ArticleBioData mining2024
The goldmine of GWAS summary statistics: a systematic review of methods and tools.
Article in BioData mining, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genomic dissection of methane emission traits in cattle: A meta-GWAS and heritability analysis across populations.PloS one · 2026Pooled it
- Effect sizes in human functional neuroimaging.Research square · 2026Article
- The Hidden Landscape of Missed Effects in Human Functional Neuroimaging.bioRxiv : the preprint server for biology · 2026Article
- PYRAMA: an open-source tool for advanced meta-analysis of genome wide association studies.Bioinformatics (Oxford, England) · 2026Article
- Polygenic risk scores: Navigating the future of precision medicine through economic, ethical, and scientific advancements.iScience · 2026Review
- A scalable framework for identifying allelic series from summary statistics.American journal of human genetics · 2025Article
- Implementing a training resource for large-scale genomic data analysis in the All of Us Researcher Workbench.American journal of human genetics · 2025Review
- metacp: a versatile software package for combining dependent or independent p-values.BMC bioinformatics · 2025Article
- fastMETA: a fast and efficient tool for multivariate meta-analysis of GWAS.Frontiers in genetics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.
Indexed as
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.