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.
Highlights
Alzheimer’s disease (AD) is the most common form of brain dementia characterized by the gradual loss of memory and other cognitive function
For M-least absolute shrinkage and selection operator (Lasso) and G-Lasso, nested 5-fold cross validation (CV) procedure was applied to tune the parameters
To provide an unbiased estimate of the prediction performance of each method tested in the experiments, all methods were evaluated using the same partition of subjects during the cross validation procedure
Summary
Alzheimer’s disease (AD) is the most common form of brain dementia characterized by the gradual loss of memory and other cognitive function. 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. We identified a functionally connected subnetwork involving 276 multi-omic biomarkers, including SNPs, genes and proteins.
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