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.
parahippocampal cortex, posterior cingulum, posterior hippocampi, amygdalae (anterior portion), thalami and putamen at the subcortical level ( Figure 1). The corresponding genetic component was comprised of 27 significant molecular genetic pathways, mostly related to neuronal functions such as myelination and nervous system development (Figure 2-3). At the cellular level, we identified pathways related to synaptic properties, and neuronal and cell projections. The obtained genes also identified the glutamatergic synapse at the cellular metabolic level, and several phospholipase C-mediated components. The model detected statistically significant association with pathological markers (MMSE: p ¼ 3.8e-2, time to conversion: p¼4.9e-3, and hippocampal volume: p¼1.5e-4) when independently tested on 553 patients with mild cognitive impairment (Figure 4). Conclusions: This study links AD-related brain measures to several biological regulatory functions mediated by common genetic variants. Most importantly, the identified metabolic and cellular pathways are linked to known biochemical mechanisms of AD, and highlight potential targets for developing novel therapeutic agents.Background: A recent genome-wide association (GWA) study identified associations with multiple neuropathological features of Alzheimer disease (AD) including neuritic plaques (NP), neurofibrillary tangles (NFT), cerebral amyloid angiopathy (CAA), hippocampal sclerosis (HS), and Lewy body disease (LBD) among autopsyconfirmed AD subjects and cognitively normal controls. We hypothesized that simultaneous consideration (multivariate analysis) of several neuropathological endophenotypes of AD and related dementias will identify genes having pleiotropic effects on multiple AD-related brain changes. Methods: Summary: GWA study data from a previous study (Beecham et al. PLoS Genet, 2014) were evaluated using the O'Brien method for multivariate analysis of dementia-related neuropathological characteristics including (1) NP and NFT, (2) NP, NFT and CAA, and (3) all available neuropathological traits. Resultswere compared with those obtained for the traits analyzed individually. Results:We observed novel genome-wide significant (GWS) association (p<5x10 -8 ) for the NP and NFT bivariate outcome with several single nucleotide polymorphisms (SNPs) in the region between NCK2 and ECRG4 (best SNP, rs34487851; meta-analysis P-value [P]¼2.4x10 -8 ), whereas the association of each of the individual traits were less significant (P[ NP]¼1.6x10 -5 , P[NFT]¼1.3x10 -4 ). Strong evidence of association was also identified with two previously established AD genes including APOE (rs6857; P: NP¼1.5x10 -46 , NFT¼4.1x10 -44 , NP-NFT¼3.8x10 -62 ) and BIN1 (rs6733839; P: NP¼7.8x10 -6 , NFT¼9.2x10 -6 , NP-NFT¼1.6x10 -7 ). Suggestive associations (P<10 -5 ) were found with SNPs near COBL and in HDAC9 for the multivariate model of NP, NFT, and CAA. Conclusions:We identified a novel locus upstream of ECGR4 that influences NP and NFT. ECGR4, esophageal cancer related gene-4 encodes a horm...
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