1. Introduction
Alzheimer’s disease (AD) is a neurodegenerative disorder and the most common cause of dementia. The disease is characterized by the loss of neurons and synaptic connections in the cortex and certain subcortical areas. AD is clinically characterized by decline in progressive executive function, perceptual speed, short-term memory and other cognitive capacities [1]. Degradation of cognitive functions has a major impact on an individual’s quality of life. According to the World Alzheimer Report 2018, there are over 50 million people suffering from AD or other dementias in the world [2]. Each year, approximately 5–7 million new cases of AD are registered in the geriatric population [3].
Two forms of AD are traditionally distinguished: early onset (EOAD, onset <65 years old) and late-onset (LOAD, onset >65 years old). EOAD accounts for 1–6% of AD cases. These cases are familial and are associated with classical Mendelian patterns of inheritance. The amyloid precursor protein (APP), presenilin1 (PSEN1), and presenilin2 (PSEN2) genes are primarily implicated in EOAD. As for the later onset form of the disease, both genetic and environmental factors are considered to impact the disease risk profile. LOAD constitutes approximately 95% of AD cases [4]. Heritability for LOAD is estimated to be between 58% and 79%, on the basis of a twin study design [5]. The genetic complexity of LOAD should not be underestimated. Due to general efforts, it has become clear that pathogenesis of LOAD is multi-faceted and is not limited to one set of simple molecular interactions.
Common neurological and psychiatric disorders, including AD, schizophrenia, bipolar disorder, Parkinson’s disease are now the subject of intensive studies. Over the last few decades great progress in researching of susceptibility genetic loci to common mental and neurological diseases has been made using new technologies and methods: genome-wide association studies (GWAS), large meta-analysis, whole-exome/genome sequencing (GWAS, meta-analysis, NGS).
Alzheimer’s disease and schizophrenia are two common diseases of the brain with significant differences in neuropathology, etiology and symptoms. Data on the common links in the pathogenesis of AD and schizophrenia are accumulating [6]. Thus, statistical evidence has shown that AD and schizophrenia have a specific molecular background. Transcriptome studies have found a similar pattern of gene expression in the superior temporal gyrus in AD and schizophrenia [7]. Recently, GWASs have identified many genomic loci characterized by small effect sizes associated with LOAD: CLU, CR1, PICALM, BIN1, ABCA7, CD33 and others [8,9,10]. It is important to keep in mind that genetic association studies have often produced false findings, and thus need validation in multiple sample sets. On the other hand, it is known that most GWAS SNPs in the associated loci with disease cannot be explained on the basis of known pathological mechanisms. Next-generation sequencing has assisted the identification of rare susceptibility modifying alleles in APP, TREM2, and PLD3 [11]. However missing heritability for LOAD remains extensive. While great progress has been made with respect to understanding the genetic landscape of LOAD, the connection between the genes and variants promoting to the risk of the disease remains unclear. The data accumulation and the use of new bioinformatics approaches including those implemented in publicly available resources can help with this problem. The biological roles in metabolic pathways of all newly identified variants, as well as functionally related genes, can be assessed using ALIGATOR, KEGG, GO, STRING, GeneMANIA [12,13,14,15,16]. Exploring potentially new protein interactions in common mental and neurological diseases using bioinformatic strategies presents potential interest. Genetic risk factors could be analyzed and integrated in terms of biological pathways and functions to better understand the contribution of genetics to the general background of neurological and mental diseases. Protein interaction data, derived from the GWAS database, can be used to build protein–protein interaction (PPI) networks from genes associated with AD, schizophrenia and cognitive performance. PPI network analysis could be an effective and powerful approach to identify potential biological pathways or key genes involved in the link of neurological and mental diseases.
In addition to the complexities of LOAD associated with numerous interconnected environmental and genetic factors, the genetic architecture of LOAD differs between ethnic groups. Our prior study estimated genetic diversity of sixteen native populations of North Eurasia using a panel of genetic markers significantly associated with AD and Schizophrenia [17].
Here we follow up on our prior research in a new cohort of the AD group and controls. In the present study, we aimed to analyze 63 of the previously reported statistically significant SNP markers associated with AD in approximately 450 Russian samples from well-characterized LOAD case–control set. In addition, our focus was on the identification of the molecular and functional pathways of these SNP markers using various statistical and bioinformatics resources.
