scholarly journals Identification of functionally connected multi-omic biomarkers for Alzheimer’s Disease using modularity-constrained Lasso

2019 ◽  
Author(s):  
Linhui Xie ◽  
Pradeep Varathan ◽  
Kwangsik Nho ◽  
Andrew J. Saykin ◽  
Paul Salama ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Performance ◽  
Large Scale ◽  
Association Studies ◽  
Interaction Network ◽  
Real Data ◽  
Genome Wide Association Studies ◽  
Penalty Term ◽  
Genome Wide

AbstractIn 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.

scholarly journals Shared Genetic Architecture Between Alzheimer’s Disease and Gastrointestinal Tract Disorders: A Large-scale Genome-wide Cross-trait Analysis

2021 ◽  
Author(s):  
Emmanuel O Adewuyi ◽  
Eleanor K O’Brien ◽  
Dale R Nyholt ◽  
Tenielle Porter ◽  
Simon M Laws
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gastrointestinal Tract ◽  
Large Scale ◽  
Association Studies ◽  
Biological Pathways ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Shared Genetic

Abstract Background: Consistent with the concept of the gut-brain phenomenon, observational studies have reported a pattern of co-occurring relationship between Alzheimer’s disease (AD) and a range of gastrointestinal tract (GIT) traits. However, it is not clear whether the reported association reflects a causal or shared genetic aetiology of GIT disorders with AD. While AD has no known curative treatments, and its pathogenesis is not clearly understood, a comprehensive assessment of its shared genetics with diseases (comorbidities) can provide a deeper understanding of its underlying biological mechanisms and enhance potential therapy development. Methods: We analysed large-scale genome-wide association studies (GWAS) summary data (sample size = 34,652 – 456,327) to comprehensively assess shared genetic overlap and causality of GIT disorders with the risk of AD. Further, we performed meta-analyses, pairwise GWAS analysis; and investigated genes and biological pathways shared by AD and GIT disorders.Results: Our analyses reveal significant concordance of SNP risk effects across AD and GIT disorders (Ppermuted = 9.99 × 10−4). Also, we found a significant positive genetic correlation between AD and each of gastroesophageal reflux disease (GERD), peptic ulcer disease (PUD), medications for GERD or PUD (PGM), gastritis-duodenitis, irritable bowel syndrome, and diverticular disease, but not inflammatory bowel disease. Mendelian randomisation analyses found no evidence for a significant causal association between AD and GIT disorders. However, shared independent genome-wide significant (Pmeta-analysis < 5 × 10-8) loci (including 1p31.3 [near gene, PDE4B], 1q32.2 [CD46], 3p21.31 [SEMA3F], 16q22.1 [MTSS2], 17q21.33 [PHB], and 19q13.32 [APOE]) were identified for AD and PGM, six of which are putatively novel. These loci were replicated using GERD and PUD GWAS and reinforced in pairwise GWAS (colocalisation) as well as gene-based analyses. Lipid metabolism, autoimmune system, lipase inhibitors, PD-1 signalling, and statin mechanisms were significantly enriched in pathway-based analyses. Conclusions: These findings support shared genetic susceptibility of GIT disorders with AD risk and provide new insights into their observed association. The identified loci and genes—PDE4B, CD46 and APOE, especially—and biological pathways—statins and lipase inhibitors, in particular—may provide novel therapeutic avenues or targets for further investigation in AD, GIT disorders, or their comorbidity.

scholarly journals Metabolome-wide association study on ABCA7 demonstrates a role for ceramide metabolism in impaired cognitive performance and Alzheimer's disease

2021 ◽  
Author(s):  
Abbas Dehghan ◽  
Rui Pinto ◽  
Ibrahim Karaman ◽  
Jian Hung ◽  
Brenan Durainayagam ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Cognitive Performance ◽  
Association Studies ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Underlying Mechanisms

Genome-wide association studies (GWAS) have identified genetic loci associated with risk of Alzheimer's disease (AD), but underlying mechanisms are largely unknown. Using untargeted mass spectrometry, we conducted a metabolome-wide association study (MWAS) that identified the association of lactosylceramides (LacCer)s with AD-related single nucleotide polymorphisms (SNPs) in ABCA7 (P = 5.0x 10-5 to 1.3 x 10-44). Independent support for the association came through the discovery of differences in concentrations of sphingomyelins, ceramides, and hexose-ceramides in brain tissue from ABCA7-null mice compared to wild type (P =0.049 -1.44 x10-5). We showed that plasma LacCer concentrations are associated with cognitive performance in humans. We found evidence for a potentially causal association of LacCer with AD risk using Mendelian randomisation analysis. Our work suggests that AD risks arising from functional variations in ABCA7 expression are mediated at least in part through ceramides, the metabolism or downstream signalling of which offers new therapeutic opportunities.

