Predictive Analysis of Brain-Derived Neurotrophic Factor and Apolipoprotein E SNPs in Alzheimer’s Pathogenesis

Predictive Analysis of Brain-Derived Neurotrophic Factor and Apolipoprotein E SNPs in Alzheimer’s Pathogenesis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictive Analysis of Brain-Derived Neurotrophic Factor and Apolipoprotein E SNPs in Alzheimer’s Pathogenesis Muralidhara Nitheesh Beliraya, Sandeep Mallya, Sudharshan Prabhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8893939/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by memory impairment, cognitive decline, and behavioral changes. Genetic susceptibility plays a central role in its development, particularly variants in the apolipoprotein E ( APOE ) gene and neurotrophic regulators such as brain-derived neurotrophic factor ( BDNF ). Nonsynonymous single-nucleotide polymorphisms (nsSNPs) in these genes can alter protein structure and function, potentially contributing to disease progression. This study used computational methods to evaluate the functional and structural consequences of nsSNPs in BDNF and APOE . A total of 3,590 SNPs in BDNF and 27,830 SNPs in APOE were retrieved from the dbSNP database. After filtering for coding-region variants with minor allele frequency ≥ 0.001, 33 BDNF and 95 APOE nsSNPs were selected for further analysis. Pathogenicity predictions were performed using SIFT and PolyPhen-2, while functional impact was assessed using CADD scores. Protein stability changes were analyzed with MUpro and I-Mutant, and potential post-translational modification sites were evaluated using GPS-based prediction. Secondary structure alterations were examined using GOR4, and three-dimensional models were generated through SWISS-MODEL and validated by Ramachandran plot analysis. Several variants, including rs1048218 ( BDNF Q75H), rs7412 ( APOE R176C), and rs769455 ( APOE R163C), showed consistent damaging predictions across multiple tools. Stability analysis indicated marked destabilization for rs1048218 and rs7412, whereas rs769455 showed variable predictions. Structural modeling suggested localized conformational changes without major disruption of the overall fold. These findings suggest that specific nsSNPs in BDNF and APOE may influence protein behavior and contribute to AD pathology. Experimental validation will be necessary to confirm their biological relevance. APOE BDNF nsSNPs single nucleotide polymorphisms protein stability computational biology pathogenicity prediction Alzheimer’s disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Alzheimer's disease (AD) is a neurodegenerative disorder characterized by damage to nerve cells in the brain. It is the most common type of dementia, primarily affecting individuals aged 60 and above ( 1 ). The key pathological hallmarks of AD include the accumulation of amyloid-ß (Aß) plaques and tau-containing neurofibrillary tangles (NFTs) ( 2 ). These changes lead to memory loss, neuronal dysfunction, cognitive decline, impaired verbal communication, neuroinflammation, and chronic neuronal loss ( 3 ). On the basis of symptoms, the progression of AD is typically categorized into three stages: early, intermediate, and late ( 4 , 5 ). Genetic factors significantly contribute to AD development, with brain-derived neurotrophic factor ( BDNF ) and apolipoprotein E ( APOE ) being considered two of the major genetic determinants ( 6 ). Although the precise mechanism underlying AD pathogenesis remains unclear, early-onset AD is often associated with mutations in the genes encoding amyloid precursor protein (APP), presenilin 1 (PSEN1), and presenilin 2 (PSEN2) ( 7 ). In contrast, APOE is a major genetic contributor to late-onset AD, accounting for approximately 95% of cases. The human APOE gene exists in three alleles, ε2 ( APOE 2), ε3 ( APOE 3), and ε4 ( APOE 4), with the ε4 allele identified as the strongest genetic risk factor for AD ( 8 ). BDNF is a neurotrophin that supports neuronal survival, differentiation, morphology, development and synaptic remodeling. Human BDNF is located on chromosome 11p14.1 ( 9 ). Studies suggest that reduced levels of BDNF are associated with Aβ accumulation, neuroinflammation, neuronal cell death, and tau phosphorylation. However, the exact mechanism by which BDNF is disrupted in AD remains unclear ( 10 ). APOE , on the other hand, is synthesized primarily by the liver and macrophages in peripheral tissues, where it plays a critical role in lipid transport and homeostasis ( 11 ). In the central nervous system (CNS), astrocytes and microglia are the main sources of APOE . During synaptic plasticity and membrane repair, APOE facilitates the delivery of cholesterol and lipids to neurons through APOE receptors. Even a single amino acid substitution can alter the binding affinity of APOE isoforms to receptors, lipids, and Aβ, influencing Aβ accumulation and distribution ( 12 ) . Single-nucleotide polymorphisms (SNPs) are the most common genetic variations in humans and are often implicated in a range of complex diseases. Among them, nonsynonymous single-nucleotide polymorphisms (nsSNPs) are critical, as they result in amino acid substitutions that can alter protein function and structure (13). These changes may affect enzyme activity, disrupt transcription factor binding and ultimately impair gene expression, contributing to various genetic disorders ( 1 ). Despite increasing genetic evidence, the specific functional consequences of nsSNPs in BDNF and APOE remain incompletely understood. To bridge this knowledge gap, in silico approaches provide a rapid and cost-effective method to predict the pathogenic potential and structural impact of these variants. This study employs a range of computational tools to analyze nsSNPs in the human BDNF and APOE genes and evaluate their potential influence on protein function and stability. Materials and Methods Data retrieval and SNP selection SNPs related to the BDNF and APOE genes were collected from the NCBI dbSNP database using a gene-focused search strategy ( 14 ). The objective of this targeted retrieval was to focus on genes that have strong genetic evidence linking them to AD susceptibility. Following data collection, a systematic filtering process was applied. Variants were restricted to coding-region polymorphisms and only nonsynonymous (missense) variants were retained. Additionally, a minor allele frequency threshold of ≥ 0.001 was applied to ensure inclusion of common variants with potential biological relevance. Intronic, synonymous, and low-frequency variants were removed from further analysis. Following systematic filtering and application of the inclusion and exclusion criteria, a refined dataset was obtained for comprehensive analysis. Each SNP entry included the SNP ID, chromosomal location, nucleotide substitution and resulting amino acid change. The overall workflow followed for variant retrieval, filtering, and analysis is illustrated in Fig. 1 . Functional and structural prioritization The retained variants were initially screened for potential functional impact using SIFT and PolyPhen-2 prediction tools. Only variants predicted to be damaging by both algorithms were prioritized for downstream structural and functional analyses to maintain analytical stringency. Computational prediction analyses Functional impact prediction The Combined Annotation Dependent Depletion (CADD) framework was employed to evaluate the functional consequences of the identified nsSNPs ( 15 ). PHRED-scaled scores were interpreted according to established guidelines: scores ≥ 10 represent the 10% most deleterious possible substitutions, scores ≥ 20 indicate the 1% most deleterious, and scores ≥ 30 represent the 0.1% most deleterious variants genome-wide. We considered all variants above a score of 20 for further analysis. Prediction of variant pathogenicity Sequence-based prediction tools were employed to evaluate the potential pathogenicity of the identified nsSNPs. The Sorting Intolerant From Tolerant (SIFT) algorithm was first applied to determine the functional impact of amino-acid substitutions based on evolutionary conservation ( 16 ). Variants exhibiting SIFT scores < 0.05 were classified as deleterious, whereas those with scores ≥ 0.05 were considered tolerated. Further to complement this analysis, the Polymorphism Phenotyping v2 (PolyPhen-2) tool was used to assess the possible structural and functional consequences of each mutation ( 17 ). PolyPhen-2 classifies variants along a probability scale from 0.00 to 1.00, where values between 0.00-0.15 indicate benign substitutions, 0.15–0.85 indicate possibly damaging effects, and 0.85-1.00 represent probably damaging variants. To improve prediction reliability, a consensus-based selection strategy was adopted. Only variants predicted to be deleterious by SIFT and simultaneously categorized as likely or probably damaging by PolyPhen-2 were retained for further downstream computational analyses. This filtering step ensured that subsequent structural and functional characterization focused on high-confidence pathogenic candidates. Structural and functional analysis Protein stability analysis Thermodynamic stability changes resulting from amino acid