2. Materials and Methods
2.1. Samples
This study involved 185 unrelated LOAD patients (mean age 72.15 ± 7.87 years) recruited and evaluated at the Department of Neurology and Neurosurgery (Siberian State Medical University, Tomsk, Russia). Subjects were included in the study after a diagnosis of LOAD according to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), the diagnostic and statistical manual of mental disorders, fourth edition, text revision (DSM-IV TR) [18] and the NINCDS-ADRDA Alzheimer’s Criteria (the National Institute of Neurological and Communicative Diseases and Stroke and the Alzheimer’s Disease and Related Disorders Association) [19]. Structural imaging based on magnetic resonance is a part of the clinical assessment of patients with suspected AD. The presence of focal symmetrical medial temporal atrophy has predictive value for AD. Our study included the cases with MRI images showing the characteristic atrophy of the hippocampus. Cases had a minimum age at disease onset of 65 years. Control group consisted of 287 unrelated cognitively normal elderly Russian participants (mean age 71.8 ± 5.7 years). The same procedures for the control group were performed as for the case group to exclude the LOAD diagnosis. Accordingly, history, neurologic examinations, magnetic resonance images of brain volumes were examined for the control group. Additionally, the MoCA test was conducted for the control group to exclude mild cognitive impairment. The MoCA was developed to be more sensitive to mild cognitive impairment in geriatric populations than other screeners (like the Mini Mental State Examination-MMSE). MoCA scores ranged between 0 and 30 points, and the higher scores indicated the better cognitive performance. In our study, the control group included people with the MoCA score ≥ 23. Controls were recruited and evaluated at the Nebbiolo Center for Clinical Trials (Tomsk).
All of the studied individuals, cases and controls were of the same ethnic (Russians) and geographical origins, living in the Tomsk region of the Russian Federation. Table 1 presents the main demographic parameters of the studied groups. The study protocol was approved by the Scientific Ethics Committee of the Research Institute of Medical Genetics (Tomsk National Research Medical Center of the Russian Academy of Sciences) (protocol number 2017/108). Individuals or legal guardians signed a written informed consent form after the study objectives and procedures had been explained.
2.2. Genotyping
Genomic DNA was extracted from the peripheral venous blood using phenol–chloroform extraction method. For this study, two multiplex panels of SNP markers that showed repeated association with cognitive performance, AD or/and schizophrenia in the GWAS were formed (see Table 2). Genetic markers were selected based on certain and clear criteria. First of all, we selected loci reaching a genomic value (p < 5.00 × 10−7) for the phenotype (cognitive functions, AD and/or schizophrenia in GWAS). The next requirement concerned the type of marker: only SNPs were selected. The minor allele frequency (MAF) should have been ≥ than 5% in at least one population group of the HapMap project or the 1000 Genomes project. Selected markers should form multiplexes using the Assay Design Suite v2.0 genotyping tool (
2.3. Statistical Analyses
Two SNPs (SORL1 rs11218343, TCF4 rs1261117) were excluded from the final analysis because they showed genotype call rates < 87%. Per-marker genotype call rates were higher than 95% for all the rest of the markers. The genotype distributions of 3 loci (LUZP2 rs1021261, CNTNAP2 rs10273775, GRIN2B rs2160519) were not consistent with the Hardy–Weinberg equilibrium in the control group. Therefore, these three polymorphisms were excluded from further analyses.
Pearson’s chi-squared test was performed to verify the Hardy–Weinberg equilibrium of each SNP in LOAD patients and controls. The genetic variants in the control group which were not in the Hardy–Weinberg equilibrium (p < 0.05) were excluded from the analysis. Allele and genotype frequencies of each SNP were compared between LOAD cases and controls using the chi-squared (χ2) test. The odds ratios (OR) and 95% confidence intervals (CIs) were calculated to assess the relative risk. The Benjamini and Hochberg false discovery rate method was used for multiple testing corrections [20]. A p value threshold of 0.05 was used to determine significance.
