Genetics of Alzheimer’s Disease: the Importance of Polygenic and Epistatic Components

Raghavan, Neha; Tosto, Giuseppe

doi:10.1007/s11910-017-0787-1

Cited by 53 publications

(35 citation statements)

References 91 publications

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“…The only other oligogenic influence was that of the well‐known APOE4 / TOMM40 variants. There was nominal association with Alzheimer's pathological diagnosis in this report, though PTPRD has not previously displayed robust associations with Alzheimer's disease diagnoses . MAPK5, a kinase associated with tangle‐related tau hyperphosphorylation, is a likely PTPRD dephosphorylation target on the basis of ptp‐3 knockout data .…”

Section: Ptprd Genomic Variation and Ptprd Associations With Human DImentioning

confidence: 52%

PTPRD: neurobiology, genetics, and initial pharmacology of a pleiotropic contributor to brain phenotypes

Uhl

Martínez

2019

Annals of the New York Academy of Sciences

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Receptor‐type protein tyrosine phosphatase, receptor type D (PTPRD) has likely roles as a neuronal cell adhesion molecule and synaptic specifier. Interest in its neurobiology and genomics has been stimulated by results from human genetics and mouse models for phenotypes related to addiction, restless leg syndrome, neurofibrillary pathology in Alzheimer's disease, cognitive impairment/intellectual disability, mood lability, and obsessive‐compulsive disorder. We review PTPRD's discovery, gene family, candidate homomeric and heteromeric binding partners, phosphatase activities, brain distribution, human genetic associations with nervous system phenotypes, and mouse model data relevant to these phenotypes. We discuss the recently reported discovery of the first small molecule inhibitor of PTPRD phosphatase, the identification of its addiction‐related effects, and the implications of these findings for the PTPRD‐associated brain phenotypes. In assembling PTPRD neurobiology, human genetics, and mouse genetic and pharmacological datasets, we provide a compelling picture of the roles played by PTPRD, its variation, and its potential as a target for novel therapeutics.

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Section: Ptprd Genomic Variation and Ptprd Associations With Human DImentioning

confidence: 52%

PTPRD: neurobiology, genetics, and initial pharmacology of a pleiotropic contributor to brain phenotypes

Uhl

Martínez

2019

Annals of the New York Academy of Sciences

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“…The etiology of LOAD is likely to be motivated by both environmental and genetic components. However, the genetic component seems to a major determinant as the heritability of the disease has been estimated to be ∼ 80% [21]. Therefore, genetic testing hold the potential to provided sufficiently accurate predictions of the disease using genetic data exclusively.…”

Section: Discussionmentioning

confidence: 99%

“…The genetic component of LOAD has been estimated to be 79%. However, recent studies on the heritability of LOAD have estimated that common genetic variants identified by genome-wide association studies (GWAS) are only capable to explain 33% of the phenotypic variance, meaning that over 40% of the genetic component remains unexplained [21].…”

Section: Introductionmentioning

confidence: 99%

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

Oriol

Clemente²,

Estrada³

2019

Preprint

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Alzheimer'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.

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“…The functional mechanisms of TREM2 in Aβ uptake by microglia are also complicated, and contradictory biological consequences are observed in mouse models (see e.g., (Gratuze et al, 2018) for a review on this topic). Moreover, adding up the APOE variant and other nine identified top SNPs accounts for a small portion (5%) of variation of age-of-onset (Raghavan and Tosto, 2017), suggesting that missing genetic mechanisms contribute to this complex disease. We expect that discovery of additional AD associated genetic variants will provide more insights into the understanding of the AD pathology.…”

Section: Introductionmentioning

confidence: 99%

Exome-wide age-of-onset analysis reveals exonic variants inERN1, TACR3andSPPL2Cassociated with Alzheimer’s disease

Loika

Park

et al. 2020

Preprint

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Despite recent discovery in GWAS of genomic variants associated with Alzheimer's disease (AD), its underlying biological mechanisms are still elusive. Discovery of novel AD-associated genetic variants, particularly in coding regions and from APOE ε 4 non-carriers, may provide more insights into the understanding of the pathology of AD. In this study, we carried out exome-wide association analysis of age-of-onset of AD with ~20,000 subjects, and placed more emphasis on APOE ε 4 non-carriers. Using Cox mixed-effects models, we find that age-of-onset shows a stronger genetic signal than AD case-control status, capturing many known variants with stronger significance, and also revealing three new variants. We identified two novel rare variants, rs56201815, a synonymous variant in ERN1, from the analysis of APOE ε 4 noncarriers, and a missense variant rs144292455 in TACR3. In addition, we detected rs12373123, a common missense variant in SPPL2C in the MAPT region, associated with age-of-onset of AD in APOE ε 4 non-carriers. In an attempt to unravel their regulatory and biological functions, we found that the minor allele of rs56201815 was associated with lower average FDG uptake across five brain regions in ADNI. Our eQTL analyses based on 6198 gene expression samples from ROSMAP and GTEx revealed that the minor allele of rs56201815 was associated with elevated expression of ERN1, a key gene triggering unfolded protein response (UPR), in multiple brain regions, including posterior cingulate cortex and nucleus accumbens. Our cell type-specific eQTL analysis of based on ~80,000 single nuclei in prefrontal cortex revealed that the protective minor allele of rs12373123 significantly increased expression of GRN in microglia, and was associated with MAPT expression in astrocytes. These findings provide novel evidence supporting the hypothesis of the potential involvement of the UPR to ER stress in the pathological pathway of AD, and also provide more insights into underlying regulatory mechanisms behind the pleiotropic effects of rs12373123 in multiple degenerative diseases including AD and Parkinson's disease.In this study, we performed exome-wide association analysis of age-of-onset of AD, in which most genetic variants are rare or low frequency, using an Alzheimer's Disease Sequencing Project (ADSP) sample of 10,216 subjects in the discovery phase. Rare coding variants often show larger effect size and their biological consequences are more explicable, but its association analysis is complicated by insufficient statistical power. Although exome-wide association of AD has recently been explored using AD status (Bis et al., 2018;Cruchaga et al., 2014;Raghavan et al., 2018), our rationale is that more AD-related rare variants can be identified using analysis of age-of-onset of AD with a Cox model given emerging evidence from a previous study showing its advantage in terms of statistical power (He and Kulminski, 2019). We attempted to replicate significant findings in four other studies, with a meta-analysis sample size of a...

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Genetics of Alzheimer’s Disease: the Importance of Polygenic and Epistatic Components

Cited by 53 publications

References 91 publications

PTPRD: neurobiology, genetics, and initial pharmacology of a pleiotropic contributor to brain phenotypes

PTPRD: neurobiology, genetics, and initial pharmacology of a pleiotropic contributor to brain phenotypes

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

Exome-wide age-of-onset analysis reveals exonic variants inERN1, TACR3andSPPL2Cassociated with Alzheimer’s disease

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