substitutions were evaluated using two complementary sequence-based prediction tools, namely, MUpro and I-Mutant ( 18 , 19 ). Both of these tools calculate Gibbs free energy changes (ΔΔG), where negative values indicate decreased stability and positive values suggest increased stability relative to that of wild-type proteins. Posttranslational modification analysis Group-based prediction system (GPS) tool was used to identify potential alterations in phosphorylation patterns ( 20 ). Both the wild-type and variant protein sequences of BDNF and APOE were analyzed under the CK1 kinase model to detect possible modifications in terms of phosphorylation site availability and kinase specificity. Secondary structure prediction The Garnier-Osguthorpe-Robson version 4 (GOR4) algorithm was utilized to predict secondary structure elements, categorizing amino acid residues into α-helices, β-strands, and random coils on the basis of primary sequence information ( 21 ). Comparative analysis between the wild-type and variant sequences was performed to detect mutation-induced alterations in secondary structure composition. Three-dimensional structural modeling and validation Three-dimensional protein structures for identified variants were generated using the SWISS-MODEL homology modeling platform ( 22 ). The models with the highest global model quality estimation (GMQE) score were selected to ensure reliability. Structural validation was performed through Ramachandran plot analysis to assess the backbone dihedral angle distributions and overall stereochemical quality. All the generated mutant models demonstrated acceptable structural quality on the basis of comprehensive MolProbity assessments, including favorable overall MolProbity scores and minimal steric clash scores, thereby supporting the reliability of the subsequent structural predictions and analyses. RESULTS Functional impact evaluation via CADD scoring Combined Annotation Dependent Depletion (CADD) analysis identified high-impact variants with PHRED scores ≥ 20. Ten BDNF variants presented scores ranging from 20.4 to 31.0, whereas 12 APOE variants presented scores between 23.3 and 56.0, indicating a substantial likelihood of deleterious functional consequences (Table 1 ). Table 1 High-impact variants with CADD PHRED scores ≥ 20. Gene rsIDs Ref Alt Raw Score PHRED BDNF rs1049779568 T G 5.3057 29.8 rs1590216799 T G 5.3686 31 rs1048218 C A 3.3057 22.1 rs139352447 G C 2.8056 20.4 rs6265 C A 3.1638 21.6 rs6265 C G 3.1699 21.6 rs6265 C T 4.1758 24.5 rs8192466 G A 3.8198 23.6 rs8192466 G T 3.3397 22.2 rs8192466 G C 2.9853 21 APOE rs752693941 G T 12.0308 56 rs769455 C T 5.1062 28.6 rs121918393 C T 4.9265 27.5 rs121918393 C A 4.2803 24.8 rs7412 C T 4.3721 25.1 rs868094551 C A 3.9388 23.9 rs767339630 G A 3.924 23.8 rs199768005 T A 3.9103 23.8 rs573658040 C T 3.6962 23.3 rs1599954391 T G 3.8682 23.7 rs140808909 G T 7.9369 36 rs557715042 G A 8.7425 38 Identification and pathogenicity prediction of non-synonymous SNPs Comprehensive database mining of dbSNP retrieved 3,590 SNPs from the BDNF gene and 27,830 SNPs from the APOE gene. Initial filtering identified 505 nonsynonymous SNPs (nsSNPs) in BDNF and 375 nsSNPs in APOE . The application of stringent inclusion criteria (coding region localization, minor allele frequency ≥ 0.001, and nonsynonymous variants) yielded a refined dataset of 33 BDNF nsSNPs and 95 APOE nsSNPs for comprehensive computational analysis. Consensus pathogenicity predictions SIFT analysis predicted 4 nsSNPs in BDNF and 17 in APOE as deleterious (score < 0.05), whereas PolyPhen-2 classified 3 BDNF variants and 4 APOE variants as probably damaging (score ≥ 0.85). Critically, three variants demonstrated concordant predictions across both algorithms: rs1048218 in BDNF and rs7412 and rs769455 in APOE (Table 2 ). The BDNF variant rs1048218 (Q75H) exhibited moderate deleteriousness (SIFT: 0.041) with a high damage probability (PolyPhen-2: 0.956). Both APOE variants presented maximal PolyPhen-2 scores (1.000) and very low SIFT scores, indicating that strong pathogenic potential affects critical arginine residues involved in lipid binding. Table 2 Consensus pathogenicity predictions for high-confidence variants. Gene SNP ID Amino Acid Change SIFT Score SIFT Prediction PolyPhen-2 Score PolyPhen-2 Prediction BDNF rs1048218 Q75H 0.041 Deleterious 0.956 Probably damaging APOE rs7412 R176C 0.001 Deleterious 1.000 Probably damaging APOE rs769455 R163C 0.0080 Deleterious 1.000 Probably damaging Protein stability analysis Thermodynamic stability assessment using MUpro and I-Mutant 2.0 revealed predominantly destabilizing effects for the consensus variants (Table 3 ). Both tools concordantly indicated substantial destabilization for rs1048218 ( BDNF , Q75H), with I-Mutant predicting a more pronounced effect (ΔΔG = -2.08 kcal/mol). For APOE rs7412 (R176C), both predictors suggested destabilization, although the effect was modest in the I-Mutant group (ΔΔG = -0.07 kcal/mol), indicating a likely mild reduction in protein stability. In contrast, APOE rs769455 (R163C) yielded conflicting predictions, suggesting marginal structural perturbations. Taken together, these findings suggest strong destabilization for BDNF Q75H, moderate destabilization for APOE R176C, and an overall neutral to marginal effect for APOE R163C. Table 3 Protein stability predictions for consensus variants. Gene RSID Mutation Tool ΔΔG (kcal/mol) Prediction (per tool) Consensus Interpretation BDNF rs1048218 Q75H MUpro -1.001 Decrease Stability Destabilizing (strong evidence, both tools agree) I-Mutant 2.0 -2.08 Decrease Stability APOE rs7412 R176C MUpro -0.859 Decrease Stability Likely destabilizing (moderate evidence, mild effect in I-Mutant) I-Mutant 2.0 -0.07 Decrease Stability rs769455 R163C MUpro -0.76 Decrease Stability Uncertain/likely neutral (predictions conflict, both near zero) I-Mutant 2.0 0.08 Increase Stability Post-translational modification analysis Phosphorylation site predictions via GPS-MSP demonstrated remarkable conservation of regulatory sites across wild-type and variant sequences (Table 4 ). All identified phosphorylation sites remained functionally intact across variants, with prediction scores consistently exceeding threshold cutoffs. This conservation suggests minimal disruption of posttranslational regulatory mechanisms. Table 4 Phosphorylation site conservation analysis. Gene Sequence Type Position Residue Kinase Score Status BDNF Wild-type/rs1048218 39 T CK1 0.203 Conserved 123 S CK1 0.183 Conserved 130 S AGC/CK1 0.135/0.176 Conserved 145 S CK1 0.116 Conserved APOE Wild-type/variants 40 S CK1 0.156 Conserved Secondary structure impact assessment Secondary structure analysis via GOR4 revealed minimal but consistent structural perturbations (Table 5 ). All the variants presented minimal α-helix reduction with compensatory increases in random coil content. Critically, no novel secondary structure elements emerged, suggesting preservation of the fundamental protein architecture. Table 5 Results of secondary structure prediction of the wild type and nsSNPs of the BDNF and APOE genes. Gene Structure Element Wild-type Variant Change (%) BDNF (rs1048218) α-helix 50 (20.24%) 48 (19.43%) -0.81 Extended strand 73 (29.55%) 73 (29.55%) 0 Random coil 124 (50.20%) 126 (51.01%) 0.81 APOE (rs7412) α-helix 259 (81.70%) 257 (81.07%) -0.63 Extended strand 9 (2.84%) 9 (2.84%) 0 Random coil 49 (15.46%) 51 (16.09%) 0.63 APOE (rs769455) α-helix 259 (81.70%) 258 (81.39%) -0.31 Extended strand 9 (2.84%) 9 (2.84%) 0 Random coil 49 (15.46%) 50 (15.77%) 0.31 Visual analysis of secondary structure distributions (Fig. 2 ) corroborated these quantitative findings. Wild-type BDNF (Fig. 2 A) displayed characteristic α-helical (red), β-sheet (blue), and coil (magenta) distributions indicative of stable neurotrophin architecture. The rs1048218 variant (Fig. 2 B) presented localized perturbations in the helix and coil regions while maintaining overall structural integrity. Similarly, wild-type APOE (Fig. 2 C) exhibited the expected predominant α-helical conformation typical of apolipoproteins. Variants rs7412 (Fig. 2 D) and rs769455 (Fig. 2 E) demonstrated subtle alterations in helical and coil compositions without introducing novel structural elements, confirming preservation of the fundamental apolipoprotein fold. All the models achieved acceptable quality metrics, with MolProbity scores ≤ 1.39 indicating reliable structural predictions. APOE variants demonstrated superior stereochemical quality with minimal Ramachandran outliers (< 1%), whereas the BDNF variant presented slightly elevated outlier percentages (4.08%), suggesting localized conformational strain. The mutation is highlighted in magenta, highlighting its spatial location within the structure (Fig. 3 A-B). The preserved structure suggests that these mutations are unlikely to disrupt overall protein folding. Ramachandran plot analysis further confirmed the structural reliability of the models (Fig. 4 B and 4 C). The results of the Ramachandran plot analysis for the BDNF and APOE mutants are shown in Table 6 . Table 6 Results of structure validation via Ramachandran plots for the BDNF and APOE gene mutants Gene rsID MolProbity score Clash