Identification of functionally connected multi-omic biomarkers for Alzheimer’s disease using modularity-constrained Lasso
Year: 2020
Type: article
Abstract: Large-scale genome wide association studies (GWASs) have led to discovery of many genetic risk factors in Alzheimer's disease (AD), such as APOE, TOMM40 and CLU. Despite the significant progress, it remains a major challenge to functionally validate these genetic findings and translate them into targetable mechanisms. Integration of multiple types of molecular data is increasingly used to address this problem. In this paper, we proposed a modularity-constrained Lasso model to jointly analyze the genotype, gene expression and protein expression data for discovery of functionally connected multi-omic biomarkers in AD. With a prior network capturing the functional relationship between SNPs, genes and proteins, the newly introduced penalty term maximizes the global modularity of the subnetwork involving selected markers and encourages the selection of multi-omic markers with dense functional connectivity, instead of individual markers. We applied this new model to the real data collected in the ROS/MAP cohort where the cognitive performance was used as disease quantitative trait. A functionally connected subnetwork involving 276 multi-omic biomarkers, including SNPs, genes and proteins, were identified to bear predictive power. Within this subnetwork, multiple trans-omic paths from SNPs to genes and then proteins were observed. This suggests that cognitive performance deterioration in AD patients can be potentially a result of genetic variations due to their cascade effect on the downstream transcriptome and proteome level.
Source: PLoS ONE
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,695
Funders: National Science Foundation, Illinois Department of Public Health, Indiana University, Indiana Clinical and Translational Sciences Institute, Rush University, Translational Genomics Research Institute, National Institutes of Health, National Institute on Aging, National Institute of Biomedical Imaging and Bioengineering, U.S. National Library of Medicine, Division of Information and Intelligent Systems, National Center for Advancing Translational Sciences
Awards: Epidemiologic Study of Neural Reserve and Neurobiology of Aging, G1171648585, G1218922180, Indiana Alzheimer Disease Center, Genetic Epidemiology of Cognitive Decline in an Aging Population Sample, G1973305328, Indiana Clinical and Translational Sciences Institute, G2796891014, Gene co-expression underlying the connectomic alterations in Alzheimer's disease, Exploring the Role of the Brain Transcriptome in Cognitive Decline, G3516344715, G3779651404, Pathway discovery, validation and compound identification for Alzheimer's disease, G4356727394, Integrative Bioinformatics Approaches to Human Brain Genomics and Connectomics, Risk Factors, Pathology, and Clinical Expressions of AD, G5018925220, Rush Alzheimer's Disease Core Center, G5090083847, Alzheimer's Disease Genetics Consortium, G541333419, G5713958695, Integrating Neuroimaging, Multi-omics, and Clinical Data in Complex Disease, G578809140, G5952929075, CRII:SCH:Computational Methods to Mine Multi-omic Data for Systems Biology of Complex Diseases, G7479973302, Memory Circuitry in MCI and Early Alzheimerâs Disease Prodrome: Molecular Drivers, G8639257087
OpenAlex ID: W3035808001