scholarly journals The clinical utility of gene testing for Alzheimer’s disease

2011 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
Emily R. Atkins ◽  
Peter K. Panegyres

Alzheimer’s disease (AD) is the largest cause of dementia, affecting 35.6 million people in 2010. Amyloid precursor protein, presenilin 1 and presenilin 2 mutations are known to cause familial early-onset AD, whereas apolipoprotein E (APOE) ε4 is a susceptibility gene for late-onset AD. The genes for phosphatidylinositol- binding clathrin assembly protein, clusterin and complement receptor 1 have recently been described by genome-wide association studies as potential risk factors for lateonset AD. Also, a genome association study using single neucleotide polymorphisms has identified an association of neuronal sortilin related receptor and late-onset AD. Gene testing, and also predictive gene testing, may be of benefit in suspected familial early-onset AD however it adds little to the diagnosis of lateonset AD and does not alter the treatment. We do not recommend APOE ε4 genotyping.

2021 ◽  
Author(s):  
M. Ilyas Kamboh

AbstractAlzheimer’s disease (AD) is a complex and multifactorial neurodegenerative disease. Due to its long clinical course and lack of an effective treatment, AD has become a major public health problem in the USA and worldwide. Due to variation in age-at-onset, AD is classified into early-onset (< 60 years) and late-onset (≥ 60 years) forms with early-onset accounting for only 5–10% of all cases. With the exception of a small number of early-onset cases that are afflicted because of high penetrant single gene mutations in APP, PSEN1, and PSEN2 genes, AD is genetically heterogeneous, especially the late-onset form having a polygenic or oligogenic risk inheritance. Since the identification of APOE as the most significant risk factor for late-onset AD in 1993, the path to the discovery of additional AD risk genes had been arduous until 2009 when the use of large genome-wide association studies opened up the discovery gateways that led the identification of ~ 95 additional risk loci from 2009 to early 2022. This article reviews the history of AD genetics followed by the potential molecular pathways and recent application of functional genomics methods to identify the causal AD gene(s) among the many genes that reside within a single locus. The ultimate goal of integrating genomics and functional genomics is to discover novel pathways underlying the AD pathobiology in order to identify drug targets for the therapeutic treatment of this heterogeneous disorder.


2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Perry G. Ridge ◽  
Mark T. W. Ebbert ◽  
John S. K. Kauwe

Alzheimer’s disease is the most common form of dementia and is the only top 10 cause of death in the United States that lacks disease-altering treatments. It is a complex disorder with environmental and genetic components. There are two major types of Alzheimer’s disease, early onset and the more common late onset. The genetics of early-onset Alzheimer’s disease are largely understood with variants in three different genes leading to disease. In contrast, while several common alleles associated with late-onset Alzheimer’s disease, including APOE, have been identified using association studies, the genetics of late-onset Alzheimer’s disease are not fully understood. Here we review the known genetics of early- and late-onset Alzheimer’s disease.


2019 ◽  
Author(s):  
Javier de Velasco Oriol ◽  
Edgar E. Vallejo ◽  
Karol Estrada ◽  

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.


2021 ◽  
Author(s):  
Adam C. Naj ◽  
Ganna Leonenko ◽  
Xueqiu Jian ◽  
Benjamin Grenier-Boley ◽  
Maria Carolina Dalmasso ◽  
...  

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.


2018 ◽  
Author(s):  
Lorenza Magno ◽  
Christian B Lessard ◽  
Marta Martins ◽  
Pedro Cruz ◽  
Matilda Katan ◽  
...  

ABSTRACTRecent Genome Wide Association Studies (GWAS) have identified novel rare coding variants in immune genes associated with late onset AD (LOAD). Amongst these, a polymorphism in Phospholipase C-gamma 2 (PLCG2) P522R, has been reported to be protective against LOAD. PLC enzymes are key elements in signal transmission networks and are potentially druggable targets. PLCG2 is highly expressed in the hematopoietic system. Hypermorphic mutations in PLCG2 in humans have been reported to cause autoinflammation and immune disorders, suggesting a key role for this enzyme in the regulation of immune cell function.We confirmed that PLCG2 expression is restricted primarily to microglia in both the healthy and AD brain. Functional analysis of the P522R variant in heterologous systems demonstrated a small hypermorphic effect of the mutation on enzyme function. PLCγ2 is therefore a potential target for modulating microglia function in AD, and a small molecule drug that weakly activates PLCγ2 may be one potential therapeutic approach.SUMMARYThe PLCG2 P522R variant is protective against Alzheimer’s disease (AD). We show that PLCG2 is expressed in CNS-resident myeloid cells, and the P522R polymorphism weakly activates enzyme function. These data suggest that activation of PLCG2 and not inhibition could be therapeutically beneficial in AD.


