C; Ling, Hua; Manolio, Teri A; Marazita, Mary L; Mathias, Rasika A; Mirel, Daniel B; Paschall, Justin; Pasquale, Louis R; Pugh, Elizabeth W; Rice, John P; Udren, Jenna; van Dam, Rob M; Wang, Xiaojing; Wiggs, Janey L; Williams, Kayleen; Yu, Kai; GENEVA Consortium
- Year
- 2010
- Journal
- Genetic epidemiology
- PMID
- 20091798
- DOI
- 10.1002/gepi.20492
- PMCID
- PMC2860056
Genome-wide association studies (GWAS) have emerged as powerful means for identifying genetic loci related to complex diseases. However, the role of environment and its potential to interact with key loci has not been adequately addressed in most GWAS. Networks of collaborative studies involving different study populations and multiple phenotypes provide a powerful approach for addressing the challenges in analysis and interpretation shared across studies. The Gene, Environment Association Studies (GENEVA) consortium was initiated to: identify genetic variants related to complex diseases; identify variations in gene-trait associations related to environmental exposures; and ensure rapid sharing of data through the database of Genotypes and Phenotypes. GENEVA consists of several academic institutions, including a coordinating center, two genotyping centers and 14 independently designed studies of various phenotypes, as well as several Institutes and Centers of the National Institutes of Health led by the National Human Genome Research Institute. Minimum detectable effect sizes include relative risks ranging from 1.24 to 1.57 and proportions of variance explained ranging from 0.0097 to 0.02. Given the large number of research participants (N>80,000), an important feature of GENEVA is harmonization of common variables, which allow analyses of additional traits. Environmental exposure information available from most studies also enables testing of gene-environment interactions. Facilitated by its sizeable infrastructure for promoting collaboration, GENEVA has established a unified framework for genotyping, data quality control, analysis and interpretation. By maximizing knowledge obtained through collaborative GWAS incorporating environmental exposure information, GENEVA aims to enhance our understanding of disease etiology, potentially identifying opportunities for intervention.
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|---|---|---|---|---|
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| Translational bioinformatics approach to combat cardiovascular disease and cancers. | Shahjahan et al. | — | 2024 | → |
| Copy Number Variations in Pancreatic Cancer: From Biological Significance to Clinical Utility. | Oketch DJA et al. | — | 2023 | → |
| The Bayesian Regularized Quantile Varying Coefficient Model. | Zhou F et al. | — | 2023 | → |
| Integrative functional linear model for genome-wide association studies with multiple traits. | Li Y et al. | — | 2022 | → |
| Network Approaches to Integrate Analyses of Genetics and Metabolomics Data with Applications to Fetal Programming Studies. | Kuang A et al. | — | 2022 | → |
| Enhancer-Gene Interaction Analyses Identified the Epidermal Growth Factor Receptor as a Susceptibility Gene for Type 2 Diabetes Mellitus. | Yang Y et al. | — | 2021 | → |
| Gene-Environment Interaction: A Variable Selection Perspective. | Zhou F et al. | — | 2021 | → |
| Systems biology in cardiovascular disease: a multiomics approach. | Joshi A et al. | — | 2021 | → |
| Gene-Based Association Mapping for Dental Caries in The GENEVA Consortium. | Wang Y et al. | — | 2020 | → |
| Genomic resources for dissecting the role of non-protein coding variation in gene-environment interactions. | Levings D et al. | — | 2020 | → |
| Incorporating multiple sets of eQTL weights into gene-by-environment interaction analysis identifies novel susceptibility loci for pancreatic cancer. | Yang T et al. | — | 2020 | → |
| Metabolomic and genetic associations with insulin resistance in pregnancy. | Liu Y et al. | — | 2020 | → |
| Molecular Genetics of Glaucoma: Subtype and Ethnicity Considerations. | Zukerman R et al. | — | 2020 | → |
| Multi-ethnic analysis shows genetic risk and environmental predictors interact to influence 25(OH)D concentration and optimal vitamin D intake. | Hatchell KE et al. | — | 2020 | → |
| Posttraumatic Stress Disorder and Inflammation: Untangling Issues of Bidirectionality. | Sumner JA et al. | — | 2020 | → |