score Ramachandran favored (%) Ramachandran outliers (%) BDNF rs1048218 1.36 0.00 91.02% 4.08% APOE rs7412 1.39 1.38 95.56% 0.63% APOE rs769455 1.39 1.38 95.56% 0.63% Ramachandran plot analysis confirmed the overall structural integrity, with the majority of residues occupying the favored conformational space (Fig. 4 A-C). The elevated outlier percentage in BDNF rs1048218 indicates potential local backbone perturbations that may influence protein dynamics without compromising global fold stability. Discussion This comprehensive computational analysis identified three high-confidence pathogenic variants-rs1048218 ( BDNF ), rs7412 ( APOE ), and rs769455 ( APOE )—with potential implications for AD pathogenesis. Convergent evidence from multiple prediction algorithms provides mechanistic insights into how these variants may contribute to neurodegeneration through distinct molecular pathways. Functional implications of BDNF rs1048218 The BDNF variant rs1048218 (Q75H) demonstrated consistent destabilizing effects across stability prediction algorithms (ΔΔG: -1.001 to -2.08 kcal/mol), indicating significant perturbation of the neurotrophin fold architecture. The glutamine-to-histidine substitution introduces a positively charged, bulkier residue that likely disrupts local hydrogen bonding networks critical for protein stability. The observed α-helix reduction and compensatory random coil increase support this interpretation, suggesting that localized structural perturbations that may impair BDNF-receptor interactions are essential for synaptic plasticity and neuronal survival. Previous studies have reported variable associations between rs1048218 and AD risk across different populations, although evidence remains limited and population-dependent ( 23 ). Our computational analysis provides mechanistic support for these associations by demonstrating how this variant may compromise BDNF 's neuroprotective signaling cascade. However, the moderate CADD scores (PHRED: 31.0) suggest subtle effects that may require additional genetic or environmental factors for phenotypic manifestation, which is consistent with variable population-specific associations. APOE variants and lipid metabolism disruption The rs7412 variant, which defines the APOE ε2 allele, presents an intriguing computational paradox. Despite consistent pathogenicity predictions (SIFT: 0.001, PolyPhen-2: 1.000), epidemiological evidence has demonstrated reduced Alzheimer's disease risk in ε2 carriers compared with ε4 carriers ( 24 ). This apparent contradiction may reflect complex relationships between protein stability and biological function. The arginine-to-cysteine substitution at position 176 occurs within the lipid-binding domain, potentially altering lipid cargo specificity rather than eliminating function. The predicted destabilization may increase APOE turnover or modify amyloid-β interactions in ways that reduce pathological aggregation, which aligns with protective epidemiological observations. The rs769455 variant exhibited conflicting stability predictions between algorithms, reflecting the subtle nature of R163C substitution effects. Located near the lipid-binding domain, this variant likely modulates lipid‒cargo interactions without drastically altering the overall structure. Few studies have identified rs769455 as a population-specific risk modifier, although its low prevalence has constrained comprehensive analysis ( 25 ). The exceptional CADD scores observed for some APOE variants (PHRED: 56.0) underscore the critical importance of intact apolipoprotein function in neurological health. Structural resilience and therapeutic implications The conservation of phosphorylation sites across all variants indicates that these mutations primarily affect protein structure rather than regulatory control mechanisms. This finding has important therapeutic implications, suggesting that interventions targeting posttranslational modifications may remain effective regardless of variant status. For BDNF , preserved CK1 and AGC kinase sites indicate normal upstream signaling pathway function, suggesting that therapeutic strategies enhancing BDNF expression or receptor sensitivity may compensate for structural deficits. The maintenance of overall protein folds despite predicted destabilization suggests considerable structural resilience in both proteins. This resilience may explain why these variants exist in populations without severe developmental consequences while contributing to late-onset disease susceptibility. The subtle secondary structure alterations (α-helix to random coil transitions) may represent adaptive flexibility, allowing proteins to maintain essential functions while altering specialized activities relevant to neurodegeneration. Conclusion Advances in genomic research have greatly improved our understanding of the genetic factors involved in AD. However, although many variants are catalogued in public databases, their exact biological effects at the protein level remain unclear. Computational approaches provide a practical strategy to narrow down large genetic datasets and identify variants that are most likely to influence disease mechanisms. In the present study, sequence and structure based bioinformatic analyses were performed to evaluate nonsynonymous variants in the BDNF and APOE genes. Three high-confidence variants of potential pathogenic relevance, namely, rs1048218 in BDNF and rs7412 and rs769455 in APOE , that may contribute to AD pathogenesis. These variants showed signs of reduced protein stability and minor structural disturbances while largely preserving the overall protein fold and regulatory features. Such changes may influence neurotrophin signaling and lipid metabolism pathways, both of which are important in Alzheimer’s disease progression. Although computational analyses are valuable for prioritizing candidate variants and reducing experimental workload, they cannot fully represent biological complexity. Differences among prediction algorithms and the absence of population-specific analysis limit the direct clinical interpretation of the results. Therefore, experimental validation using biochemical assays, expression studies, and clinical correlation will be necessary to confirm their pathogenic relevance. Overall, the identified variants provide useful starting points for future functional investigations. Further studies integrating laboratory validation and population genetics will help clarify their roles in disease development and may support the design of targeted therapeutic strategies for AD. Declarations Author contributions MNB: literature review, interpretation of data, original drafting of the manuscript, revision of the manuscript; SM: data compilation, interpretation of data, original drafting of the manuscript, revision of the manuscript; SP: study conception, literature review, critical revision of the manuscript. Funding declaration This work was supported by the Indian Council of Medical Research (Sanction number 54/8/GER/2019-NCD-II), DBT-BUILDER (BT/INF/22/SP43065/2021), Govt. of India, Manipal Research Board (MRB) Grant, and MAHE Seed Money Grant. Declaration of competing interest The authors declare that they have no competing interests. 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BMC Bioinformatics 12:1–9 Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, Heer FT, de Beer TA, Rempfer C, Bordoli L, Lepore R (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46:296–303 Lin WJ, Salton SR (2013) The Regulated Secretory Pathway and Human Disease: Insights from Gene Variants and Single Nucleotide Polymorphisms. Front Endocrinol 4:96 Serrano-Pozo A, Das S, Hyman BT (2021) APOE and Alzheimer’s disease: advances in genetics, pathophysiology, and therapeutic approaches. Lancet Neurol 20:68–80 Lord J, Lu AJ, Cruchaga C (2014) Identification of rare variants in Alzheimer’s disease. Front Genet 5:369 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8893939","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600145866,"identity":"4ac2b250-3460-47f9-892e-8e8362e7f7ee","order_by":0,"name":"Muralidhara Nitheesh Beliraya","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Muralidhara","middleName":"Nitheesh","lastName":"Beliraya","suffix":""},{"id":600145867,"identity":"fd18cbe6-37de-4fec-b969-1351866a41dc","order_by":1,"name":"Sandeep Mallya","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Sandeep","middleName":"","lastName":"Mallya","suffix":""},{"id":600145868,"identity":"43adb46e-3284-4421-8bf9-a92d0e2fc6fb","order_by":2,"name":"Sudharshan Prabhu","email":"data:image/png;base64,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","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":true,"prefix":"","firstName":"Sudharshan","middleName":"","lastName":"Prabhu","suffix":""}],"badges":[],"createdAt":"2026-02-16 14:25:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8893939/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8893939/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103932188,"identity":"1e7ea69d-3aef-4aad-8fcc-4b9af5352248","added_by":"auto","created_at":"2026-03-04 16:51:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":364483,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow for SNP retrieval, inclusion, and analysis using in silico approaches.