2017 ◽  
Author(s):  
Sourena Soheili-Nezhad

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.


2013 ◽  
Author(s):  
Charalampos S Floudas ◽  
Nara Um ◽  
M. Ilyas Kamboh ◽  
Michael M Barmada ◽  
Shyam Visweswaran

Background Identifying genetic interactions in data obtained from genome-wide association studies (GWASs) can help in understanding the genetic basis of complex diseases. The large number of single nucleotide polymorphisms (SNPs) in GWASs however makes the identification of genetic interactions computationally challenging. We developed the Bayesian Combinatorial Method (BCM) that can identify pairs of SNPs that in combination have high statistical association with disease. Results We applied BCM to two late-onset Alzheimer’s disease (LOAD) GWAS datasets to identify SNP-SNP interactions between a set of known SNP associations and the dataset SNPs. For evaluation we compared our results with those from logistic regression, as implemented in PLINK. Gene Ontology analysis of genes from the top 200 dataset SNPs for both GWAS datasets showed overrepresentation of LOAD-related terms. Four genes were common to both datasets: APOE and APOC1, which have well established associations with LOAD, and CAMK1D and FBXL13, not previously linked to LOAD but having evidence of involvement in LOAD. Supporting evidence was also found for additional genes from the top 30 dataset SNPs. Conclusion BCM performed well in identifying several SNPs having evidence of involvement in the pathogenesis of LOAD that would not have been identified by univariate analysis due to small main effect. These results provide support for applying BCM to identify potential genetic variants such as SNPs from high dimensional GWAS datasets.


2018 ◽  
Author(s):  
BW Kunkle ◽  
B Grenier-Boley ◽  
R Sims ◽  
JC Bis ◽  
AC Naj ◽  
...  

IntroductionLate-onset Alzheimer’s disease (LOAD, onset age > 60 years) is the most prevalent dementia in the elderly1, and risk is partially driven by genetics2. Many of the loci responsible for this genetic risk were identified by genome-wide association studies (GWAS)3–8. To identify additional LOAD risk loci, the we performed the largest GWAS to date (89,769 individuals), analyzing both common and rare variants. We confirm 20 previous LOAD risk loci and identify four new genome-wide loci (IQCK, ACE, ADAM10, and ADAMTS1). Pathway analysis of these data implicates the immune system and lipid metabolism, and for the first time tau binding proteins and APP metabolism. These findings show that genetic variants affecting APP and Aβ processing are not only associated with early-onset autosomal dominant AD but also with LOAD. Analysis of AD risk genes and pathways show enrichment for rare variants (P = 1.32 × 10−7) indicating that additional rare variants remain to be identified.


2020 ◽  
Vol 21 (7) ◽  
pp. 2381
Author(s):  
Chen-Ling Gan ◽  
Tao Zhang ◽  
Tae Ho Lee

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive cognitive dysfunction and behavioral impairment. In China, the number of AD patients is growing rapidly, which poses a considerable burden on society and families. In recent years, through the advancement of genome-wide association studies, second-generation gene sequencing technology, and their application in AD genetic research, more genetic loci associated with the risk for AD have been discovered, including KCNJ15, TREM2, and GCH1, which provides new ideas for the etiology and treatment of AD. This review summarizes three early-onset AD causative genes (APP, PSEN1, and PSEN2) and some late-onset AD susceptibility genes and their mutation sites newly discovered in China, and briefly introduces the potential mechanisms of these genetic susceptibilities in the pathogenesis of AD, which would help in understanding the genetic mechanisms underlying this devastating disease.


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