Genetics: Insights into Alzheimer’s Disease

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

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 4645

Special Issue Editors


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Guest Editor
1. Department of Psychiatry, Washington University in Saint Louis School of Medicine, 4444 Forest Park, Campus Box 8134, Saint Louis, MO, 63110, USA
2. NeuroGenomics and Informatics Center, Washington University in Saint Louis School of Medicine, Saint Louis, MO, 63110, USA
3. Department of Neurology, Washington University in Saint Louis School of Medicine, Saint Louis, MO, 63110, USA
Interests: biomarkers; dementia; genetics; stroke; bioinformatics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, KY, 40536 USA
2. Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA
3. Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY, 40536, USA
4. Department of Microbiology, Immunology, and Molecular Genetics, University of Kentucky, Lexington, KY, 40536 USA
Interests: bioinformatics; Alzheimer’s disease; genetics; evolution; machine learning; algorithms; pedigree analysis; rare variant analysis; codon usage bias; synonymous variation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Alzheimer’s disease is the most common form of dementia and often has devastating effects on affected individuals and their families and caregivers. Although recent large-scale analyses have revealed the genetic landscape of Alzheimer’s disease, most of the phenotypic variance attributed to genetics remains unexplained. Moreover, the identity of the genes driving the association or the implications for disease progression in most cases where loci have been identified remain largely unknown.

This Special Issue entitled “Genetics: Insights into Alzheimer’s Disease”, is the second issue in a series that studies the Genetics of Alzheimer’s Disease. The first issue, published in 2021, comprised 10 manuscripts and can be found at https://www.mdpi.com/journal/genes/special_issues/genetics_alzheimer. With this current Special Issue, the intention is to provide a platform for a wide range of reviews, research articles, communications, and technical notes related to the genetics of either late- or early-onset Alzheimer’s disease. We encourage the submission of manuscripts that have a strong genetic component and that may cover, while not being limited to, the following topics: machine learning of genetic markers associated with Alzheimer’s disease, genome-wide association studies, functional studies of Alzheimer’s disease-related genes or variants, personalized genetics, gene expression analyses, clinical trials with a genetic component, rare variant analyses, and other bioinformatics analyses of Alzheimer’s disease using DNA or RNA sequencing data. Please contact the Guest Editors with questions related to the scope of this Special Issue.

Dr. Justin Miller
Dr. Laura Ibanez
Guest Editors

Manuscript Submission Information

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Keywords

  • Alzheimer’s disease
  • genetics
  • early-onset Alzheimer’s disease
  • machine learning
  • bioinformatics sequencing
  • dementia
  • clinical trial
  • rare variant
  • gene expression

Published Papers (2 papers)

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Research

16 pages, 3790 KiB  
Article
Expression of INPP5D Isoforms in Human Brain: Impact of Alzheimer’s Disease Neuropathology and Genetics
by Diana J. Zajac, James Simpson, Eric Zhang, Ishita Parikh and Steven Estus
Genes 2023, 14(3), 763; https://doi.org/10.3390/genes14030763 - 21 Mar 2023
Cited by 9 | Viewed by 2185
Abstract
The single nucleotide polymorphisms rs35349669 and rs10933431 within Inositol Polyphosphate-5-Phosphatase D (INPP5D) are strongly associated with Alzheimer’s Disease risk. To better understand INPP5D expression in the brain, we investigated INPP5D isoform expression as a function of rs35349669 and rs10933431, as well [...] Read more.
The single nucleotide polymorphisms rs35349669 and rs10933431 within Inositol Polyphosphate-5-Phosphatase D (INPP5D) are strongly associated with Alzheimer’s Disease risk. To better understand INPP5D expression in the brain, we investigated INPP5D isoform expression as a function of rs35349669 and rs10933431, as well as Alzheimer’s disease neuropathology, by qPCR and isoform-specific primers. In addition, INPP5D allelic expression imbalance was evaluated relative to rs1141328 within exon 1. Expression of INPP5D isoforms associated with transcription start sites in exon 1 and intron 14 was increased in individuals with high Alzheimer’s disease neuropathology. In addition, a novel variant with 47bp lacking from exon 12 increased expression in Alzheimer’s Disease brains, accounting for 13% of total INPP5D expression, and was found to undergo nonsense-mediated decay. Although inter-individual variation obscured a possible polymorphism effect on INPP5D isoform expression as measured by qPCR, rs35349669 was associated with rs1141328 allelic expression imbalance, suggesting that rs35349669 is significantly associated with full-length INPP5D isoform expression. In summary, expression of INPP5D isoforms with start sites in exon 1 and intron 14 are increased in brains with high Alzheimer’s Disease neuropathology, a novel isoform lacking the phosphatase domain was significantly increased with the disease, and the polymorphism rs35349669 correlates with allele-specific full-length INPP5D expression. Full article
(This article belongs to the Special Issue Genetics: Insights into Alzheimer’s Disease)
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13 pages, 820 KiB  
Article
Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins
by Katrisa M. Ward, Brandon D. Pickett, Mark T. W. Ebbert, John S. K. Kauwe and Justin B. Miller
Genes 2022, 13(8), 1346; https://doi.org/10.3390/genes13081346 - 27 Jul 2022
Viewed by 1810
Abstract
Protein–protein functional interactions arise from either transitory or permanent biomolecular associations and often lead to the coevolution of the interacting residues. Although mutual information has traditionally been used to identify coevolving residues within the same protein, its application between coevolving proteins remains largely [...] Read more.
Protein–protein functional interactions arise from either transitory or permanent biomolecular associations and often lead to the coevolution of the interacting residues. Although mutual information has traditionally been used to identify coevolving residues within the same protein, its application between coevolving proteins remains largely uncharacterized. Therefore, we developed the Protein Interactions Calculator (PIC) to efficiently identify coevolving residues between two protein sequences using mutual information. We verified the algorithm using 2102 known human protein interactions and 233 known bacterial protein interactions, with a respective 1975 and 252 non-interacting protein controls. The average PIC score for known human protein interactions was 4.5 times higher than non-interacting proteins (p = 1.03 × 10−108) and 1.94 times higher in bacteria (p = 1.22 × 10−35). We then used the PIC scores to determine the probability that two proteins interact. Using those probabilities, we paired 37 Alzheimer’s disease-associated proteins with 8608 other proteins and determined the likelihood that each pair interacts, which we report through a web interface. The PIC had significantly higher sensitivity and residue-specific resolution not available in other algorithms. Therefore, we propose that the PIC can be used to prioritize potential protein interactions, which can lead to a better understanding of biological processes and additional therapeutic targets belonging to protein interaction groups. Full article
(This article belongs to the Special Issue Genetics: Insights into Alzheimer’s Disease)
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