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8893939/v1/1c8f3ecc895a632aae459224.png"},{"id":103932190,"identity":"2906b2b7-77f5-4f17-8d9e-c948e4e29138","added_by":"auto","created_at":"2026-03-04 16:51:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1777111,"visible":true,"origin":"","legend":"\u003cp\u003eSecondary structure prediction profiles generated by the GOR4 algorithm for wild-type and variant proteins. The plots display secondary structure probability distributions across the protein sequence (the x-axis represents the amino acid position), with colored lines indicating different structural elements: red (α-helix), blue (β-sheet), and magenta (random coil). \u003cstrong\u003e(A)\u003c/strong\u003e Wild-type BDNF protein showing a characteristic neurotrophin secondary structure distribution with balanced α-helical and β-sheet regions interspersed with flexible coil regions. \u003cstrong\u003e(B)\u003c/strong\u003e \u003cem\u003eBDNF\u003c/em\u003e rs1048218 variant demonstrating minimal perturbations in secondary structure propensities, with subtle shifts in the α-helix and compensatory increases in random coil regions while maintaining overall structural architecture. \u003cstrong\u003e(C)\u003c/strong\u003e Wild-type APOE protein exhibiting the characteristic apolipoprotein secondary structure profile dominated by α-helical content with minimal β-sheet structure. \u003cstrong\u003e(D)\u003c/strong\u003e APOE rs7412 variant showing conserved secondary structure elements with minor α-helix reduction and slight random coil increase. \u003cstrong\u003e(E)\u003c/strong\u003e The APOE rs769455 variant displayssimilar conservation patterns, with minimal α-helix reduction and modest random coil increase.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8893939/v1/18a33f0314594babe1d66b08.png"},{"id":103932191,"identity":"5cba59c4-e116-4ca3-ae2a-01d8c57b2012","added_by":"auto","created_at":"2026-03-04 16:51:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":172178,"visible":true,"origin":"","legend":"\u003cp\u003eHomology-based three-dimensional structural models of pathogenic variants in the BDNF and APOE proteins. The proteins are represented as ribbon diagrams in the rainbow (blue to red) scheme from the N-terminal to the C-terminal. (\u003cstrong\u003eA)\u003c/strong\u003e BDNF mutant structure with α-helices and β-strands and the mutation site colored magenta, suggesting possible local folding changes.\u003cstrong\u003e (B)\u003c/strong\u003e APOE mutant structure with the mutation sites shown in magenta within α-helical-rich domains, suggesting a potential impact on folding, spatial organization, and overall protein stability and function.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8893939/v1/f13cc4e4e2d45491dbb309e6.png"},{"id":103932189,"identity":"b1a6f0f6-d9e3-4881-9ef8-e0680dc7c148","added_by":"auto","created_at":"2026-03-04 16:51:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":238383,"visible":true,"origin":"","legend":"\u003cp\u003eRamachandran plot validation of three-dimensional structural models for BDNF and APOE variants. Plots display phi (φ) and psi (ψ) backbone dihedral angles for all amino acid residues, with contoured regions indicating sterically allowed conformations. (\u003cstrong\u003eA)\u003c/strong\u003e The rs1048218 variant of the \u003cem\u003eBDNF\u003c/em\u003e gene shows residues falling within favored and allowed regions with a slightly higher percentage of outliers. (\u003cstrong\u003eB)\u003c/strong\u003e The rs7412 variant of the \u003cem\u003eAPOE\u003c/em\u003e gene shows a high percentage of residues in the favored and allowed regions. Variantsshowing no significant outliers. (\u003cstrong\u003eC) \u003c/strong\u003eThe rs769455 variant of the \u003cem\u003eAPOE\u003c/em\u003e gene shows a high percentage of residues in the favored and allowed regions. All the models meet acceptable quality thresholds for structural reliability (MolProbity scores ≤1.39).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8893939/v1/3d85d4876ba154451d8e3578.png"},{"id":108219818,"identity":"e34ed2de-17af-467c-b802-da5078cf55f8","added_by":"auto","created_at":"2026-04-30 15:10:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3270677,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8893939/v1/f084a285-176b-4ee8-b922-fab118c6a93c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Analysis of Brain-Derived Neurotrophic Factor and Apolipoprotein E SNPs in Alzheimer’s Pathogenesis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer's disease (AD) is a neurodegenerative disorder characterized by damage to nerve cells in the brain. It is the most common type of dementia, primarily affecting individuals aged 60 and above (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The key pathological hallmarks of AD include the accumulation of amyloid-\u0026szlig; (A\u0026szlig;) plaques and tau-containing neurofibrillary tangles (NFTs) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These changes lead to memory loss, neuronal dysfunction, cognitive decline, impaired verbal communication, neuroinflammation, and chronic neuronal loss (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). On the basis of symptoms, the progression of AD is typically categorized into three stages: early, intermediate, and late (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenetic factors significantly contribute to AD development, with brain-derived neurotrophic factor (\u003cem\u003eBDNF\u003c/em\u003e) and apolipoprotein E (\u003cem\u003eAPOE\u003c/em\u003e) being considered two of the major genetic determinants (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Although the precise mechanism underlying AD pathogenesis remains unclear, early-onset AD is often associated with mutations in the genes encoding amyloid precursor protein (APP), presenilin 1 (PSEN1), and presenilin 2 (PSEN2) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In contrast, \u003cem\u003eAPOE\u003c/em\u003e is a major genetic contributor to late-onset AD, accounting for approximately 95% of cases. The human \u003cem\u003eAPOE\u003c/em\u003e gene exists in three alleles, ε2 (\u003cem\u003eAPOE\u003c/em\u003e2), ε3 (\u003cem\u003eAPOE\u003c/em\u003e3), and ε4 (\u003cem\u003eAPOE\u003c/em\u003e4), with the ε4 allele identified as the strongest genetic risk factor for AD (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eBDNF\u003c/em\u003e is a neurotrophin that supports neuronal survival, differentiation, morphology, development and synaptic remodeling. Human \u003cem\u003eBDNF\u003c/em\u003e is located on chromosome 11p14.1 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Studies suggest that reduced levels of \u003cem\u003eBDNF\u003c/em\u003e are associated with Aβ accumulation, neuroinflammation, neuronal cell death, and tau phosphorylation. However, the exact mechanism by which \u003cem\u003eBDNF\u003c/em\u003e is disrupted in AD remains unclear (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). \u003cem\u003eAPOE\u003c/em\u003e, on the other hand, is synthesized primarily by the liver and macrophages in peripheral tissues, where it plays a critical role in lipid transport and homeostasis (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In the central nervous system (CNS), astrocytes and microglia are the main sources of \u003cem\u003eAPOE\u003c/em\u003e. During synaptic plasticity and membrane repair, \u003cem\u003eAPOE\u003c/em\u003e facilitates the delivery of cholesterol and lipids to neurons through \u003cem\u003eAPOE\u003c/em\u003e receptors. Even a single amino acid substitution can alter the binding affinity of \u003cem\u003eAPOE\u003c/em\u003e isoforms to receptors, lipids, and Aβ, influencing Aβ accumulation and distribution (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eSingle-nucleotide polymorphisms (SNPs) are the most common genetic variations in humans and are often implicated in a range of complex diseases. Among them, nonsynonymous single-nucleotide polymorphisms (nsSNPs) are critical, as they result in amino acid substitutions that can alter protein function and structure (13). These changes may affect enzyme activity, disrupt transcription factor binding and ultimately impair gene expression, contributing to various genetic disorders (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite increasing genetic evidence, the specific functional consequences of nsSNPs in \u003cem\u003eBDNF\u003c/em\u003e and \u003cem\u003eAPOE\u003c/em\u003e remain incompletely understood. To bridge this knowledge gap, \u003cem\u003ein silico\u003c/em\u003e approaches provide a rapid and cost-effective method to predict the pathogenic potential and structural impact of these variants. This study employs a range of computational tools to analyze nsSNPs in the human \u003cem\u003eBDNF\u003c/em\u003e and \u003cem\u003eAPOE\u003c/em\u003e genes and evaluate their potential influence on protein function and stability.