| Ancestry-specific polygenic scores and SNP heritability of 25(OH)D in African- and European-ancestry populations | Hatchell KE et al. | — | 2019 | — |
| Ancestry-specific polygenic scores and SNP heritability of 25(OH)D in African- and European-ancestry populations. | Hatchell KE et al. | — | 2019 | → |
| Consortium-based genome-wide meta-analysis for childhood dental caries traits. | Haworth S et al. | — | 2018 | → |
| Improving machine learning reproducibility in genetic association studies with proportional instance cross validation (PICV). | Piette ER et al. | — | 2018 | → |
| Landscape features along migratory routes influence adaptive genomic variation in anadromous steelhead (Oncorhynchus mykiss). | Micheletti SJ et al. | — | 2018 | → |
| An American Thoracic Society/National Heart, Lung, and Blood Institute Workshop Report: Addressing Respiratory Health Equality in the United States. | Celedón JC et al. | — | 2017 | → |
| Drivers of the immunopathogenesis in systemic lupus erythematosus. | Rose T et al. | — | 2017 | → |
| Gene-Hormone Therapy Interaction and Fracture Risk in Postmenopausal Women. | Wang Y et al. | — | 2017 | → |
| Genome-wide analyses of non-syndromic cleft lip with palate identify 14 novel loci and genetic heterogeneity. | Yu Y et al. | — | 2017 | → |
| Genome-wide meta-analysis identifies a novel susceptibility signal at CACNA2D3 for nicotine dependence. | Yin X et al. | — | 2017 | → |
| Network-based regularization for high dimensional SNP data in the case-control study of Type 2 diabetes. | Ren J et al. | — | 2017 | → |
| The influence of genetic susceptibility and calcium plus vitamin D supplementation on fracture risk. | Wang Y et al. | — | 2017 | → |
| A General and Robust Framework for Secondary Traits Analysis. | Song X et al. | — | 2016 | → |
| A global test for gene-gene interactions based on random matrix theory. | Frost HR et al. | — | 2016 | → |
| Assessing the Association of Mitochondrial Genetic Variation With Primary Open-Angle Glaucoma Using Gene-Set Analyses. | Khawaja AP et al. | — | 2016 | → |
| Association involving serotonin transporter gene linked polymorphic region and bipolar disorder type 1 in Iranian population. | Ghadiri M et al. | — | 2016 | → |
| Chemoprevention in African American Men With Prostate Cancer. | Kumar NB et al. | — | 2016 | → |
| Genetic factors influencing risk to orofacial clefts: today's challenges and tomorrow's opportunities. | Beaty TH et al. | — | 2016 | → |
| A genome-wide study of inherited deletions identified two regions associated with nonsyndromic isolated oral clefts. | Younkin SG et al. | — | 2015 | → |
| A Scale-Corrected Comparison of Linkage Disequilibrium Levels between Genic and Non-Genic Regions. | Berger S et al. | — | 2015 | → |
| Challenges in Studying Modifiable Risk Factors for Birth Defects. | Tinker SC et al. | — | 2015 | → |
| Contrasting genetic architectures in different mouse reference populations used for studying complex traits. | Buchner DA et al. | — | 2015 | → |
| Detecting gene-environment interactions in human birth defects: Study designs and statistical methods. | Tai CG et al. | — | 2015 | → |
| Efficient Identification of Null-Allele Single Nucleotide Polymorphism Markers. | Özbek U et al. | — | 2015 | → |
| Genetic susceptibility to dental caries differs between the sexes: a family-based study. | Shaffer JR et al. | — | 2015 | → |
| Genomic heritability: what is it? | de Los Campos G et al. | — | 2015 | → |
| Risk prediction models for oral clefts allowing for phenotypic heterogeneity. | Wen Y et al. | — | 2015 | → |
| The primary open-angle african american glaucoma genetics study: baseline demographics. | Charlson ES et al. | — | 2015 | → |
| A genome-wide study of de novo deletions identifies a candidate locus for non-syndromic isolated cleft lip/palate risk. | Younkin SG et al. | — | 2014 | → |
| A set-based association test identifies sex-specific gene sets associated with type 2 diabetes. | He T et al. | — | 2014 | → |
| Calibrating longitudinal cognition in Alzheimer's disease across diverse test batteries and datasets. | Gross AL et al. | — | 2014 | → |
| CLEFT LIP, ALVEOLUS AND PALATE IN AFRICAN NATIVES: AN UPDATE ON DEMOGRAPHICS AND MANAGEMENT OUTCOME. | Akintububo OB et al. | — | 2014 | → |
| Data compatibility in the addiction sciences: an examination of measure commonality. | Conway KP et al. | — | 2014 | → |
| DNA copy number variants of known glaucoma genes in relation to primary open-angle glaucoma. | Liu Y et al. | — | 2014 | → |
