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Record W2096095753 · doi:10.1002/gepi.20492

The gene, environment association studies consortium (GENEVA): maximizing the knowledge obtained from GWAS by collaboration across studies of multiple conditions

2010· article· en· W2096095753 on OpenAlexfundno aff
Marilyn C. Cornelis, Arpana Agrawal, John W. Cole, Nadia N. Hansel, Kathleen C. Barnes, Terri H. Beaty, Siiri Bennett, Laura J. Bierut, Eric Boerwinkle, Kimberly F. Doheny, Bjarke Feenstra, Eleanor Feingold, Myriam Fornage, Christopher A. Haiman, Emily Harris, M. Geoffrey Hayes, John A. Heit, Frank B. Hu, Jae H. Kang, Cathy C. Laurie, Hua Ling, Teri A. Manolio, Mary L. Marazita, Rasika A. Mathias, Daniel B. Mirel, Justin Paschall, Louis R. Pasquale, Elizabeth Pugh, John P. Rice, Jenna Udren, Rob M. van Dam, Xiaojing Wang, Janey L. Wiggs, Kayleen Williams, Kai Yu

Bibliographic record

VenueGenetic Epidemiology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsnot available
FundersNational Center for Research ResourcesNational Institute of Dental and Craniofacial ResearchWellcome TrustNational Human Genome Research InstituteNational Institute on Drug AbuseNational Heart, Lung, and Blood InstituteNational Cancer InstituteNational Institute on Alcohol Abuse and AlcoholismCanadian Institutes of Health ResearchNational Institutes of HealthNational Institute of Diabetes and Digestive and Kidney DiseasesJohns Hopkins UniversityBroad InstituteNational Eye Institute
KeywordsGenome-wide association studyGenetic associationGenotypingBiologyComputational biologyGeneticsData scienceComputer scienceGeneSingle-nucleotide polymorphismGenotype

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.216
metaresearch head score (Gemma)0.136
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.216
Threshold uncertainty score0.967

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2160.136
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0110.016
Science and technology studies0.0030.006
Scholarly communication0.0120.008
Open science0.0070.032
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0050.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.351
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations157
Published2010
Admission routes1
Has abstractyes

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