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData retrieval and SNP selection\u003c/h2\u003e \u003cp\u003eSNPs related to the \u003cem\u003eBDNF\u003c/em\u003e and \u003cem\u003eAPOE\u003c/em\u003e genes were collected from the NCBI dbSNP database using a gene-focused search strategy (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The objective of this targeted retrieval was to focus on genes that have strong genetic evidence linking them to AD susceptibility. Following data collection, a systematic filtering process was applied. Variants were restricted to coding-region polymorphisms and only nonsynonymous (missense) variants were retained. Additionally, a minor allele frequency threshold of \u0026ge;\u0026thinsp;0.001 was applied to ensure inclusion of common variants with potential biological relevance. Intronic, synonymous, and low-frequency variants were removed from further analysis.\u003c/p\u003e \u003cp\u003eFollowing systematic filtering and application of the inclusion and exclusion criteria, a refined dataset was obtained for comprehensive analysis. Each SNP entry included the SNP ID, chromosomal location, nucleotide substitution and resulting amino acid change. The overall workflow followed for variant retrieval, filtering, and analysis is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunctional and structural prioritization\u003c/h3\u003e\n\u003cp\u003eThe retained variants were initially screened for potential functional impact using SIFT and PolyPhen-2 prediction tools. Only variants predicted to be damaging by both algorithms were prioritized for downstream structural and functional analyses to maintain analytical stringency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eComputational prediction analyses\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunctional impact prediction\u003c/h2\u003e \u003cp\u003eThe Combined Annotation Dependent Depletion (CADD) framework was employed to evaluate the functional consequences of the identified nsSNPs (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). PHRED-scaled scores were interpreted according to established guidelines: scores\u0026thinsp;\u0026ge;\u0026thinsp;10 represent the 10% most deleterious possible substitutions, scores\u0026thinsp;\u0026ge;\u0026thinsp;20 indicate the 1% most deleterious, and scores\u0026thinsp;\u0026ge;\u0026thinsp;30 represent the 0.1% most deleterious variants genome-wide. We considered all variants above a score of 20 for further analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrediction of variant pathogenicity\u003c/h3\u003e\n\u003cp\u003eSequence-based prediction tools were employed to evaluate the potential pathogenicity of the identified nsSNPs. The Sorting Intolerant From Tolerant (SIFT) algorithm was first applied to determine the functional impact of amino-acid substitutions based on evolutionary conservation (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Variants exhibiting SIFT scores\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were classified as deleterious, whereas those with scores\u0026thinsp;\u0026ge;\u0026thinsp;0.05 were considered tolerated. Further to complement this analysis, the Polymorphism Phenotyping v2 (PolyPhen-2) tool was used to assess the possible structural and functional consequences of each mutation (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). PolyPhen-2 classifies variants along a probability scale from 0.00 to 1.00, where values between 0.00-0.15 indicate benign substitutions, 0.15\u0026ndash;0.85 indicate possibly damaging effects, and 0.85-1.00 represent probably damaging variants. To improve prediction reliability, a consensus-based selection strategy was adopted. Only variants predicted to be deleterious by SIFT and simultaneously categorized as likely or probably damaging by PolyPhen-2 were retained for further downstream computational analyses. This filtering step ensured that subsequent structural and functional characterization focused on high-confidence pathogenic candidates.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStructural and functional analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eProtein stability analysis\u003c/h2\u003e \u003cp\u003eThermodynamic stability changes resulting from amino acid substitutions were evaluated using two complementary sequence-based prediction tools, namely, MUpro and I-Mutant (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Both of these tools calculate Gibbs free energy changes (ΔΔG), where negative values indicate decreased stability and positive values suggest increased stability relative to that of wild-type proteins.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003ePosttranslational modification analysis\u003c/h3\u003e\n\u003cp\u003eGroup-based prediction system (GPS) tool was used to identify potential alterations in phosphorylation patterns (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Both the wild-type and variant protein sequences of \u003cem\u003eBDNF\u003c/em\u003e and \u003cem\u003eAPOE\u003c/em\u003e were analyzed under the CK1 kinase model to detect possible modifications in terms of phosphorylation site availability and kinase specificity.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSecondary structure prediction\u003c/h2\u003e \u003cp\u003eThe Garnier-Osguthorpe-Robson version 4 (GOR4) algorithm was utilized to predict secondary structure elements, categorizing amino acid residues into α-helices, β-strands, and random coils on the basis of primary sequence information (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Comparative analysis between the wild-type and variant sequences was performed to detect mutation-induced alterations in secondary structure composition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThree-dimensional structural modeling and validation\u003c/h2\u003e \u003cp\u003eThree-dimensional protein structures for identified variants were generated using the SWISS-MODEL homology modeling platform (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The models with the highest global model quality estimation (GMQE) score were selected to ensure reliability. Structural validation was performed through Ramachandran plot analysis to assess the backbone dihedral angle distributions and overall stereochemical quality.\u003c/p\u003e \u003cp\u003eAll the generated mutant models demonstrated acceptable structural quality on the basis of comprehensive MolProbity assessments, including favorable overall MolProbity scores and minimal steric clash scores, thereby supporting the reliability of the subsequent structural predictions and analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFunctional impact evaluation via CADD scoring\u003c/h2\u003e \u003cp\u003eCombined Annotation Dependent Depletion (CADD) analysis identified high-impact variants with PHRED scores\u0026thinsp;\u0026ge;\u0026thinsp;20. Ten \u003cem\u003eBDNF\u003c/em\u003e variants presented scores ranging from 20.4 to 31.0, whereas 12 \u003cem\u003eAPOE\u003c/em\u003e variants presented scores between 23.3 and 56.0, indicating a substantial likelihood of deleterious functional consequences (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHigh-impact variants with CADD PHRED scores\u0026thinsp;\u0026ge;\u0026thinsp;20.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ersIDs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRaw Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePHRED\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cem\u003eBDNF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1049779568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.3057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1590216799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.3686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1048218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.3057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers139352447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.8056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers6265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.1638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers6265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.1699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers6265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.1758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers8192466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.8198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers8192466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.3397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers8192466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.9853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers752693941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.0308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers769455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.1062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers121918393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.9265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers121918393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.2803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers7412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.3721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers868094551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.9388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers767339630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers199768005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.9103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers573658040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.6962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1599954391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.8682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers140808909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.9369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers557715042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.7425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and