| Environmental influences on systemic lupus erythematosus expression. | Kamen DL | — | 2014 | → |
| Gene-environment interaction in autoimmune disease. | Ellis JA et al. | — | 2014 | → |
| Genome-wide association study and meta-analysis of intraocular pressure. | Ozel AB et al. | — | 2014 | → |
| Harmonizing the classification of age-related macular degeneration in the three-continent AMD consortium. | Klein R et al. | — | 2014 | → |
| Integrative analysis of gene-environment interactions under a multi-response partially linear varying coefficient model. | Wu C et al. | — | 2014 | → |
| An analytical comparison of the principal component method and the mixed effects model for association studies in the presence of cryptic relatedness and population stratification. | Wang K et al. | — | 2013 | → |
| A novel method for identifying nonlinear gene-environment interactions in case-control association studies. | Wu C et al. | — | 2013 | → |
| CDKN2B-AS1 genotype-glaucoma feature correlations in primary open-angle glaucoma patients from the United States. | Pasquale LR et al. | — | 2013 | → |
| Cohort Profile: the Dongfeng-Tongji cohort study of retired workers. | Wang F et al. | — | 2013 | → |
| Estrogen pathway polymorphisms in relation to primary open angle glaucoma: an analysis accounting for gender from the United States. | Pasquale LR et al. | — | 2013 | → |
| Genes, environments, and developmental research: methods for a multi-site study of early substance abuse. | Costello EJ et al. | — | 2013 | → |
| Genome-wide association study identified the human leukocyte antigen region as a novel locus for plasma beta-2 microglobulin. | Tin A et al. | — | 2013 | → |
| Genome-wide association study of dermatomyositis reveals genetic overlap with other autoimmune disorders. | Miller FW et al. | — | 2013 | → |
| Genome-wide association study of primary tooth eruption identifies pleiotropic loci associated with height and craniofacial distances. | Fatemifar G et al. | — | 2013 | → |
| Incidental genetic findings in randomized clinical trials: recommendations from the Genomics and Randomized Trials Network (GARNET). | Bookman EB et al. | — | 2013 | → |
| Prediction of complex human traits using the genomic best linear unbiased predictor. | de Los Campos G et al. | — | 2013 | → |
| Replication of genome wide association studies of alcohol dependence: support for association with variation in ADH1C. | Biernacka JM et al. | — | 2013 | → |
| The NEIGHBOR consortium primary open-angle glaucoma genome-wide association study: rationale, study design, and clinical variables. | Wiggs JL et al. | — | 2013 | → |
| Variation in HNF1B and Obesity May Influence Prostate Cancer Risk in African American Men: A Pilot Study. | Chornokur G et al. | — | 2013 | → |
| A genome-wide association study of venous thromboembolism identifies risk variants in chromosomes 1q24.2 and 9q. | Heit JA et al. | — | 2012 | → |
| A high-performance computing toolset for relatedness and principal component analysis of SNP data. | Zheng X et al. | — | 2012 | → |
| Challenges and opportunities in genome-wide environmental interaction (GWEI) studies. | Aschard H et al. | — | 2012 | → |
| Chromosomal variation in lymphoblastoid cell lines. | Shirley MD et al. | — | 2012 | → |
| Common variants at 9p21 and 8q22 are associated with increased susceptibility to optic nerve degeneration in glaucoma. | Wiggs JL et al. | — | 2012 | → |
| Copy number variations in 6q14.1 and 5q13.2 are associated with alcohol dependence. | Lin P et al. | — | 2012 | → |
| Cumulative meta-analysis for genetic association: when is a new study worthwhile? | Rotondi MA et al. | — | 2012 | → |
| Detectable clonal mosaicism from birth to old age and its relationship to cancer. | Laurie CC et al. | — | 2012 | → |
| Fast detection of de novo copy number variants from SNP arrays for case-parent trios. | Scharpf RB et al. | — | 2012 | → |
| From mouse to humans: discovery of the CACNG2 pain susceptibility gene. | Nissenbaum J | — | 2012 | → |
| Genetics of nonsyndromic orofacial clefts. | Rahimov F et al. | — | 2012 | → |
| Genetic susceptibility to dental caries on pit and fissure and smooth surfaces. | Shaffer JR et al. | — | 2012 | → |
| Genome-wide analysis of central corneal thickness in primary open-angle glaucoma cases in the NEIGHBOR and GLAUGEN consortia. | Ulmer M et al. | — | 2012 | → |