Alzheimer's disease genetics: lessons to improve disease modelling

2011 ◽  
Vol 39 (4) ◽  
pp. 910-916 ◽  
Author(s):  
Rita J. Guerreiro ◽  
John Hardy
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Current Knowledge ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Complete Understanding ◽  
Genome Wide ◽  
History Of ◽  
Ad Alzheimer’S Disease

In the present review, we look back at the recent history of GWAS (genome-wide association studies) in AD (Alzheimer's disease) and integrate the major findings with current knowledge of biological processes and pathways. These topics are essential for the development of animal models, which will be fundamental to our complete understanding of AD.

scholarly journals Predicting late-onset Alzheimer’s disease from genomic data using deep neural networks

2019 ◽  
Author(s):  
Javier de Velasco Oriol ◽  
Edgar E. Vallejo ◽  
Karol Estrada ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Genetic Variants ◽  
Deep Neural Networks ◽  
Late Onset ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Clinical Markers ◽  
Genome Wide

AbstractAlzheimer’s disease (AD) is the leading form of dementia. Over 25 million cases have been estimated worldwide and this number is predicted to increase two-fold every 20 years. Even though there is a variety of clinical markers available for the diagnosis of AD, the accurate and timely diagnosis of this disease remains elusive. Recently, over a dozen of genetic variants predisposing to the disease have been identified by genome-wide association studies. However, these genetic variants only explain a small fraction of the estimated genetic component of the disease. Therefore, useful predictions of AD from genetic data could not rely on these markers exclusively as they are not sufficiently informative predictors. In this study, we propose the use of deep neural networks for the prediction of late-onset Alzheimer’s disease from a large number of genetic variants. Experimental results indicate that the proposed model holds promise to produce useful predictions for clinical diagnosis of AD.

scholarly journals Genome-Wide Meta-Analysis of Late-Onset Alzheimer's Disease Using Rare Variant Imputation in 324,809 Subjects Identifies Novel Rare Variant Locus NCK2: The International Genomics of Alzheimer's Project (IGAP)

2021 ◽  
Author(s):  
Adam C. Naj ◽  
Ganna Leonenko ◽  
Xueqiu Jian ◽  
Benjamin Grenier-Boley ◽  
Maria Carolina Dalmasso ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Rare Variant ◽  
Late Onset ◽  
Sequence Data ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
The Uk

Risk for late-onset Alzheimer's disease (LOAD) is driven by multiple loci primarily identified by genome-wide association studies, many of which are common variants with minor allele frequencies (MAF)>0.01. To identify additional common and rare LOAD risk variants, we performed a GWAS on 25,170 LOAD subjects and 41,052 cognitively normal controls in 44 datasets from the International Genomics of Alzheimer's Project (IGAP). Existing genotype data were imputed using the dense, high-resolution Haplotype Reference Consortium (HRC) r1.1 reference panel. Stage 1 associations of P<10-5 were meta-analyzed with the European Alzheimer's Disease Biobank (EADB) (n=20,301 cases; 21,839 controls) (stage 2 combined IGAP and EADB). An expanded meta-analysis was performed using a GWAS of parental AD/dementia history in the UK Biobank (UKBB) (n=35,214 cases; 180,791 controls) (stage 3 combined IGAP, EADB, and UKBB). Common variant (MAF≥0.01) associations were identified for 29 loci in stage 2, including novel genome-wide significant associations at TSPAN14 (P=2.33×10-12), SHARPIN (P=1.56×10-9), and ATF5/SIGLEC11 (P=1.03[mult]10-8), and newly significant associations without using AD proxy cases in MTSS1L/IL34 (P=1.80×10-8), APH1B (P=2.10×10-13), and CLNK (P=2.24×10-10). Rare variant (MAF<0.01) associations with genome-wide significance in stage 2 included multiple variants in APOE and TREM2, and a novel association of a rare variant (rs143080277; MAF=0.0054; P=2.69×10-9) in NCK2, further strengthened with the inclusion of UKBB data in stage 3 (P=7.17×10-13). Single-nucleus sequence data shows that NCK2 is highly expressed in amyloid-responsive microglial cells, suggesting a role in LOAD pathology.

scholarly journals Alzheimer's disease: the large gene instability hypothesis

2017 ◽  
Author(s):  
Sourena Soheili-Nezhad
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Association Studies ◽  
Phase Iii ◽  
Testable Hypothesis ◽  
Genome Wide Association Studies ◽  
Evolutionary Forces ◽  
Large Gene ◽  
Genome Wide

All drug trials of the Alzheimer's disease (AD) have failed to slow the progression of dementia in phase III studies, and the most effective therapeutic strategy remains controversial due to the poorly understood disease mechanisms. For AD drug design, amyloid beta (Aβ) and its cascade have been the primary focus since decades ago, but mounting evidence indicates that the underpinning molecular pathways of AD are more complex than the classical reductionist models. Several genome-wide association studies (GWAS) have recently shed light on dark aspects of AD from a hypothesis-free perspective. Here, I use this novel insight to suggest that the amyloid cascade hypothesis may be a wrong model for AD therapeutic design. I review 23 novel genetic risk loci and show that, as a common theme, they code for receptor proteins and signal transducers of cell adhesion pathways, with clear implications in synaptic development, maintenance, and function. Contrary to the Aβ-based interpretation, but further reinforcing the unbiased genome-wide insight, the classical hallmark genes of AD including the amyloid precursor protein (APP), presenilins (PSEN), and APOE also take part in similar pathways of growth cone adhesion and contact-guidance during brain development. On this basis, I propose that a disrupted synaptic adhesion signaling nexus, rather than a protein aggregation process, may be the central point of convergence in AD mechanisms. By an exploratory bioinformatics analysis, I show that synaptic adhesion proteins are encoded by largest known human genes, and these extremely large genes may be vulnerable to DNA damage accumulation in aging due to their mutational fragility. As a prototypic example and an immediately testable hypothesis based on this argument, I suggest that mutational instability of the large Lrp1b tumor suppressor gene may be the primary etiological trigger for APOE-dab1 signaling disruption in late-onset AD. In conclusion, the large gene instability hypothesis suggests that evolutionary forces of brain complexity have led to emergence of large and fragile synaptic genes, and these unstable genes are the bottleneck etiology of aging disorders including senile dementias. A paradigm shift is warranted in AD prevention and therapeutic design.

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