pathogenicity prediction of non-synonymous SNPs\u003c/h2\u003e \u003cp\u003eComprehensive database mining of dbSNP retrieved 3,590 SNPs from the \u003cem\u003eBDNF\u003c/em\u003e gene and 27,830 SNPs from the \u003cem\u003eAPOE\u003c/em\u003e gene. Initial filtering identified 505 nonsynonymous SNPs (nsSNPs) in \u003cem\u003eBDNF\u003c/em\u003e and 375 nsSNPs in \u003cem\u003eAPOE\u003c/em\u003e. The application of stringent inclusion criteria (coding region localization, minor allele frequency\u0026thinsp;\u0026ge;\u0026thinsp;0.001, and nonsynonymous variants) yielded a refined dataset of 33 \u003cem\u003eBDNF\u003c/em\u003e nsSNPs and 95 \u003cem\u003eAPOE\u003c/em\u003e nsSNPs for comprehensive computational analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eConsensus pathogenicity predictions\u003c/h2\u003e \u003cp\u003eSIFT analysis predicted 4 nsSNPs in \u003cem\u003eBDNF\u003c/em\u003e and 17 in \u003cem\u003eAPOE\u003c/em\u003e as deleterious (score\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas PolyPhen-2 classified 3 \u003cem\u003eBDNF\u003c/em\u003e variants and 4 \u003cem\u003eAPOE\u003c/em\u003e variants as probably damaging (score\u0026thinsp;\u0026ge;\u0026thinsp;0.85). Critically, three variants demonstrated concordant predictions across both algorithms: rs1048218 in \u003cem\u003eBDNF\u003c/em\u003e and rs7412 and rs769455 in \u003cem\u003eAPOE\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The \u003cem\u003eBDNF\u003c/em\u003e variant rs1048218 (Q75H) exhibited moderate deleteriousness (SIFT: 0.041) with a high damage probability (PolyPhen-2: 0.956). Both \u003cem\u003eAPOE\u003c/em\u003e variants presented maximal PolyPhen-2 scores (1.000) and very low SIFT scores, indicating that strong pathogenic potential affects critical arginine residues involved in lipid binding.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConsensus pathogenicity predictions for high-confidence variants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNP ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmino Acid Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSIFT Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSIFT Prediction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePolyPhen-2 Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePolyPhen-2 Prediction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBDNF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1048218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ75H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProbably damaging\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers7412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR176C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProbably damaging\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers769455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR163C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProbably damaging\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eProtein stability analysis\u003c/h2\u003e \u003cp\u003eThermodynamic stability assessment using MUpro and I-Mutant 2.0 revealed predominantly destabilizing effects for the consensus variants (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Both tools concordantly indicated substantial destabilization for rs1048218 (\u003cem\u003eBDNF\u003c/em\u003e, Q75H), with I-Mutant predicting a more pronounced effect (ΔΔG = -2.08 kcal/mol). For \u003cem\u003eAPOE\u003c/em\u003e rs7412 (R176C), both predictors suggested destabilization, although the effect was modest in the I-Mutant group (ΔΔG = -0.07 kcal/mol), indicating a likely mild reduction in protein stability. In contrast, \u003cem\u003eAPOE\u003c/em\u003e rs769455 (R163C) yielded conflicting predictions, suggesting marginal structural perturbations. Taken together, these findings suggest strong destabilization for \u003cem\u003eBDNF\u003c/em\u003e Q75H, moderate destabilization for \u003cem\u003eAPOE\u003c/em\u003e R176C, and an overall neutral to marginal effect for \u003cem\u003eAPOE\u003c/em\u003e R163C.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProtein stability predictions for consensus variants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRSID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMutation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔΔG (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrediction (per tool)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConsensus Interpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eBDNF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ers1048218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQ75H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMUpro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDecrease Stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDestabilizing (strong evidence, both tools agree)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI-Mutant 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDecrease Stability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ers7412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR176C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMUpro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDecrease Stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLikely destabilizing (moderate evidence, mild effect in I-Mutant)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI-Mutant 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDecrease Stability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ers769455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR163C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMUpro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDecrease Stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUncertain/likely neutral (predictions conflict, both near zero)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI-Mutant 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncrease Stability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePost-translational modification analysis\u003c/h2\u003e \u003cp\u003ePhosphorylation site predictions via GPS-MSP demonstrated remarkable conservation of regulatory sites across wild-type and variant sequences (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). All identified phosphorylation sites remained functionally intact across variants, with prediction scores consistently exceeding threshold cutoffs. This conservation suggests minimal disruption of posttranslational regulatory mechanisms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhosphorylation site conservation analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResidue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKinase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eBDNF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWild-type/rs1048218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConserved\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConserved\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAGC/CK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.135/0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConserved\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConserved\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWild-type/variants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConserved\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSecondary structure impact assessment\u003c/h2\u003e \u003cp\u003eSecondary structure analysis via GOR4 revealed minimal but consistent structural perturbations (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). All the variants presented minimal α-helix reduction with compensatory increases in random coil content. Critically, no novel secondary structure elements emerged, suggesting preservation of the fundamental protein architecture.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of secondary structure prediction of the wild type and nsSNPs of the \u003cem\u003eBDNF\u003c/em\u003e and \u003cem\u003eAPOE\u003c/em\u003e genes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStructure Element\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWild-type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChange (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eBDNF\u003c/em\u003e (rs1048218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα-helix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50 (20.