| Title: | Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel |
| Journal: | BMC Genomics |
| Published: | 1 May 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40312319/ |
| DOI: | https://doi.org/10.1186/s12864-025-11588-9 |
| URL: | https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-025-11588-9 |
Publication 15709
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Abstract
BackgroundAccurate genetic risk prediction and understanding the mechanisms underlying complex diseases are essential for effective intervention and precision medicine. However, current methods often struggle to capture the intricate and subtle genetic interactions contributing to disease risk. This challenge may be further exacerbated by the curse of dimensionality when considering large-scale pairwise genetic combinations with limited samples. Overcoming these limitations could transform biomedicine by providing deeper insights into disease mechanisms, moving beyond black-box models and single-locus analyses, and enabling a more comprehensive understanding of cross-disease patterns.ResultsWe developed Ge-SAND (Genomic Embedding Self-Attention Neurodynamic Decoder), an explainable deep learning-driven framework designed to uncover complex genetic interactions at scales exceeding 106 in parallel for accurate disease risk prediction. Ge-SAND leverages genotype and genomic positional information to identify both intra- and interchromosomal interactions associated with disease phenotypes, providing comprehensive insights into pathogenic mechanisms crucial for disease risk prediction. Applied to simulated datasets and UK Biobank cohorts for Crohn's disease, schizophrenia, and Alzheimer's disease, Ge-SAND achieved up to a 20% improvement in AUC-ROC compared to mainstream methods. Beyond its predictive accuracy, through self-attention-based interaction networks, Ge-SAND provided insights into large-scale genotype relationships and revealed genetic mechanisms underlying these complex diseases. For instance, Ge-SAND identified potential genetic interaction pairs, including novel relationships such as ISOC1 and HOMER2, potentially implicating the brain-gut axis in Crohn's and Alzheimer's diseases.ConclusionGe-SAND is a novel deep-learning approach designed to address the challenges of capturing large-scale genetic interactions. By integrating disease risk prediction with interpretable insights into genetic mechanisms, Ge-SAND offers a valuable tool for advancing genomic research and precision medicine.</p>
8 Keywords
- Alzheimer Disease
- Crohn Disease
- Deep Learning
- Epistasis, Genetic
- Genetic Predisposition to Disease
- Genomics
- Humans
- Schizophrenia
11 Authors
- Lihang Ye
- Liubin Zhang
- Bin Tang
- Junhao Liang
- Ruijie Tan
- Hui Jiang
- Wenjie Peng
- Nan Lin
- Kun Li
- Chao Xue
- Miaoxin Li
1 Application
| Application ID | Title |
|---|---|
| 86920 | Evaluating rare and common genetic variants and interaction with environment affecting mental diseases |
Enabling scientific discoveries that improve human health