Genetics and Genomics of Alzheimer’s Disease

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Human Genomics and Genetic Diseases".

Deadline for manuscript submissions: closed (29 May 2020) | Viewed by 9102

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Guest Editor
Department of Biology, Brigham Young University, Provo, UT, USA
Interests: Alzheimer's association; bioinformatics; genetics; genomics
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Special Issue Information

Dear Colleagues,

Alzheimer’s disease (AD) is an age-related neurological disorder, the most common cause form of dementia, and a top 10 cause of death. On average, affected individuals live seven years after diagnosis. In most cases, the primary caretakers are immediate family members, frequently an unaffected, elderly, spouse. In the past 20 years, the number of AD deaths has more than doubled, and the incidence of AD is expected to continue its rapid increase. Despite significant efforts, there are currently no disease-altering treatments, and relatively few clinical trials underway.

AD begins many years before the onset of symptoms. A hallmark pathology of AD is the accumulation of Aβ fragments in the brain, one of which leads to neuronal death. Amyloid precursor protein (APP) is cleaved by α- and β-secretases to create different Aβ fragments, one of which, Aβ42, self-aggregates creating toxic plaques in the brain.

In the past 10 years, significant progress has been made towards solving the genetic architecture of AD. More than 20 different genes have been implicated in genome-wide association studies (GWAS), the majority of which are not functional and have relatively small effects on risk for disease. More recently, numerous rare genomic variants have been reported that influence risk for AD. These rare variants have a large effect on risk for disease and are more likely to be functional.

In this Special Issue of Genes, we extend an invitation for reviews on the current state of genetics and genomics in Alzheimer’s Disease, as well as original research articles. We are especially interested in contributions that utilize novel methods, describe protective variants, consider alternative hypotheses to AD, or contribute to our ability to diagnose AD early. However, we will consider any manuscript describing the genomics of AD.

Dr. Perry G. Ridge
Guest Editor

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Keywords

  • Alzheimer’s disease
  • AD
  • genetic markers
  • genome sequencing
  • neurological disorder

Published Papers (3 papers)

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Research

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9 pages, 1229 KiB  
Article
Predicting Clinical Dementia Rating Using Blood RNA Levels
by Justin B. Miller and John S. K. Kauwe
Genes 2020, 11(6), 706; https://0-doi-org.brum.beds.ac.uk/10.3390/genes11060706 - 26 Jun 2020
Cited by 10 | Viewed by 2826
Abstract
The Clinical Dementia Rating (CDR) is commonly used to assess cognitive decline in Alzheimer’s disease patients and is included in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We divided 741 ADNI participants with blood microarray data into three groups based on their most [...] Read more.
The Clinical Dementia Rating (CDR) is commonly used to assess cognitive decline in Alzheimer’s disease patients and is included in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We divided 741 ADNI participants with blood microarray data into three groups based on their most recent CDR assessment: cognitive normal (CDR = 0), mild cognitive impairment (CDR = 0.5), and probable Alzheimer’s disease (CDR ≥ 1.0). We then used machine learning to predict cognitive status using only blood RNA levels. Only one probe for chloride intracellular channel 1 (CLIC1) was significant after correction. However, by combining individually nonsignificant probes with p-values less than 0.1, we averaged 87.87% (s = 1.02) predictive accuracy for classifying the three groups, compared to a 55.46% baseline for this study due to unequal group sizes. The best model had an overall precision of 0.902, recall of 0.895, and a receiver operating characteristic (ROC) curve area of 0.904. Although we identified one significant probe in CLIC1, CLIC1 levels alone were not sufficient to predict dementia status and cannot be used alone in a clinical setting. Additional analyses combining individually suggestive, but nonsignificant, blood RNA levels were significantly predictive and may improve diagnostic accuracy for Alzheimer’s disease. Therefore, we propose that patient features that do not individually predict cognitive status might still contribute to overall cognitive decline through interactions that can be elucidated through machine learning. Full article
(This article belongs to the Special Issue Genetics and Genomics of Alzheimer’s Disease)
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Review

