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Database Commons

a catalog of worldwide biological databases

Database Profile

General information

URL: https://ngdc.cncb.ac.cn/gwas/
Full name: Genome-Wide SNP-trait Associations Atlas
Description: GWAS Atlas is a public and manually curated database of published genome-wide SNP-trait associations for plants and animals.
Year founded: 2020
Last update: 2022-06
Version:
Accessibility:
Manual:
Accessible
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Country/Region: China

Classification & Tag

Data type:
DNA
Data object:
Database category:
Major species:
Keywords:

Contact information

University/Institution: Beijing Institute of Genomics, Chinese Academy of Sciences
Address: 1 Beichen West Road, Chaoyang District, Beijing 100101, China
City: Beijing
Province/State: Beijing
Country/Region: China
Contact name (PI/Team): Shuhui Song
Contact email (PI/Helpdesk): songshh@big.ac.cn

Publications

36263826
GWAS Atlas: an updated knowledgebase integrating more curated associations in plants and animals. [PMID: 36263826]
Xiaonan Liu, Dongmei Tian, Cuiping Li, Bixia Tang, Zhonghuang Wang, Rongqin Zhang, Yitong Pan, Yi Wang, Dong Zou, Zhang Zhang, Shuhui Song

GWAS Atlas (https://ngdc.cncb.ac.cn/gwas/) is a manually curated resource of genome-wide genotype-to-phenotype associations for a wide range of species. Here, we present an updated implementation of GWAS Atlas by curating and incorporating more high-quality associations, with significant improvements and advances over the previous version. Specifically, the current release of GWAS Atlas incorporates a total of 278,109 curated genotype-to-phenotype associations for 1,444 different traits across 15 species (10 plants and 5 animals) from 830 publications and 3,432 studies. A collection of 6,084 lead SNPs of 439 traits and 486 experiment-validated causal variants of 157 traits are newly added. Moreover, 1,056 trait ontology terms are newly defined, resulting in 1,172 and 431 terms for Plant Phenotype and Trait Ontology and Animal Phenotype and Trait Ontology, respectively. Additionally, it is equipped with four online analysis tools and a submission platform, allowing users to perform data analysis and data submission. Collectively, as a core resource in the National Genomics Data Center, GWAS Atlas provides valuable genotype-to-phenotype associations for a diversity of species and thus plays an important role in agronomic trait study and molecular breeding.

Nucleic Acids Res. 2023:51(D1) | 5 Citations (from Europe PMC, 2024-04-06)
31566222
GWAS Atlas: a curated resource of genome-wide variant-trait associations in plants and animals. [PMID: 31566222]
Tian D, Wang P, Tang B, Teng X, Li C, Liu X, Zou D, Song S, Zhang Z.

GWAS Atlas (https://bigd.big.ac.cn/gwas/) is a manually curated resource of genome-wide variant-trait associations for a wide range of species. Unlike existing related resources, it features comprehensive integration of a high-quality collection of 75 467 variant-trait associations for 614 traits across 7 cultivated plants (cotton, Japanese apricot, maize, rapeseed, rice, sorghum and soybean) and two domesticated animals (goat and pig), which were manually curated from 254 publications. We integrated these associations into GWAS Atlas and presented them in terms of variants, genes, traits, studies and publications. More importantly, all associations and traits were annotated and organized based on a suite of ontologies (Plant Trait Ontology, Animal Trait Ontology for Livestock, etc.). Taken together, GWAS Atlas integrates high-quality curated GWAS associations for animals and plants and provides user-friendly web interfaces for data browsing and downloading, accordingly serving as a valuable resource for genetic research of important traits and breeding application.

Nucleic Acids Res. 2020:48(D1) | 56 Citations (from Europe PMC, 2024-04-06)

Ranking

All databases:
698/6000 (88.383%)
Genotype phenotype and variation:
89/852 (89.671%)
698
Total Rank
60
Citations
15
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Record metadata

Created on: 2018-01-28
Curated by:
Yuanyuan Cheng [2023-08-22]
Dong Zou [2019-12-03]
Zhang Zhang [2019-11-12]
Dong Zou [2019-09-09]
Zhang Zhang [2019-06-21]
huma shireen [2018-04-16]
Zhaohua Li [2018-01-27]