GWAS Atlas
A curated resource of genome-wide variant-trait associations

1. Introduction

GWAS Atlas is a manually curated resource of genome-wide genotype-phenotype (G2P) associations for a wide range of species. The current release of GWAS Atlas features a comprehensive collection 278109 curated G2Ps for 1444 traits across 10 plants and 5 animals, which were manually curated from 830 publications. More importantly, all traits were annotated and organized based on a suite of reference ontology (PTO, Plant Trait Ontology; ATOL, Animal Trait Ontology for Livestock) and our customized ontology (PPTO, Plant Phenotype and Trait Ontology; APTO, Animal Phenotype and Trait Ontology) terms. 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.

2. Data curation

2.1. Overview of curation processes

To provide high-quality information curated from GWAS publications, we set up a standardized curation process involving literature search, information retrieval, integration & annotation and database construction.

Curation Process


Figure 1 Overview of GWAS Atlas Curation Processes

2.2. Literature retrieval

We performed literature search in PubMed using species name and GWAS as keywords. Publications are eligible for inclusion in GWAS Atlas if they contain significant GWAS associations with necessary description on biological traits.

2.3. Curation models

We manually curate the study and G2P information from publications. As one publication may contain multiple studies with different experimental designs, we record species name, sampling spot, year, condition, population, sample size, genotyping technology, association model, association number, and PMID for each study. Regarding GWAS association, we collect species name, genome version, genomic position, variant ID, traits, GWAS association P-value, R2 and mapped genes.

Table 1: The curation model for genome-wide genotype-phenotype associations
Data type Examples
Genomic Location chr1:129845
Reference Genome Wm82.a2.v1
Environment field
Sampling Spot Beijing, China
Sampling Year 2016
Condition salt stress
Population 319 landrace, 245 cultivared soybean accessions
Sample Size 127
Tissue leaf
Trait plant height
Genotype Technology Controlled vocabulary in table3
Association Model Controlled vocabulary in table4
P-value 0.00000077
R2(%) 18.1
Gene ID Glyma.01G003300
Gene Symbol ET2
PMID 29081789
Journal Frontiers in Plant Science
Title Genetic architecture of natural variation in rice nonphotochemical quenching capacity revealed by genome-wide association study
Table 2: The curation model for causal variants
Data type Examples
Genomic Location chr3:45301350
Reference Genome Wm82.a2.v1
Gene Symbol MYB4
Gene ID Glyma.03G258700
Reference allele A, T, C, G
Alternative allele A, T, C, G
Area Controlled vocabulary in table5
Trait flavone content
Trait Impact Controlled vocabulary in table6
Allele Impact Controlled vocabulary in table7
Causal Type Controlled vocabulary in table8
PMID 32082354
Table 3: Controlled vocabulary for genotyping technology
Tech ID Genotyping Technology Abbreviation Name
1 Whole Genome Sequencing WGS
2 Genotyping by Sequencing GBS
3 Genotyping by Array Array
4 Specific-Locus Amplified Fragment Sequencing SLAF-seq
5 Whole Exome Sequencing WES
6 RNA Sequencing RNA-seq
7 Restriction-site Associated DNA Sequencing RAD-seq
8 Droplet-assisted RNA Targeting by Single-cell Sequencing DART-seq
9 Polymerase Chain Reaction PCR
10 Unclassified other
Table 4: Controlled vocabulary for GWAS association model
Model ID Model Name Abbreviation Name
1 Mixed Linear Model MLM
2 General Linear Model GLM
3 Logistic Regression Model LRM
4 Compressed Mixed Linear Model CMLM
5 Unified Mixed Linear Model UMLM
6 Efficient Mixed Model EMMAX
7 Multi-Locus Mixed Model MLMM
8 Bayesian Sparse Linear Mixed Model BSLMM
9 Factored Spectrally Transformed Linear Mixed Model FaST-LMM
10 Fixed and random model Circulating Probability Unification FarmCPU
11 Joint-Linkage Model JLM
12 Additive Inherence Model Additive model
13 Fisher's Exact Test Fisher's exact test
14 Least Squares Regression Model LSR
15 Chi Square test X² test
16 Case Control Model Case Control Model
17 Multi-Locus Random-SNP-Effect Mixed Linear Model mrMLM
18 Fast-Multi-Locus Random-SNP-Effect Mixed Linear Model FASTmrEMMA
19 Unclassified other
Table 5: Controlled vocabulary for gene area
Area ID Area Name
1 3_prime_UTR
2 5_prime_UTR
3 exon
4 intron
5 CDS
6 promoter
7 upstream
8 downstream
Table 6: Controlled vocabulary for trait impact
Vocabulary ID Vocabulary Name
1 increasing
2 decreasing
3 early
4 delaying
5 other
Table 7: Controlled vocabulary for allele impact
Vocabulary ID Vocabulary Name
1 inferior
2 superior
3 other
Table 8: Controlled vocabulary for causal type
Vocabulary ID Vocabulary Name
1 causal
2 potential causal