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48 (19.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtended strand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73 (29.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73 (29.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRandom coil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124 (50.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126 (51.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e (rs7412)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα-helix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e259 (81.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e257 (81.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtended strand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (2.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (2.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRandom coil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (15.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51 (16.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e (rs769455)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα-helix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e259 (81.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e258 (81.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtended strand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (2.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (2.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRandom coil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (15.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50 (15.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eVisual analysis of secondary structure distributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) corroborated these quantitative findings. Wild-type \u003cem\u003eBDNF\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) displayed characteristic α-helical (red), β-sheet (blue), and coil (magenta) distributions indicative of stable neurotrophin architecture. The rs1048218 variant (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) presented localized perturbations in the helix and coil regions while maintaining overall structural integrity. Similarly, wild-type \u003cem\u003eAPOE\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) exhibited the expected predominant α-helical conformation typical of apolipoproteins. Variants rs7412 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) and rs769455 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) demonstrated subtle alterations in helical and coil compositions without introducing novel structural elements, confirming preservation of the fundamental apolipoprotein fold.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll the models achieved acceptable quality metrics, with MolProbity scores\u0026thinsp;\u0026le;\u0026thinsp;1.39 indicating reliable structural predictions. \u003cem\u003eAPOE\u003c/em\u003e variants demonstrated superior stereochemical quality with minimal Ramachandran outliers (\u0026lt;\u0026thinsp;1%), whereas the \u003cem\u003eBDNF\u003c/em\u003e variant presented slightly elevated outlier percentages (4.08%), suggesting localized conformational strain. The mutation is highlighted in magenta, highlighting its spatial location within the structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). The preserved structure suggests that these mutations are unlikely to disrupt overall protein folding. Ramachandran plot analysis further confirmed the structural reliability of the models (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The results of the Ramachandran plot analysis for the \u003cem\u003eBDNF\u003c/em\u003e and \u003cem\u003eAPOE\u003c/em\u003e mutants are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of structure validation via Ramachandran plots for the \u003cem\u003eBDNF\u003c/em\u003e and \u003cem\u003eAPOE\u003c/em\u003e gene mutants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ersID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMolProbity score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClash score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRamachandran favored (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRamachandran outliers (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBDNF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1048218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.08%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers7412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers769455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRamachandran plot analysis confirmed the overall structural integrity, with the majority of residues occupying the favored conformational space (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C). The elevated outlier percentage in \u003cem\u003eBDNF\u003c/em\u003e rs1048218 indicates potential local backbone perturbations that may influence protein dynamics without compromising global fold stability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis comprehensive computational analysis identified three high-confidence pathogenic variants-rs1048218 (\u003cem\u003eBDNF\u003c/em\u003e), rs7412 (\u003cem\u003eAPOE\u003c/em\u003e), and rs769455 (\u003cem\u003eAPOE\u003c/em\u003e)\u0026mdash;with potential implications for AD pathogenesis. Convergent evidence from multiple prediction algorithms provides mechanistic insights into how these variants may contribute to neurodegeneration through distinct molecular pathways.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional implications of\u003c/b\u003e \u003cb\u003eBDNF\u003c/b\u003e \u003cb\u003ers1048218\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eBDNF\u003c/em\u003e variant rs1048218 (Q75H) demonstrated consistent destabilizing effects across stability prediction algorithms (ΔΔG: -1.001 to -2.08 kcal/mol), indicating significant perturbation of the neurotrophin fold architecture. The glutamine-to-histidine substitution introduces a positively charged, bulkier residue that likely disrupts local hydrogen bonding networks critical for protein stability. The observed α-helix reduction and compensatory random coil increase support this interpretation, suggesting that localized structural perturbations that may impair BDNF-receptor interactions are essential for synaptic plasticity and neuronal survival.\u003c/p\u003e \u003cp\u003ePrevious studies have reported variable associations between rs1048218 and AD risk across different populations, although evidence remains limited and population-dependent (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Our computational analysis provides mechanistic support for these associations by demonstrating how this variant may compromise \u003cem\u003eBDNF\u003c/em\u003e's neuroprotective signaling cascade. However, the moderate CADD scores (PHRED: 31.0) suggest subtle effects that may require additional genetic or environmental factors for phenotypic manifestation, which is consistent with variable population-specific associations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAPOE\u003c/b\u003e \u003cb\u003evariants and lipid metabolism disruption\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe rs7412 variant, which defines the \u003cem\u003eAPOE\u003c/em\u003e ε2 allele, presents an intriguing computational paradox. Despite consistent pathogenicity predictions (SIFT: 0.001, PolyPhen-2: 1.000), epidemiological evidence has demonstrated reduced Alzheimer's disease risk in ε2 carriers compared with ε4 carriers (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This apparent contradiction may reflect complex relationships between protein stability and biological function. The arginine-to-cysteine substitution at position 176 occurs within the lipid-binding domain, potentially altering lipid cargo specificity rather than eliminating function. The predicted destabilization may increase \u003cem\u003eAPOE\u003c/em\u003e turnover or modify amyloid-β interactions in ways that reduce pathological aggregation, which aligns with protective epidemiological observations.\u003c/p\u003e \u003cp\u003eThe rs769455 variant exhibited conflicting stability predictions between algorithms, reflecting the subtle nature of R163C substitution effects. Located near the lipid-binding domain, this variant likely modulates lipid‒cargo interactions without drastically altering the overall structure. Few studies have identified rs769455 as a population-specific risk modifier, although its low prevalence has constrained comprehensive analysis (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The exceptional CADD scores observed for some \u003cem\u003eAPOE\u003c/em\u003e variants (PHRED: 56.0) underscore the critical importance of intact apolipoprotein function in neurological health.