Considerations for integrative multi‐omic approaches to explore Alzheimer's disease mechanisms

Abstract

The past decade has seen the maturation of multiple different forms of high‐dimensional molecular profiling to the point that these methods could be deployed in initially hundreds and more recently thousands of human samples. In the field of Alzheimer's disease (AD), these profiles have been applied to the target organ: the aging brain. In a growing number of cases, the same samples were profiled with multiple different approaches, yielding genetic, transcriptomic, epigenomic and proteomic data. Here, we review lessons learned so far as we move beyond quantitative trait locus (QTL) analyses which map the effect of genetic variation on molecular features to integrate multiple levels of “omic” data in an effort to identify the molecular drivers of AD. One thing is clear: no single layer of molecular or “omic” data is sufficient to capture the variance of AD or aging‐related cognitive decline. Nonetheless, reproducible findings are emerging from current efforts, and there is evidence of convergence using different approaches. Thus, we are on the cusp of an acceleration of truly integrative studies as the availability of large numbers of well‐characterized brain samples profiled in three or more dimensions enables the testing, comparison and refinement of analytic methods with which to dissect the molecular architecture of the aging brain.

Bioinformatics pipeline to guide late‐onset Alzheimer's disease (LOAD) post‐GWAS studies: Prioritizing transcription regulatory variants within LOAD‐associated regions

“…Molecular profiling and integrative multi‐omics approaches are the current focus in LOAD genetic research toward the identification the molecular drivers of LOAD. 47 , 48 , 49 , 50 Several bioinformatics approaches have been described to study the functional role of GWAS‐enhancer elements and variants on gene expression and in turn, development or progression of neurodegenerative diseases including LOAD. These approaches have included fine mapping DNA methylation sites in prefrontal cortex neurons from brains with different degrees of Alzheimer's disease pathology, 51 cataloguing enhancers in LOAD regions and mapping promoter‐enhancer interaction using Circular Chromosomal Conformation Capture (4C) data to prioritize genes for experimental follow‐up, 28 and integrating datasets of enhancer activity, TF binding sites, and eQTL 10 , 52 , 53 to characterize the effects of non‐coding genetic variation associated with LOAD risk.…”

Epigenome‐wide association studies in Alzheimer’s disease; achievements and challenges

“…The recent development of powerful computational frameworks now offers multilayer inter‐regulatory approaches to understand the development and course of AD, while accounting for inter‐individual differences in genotype and exposure to environmental risk factors. As such, multi‐omics approaches––that include an integrative analysis of various layers of epigenetic regulation––and associated data science tools show great promise in the development of novel diagnostic tools and treatment strategies for AD, with further details on this approach provided elsewhere in this mini‐symposium [36].…”

Multi-‘Omic Integration via Similarity Network Fusion to Detect Molecular Subtypes of Aging

“…Importantly, it has been shown that several multi-'omic data types, including histone acetylation [15], metabolomics [16][17][18][19][20] and proteomics [21], are not only associated with AD neuropathologies, but also contributed information to associations that is missed with RNAseq alone [8,21]. As such, integrating data modalities into subtyping pipelines has been an active area of research [22,23], and large-scale cohort studies of aging that include brain donation and multi-'omic characterization, such as those from the Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD) consortium, now offer opportunities for developing highly integrative models of cognitive decline [24]. Methods development in high-dimensional feature integration have also facilitated these analyses [25,26] , though not yet in pathological aging or AD.…”