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Updated: Jun 11, 2025
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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
Published on: January 9, 2020
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LD-informed deep learning for Alzheimer's gene loci detection using WGS data
Taeho Jo1, Paula Bice1, Kwangsik Nho1,2
1Indiana Alzheimer Disease Research Center and Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
Medrxiv : the Preprint Server for Health Sciences
|October 7, 2024
Summary
Deep-Block, a novel AI framework, identifies genetic factors for Alzheimer's disease (AD) by analyzing large genomic datasets. It pinpoints both known and new genetic variants associated with AD risk.
Area of Science:
- Genomics
- Artificial Intelligence
- Neuroscience
Background:
- Genomic datasets are growing exponentially, requiring advanced tools for genetic loci identification.
- Alzheimer's disease (AD) research needs efficient methods to analyze large-scale, high-throughput sequencing data.
- Identifying genetic factors for AD is crucial for understanding disease mechanisms and developing targeted therapies.
Purpose of the Study:
- To introduce Deep-Block, a multi-stage deep learning framework for identifying genetic regions associated with Alzheimer's disease.
- To incorporate biological knowledge into an AI architecture for enhanced genetic analysis.
- To identify novel single nucleotide polymorphisms (SNPs) contributing to AD risk.
Main Methods:
- Genome segmentation using linkage disequilibrium (LD) patterns.
- Sparse attention mechanisms for selecting relevant LD blocks.
- TabNet and Random Forest algorithms for SNP feature importance quantification.
- Application to a large whole genome sequencing (WGS) dataset from the Alzheimer's Disease Sequencing Project (ADSP).
Main Results:
- Identification and ranking of 30,218 LD blocks based on AD relevance.
- Discovery of novel SNPs within the top 1,500 LD blocks, alongside confirmation of known variants like APOE rs429358.
- Functional evidence for identified variants through expression Quantitative Trait Loci (eQTL) analysis across 13 brain regions.
- Cross-validation against established AD loci from the European Alzheimer's and Dementia Biobank (EADB) and GWAS catalog.
Conclusions:
- Deep-Block effectively processes large-scale sequencing data, preserving SNP interactions and minimizing information loss.
- Tissue-specific eQTL analysis supports the functional relevance of identified variants in brain regions.
- The framework successfully identified known and novel genetic variants, advancing the understanding of AD's genetic architecture.