2.4. Variant unifying and annotation

As the genome sequence is continuously updating, we unified the genomic lociation of variants which were collected from different publications to the latest version of the reference genome in GVM using sequence-based searching. If there are variant records in the GVM database, we use the reference identifier in VarID and redirect the user to the variant view in GVM. All variants were annotated by VEP.

2.5. Trait term annotation

To unify the representation of biological traits, trait entities are mapped to a suite of reference ontologies (PTO; ATOL) and species-specific ontology (CO) using the ‘term search’ in Planteome API and Livestock Ontologies. Since not all curated traits are included in existing ontologies, we additionally establish PPTO and APTO by integrating more comprehensive terms based on Open Biological and Biomedical Ontologies (OBO) format.

3. GWAS tools

3.1 LeadSNPFinder

For a given genomic region, there are many variants that are associated with the same phenotype. Actually, some variants are defined because of the fact that they are genetically linked to the causal variant. Thus, we can filter some variants using the genetic linkage parameter R2. A smaller R2 value indicates that the two alleles are independent from each other.

Lead SNPs in this database has been defined using PLINK. The parameters are set as follows:

First round: --clump-p1 5e-8 --clump-p2 0.05 --clump-r2 0.6 --clump-kb 1000

Second round: --clump-p1 5e-8 --clump-p2 0.05 --clump-r2 0.1 --clump-kb 1000

Users can calculate lead SNPs with their own GWAS summary data using the LeadSNPFinder tool.

3.2 GeneFinder

Based on GWAS summary data, the MAGMA tool is used to compute gene-based P-values (gene analysis) with default parameters. Before this, SNPs need to be mapped to genes.

Users can find gene-phenotype associations using the GeneFinder tool.

4. Data search

In the 'Search' module, we support user to query term keywords (e.g. height), gene ID (e.g. Zm00001d021954), and genomic location (e.g. chr1:14702150-37601000). The related traits, genes, and variants, and all eligible search results will be listed.

5. Data download

If you are interested in the associations of a species, please visit the 'Download' page, which includes associations for each specie stored in text format (.txt and .xlsx). Our customized ontologies including PPTO and APTO were also available in obo format.

6. Data submission

GWAS Atlas is equipped with a submission platform for GWAS studies or summaries, allowing users to perform data submission.

7. Support

7.1. Funding Support

  • Strategic Priority Research Program of Chinese Academy of Sciences (XDA08020102)
  • The 13th Five-year Informatization Plan of Chinese Academy of Sciences (XXH13505-05)
  • The Youth Innovation Promotion Association of Chinese Academy of Sciences (2018134)

7.2. Comments & Collaborations

We look forward to worldwide comments, suggestions and guidance from colleagues and peers with common research interests. We also invite the scientific community to submit their analysis results of GWAS to GWAS Atlas and to build collaborations in improving the functionalities of GWAS Atlas.

7.3. FeedBack

We would love to hear from you for any questions or comments. Please find our contact information here.

Telephone: +86 (10) 8409-7620
Fax: +86 (10) 8409-7298
Email: gwas@big.ac.cn
Postal Address:
The GWAS Team, National Genomics Data Center, Beijing Institute of Genomics (BIG), Chinese Academy of Sciences (CAS)
NO 1 Beichen West Road, Chaoyang District, Beijing 100101, China

7.4. Documentation in Chinese

The Documentation in Chinese can be downloaded here (pdf format).