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStructural resilience and therapeutic implications\u003c/h2\u003e \u003cp\u003eThe conservation of phosphorylation sites across all variants indicates that these mutations primarily affect protein structure rather than regulatory control mechanisms. This finding has important therapeutic implications, suggesting that interventions targeting posttranslational modifications may remain effective regardless of variant status. For \u003cem\u003eBDNF\u003c/em\u003e, preserved CK1 and AGC kinase sites indicate normal upstream signaling pathway function, suggesting that therapeutic strategies enhancing \u003cem\u003eBDNF\u003c/em\u003e expression or receptor sensitivity may compensate for structural deficits.\u003c/p\u003e \u003cp\u003eThe maintenance of overall protein folds despite predicted destabilization suggests considerable structural resilience in both proteins. This resilience may explain why these variants exist in populations without severe developmental consequences while contributing to late-onset disease susceptibility. The subtle secondary structure alterations (α-helix to random coil transitions) may represent adaptive flexibility, allowing proteins to maintain essential functions while altering specialized activities relevant to neurodegeneration.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAdvances in genomic research have greatly improved our understanding of the genetic factors involved in AD. However, although many variants are catalogued in public databases, their exact biological effects at the protein level remain unclear. Computational approaches provide a practical strategy to narrow down large genetic datasets and identify variants that are most likely to influence disease mechanisms.\u003c/p\u003e \u003cp\u003eIn the present study, sequence and structure based bioinformatic analyses were performed to evaluate nonsynonymous variants in the \u003cem\u003eBDNF\u003c/em\u003e and \u003cem\u003eAPOE\u003c/em\u003e genes. Three high-confidence variants of potential pathogenic relevance, namely, rs1048218 in \u003cem\u003eBDNF\u003c/em\u003e and rs7412 and rs769455 in \u003cem\u003eAPOE\u003c/em\u003e, that may contribute to AD pathogenesis. These variants showed signs of reduced protein stability and minor structural disturbances while largely preserving the overall protein fold and regulatory features. Such changes may influence neurotrophin signaling and lipid metabolism pathways, both of which are important in Alzheimer\u0026rsquo;s disease progression. Although computational analyses are valuable for prioritizing candidate variants and reducing experimental workload, they cannot fully represent biological complexity. Differences among prediction algorithms and the absence of population-specific analysis limit the direct clinical interpretation of the results. Therefore, experimental validation using biochemical assays, expression studies, and clinical correlation will be necessary to confirm their pathogenic relevance.\u003c/p\u003e \u003cp\u003eOverall, the identified variants provide useful starting points for future functional investigations. Further studies integrating laboratory validation and population genetics will help clarify their roles in disease development and may support the design of targeted therapeutic strategies for AD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econtributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMNB: literature review, interpretation of data, original drafting of the manuscript, revision of the manuscript; SM: data compilation, interpretation of data, original drafting of the manuscript, revision of the manuscript; SP: study conception, literature review, critical revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Indian Council of Medical Research (Sanction number 54/8/GER/2019-NCD-II), DBT-BUILDER (BT/INF/22/SP43065/2021), Govt. of India, Manipal Research Board (MRB) Grant, and MAHE Seed Money Grant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRedhwan A, Adnan M, Bakhsh HR, Alshammari N, Surti M, Parashar M et al (2025) Computational Identification and Functional Analysis of Potentially Pathogenic nsSNPs in the NLRP3 Gene Linked to Alzheimer\u0026rsquo;s Disease. Cell Biochem Biophys 83:357\u0026ndash;375\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu E, Zhang Y, Wang JZ (2024) Updates in Alzheimer's disease: from basic research to diagnosis and therapies. Transl Neurodegener 13:45\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFakorede S, Lateef OM, Garuba WA, Akosile PO, Okon DA, Aborode AT (2025) Dual impact of neuroinflammation on cognitive and motor impairments in Alzheimer\u0026rsquo;s disease. J Alzheimers Dis Rep ;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed H, Soliman H, Elmogy M (2022) Early detection of Alzheimer\u0026rsquo;s disease using single nucleotide polymorphisms analysis based on gradient boosting tree. Comput Biol Med 146:105622\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMangalmurti A, Lukens JR (2022) How neurons die in Alzheimer\u0026rsquo;s disease: Implications for neuroinflammation. 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BMC Bioinformatics 12:1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, Heer FT, de Beer TA, Rempfer C, Bordoli L, Lepore R (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46:296\u0026ndash;303\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin WJ, Salton SR (2013) The Regulated Secretory Pathway and Human Disease: Insights from Gene Variants and Single Nucleotide Polymorphisms. Front Endocrinol 4:96\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSerrano-Pozo A, Das S, Hyman BT (2021) APOE and Alzheimer\u0026rsquo;s disease: advances in genetics, pathophysiology, and therapeutic approaches. Lancet Neurol 20:68\u0026ndash;80\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLord J, Lu AJ, Cruchaga C (2014) Identification of rare variants in Alzheimer\u0026rsquo;s disease. Front Genet 5:369\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"APOE, BDNF, nsSNPs, single nucleotide polymorphisms, protein stability, computational biology, pathogenicity prediction, Alzheimer’s disease","lastPublishedDoi":"10.21203/rs.3.rs-8893939/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8893939/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is a progressive neurodegenerative disorder marked by memory impairment, cognitive decline, and behavioral changes. Genetic susceptibility plays a central role in its development, particularly variants in the apolipoprotein E (\u003cem\u003eAPOE\u003c/em\u003e) gene and neurotrophic regulators such as brain-derived neurotrophic factor (\u003cem\u003eBDNF\u003c/em\u003e). Nonsynonymous single-nucleotide polymorphisms (nsSNPs) in these genes can alter protein structure and function, potentially contributing to disease progression. This study used computational methods to evaluate the functional and structural consequences of nsSNPs in \u003cem\u003eBDNF\u003c/em\u003e and \u003cem\u003eAPOE\u003c/em\u003e. A total of 3,590 SNPs in \u003cem\u003eBDNF\u003c/em\u003e and 27,830 SNPs in \u003cem\u003eAPOE\u003c/em\u003e were retrieved from the dbSNP database. After filtering for coding-region variants with minor allele frequency\u0026thinsp;\u0026ge;\u0026thinsp;0.001, 33 \u003cem\u003eBDNF\u003c/em\u003e and 95 \u003cem\u003eAPOE\u003c/em\u003e nsSNPs were selected for further analysis. Pathogenicity predictions were performed using SIFT and PolyPhen-2, while functional impact was assessed using CADD scores. Protein stability changes were analyzed with MUpro and I-Mutant, and potential post-translational modification sites were evaluated using GPS-based prediction. Secondary structure alterations were examined using GOR4, and three-dimensional models were generated through SWISS-MODEL and validated by Ramachandran plot analysis. Several variants, including rs1048218 (\u003cem\u003eBDNF\u003c/em\u003e Q75H), rs7412 (\u003cem\u003eAPOE\u003c/em\u003e R176C), and rs769455 (\u003cem\u003eAPOE\u003c/em\u003e R163C), showed consistent damaging predictions across multiple tools. Stability analysis indicated marked destabilization for rs1048218 and rs7412, whereas rs769455 showed variable predictions. Structural modeling suggested localized conformational changes without major disruption of the overall fold. These findings suggest that specific nsSNPs in \u003cem\u003eBDNF\u003c/em\u003e and \u003cem\u003eAPOE\u003c/em\u003e may influence protein behavior and contribute to AD pathology. Experimental validation will be necessary to confirm their biological relevance.\u003c/p\u003e","manuscriptTitle":"Predictive Analysis of Brain-Derived Neurotrophic Factor and Apolipoprotein E SNPs in Alzheimer’s Pathogenesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-04 16:51:05","doi":"10.21203/rs.3.rs-8893939/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed04dab9-6bd8-42d8-a846-a2bb066cbf99","owner":[],"postedDate":"March 4th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-04-30T14:58:43+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T15:10:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-04 16:51:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8893939","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8893939","identity":"rs-8893939","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}