Deep learning-based identification of genetic variants: application to Alzheimer’s disease classification
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Abstract
Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in genome-wide association studies (GWAS) with high-dimensional genomic data. Here we propose a novel three-step approach (SWAT-CNN) for identification of genetic variants using deep learning to identify phenotype-related single nucleotide polymorphisms (SNPs) that can be applied to develop accurate disease classification models. In the first step, we divided the whole genome into nonoverlapping fragments of an optimal size and then ran convolutional neural network (CNN) on each fragment to select phenotype-associated fragments. In the second step, using a Sliding Window Association Test (SWAT), we ran CNN on the selected fragments to calculate phenotype influence scores (PIS) and identify phenotype-associated SNPs based on PIS. In the third step, we ran CNN on all identified SNPs to develop a classification model. We tested our approach using GWAS data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) including (N = 981; cognitively normal older adults (CN) = 650 and AD = 331). Our approach identified the well-known APOE region as the most significant genetic locus for AD. Our classification model achieved an area under the curve (AUC) of 0.82, which was compatible with traditional machine learning approaches, random forest and XGBoost. SWAT-CNN, a novel deep learning–based genome-wide approach, identified AD-associated SNPs and a classification model for AD and may hold promise for a range of biomedical applications.
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Taeho et al. achieved 0.82 AAUC on ADNI using SNP gene-based data.
“| Taeho et al. [85] | SNP | Gene-based | ADNI | 0.82 (AAUC) |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [85]
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GENETIC BIOMARKERS DETECTION FOR ALZHEIMER’S DISEASE**
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- Authors
- Jo, T., Nho, K., Bice, P., Saykin, A. J., & Initiative, F. T. A. D. N.
- Affiliations
- Indiana University Bloomington (US)
- Indiana University Indianapolis (US)
- Corresponding author
- Nho, Kwangsik
- Saykin, Andrew J
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-02-20
- Volume
- 23
- Issue
- 2
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC8921609
- OpenAlex
- W4211064437
- Cited by
- 80
- References
- 96
- Citations by year
- 2026: 11 · 2025: 23 · 2024: 20 · 2023: 20 · 2022: 6
- FWCI
- 10.40
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Briefings in Bioinformatics (publishedVersion)
- medRxiv (acceptedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- IUScholarWorks (Indiana University) (submittedVersion)
- Funders
- National Institutes of Health (U01 AG024904, P30 AG010133, P30 AG072976, R01 AG019771, R01 AG057739, R01 LM013463, R01 AG068193, T32 AG071444, U01 AG068057, R01 LM012535, R03 AG063250)
- Department of Defense (W81XWH-12-2-0012)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Alzheimer's disease, genetic variant identification, single nucleotide polymorphisms, genome-wide association study, convolutional neural networks, apolipoprotein E, disease classification, ADNI
- MeSH terms
- Deep Learning, Aged, Alzheimer Disease, Humans, Magnetic Resonance Imaging, Genome-Wide Association Study, Cognitive Dysfunction
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