AI GWAS Methods
Curation of Methods within GWAS — listings under the AI tab.
AI-enhanced GWAS Methods
Machine learning approaches that improve statistical power or discover novel genetic architecture:
- Non-linear covariate adjustment using deep neural networks (DeepNull, Hormozdiari et al. PMID 36195755, Nat Commun 2022).
- Neural network-based association tests with built-in epistasis detection (GWANN, Holzinger et al. PMID 38918402, Nat Genet 2024).
- Transformer models for discovering trait-relevant genetic variants by learning from summary statistics (InsightGWAS, Song et al. bioRxiv 2025).
- Power-boosting methods that combine multiple association statistics (Quickdraws, Shi et al. Nat Genet 2025).
Summary Table
Click a column header to sort the table.
| NAME | Main citation | YEAR |
|---|---|---|
| DeepNull | McCaw ZR et al., Nat Commun, 2022 |
2022 |
| GWANN | Ghose U et al., Brief Bioinform, 2024 |
2024 |
| InsightGWAS (Migraine) | Meng Z et al., Nat Commun, 2025 |
2025 |
| Quickdraws | Loya H et al., Nat Genet, 2025 |
2025 |
DeepNull
PUBMED_LINK
FULL NAME
DeepNull - Deep Learning for Non-linear Covariate Adjustment in GWAS
DESCRIPTION
DeepNull is a method that identifies and adjusts for non-linear and interactive covariate effects in GWAS using a deep neural network. It maintains tight control of type I error while increasing statistical power by up to 20% in the presence of non-linear covariate effects. Published in Nature Communications.
TITLE
DeepNull models non-linear covariate effects to improve phenotypic prediction and association power.
ABSTRACT
Genome-wide association studies (GWASs) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce DeepNull, a method that identifies and adjusts for non-linear and interactive covariate effects using a deep neural network, maintaining tight control of the type I error while increasing statistical power by up to 20%.
DOI
10.1038/s41467-021-27930-0
GWANN
PUBMED_LINK
FULL NAME
GWANN - Genome-Wide Association Neural Networks
DESCRIPTION
GWANN (Genome-Wide Association Neural Networks) is a novel approach that uses neural networks to perform gene-level association studies. Applied to Alzheimer's disease in UK Biobank, GWANN identifies genes linked to family history of AD by aggregating SNP-level information at the gene level through neural network architectures. Published in Briefings in Bioinformatics.
TITLE
Genome-wide association neural networks identify genes linked to family history of Alzheimer's disease.
ABSTRACT
Augmenting traditional genome-wide association studies (GWAS) with advanced machine learning algorithms can allow the detection of novel signals in available cohorts. We introduce "genome-wide association neural networks (GWANN)", a novel approach that uses neural networks (NNs) to perform a gene-level association study with family history of Alzheimer's disease (AD) in UK Biobank.
DOI
10.1093/bib/bbae704
InsightGWAS (Migraine) (InsightGWAS)
PUBMED_LINK
FULL NAME
InsightGWAS - Transformer-based Deep Learning Enhances Migraine GWAS
DESCRIPTION
InsightGWAS is a Transformer-based model that enhances genetic discovery for migraine GWAS by integrating functional annotations and leveraging transfer learning from GWAS datasets of major depressive disorder. It identified 293 previously unreported loci from 53,109 cases and 230,876 controls. Published in Nature Communications.
TITLE
Transformer-based deep learning enhances discovery in migraine GWAS.
ABSTRACT
Migraine is a complex neurological disorder with substantial heritability, yet genome-wide association studies (GWAS) have explained only a fraction of its genetic component. We developed InsightGWAS, a Transformer-based model, to enhance genetic discovery for migraine by integrating functional annotations and leveraging transfer learning from GWAS datasets of major depressive disorder (MDD), identifying 293 previously unreported loci.
DOI
10.1038/s41467-025-65991-7
Quickdraws
PUBMED_LINK
FULL NAME
Quickdraws - Scalable Variational Inference for Mixed-Model GWAS
DESCRIPTION
Quickdraws is a method that increases association power in quantitative and binary traits for GWAS without sacrificing computational efficiency, leveraging a spike-and-slab prior on variant effects, stochastic variational inference, and graphics processing unit acceleration. Published in Nature Genetics.
TITLE
A scalable variational inference approach for increased mixed-model association power.
ABSTRACT
The rapid growth of modern biobanks is creating new opportunities for large-scale genome-wide association studies (GWASs) and the analysis of complex traits. However, performing GWASs on millions of samples often leads to trade-offs between computational efficiency and statistical power, reducing the benefits of large-scale data collection efforts. We developed Quickdraws, a method that increases association power in quantitative and binary traits without sacrificing computational efficiency, leveraging a spike-and-slab prior on variant effects, stochastic variational inference and graphics processing unit acceleration.
DOI
10.1038/s41588-024-02044-7