The linkage disequilibrium (LD) between SNP pairs in the genomic region of APOE- TOMM40 locus was quantified using Haploview version 4.2 software [21]. Haplotype frequencies were determined using the EM algorithm. The LD block structure was determined using the Solid Spine method provided by the Haploview 4.2. The degree of genetic linkage between the 8 SNPs in study groups was estimated as Lewontin’s coefficient D’ and Pearson’s correlation coefficient r2, where no color (D’ = 0) indicates that LD is weak or nonexistent and the black (D’ = 1) indicates that there exists strong pairwise LD between SNPs (Figure 1).
Generalized multifactor dimensionality reduction (GMDR) analysis was carried out for the detection and characterization of gene–gene interactions. This is a generalized MDR framework based on the score of a generalized linear model [22]. The best interaction model was selected on the basis of maximum testing balanced accuracy (TBA) and cross-validation consistency (CVC). Permutation (p) results were considered to be statistically significant at the 0.05 level. In our study, we used open-source software GMDR (
The protein–protein interaction network of the studied proteins was constructed with the online analysis tool STRING v11.0 (
3. Results
The minor allele frequencies of 58 SNPs in LOAD patients and in controls are presented in a Table 2. Almost all the allele and genotype frequencies for the investigated loci were within the range reported in other populations of the European descent in the 1000 Genomes project (
The statistical power of the study was estimated for the Genetic Association Study (GAS) Power Calculator (
Out of the 58 markers tested, nine showed significant association to LOAD (Table 3). Two variants demonstrated marginal p values for the allele and/or genotype frequency difference between the LOAD and control subjects: FBXO40 rs3772130 and NECTIN2 rs6859. After correction for multiple testing the results remained statistically significant for five genetic variants in three genes: TOMM40 rs2075650, APOE ε4, rs429358 and rs769449, NECTIN rs6857 (Table 3). All of these markers were located in the same region of the chromosome 19.
Due to the fact that it is important for the LD analysis that the markers are in the same chromosome region, we included five SNPs in the LD analysis, which in our study were significantly associated with LOAD: TOMM40 rs2075650, APOE rs7412, rs429358 and rs769449, NECTIN rs6857. All of these markers were located in the same region of chromosome 19 (the APOE-TOMM40 region). Markers TOMM40 rs157580, NECTIN rs6859, APOC1 rs4420638 were genotyped, but were not significantly associated with the LOAD in our study. These markers were also analyzed using the LD analysis, as they were also located in the APOE-TOMM40 region of chromosome 19, like the other 5 markers. The theoretically possible number of haplotypes was 256. A total of 44 haplotypes were detected in our samples. Twenty haplotypes were revealed in the control group, while 24 haplotypes were found in the cases. Fourteen identical haplotypes were observed in both groups. Identical haplotypes and their frequencies are presented in a Table 4.
Figure 2 demonstrates haplotypes with frequencies above 1% in the case and the control groups.
The number of main haplotypes (frequency above 10%) was four in the control group and three in LOAD patients (Figure 2). Results of the analysis based on comparison of the haplotype frequencies showed that risk haplotypes ATAGACCG (χ2 = 7.313, p = 0.0068; OR 1.90, 95% CI 1.19–3.05) and ATAGACCA (χ2 = 14.589, p = 0.0001; OR 17.43, 95% CI 2.24–135.62) were significantly associated with LOAD. Moreover, we identified one protective haplotype GCAAGTCA (χ2 = 6.822, p = 0.009; OR 0.63, 95% CI 0.44–0.89) generated from these eight SNPs. All of the above associations remain significant after permutation test (p < 0.05).
The structure of LD among eight SNPs in the APOE-TOMM40 locus for the cases and controls is shown in Figure 1. Analysis of LD in cases and controls demonstrated that these groups were characterized by different structures of haplotype blocks at APOE-TOMM40 locus (Figure 1a,b). In controls, the first block consisted of four SNPs (rs6859, rs6857, rs157580, rs2075650) and the second included three SNPs (rs769449, rs429358, rs7412), while in the cases, the first block comprised four SNPs (rs6857, rs157580, rs2075650, rs769449) and the second block was made up of two closely located SNPs (rs429358, rs7412). Notably, this difference demonstrates the high level of LD in case group between rs429358 and rs7412 from the APOE gene, which are formed the APOE isoforms (ε2, ε3, ε4). The haplotype block structure for control group demonstrates the blocks that include genes: Block1-NECTIN2-TOMM40, Block2–APOE.
