A large number of genetic variations have been identified to be associated with Alzheimer’s disease (AD) and related quantitative traits. However, majority of existing studies focused on single types of omics data, lacking the power of generating a community including multi-omic markers and their functional connections. Because of this, the immense value of multi-omics data on AD has attracted much attention. Leveraging genomic, transcriptomic and proteomic data, and their backbone network through functional relations, we proposed a modularity-constrained logistic regression model to mine the association between disease status and a group of functionally connected multi-omic features, i.e. single-nucleotide polymorphisms (SNPs), genes and proteins. This new model was applied to the real data collected from the frontal cortex tissue in the Religious Orders Study and Memory and Aging Project cohort. Compared with other state-of-art methods, it provided overall the best prediction performance during cross-validation. This new method helped identify a group of densely connected SNPs, genes and proteins predictive of AD status. These SNPs are mostly expression quantitative trait loci in the frontal region. Brain-wide gene expression profile of these genes and proteins were highly correlated with the brain activation map of ‘vision’, a brain function partly controlled by frontal cortex. These genes and proteins were also found to be associated with the amyloid deposition, cortical volume and average thickness of frontal regions. Taken together, these results suggested a potential pathway underlying the development of AD from SNPs to gene expression, protein expression and ultimately brain functional and structural changes.

In the past decade, a large number of genetic biomarkers have been discovered through large-scale genome wide association studies (GWASs) in Alzheimer's disease (AD), such as APOE, TOMM40 and CLU. Despite this significant progress, existing genetic findings are largely passengers not directly involved in the driver events, which presents challenges for replication and translation into targetable mechanisms. In this paper, leveraging the protein interaction network, we proposed a modularity-constrained Lasso model to jointly analyze the genotype, gene expression and protein expression data. 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 in ROS/MAP cohort for discovery of biomarkers related to cognitive performance. 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, suggesting that cognitive performance can be potentially affected by the genetic mutations due to their cascade effect on the expression of downstream genes and proteins.

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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.

Background Large‐scale genome wide association studies (GWASs) have identified many genetic variants associated with Alzheimer’s disease (AD) and related traits. While many GWAS findings were found to co‐localize with expression quantitative trait loci (eQTL), it is of great interest to investigate the functional effect of GWAS hits on the downstream transcriptomic level. Considering the lack of gene expression data from the brain tissue, we propose to integrate the summary statistics from AD GWAS and brain eQTL analysis to investigate potential transcriptomic alterations inside AD brains. We further validate our findings using the brain gene expression data from the ROS/MAP cohort. Method We used the GWAS summary statistics from IGAP[1] and brain eQTL results from BRAINEAC[2]. Summary Mendelian Randomization (SMR) and Heterogeneity in Dependent Instruments (HEIDI) tests were applied to identify highly significant genes related to AD in temporal cortex, frontal cortex and hippocampal regions. For significant genes identified from each region, we further performed differential gene expression analysis using the RNA‐Seq data from the corresponding brain tissue in the Mayo Clinic cohort. SNPs from these genes were tested for association with FDG intensity and thickness of corresponding brain regions using the data from ADNI. Finally, we performed pathway analysis using ClueGO. Result SMR and HEIDI test identified 32 genes significantly associated with AD in the transcriptomic level, but only in temporal cortex. 10 of them were further validated with altered expression in AD temporal cortex region in the Mayo cohort (Fig.1). 19 SNPs from these genes were significantly related to the FDG intensity and thickness of temporal cortex regions, predominantly present in TOMM40 and NECTIN genes. Top pathways enriched by these genes are Neutrophil degranulation and cell surface interactions at vascular wall (Fig.2). Conclusion We identified several SNPs with potential regulatory role in mediating the expression level of genes, which are found altered in AD brains and associated with multiple neuroimaging phenotypes. With evidence from multiple sources, these SNPs, their downstream genes and related pathways could serve as potential targets for further therapeutic intervention of AD.