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Theron, D.; Hopkins, L.N.; Sutherland, H.G.; Griffiths, L.R.; Fernandez, F. Alzheimer’s Disease Genetic Susceptibility. Encyclopedia. Available online: https://encyclopedia.pub/entry/49045 (accessed on 27 July 2024).
Theron D, Hopkins LN, Sutherland HG, Griffiths LR, Fernandez F. Alzheimer’s Disease Genetic Susceptibility. Encyclopedia. Available at: https://encyclopedia.pub/entry/49045. Accessed July 27, 2024.
Theron, Danelda, Lloyd N. Hopkins, Heidi G. Sutherland, Lyn R. Griffiths, Francesca Fernandez. "Alzheimer’s Disease Genetic Susceptibility" Encyclopedia, https://encyclopedia.pub/entry/49045 (accessed July 27, 2024).
Theron, D., Hopkins, L.N., Sutherland, H.G., Griffiths, L.R., & Fernandez, F. (2023, September 12). Alzheimer’s Disease Genetic Susceptibility. In Encyclopedia. https://encyclopedia.pub/entry/49045
Theron, Danelda, et al. "Alzheimer’s Disease Genetic Susceptibility." Encyclopedia. Web. 12 September, 2023.
Alzheimer’s Disease Genetic Susceptibility
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This entry is adapted from the peer-reviewed paper 10.3390/ijms241713480

Alzheimer’s disease (AD) is the most common form of dementia that affects millions of individuals worldwide. Although the research over the last decades has provided new insight into AD pathophysiology, there is currently no cure for the disease. AD is often only diagnosed once the symptoms have become prominent, particularly in the late-onset (sporadic) form of AD. Consequently, it is essential to further new avenues for early diagnosis. With advances in genomic analysis and a lower cost of use, the exploration of genetic markers alongside RNA molecules can offer a key avenue for early diagnosis.

Alzheimer’s disease genes genetics Late Onset Alzheimer's Disease Sporadic Alzheimer's Disease RNA

1. Introduction

With recent advances in technology leading to lower costs, genetic screening has become increasingly utilised in the diagnosis of a range of diseases, including for the diagnosis of progressive neurological disorders such as Spino–Cerebellar Ataxia (SCA) [1]. Although the familial form of AD has strong genetic origins with well-characterised pathogenic variants (Amyloid Protein Precursor (APP), Presenilin-1 (PSEN1) and Presenilin-2 (PSEN2) [2][3]), the genetic landscape of sporadic AD is more complex and remains less well understood. Taking into account the multifactorial aetiology of AD (including environmental factors) [4], it is essential to consider not only the multiple genes involved in AD but also the regulation of the transcriptome. Small non-coding molecules, such as microRNAs, circular-RNAs, and long non-coding RNAs, can regulate gene expression at the transcriptional and post-transcriptional level. Consequently, it is essential to further explore the interplay between the genome and its regulation via non-coding RNAs, which may be influenced by and respond to environmental factors.

2. AD Genetic Susceptibility

A wide variety of genes implicated in the development of AD have been identified as possible diagnostic and therapeutic targets. Altogether, approximately 70% of sporadic AD’s heritability may be explained by genetic factors, including genes associated with multiple neuropathological events, as summarised in Table 1 and illustrated in Figure 1 [5][6][7]. Polymorphisms in these genes impact neuroinflammation, formation and clearance of abnormal/pathogenic proteins (Aβ peptides and phosphorylated-tau/tau), neural repair, and synaptic signalling. While not all variants are specific to AD, rare AD-related variants have been found to be significantly associated with lipid metabolism and amyloid processing [7].
Figure 1. Genes involved in neuronal dysfunction in AD pathophysiology.
Table 1. Mechanisms of AD Pathology and Associated Genes Reported by GWAS.
Mechanism of AD
Pathophysiology		 Genes
Amyloid Pathway
Amyloid angiopathy		 APP, ABCA7, ADAM10, BIN1, CD2AP, PICALM, SORL1, CR1, CLU, CD33, FERMT2, CASS4, PTK2B, SLC10A2, UNC5C, PLD3, SLC24A4, RIN3, DSG2, CST3, BCHE, CTSD, MEF2C, AB13, PLCG2, SHARPIN, APH1B, ACE, IGF1, INSR, LKB1, PSEN1, PSEN2, APOE, HLA-DRB5, HLA-DRB1
Tau Pathology BIN1, CD2AP, PICALM, MAPT, CASS4, PTK2B, UNC5C, SLC24A4, RIN3, BCHE, HS3ST1, ACE, IGFQ, INSR, LKBQ
Lipid transport
Lipid Metabolism		 APOE, ABCA7, PLCG2A, SORL1, CLU, PICALM, SLC10A2, SLC24A4, RIN3, ECHDC3
Dendrites CD2AP, COBL
Neuroinflammation
microglial activation		 CR1, TREM2, CLU, MS4A4E, ABCA7, CD33, MS4A6A, INPP5D, PLCG2, PTK2K, FBXL7, HLA-DRB5, HLA-DRB1, BIN1, MTHFR, MEF2C, AB13, SHARPIN, MINK1, ACE, SCIMP, PILRA, SPI1
Protein aggregation APOE, PFDN1, CLU
Mitochondrial Function MTHFD1L, ECHDC3, TOMM40
Synaptic dysfunction PICALM, PTK2B, SLC10A2, MEF2C, MINK1, APH1B
Blood Brain Barrier disruption, vascular damage CD2AP, EPHA1, MTHFR
Apoptotic genes FBXL7, CLU
Oxidative Stress MEF2C, NME8, TOMM40, MEF2C, MINK1, ACE
Cell Cycle and transport BIN1, SORL1, PILCAM, CD2AP, EPHA1, RANDBP2, UNC5C, NME8, EPHA1, RANBP2, UNC5C, NME8, COBL, USP6NL, CD2AP
Summary of key genes associated with AD reported by GWAS replicated genetic studies. Adapted from [5][8][9].

