Molecular Genetics of Alzheimer's Disease

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Molecular Genetics".

Deadline for manuscript submissions: closed (15 September 2022) | Viewed by 1895

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Guest Editor
Division of Translational Brain Sciences, Department of Neurology & Pathology, and the Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC 27710, USA
Interests: age-related neurodegenerative diseases; neurogenetics; functional genomics; regulation of gene expression; precision medicine and ‘next generation’ gene therapy approaches for Alzheimer’s-Parkinson’s
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Guest Editor
Director of Viral Vector Core, Department of Neurobiology, Duke University School of Medicine, Durham, NC 27710, USA
Interests: gene therapy; neurodegenerative diseases; gene regulation; epigenetic
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Alzheimer's disease (AD) has a very strong genetic component, with heredity estimated at between 58 and 79%. The vast majority of AD cases are late-onset (LOAD) ‘sporadic’ forms, with no obvious familial inheritance pattern. Thirty years ago, the first and strongest genetic risk factor of LOAD—the e4 allele of the apolipoprotein E gene (APOE e4)—was discovered by linkage analysis of pedigrees. Advances in genetic and genomic technologies over the following years have greatly enhanced our knowledge concerning the genetic underpinning LOAD, and we are now aware of numerous regions in the genome that confer risk for LOAD. Functional genomic studies, including emerging integrative approaches and single-cell techniques, show promise in identifying the specific target genes through which LOAD risk loci act. However, the genetic architecture underlying LOAD is far from being fully understood. Decoding the genetic architecture of LOAD is an essential part of efforts to understand the pathophysiological processes of the disease, and will yield translational knowledge that is crucial for the development of new therapeutic targets and biomarkers for LOAD.

Original manuscripts and reviews that discuss any aspect of the genetic architecture of LOAD, related mechanistic insights and translational implications are welcome.

Prof. Dr. Ornit Chiba-Falek
Dr. Boris Kantor
Guest Editors

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Keywords

  • Alzheimer’s Disease
  • polygenic risk score
  • genome wide association studies
  • transcriptomic
  • epigenomic

Published Papers (1 paper)

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Research

12 pages, 1551 KiB  
Article
Differential Gene Expression and DNA Methylation in the Risk of Depression in LOAD Patients
by Suraj Upadhya, Daniel Gingerich, Michael William Lutz and Ornit Chiba-Falek
Biomolecules 2022, 12(11), 1679; https://doi.org/10.3390/biom12111679 - 12 Nov 2022
Cited by 2 | Viewed by 1589
Abstract
Depression is common among late-onset Alzheimer’s Disease (LOAD) patients. Only a few studies investigated the genetic variability underlying the comorbidity of depression in LOAD. Moreover, the epigenetic and transcriptomic factors that may contribute to comorbid depression in LOAD have yet to be studied. [...] Read more.
Depression is common among late-onset Alzheimer’s Disease (LOAD) patients. Only a few studies investigated the genetic variability underlying the comorbidity of depression in LOAD. Moreover, the epigenetic and transcriptomic factors that may contribute to comorbid depression in LOAD have yet to be studied. Using transcriptomic and DNA-methylomic datasets from the ROSMAP cohorts, we investigated differential gene expression and DNA-methylation in LOAD patients with and without comorbid depression. Differential expression analysis did not reveal significant association between differences in gene expression and the risk of depression in LOAD. Upon sex-stratification, we identified 25 differential expressed genes (DEG) in males, of which CHI3L2 showed the strongest upregulation, and only 3 DEGs in females. Additionally, testing differences in DNA-methylation found significant hypomethylation of CpG (cg20442550) on chromosome 17 (log2FC = −0.500, p = 0.004). Sex-stratified differential DNA-methylation analysis did not identify any significant CpG probes. Integrating the transcriptomic and DNA-methylomic datasets did not discover relationships underlying the comorbidity of depression and LOAD. Overall, our study is the first multi-omics genome-wide exploration of the role of gene expression and epigenome alterations in the risk of comorbid depression in LOAD patients. Furthermore, we discovered sex-specific differences in gene expression underlying the risk of depression symptoms in LOAD. Full article
(This article belongs to the Special Issue Molecular Genetics of Alzheimer's Disease)
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