ArticleCell genomics2021
Workshop proceedings: GWAS summary statistics standards and sharing.
Article in Cell genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- A Recipe for a Good π. How to Properly Estimate Population Genetics Summary Statistics and Why we Should Systematically Report Them.Genome biology and evolution · 2026Article
- Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies.Cell genomics · 2025Review
- MUTATE: a human genetic atlas of multiorgan artificial intelligence endophenotypes using genome-wide association summary statistics.Briefings in bioinformatics · 2025Article
- The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity.Nucleic acids research · 2025Article
- TidyGWAS: a scalable approach for standardized cleaning of genome-wide association study summary statistics.Bioinformatics advances · 2025Article
- The PRIMED Consortium: Reducing disparities in polygenic risk assessment.American journal of human genetics · 2024Article
- The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity.bioRxiv : the preprint server for biology · 2024Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Integrative cross-omics and cross-context analysis elucidates molecular links underlying genetic effects on complex traits.Nature communications · 2024Article
- Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality.Genetics · 2024Article
- Enabling tradeoffs in privacy and utility in genomic data Beacons and summary statistics.Genome research · 2023Article
- Improve the model of disease subtype heterogeneity by leveraging external summary data.PLoS computational biology · 2023Article
- eXclusionarY: 10 years later, where are the sex chromosomes in GWASs?American journal of human genetics · 2023Review
- Processing genome-wide association studies within a repository of heterogeneous genomic datasets.BMC genomic data · 2023Article
- Sharing GWAS summary statistics results in more citations.Communications biology · 2023Article
- The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource.Nucleic acids research · 2023Article
- SysBiolPGWAS: simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasets.Bioinformatics (Oxford, England) · 2023Article
- SumStatsRehab: an efficient algorithm for GWAS summary statistics assessment and restoration.BMC bioinformatics · 2022Article
- Identifying and correcting for misspecifications in GWAS summary statistics and polygenic scores.HGG advances · 2022Article
- PLCOjs, a FAIR GWAS web SDK for the NCI Prostate, Lung, Colorectal and Ovarian Cancer Genetic Atlas project.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
Genome-wide association studies (GWASs) have enabled robust mapping of complex traits in humans. The open sharing of GWAS summary statistics (SumStats) is essential in facilitating the larger meta-analyses needed for increased power in resolving the genetic basis of disease. However, most GWAS SumStats are not readily accessible because of limited sharing and a lack of defined standards. With the aim of increasing the availability, quality, and utility of GWAS SumStats, the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS Catalog organized a community workshop to address the standards, infrastructure, and incentives required to promote and enable sharing. We evaluated the barriers to SumStats sharing, both technological and sociological, and developed an action plan to address those challenges and ensure that SumStats and study metadata are findable, accessible, interoperable, and reusable (FAIR). We encourage early deposition of datasets in the GWAS Catalog as the recognized central repository. We recommend standard requirements for reporting elements and formats for SumStats and accompanying metadata as guidelines for community standards and a basis for submission to the GWAS Catalog. Finally, we provide recommendations to enable, promote, and incentivize broader data sharing, standards and FAIRness in order to advance genomic medicine.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.