| Genome-wide association scan of dental caries in the permanent dentition. | Wang X et al. | — | 2012 | → |
| Genome-wide association study of lung function decline in adults with and without asthma. | Imboden M et al. | — | 2012 | → |
| GWAS reveals new recessive loci associated with non-syndromic facial clefting. | Camargo M et al. | — | 2012 | → |
| GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies. | Gogarten SM et al. | — | 2012 | → |
| Heritable patterns of tooth decay in the permanent dentition: principal components and factor analyses. | Shaffer JR et al. | — | 2012 | → |
| Measuring alcohol consumption for genomic meta-analyses of alcohol intake: opportunities and challenges. | Agrawal A et al. | — | 2012 | → |
| Population-based case-control association studies. | Hancock DB et al. | — | 2012 | → |
| Rare variants in ischemic stroke: an exome pilot study. | Cole JW et al. | — | 2012 | → |
| The evolution of human genetic studies of cleft lip and cleft palate. | Marazita ML | — | 2012 | → |
| Using PhenX measures to identify opportunities for cross-study analysis. | Pan H et al. | — | 2012 | → |
| Variation in actual relationship among descendants of inbred individuals. | Hill WG et al. | — | 2012 | → |
| A genome-wide association study of DSM-IV cannabis dependence. | Agrawal A et al. | — | 2011 | → |
| Assessing and managing risk when sharing aggregate genetic variant data. | Craig DW et al. | — | 2011 | → |
| Changes: choosing a target? | Eckelman W | — | 2011 | → |
| Cleft lip and palate: understanding genetic and environmental influences. | Dixon MJ et al. | — | 2011 | → |
| Common variants near CAV1 and CAV2 are associated with primary open-angle glaucoma in Caucasians from the USA. | Wiggs JL et al. | — | 2011 | → |
| Contemporary human genetic strategies in aging research. | Bloss CS et al. | — | 2011 | → |
| Copy number variation accuracy in genome-wide association studies. | Lin P et al. | — | 2011 | → |
| Database of genetic studies of bipolar disorder. | Piletz JE et al. | — | 2011 | → |
| GALC deletions increase the risk of primary open-angle glaucoma: the role of Mendelian variants in complex disease. | Liu Y et al. | — | 2011 | → |
| Gene-environment interplay in common complex diseases: forging an integrative model—recommendations from an NIH workshop. | Bookman EB et al. | — | 2011 | → |
| Genome partitioning of genetic variation for complex traits using common SNPs. | Yang J et al. | — | 2011 | → |
| Genome-wide association scan for childhood caries implicates novel genes. | Shaffer JR et al. | — | 2011 | → |
| Genome-wide association study identifies four loci associated with eruption of permanent teeth. | Geller F et al. | — | 2011 | → |
| Genome-wide association study identifies novel loci predisposing to cutaneous melanoma. | Amos CI et al. | — | 2011 | → |
| Genome-wide association study of conduct disorder symptomatology. | Dick DM et al. | — | 2011 | → |
| Mayo Genome Consortia: a genotype-phenotype resource for genome-wide association studies with an application to the analysis of circulating bilirubin levels. | Bielinski SJ et al. | — | 2011 | → |
| Phenotype harmonization and cross-study collaboration in GWAS consortia: the GENEVA experience. | Bennett SN et al. | — | 2011 | → |
| Public health in the genomic era: will Public Health Genomics contribute to major changes in the prevention of common diseases? | Cleeren E et al. | — | 2011 | → |
| The case for launch of an international DNA-based birth cohort study. | Rudan I et al. | — | 2011 | → |
| Using the PhenX Toolkit to Add Standard Measures to a Study. | Hendershot T et al. | — | 2011 | → |
| Variation in actual relationship as a consequence of Mendelian sampling and linkage. | Hill WG et al. | — | 2011 | → |
| Genome-wide association studies and large-scale collaborations in epidemiology. | Psaty BM et al. | — | 2010 | → |
| Pharmacogenomics--10 years of progress: a cardiovascular perspective. | Winkelmann BR et al. | — | 2010 | → |
| Quality control and quality assurance in genotypic data for genome-wide association studies. | Laurie CC et al. | — | 2010 | → |
| Quality, quantity and harmony: the DataSHaPER approach to integrating data across bioclinical studies. | Fortier I et al. | — | 2010 | → |
| Smoking and atherosclerotic cardiovascular disease: part IV: genetic markers associated with smoking. | Winkelmann BR et al. | — | 2010 | → |