2.1. Genes Related to AD Hallmarks of Pathophysiology

The development of amyloid plaques involves the interplay of several regulatory genes and AD pathways (Figure 1). The Amyloid-Precursor Protein (APP) gene encodes for the APP protein, which is cleaved by β-secretase (BACE) and γ-secretase to produce Aβ peptides. These peptides aggregate in excess in AD, forming characteristic amyloid plaques. Duplications or mutations of the β-cleavage site result in varying levels of pathogenic Aβ fragmentation, while mutations at different sites can shift the cleavage site, cause a conformational change, or alter protofibril and fibril formation [10]. Mutation near the BACE cleavage site, A673T (Icelandic), leads to a decrease in the production of Aβ40 [11]. In contrast, the A673V (A2V) mutation leads to increased pathogenic Aβ production [12].
In addition to the BACE gene, the Presenilin genes (PSEN1 and PSEN2) encode for key subunits of γ-secretase responsible for final APP cleavage into Aβ. These genes have been highly investigated, particularly for their role as risk factors for the familial form of AD [13]. Abnormal PSEN1 and PSEN2 result in the premature release of APP fragments during γ-cleavage, resulting in longer, incompletely proteolyzed amyloid fragments [13]. Functional polymorphisms (M139V, M146I, R278I) of PSEN1 lead to variability in PSEN1 protein lengths, γ-secretase function, and ratio of Aβ cleavages [14]. Mutations of D9E in PSEN1 remove a hydrophilic domain modifying the γ-secretase active site, while mutations at M139T impact APP binding [15]. 
One of the primary genes influencing the risk of AD development is Apolipoprotein E (APOE). Three common APOE alleles (E2, E3, E4) have been well-characterised in various populations and may act in conjunction with various other AD risk factor genes [16][17]. The APOE2 allele has been reported as protective in AD, improving cholesterol efflux and neuronal growth and repair [18][19].

