Nat Commun November 2017 PMID 29184056

Functional mapping and annotation of genetic associations with FUMA.

Kyoko Watanabe, Erdogan Taskesen, Arjen van Bochoven, Danielle Posthuma

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Peak: 2 in Jun 2023

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Mar 2022 – Aug 2023

Where it's discussed

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AI synopsis

What people are saying

Several discussions praise FUMA's integration of biological data and visualization tools, describing it as a 'game changer' for post-GWAS analysis; the tone underscores enthusiasm about its potential to improve research efficiency and discovery, particularly by reducing time-consuming tasks and facilitating clearer interpretation of complex data.

AgreementModerate agreement

Most discussions acknowledge the usefulness of FUMA in streamlining post-GWAS annotation and data visualization, indicating general support.

InterestHigh interest

Posts show high interest by highlighting how FUMA can address key challenges in identifying causal variants and improve research workflows.

EngagementModerate engagement

Some discussions delve into specific features like gene prioritization and visual tools, reflecting active engagement.

ImpactModerate impact

Comments suggest that FUMA could significantly accelerate genetic research, implying a moderate to high impact on the field.

Abstract in brief

The main challenge in genomewide association studies (GWAS) is identifying causal variants since most significant hits are in noncoding regions and linkage disequilibrium spreads effects across multiple variants.
Post-GWAS annotation is essential for selecting the most likely causal variants, but existing resources are often time-consuming and lack integrated visual tools.
FUMA is a web-based platform that integrates various biological data to help annotate GWAS results, prioritize genes, and visualize data, thus streamlining the process of generating testable hypotheses about causal genetic variants.]

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