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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Identification of functionally connected multi-omic biomarkers for Alzheimer's disease using modularity-constrained
Linhui Xie1, Pradeep Varathan2, Kwangsik Nho3
1Department of Electrical and Computer Engineering, Indiana University Purdue University Indianapolis, Indianapolis, Indiana, United States of America.
Plos One
|June 20, 2020
Summary
Researchers identified 276 multi-omic biomarkers for Alzheimer's disease (AD) using a novel model. This approach connects genetic variations (SNPs) to gene and protein expression changes, revealing pathways impacting cognitive decline.
Area of Science:
- Genomics
- Proteomics
- Systems Biology
- Neuroscience
Background:
- Genome-Wide Association Studies (GWAS) have identified numerous Alzheimer's disease (AD) genetic risk factors.
- Functional validation of these genetic discoveries and translation into therapeutic targets remain significant challenges.
- Integrating multi-omic data (genotype, gene expression, protein expression) is crucial for understanding complex diseases like AD.
Purpose of the Study:
- To develop a novel computational model for jointly analyzing multi-omic data.
- To discover functionally connected multi-omic biomarkers for Alzheimer's disease.
- To identify trans-omic pathways linking genetic variations to disease phenotypes.
Main Methods:
- Proposed a modularity-constrained Lasso model for integrated genotype, gene expression, and protein expression analysis.
- Incorporated a prior network to capture functional relationships between Single Nucleotide Polymorphisms (SNPs), genes, and proteins.
- Applied the model to the ROS/MAP cohort data, using cognitive performance as the quantitative trait.
Main Results:
- Identified a functionally connected subnetwork of 276 multi-omic biomarkers with predictive power for cognitive performance in AD.
- Observed multiple trans-omic paths from SNPs to genes and then to proteins within the identified subnetwork.
- Demonstrated the model's ability to select markers with dense functional connectivity, rather than individual markers.
Conclusions:
- The identified multi-omic biomarkers and pathways offer insights into the functional consequences of genetic variations in AD.
- Cognitive decline in AD may result from a cascade effect of genetic variations on downstream gene and protein expression.
- This approach facilitates the discovery of targetable mechanisms for Alzheimer's disease.
