sc2GWAS: a comprehensive platform linking single cell and GWAS traits of human
Open accessAbstract
Identifying cell populations associated with risk variants is essential for uncovering cell-specific mechanisms that drive disease development and progression. Integrating genome-wide association studies (GWAS) with single-cell RNA sequencing (scRNA-seq) has become an effective strategy for detecting trait–cell relationships. The accumulation of trait-related single cell data has led to an urgent need for its comprehensively processing. To address this, we developed sc2GWAS (https://bio.liclab.net/sc2GWAS/), which aims to document large-scale GWAS trait–cell regulatory pairs at single-cell resolution and provide comprehensive annotations and enrichment analyses for these related pairs. The current version of sc2GWAS curates a total of 15 078 310 candidate trait–cell pairs from > 6 300 000 individual cells, offering a valuable resource for exploring complex regulatory relationships between traits and cells. We applied strict quality control measures on both scRNA-seq data and GWAS data, ensuring the reliability and accuracy of the datasets for the identification of trait-relevant cells and genes. In addition, sc2GWAS provides ranked lists of trait-relevant genes and extensive (epi) genetic annotations, making it a valuable resource for downstream analyses. We demonstrate the utility of the platform by investigating Alzheimer’s disease, where we identified significant associations between the disease and microglial cells, with the APOE gene emerging as particularly significant. This platform facilitates detailed research into complex trait–cell and trait–gene interactions, we anticipate that sc2GWAS will become a comprehensive and valuable platform for exploring GWAS trait–cell regulatory mechanisms.
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Single-cell network inference holds potential to resolve these ambiguities but faces its own limitations, including noise sensitivity and reliance on aggressive data denoising strategies.
“Single-cell network inference holds potential to resolve these ambiguities but faces its own limitations, including noise sensitivity and reliance on aggressive data denoising strategies261,315.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [261]
The Introduction says single-cell resolution can help investigate regulatory relationships that cell-population analyses overlook. The Methods describe reducing noise with MAGIC and avoiding over-smoothing when annotating cell types. The paper does not establish that single-cell network inference specifically is noise-sensitive or relies on aggressive denoising. (LLM, conf=0.82)
Experimental — accuracy not yet measuredTools like sc2GWAS connect genome-wide association loci to cellular transcriptomic signatures, identifying microglia and astrocytes as key mediators in AD.
“Tools like sc2GWAS connect genome-wide association loci to cellular transcriptomic signatures, identifying microglia and astrocytes as key mediators in AD151,261.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [261]
The Abstract describes sc2GWAS as integrating GWAS and single-cell RNA-seq data, and the Alzheimer’s disease case study identifies a significant association with microglial cells (page not specified). The case study’s analyzed cell types did not include astrocytes, so it does not support the claim that sc2GWAS identified them as key mediators in AD. (LLM, conf=0.90)
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DisCP-Atlas: a comprehensive resource mapping cellular processes to complex diseases
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Spatial2GWAS: a database for linking spatial transcriptomic regions with GWAS traits
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Unveiling the genetic overlap and causal links between gastroesophageal reflux disease and asthma
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Functional Analysis of miR-148a: A Differentially Expressed microRNA in Hemifacial Microsomia
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Self-citationCRISPR-Edited Cell Lines: A New Era in Functional Oncology Research
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Single-Cell Studies Advance Understanding of the Genetic and Molecular Basis of Atherosclerosis
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scTraitReg: a single-cell multi-omic database of trait-relevant transcription factor eRegulons
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CircPTK2 as a Valuable Biomarker and Treatment Target in Cancer
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- Authors
- Yin, M., Feng, C., Yu, Z., Zhang, Y., Li, Y., Wang, X., Song, C., Guo, M., & Li, C.
- Affiliations
- First Affiliated Hospital of University of South China (CN)
- University of South China (CN)
- Beijing University of Civil Engineering and Architecture (CN)
- Corresponding author
- Guo, Maozu
- Li, Chunquan
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-11-20
- Volume
- 53
- Issue
- D1
- Pages
- D1151-D1161
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC11701642
- OpenAlex
- W4404535400
- Cited by
- 51
- References
- 59
- Citations by year
- 2026: 6 · 2025: 43 · 2024: 2
- FWCI
- 6.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- The Science and Technology Innovation Talent Program of Hunan Province of China (2024RC1062, 2024RC3212)
- National Natural Science Foundation of China (62171166, 62302206, 62031003)
- Research Foundation of the First Affiliated Hospital of University of South China for Advanced Talents (20210002-1005 USCAT-2021-01)
- Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases (2023TP1047)
- Natural Science Foundation of Hunan Province (2023JJ40594, 2023JJ30536)
- University of South China (20224310NHYCG05)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- single-cell RNA sequencing, genome-wide association study, gene regulatory networks, Alzheimer's disease, microglia, apolipoprotein E, bioinformatics platform
- MeSH terms
- Alzheimer Disease, Humans, Software, Sequence Analysis, RNA, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome-Wide Association Study, Single-Cell Analysis
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