2.2. Candidate Genes Involved in the Regulation of AD Pathways

One of the most significant genes associated with sporadic AD after APOE4 is Bridging integrator 1 (BIN1) according to GWAS studies (p < 3.92 × 10−58 [20] and 3.38 × 10−44 [21]) and gene ranking prioritisation studies (Table 2). BIN1 is highly involved in lipidic membrane trafficking, endocytosis, and cytoskeleton regulation through its SH3 domain interacting with tau [22][23][24]. BIN1 variants (such as rs744373 and rs59335482) are associated with increased tau (PET-tau, CSF t-tau, CSF p-tau), and subsequent memory deficits [22][25].
Table 2. Candidate genes ranking for sporadic AD.
Ranking Gene p-Value Source
1 BIN1 2.06 × 10−30 [5]
1.10 × 10−54 [26]
7.30 × 10−49 [27]
2 PICALM 4.29 × 10−14 [5]
5.21 × 10−26 [26]
5.10 × 10−36 [27]
3 CLU 6.60 × 10−13 [5]
7.71 × 10−26 [26]
1.10 × 10−28 [27]
4 CR1 1.51 × 10−14 [5]
1.40 × 10−23 [26]
1.60 × 10−28 [27]
5 MS4A 2.87 × 10−20 [5]
9.33 × 10−20 [26]
1.10 × 10−18 [27]
6 TREM2 2.34 × 10−11 [5]
1.83 × 10−23 [26]
7 PILRA 7.41 × 10−10 [5]
3.28 × 10−18 [26]
1.10 × 10−18 [27]
8 SORL1 1.76 × 10−8 [5]
5.59 × 10−14 [26]
4.80 × 10−17 [27]
9 HLA 2.88 × 10−15 [26]
1.20 × 10−14 [27]
10 CD2AP 2.95 × 10−12 [5]
1.11 × 10−11 [26]
5.80 × 10−14 [27]
11 ABCA7 2.36 × 10−9 [5]
2.41 × 10−13 [26]
3.70 × 10−10 [27]
12 SLC24A4 3.55 × 10−7 [5]
7.45 × 10−14 [26]
1.10 × 10−10 [27]
14 CLNK/EPHA1/ECHDC3/HS3ST1 5.02 × 10−8 [5]
1.08 × 10−11 [26]
3.40 × 10−7 [27]
15 ADAM10 2.67 × 10−11 [26]
5.50 × 10−11 [27]
16 CASS4 1.56 × 10−6 [5]
1.07 × 10−10 [26]
Ranking based on p-values reported in various GWAS for AD [5][26][27]. p-values refer to the significance of the association between the genetic loci and AD genetic score risk calculations.
After APOE and BIN1, GWAS have identified the Phosphatidylinositol-binding clathrin assembly protein (PICALM) gene as the most significant genetic susceptibility locus for AD (Table 2) [28]. PICALM initiates clathrin-mediated endocytosis and the autophagy neuronal process [28]. PICALM additionally regulates the internalisation of LRP1 (low-density lipoprotein-receptor-related protein-1), β-secretase (BACE1) and γ-secretase expression [28]. The PICALM.4 isoform is reported to have increased expression in early AD, contributing to the accumulation of endosomal-contained immature protease cathepsin D (CTSD) and reduction of Aβ clearance [29]. 
ATP-binding cassette, sub-family A, member 7 (ABCA7), has also been reported to regulate APOE assembly into high-density lipoproteins, as well as controlling glial cell states, leading to a potential protective role in AD [30]. In a Chinese cohort, SNPs in ABCA7 have shown a strong association with AD. The G allele of rs3764650 increased AD susceptibility while the A allele in rs4147929 led to higher morbidity [31]. 
TREM2 is cleaved by ADAM17 and ADAM10 (significant α-secretases in the brain) to produce soluble TREM2 (sTREM2). Cleavage by γ-secretase produces DAP12, used for signalling [32]. Although some ADAM10 variants have been associated with higher susceptibility to develop the sporadic form of AD [33], a clear genetic correlation of α-secretase haploinsufficiency with clinical symptoms remains to be established; however, variants in TREM2, such as R47H and R62H, have been associated with decreased metabolic function (glycolysis, ATP levels, mTOR activation), leading to a reduction of Aβ toxicity and Aβ plaque formation [32]. Caucasian individuals carrying the R47H variant have an odds ratio for the development of AD similar to that of individuals carrying the significantly more common APOE4 allele [32].
Myeloid cell surface antigen CD33, expressed on the surface of microglial cells, along with CR1 encoding complement receptor 1 (CR1), could play a role in AD by modulating microglial activation [34]. CR1 dysfunction decreases amyloid clearance and leads to improper activation of C3b, leading to synaptic damage [35]. CR1 has 4 alleles, with the second allele (CR1*2) increasing AD risk by 30% [35]. 
GWAS also identified CD2 associated protein (CD2AP), encoding for a neuronal protein essential for signal transduction and cytoskeleton function, as a gene highly associated with the sporadic form of AD (p = 1.70 × 10−17) [20]. The SNP rs9296559 in CD2AP was found to be strongly associated with CSF p-tau and t-tau levels in early stages of AD [36]. In addition, CD2AP expression was reported to be strongly positively associated with the Braak neurofibrillary stage [37], confirming a role for this gene in AD pathogenesis.
While GWAS have been essential in establishing the foundational genetic architecture of AD, the prioritization of genes identified in these GWAS take into account the regulation of AD susceptibility, candidate causal genes related to the underlying molecular pathways of AD, network analysis, quantitative trait loci, and AD pathophysiology [5][26][27]. Table 2 reports the ranking of key genes discussed above and reinforces the importance of prioritizing genes that modulate AD susceptibility tested in peripheral tissues.

3. RNA Regulation

RNA regulation, including of non-coding RNAs, plays an essential role in the modulation of the transcriptome in healthy and pathophysiological contexts. In neurodegenerative diseases such as AD, understanding this context may lead to new avenues of diagnosis and therapy. Three classes of non-coding RNA, miRNAs, circRNAs and lncRNAs, have recently gained greater attention, particularly for their regulatory functions in multifactorial diseases including AD pathogenesis [38][39].

