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AI GWAS Review

Curation of Review within GWAS — listings under the AI tab.

AI-enhanced GWAS

The GWAS topic within AI covers methods that use machine learning to boost genome-wide association studies. Main trajectories:

  • Imaging GWAS: Early work applied supervised CNNs (U-Net, ResNet) to medical images for trait quantification and GWAS (Haas PMID 34957434, Cell Genomics 2021; Khurshid PMID 36944631, Nat Commun 2023). Recent work uses self-supervised contrastive learning to bypass manual annotation, running GWAS directly on image embeddings (iGWAS, Kirchler et al. PMID 39020183, Nature Genetics 2024).

  • AI Phenotyping: Ensemble ML and multimodal topic models (EHR + genetics) for phenotype extraction and GWAS on imputed phenotypes (MILTON, Garg et al. PMID 39471869, Nat Genet 2024; MixEHR-SAGE, Cui et al. PMID 39843619, Nat Med 2026).

  • AI Association Methods: Neural network-based association tests (GWANN, Holzinger et al. PMID 38918402, Nat Genet 2024) and Transformer-based models for discovering novel loci (InsightGWAS, Song et al. bioRxiv 2025).

  • Post-GWAS AI: Gene prioritization using polygenic features (PoPS, Weeks et al. PMID 37106029, Nat Genet 2023).

  • Methodology reviews: Causal ML for single-cell genomics (Tejada-Lapuerta et al. PMID 40336376, Nat Genet 2025).

Summary Table

Click a column header to sort the table.

NAME Main citation YEAR
Causal ML for scGenomics
Tejada-Lapuerta A et al., Nat Genet, 2025
 2025
DL for PRS Survey
Schuran M et al., Brief Bioinform, 2025
 2025

Causal ML for scGenomics (Causal ML sc)

AI GWAS Causal ML Single Cell Machine Learning Nat Genet
PUBMED_LINK
40164735
FULL NAME
Causal Machine Learning for Single-Cell Genomics
DESCRIPTION
A Perspective from Nature Genetics delineating the application of causal machine learning to single-cell genomics. Discusses causal models, challenges in inferring causative roles of genes from single-cell omics data combined with perturbation screens, and the potential for integrating causal ML with GWAS to understand disease mechanisms at single-cell resolution.
TITLE
Causal machine learning for single-cell genomics.
ABSTRACT
Advances in single-cell '-omics' allow unprecedented insights into the transcriptional profiles of individual cells and, when combined with large-scale perturbation screens, enable measuring of the effect of targeted perturbations on the whole transcriptome. In this Perspective, we delineate the application of causal machine learning to single-cell genomics and its associated challenges, presenting the causal model most commonly applied to single-cell biology.
DOI
10.1038/s41588-025-02124-2

DL for PRS Survey (DL PRS Survey)

AI GWAS Polygenic Risk Score Deep Learning Survey Review Brief Bioinform
PUBMED_LINK
40802796
FULL NAME
A Survey on Deep Learning for Polygenic Risk Scores
DESCRIPTION
A comprehensive survey of deep learning approaches for polygenic risk scores (PRS). Reviews how neural networks can model non-linear relationships between genetic variants and disease risk, going beyond traditional linear PRS methods, and assesses their performance across different traits and architectures. Published in Briefings in Bioinformatics.
TITLE
A survey on deep learning for polygenic risk scores.
ABSTRACT
Polygenic risk scores (PRS) combine the effects of multiple genetic variants to predict an individual's genetic predisposition to a disease. PRS typically rely on linear models, which assume that all genetic variants act independently. There is growing interest in applying deep learning neural networks to model PRS given their ability to model non-linear relationships. We conducted a survey of the literature to investigate how neural networks are being applied to PRS.
DOI
10.1093/bib/bbaf373