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11 pages, 645 KiB  
Review
The Role of BMI1 in Late-Onset Sporadic Alzheimer’s Disease
by Ryan Hogan, Anthony Flamier, Eleonora Nardini and Gilbert Bernier
Genes 2020, 11(7), 825; https://0-doi-org.brum.beds.ac.uk/10.3390/genes11070825 - 21 Jul 2020
Cited by 6 | Viewed by 3577
Abstract
Late-onset sporadic Alzheimer’s disease (LOAD) seems to contain a “hidden” component that cannot be explained by classical Mendelian genetics, with advanced aging being the strongest risk factor. More surprisingly, whole genome sequencing analyses of early-onset sporadic Alzheimer’s disease cohorts also revealed that most [...] Read more.
Late-onset sporadic Alzheimer’s disease (LOAD) seems to contain a “hidden” component that cannot be explained by classical Mendelian genetics, with advanced aging being the strongest risk factor. More surprisingly, whole genome sequencing analyses of early-onset sporadic Alzheimer’s disease cohorts also revealed that most patients do not present classical disease-associated variants or mutations. In this short review, we propose that BMI1 is possibly epigenetically silenced in LOAD. Reduced BMI1 expression is unique to LOAD compared to familial early-onset AD (EOAD) and other related neurodegenerative disorders; moreover, reduced expression of this single gene is sufficient to reproduce most LOAD pathologies in cellular and animal models. We also show the apparent amyloid and Tau-independent nature of this epigenetic alteration of BMI1 expression. Lastly, examples of the mechanisms underlying epigenetic dysregulation of other LOAD-related genes are also illustrated. Full article
(This article belongs to the Special Issue Genetics and Genomics of Alzheimer’s Disease)
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Other

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7 pages, 635 KiB  
Brief Report
Cognitive Decline in Alzheimer’s Disease: Limited Clinical Utility for GWAS or Polygenic Risk Scores in a Clinical Trial Setting
by Jack Euesden, Sivakumar Gowrisankar, Angela Xiaoyan Qu, Pamela St. Jean, Arlene R. Hughes and David J. Pulford
Genes 2020, 11(5), 501; https://0-doi-org.brum.beds.ac.uk/10.3390/genes11050501 - 02 May 2020
Cited by 2 | Viewed by 2267
Abstract
Introduction: Alzheimer’s disease (AD) is a progressive and irreversible neurological disease. The genetics and molecular mechanisms underpinning differential cognitive decline in AD are not well understood; the genetics of AD risk have been studied far more assiduously. Materials and Methods: Two [...] Read more.
Introduction: Alzheimer’s disease (AD) is a progressive and irreversible neurological disease. The genetics and molecular mechanisms underpinning differential cognitive decline in AD are not well understood; the genetics of AD risk have been studied far more assiduously. Materials and Methods: Two phase III clinical trials measuring cognitive decline over 48 weeks using Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog, n = 2060) and Clinical Dementia Rating-Sum of Boxes (CDR-SB, n = 1996) were retrospectively genotyped. A Genome-Wide Association Study (GWAS) was performed to identify and replicate genetic variants associated with cognitive decline. The relationship between polygenic risk score (PRS) and cognitive decline was tested to investigate the predictive power of aggregating many variants of individually small effect. Results: No loci met candidate gene or genome-wide significance. PRS explained a very small percentage of variance in rates of cognitive decline (ADAS-cog: 0.54%). Conclusions: These results suggest that incorporating genetic information in the prediction of cognitive decline in AD currently appears to have limited utility in clinical trials, consistent with small effect sizes estimated elsewhere. If AD progression is more heritable soon after disease onset, genetics may have more clinical utility. Full article
(This article belongs to the Special Issue Genetics and Genomics of Alzheimer’s Disease)
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