3.1. Micro-RNAs

MiRNAs are small non-coding RNAs, 18–25 nucleotides long, able to bind to complementary sequence elements in the messenger RNA (mRNA) of protein-coding genes (“target genes”). This binding, specifically occurring in the 3′ untranslated region, may result in translational repression or degradation of target messenger RNAs (mRNAs) via the recruitment and action of micro-ribonucleoprotein complexes [40]. Alterations in miRNA expression profiles have been reported in AD pathogenesis, impacting key AD-associated pathways and processes, including both amyloid and tau pathologies.

3.2. Emerging Regulators of the Transcriptome: circRNAs and lncRNAs

CircRNAs are a type of noncoding RNA characterised by a covalent closed-loop structure that differentiates them from other noncoding RNAs, such as lncRNAs and miRNAs. Over 180,000 circRNAs were recently found to be present in human transcriptomes, with their expression being associated with both healthy and pathological conditions [41][42]. Although still being explored, the role of circRNA can occur by acting as miRNAs or RNA binding protein sponges to regulate target gene expression, regulating gene splicing, and acting as templates for protein translation in multiple biological processes [43][44]. Due to the tendency for circRNAs to accumulate during healthy brain aging, they may be considered to be suitable markers or treatment candidates for age-related neurodegenerative diseases, such as AD [45].
CircRNAs can affect the expression of many protein-coding genes including APP and BACE1, which are involved in the regulation of amyloid production. For example, the expression of circRNA ciRS-7, which originates from cerebellar degeneration-related protein 1 antisense transcript (CDR1AS), leads to the activation of Ubiquitin C-Terminal Hydrolase L1 (UCHL1), which then promotes APP and BACE1 degradation, consequently leading to impeded amyloid plaque growth [46]. 
Although some studies have identified a six-circRNA panel that differentiated between AD patients and controls, with the potential ability to discriminate between AD patients and other types of dementias [47], a limited number of studies have explored circRNAs in peripheral tissues in the context of AD. The expression of both circ_0003391 and circ_HAUS4 were positively correlated with several cognitive tests including MMSE, while the expression of circ_GPHN and circ_AXL were negatively correlated with MMSE [48][49]. 
Another subclass of non-coding RNAs, lncRNAs, have also emerged recently as significant regulators of the transcriptome. Over 50,000 human lncRNAs have currently been identified [50]. These molecules have at least 200 nucleotides and can interact with DNA, RNA, and RNA-binding proteins at the transcriptional, post-transcriptional, and post-translational level [51].

4. Summary

Pre-clinical use of genetic testing can provide an early diagnostic tool for AD, investigating not only the key hallmarks of AD (as often tested via invasive CSF lumbar puncture procedures and expensive, multi-day PET scan procedures to visualize early abnormal protein accumulations), but also the subtle pathological drivers of AD, including microglial dysfunction, deficient repair and clearance or damaged cells, dysfunctional complement activation and impaired cerebral vasculature. Taken as a whole, preliminary genetic testing using low-cost and non-invasive peripheral sampling, including blood and saliva, can provide a more comprehensive picture of these early stages of AD. This is critical, as novel frontline treatments for AD currently function to decrease and subdue the formation of key AD pathologies (such as amyloid-beta plaques and tau neurofibrillary tangles) but cannot repair any damage already done by this devastating pathology.
Many of the genes identified via genetic studies and discussed here are involved in more than one pathway underlying AD pathophysiology (such as APOE, PICALM, CLU, CD2AP, TREM2, BIN1), supporting the fact that disease and therapy should be considered convergently instead of in isolation. A multi-genic approach, along with the assessment of additional risk factors involved with these genes, must be considered during the development of a potential diagnostic tool. This report also highlights the significance of RNA molecules regulating the transcriptome, particularly related to expression of genes involved in AD pathophysiology. Due to their effects on target genes in AD pathways, miRNAs have been highly studied in the last decade for their diagnostic and therapeutic potential [52]. Although miRNAs are very targeted molecules, they need to pass through the brain–blood barrier to effect gene expression in the brain and only a low number can get through even if administered in high doses systemically. Consequently, their major potential may be for diagnostic purposes. Considering that all RNA molecules are present in both brain and peripheral tissues (CSF, blood), they have the potential to assist AD prognosis.

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Subjects: Neurosciences
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Danelda Theron
,
Lloyd N. Hopkins
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Heidi G. Sutherland
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Lyn R. Griffiths
,
Francesca Fernandez
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Update Date: 14 Sep 2023
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