Although not every studied variant in genes was significantly associated with AD by univariate (case–control) analysis, there were gene–gene interactions among variants using generalized multifactor dimensionality reduction (GMDR) method. GMDR combines genotypes into “high-risk” and “low-risk” groups in order to reduce multidimensional data into only one dimension. In our study, we analyzed all possible combinations of 44 genes both in cases and in control subjects. There were three significant models as a result of the GMDR analysis: one-factor, two-factor and three-factor model (Table 5).
Among these results, we selected two models (a two-factor and a three-factor) as the best SNP combinations, based on the best Testing Balanced Accuracy (TBA) in data and their high cross-validation consistency (CVC) value. Figure 3 shows the score distributions in the best models. Seven genotype combinations with an increased risk of developing LOAD and only one combination of low-risk genotypes were found for the two-factor model (Figure 3a). The combination of low-risk genotypes showed statistically significant differences between the studied groups: TCF4 rs17594526 “CC” and APOE rs429358 “TT” (p < 0.001; OR = 0.25; 95% CI: 0.14–0.45). Fourteen genotype combinations with an increased risk of developing LOAD and five combinations of low-risk genotypes were found for the three-locus model (Figure 3b). Since we did not have all genotypes for the three-locus model in GMDR analysis, we designated the data in this model as preliminary. The one-locus model was computed for APOE rs429358 and the empirical p value for prediction error using permutation testing was 0.0006, indicating the interactions among the three variants contributed to a higher risk of AD than did single variant alone (Table 5).
In the present study, we conducted a protein–protein network interaction (PPI) search with proteins of AD, schizophrenia and cognitive performance collected via the GWAS database using STRING (v11.0). This exploratory bioinformatic analysis demonstrated a potential network of interaction between proteins connected to AD, schizophrenia and cognitive performance. PPI network analysis of 41 studied genes showed statistically significant connectivity among proteins. Interaction enrichment had p-value < 1.0 × 10−16 for our gene set. This means that such enrichment demonstrates that the proteins are at least partly biologically connected, as a group. The PPI network contained 24 nodes and 39 edges (Figure 4). The STRING indicated that the proteins formed three subnetworks of interconnected gene products (Figure 4). Each subnetwork had a central protein. The first subnetwork consisted of 10 proteins with APOE in the center: CD33, CR1, CLU, RELN, SORL1, NECTIN2 (PRVL2), TOMM40, APOC, MPC2. The second subnetwork included 6 proteins with CACNA1C as the center component: GRIN2B, TENM4, CSMD1, NT5C2, POM121L2. In addition, the third protein subgroup had 8 nodes with CTNNA2 in the center part: FBXO40, CNTN4, CTNNA2, CDON, CAMD2, TCF4, CHD6. Proteins with a large number of interactions were identified (with edges from 8 to 5). The eight genes with strong connections (“multiple interactions”) were APOE, SORL1, APOC1, CD33, CLU, TOMM40, CNTNAP2 and CACNA1C (Figure 4). The largest number of connections was established for the APOE protein (8). This large number of interactions is not surprising, given the high known effect of the APOE gene for phenotypic modifications for several medical traits [23,24,25]. In addition to proteins with a large number of interactions, there are other proteins that can connect or act as bridges between subnetworks. The four “bridge” genes were APOE, RELN, CNTNAP2 and GRIN2B. It should be noted that APOE and CNTNAP2 represent both types of genes (“multiple interactions” and “bridge”).
To evaluate molecular function, cellular component, and biological process of our gene set we used bioinformatics resource Gene Ontology (GO,
4. Discussion
AD is the most common form of age-related neurodegenerative disease. To study such complex diseases, it is necessary to apply various types of approaches. In this study, we performed several different analyses to excavate loci involved in development of the late onset AD in the Russian population. First of all, we focused on the potential associations of SNPs previously identified in GWAS. Since not only AD is characterized by a decrease in cognitive function, we also included GWAS loci for other reported traits in our analysis, such as schizophrenia, unipolar depression, bipolar disorder, attention deficit hyperactivity disorder, autism spectrum disorder and others.
Similar study designs have been reported in other papers for various disorders: AD and Parkinson’s disease, AD and ischemic stroke, schizophrenia and bipolar disorder, and others [26,27]. A recent study demonstrated an unbiased link between polygenic risk for schizophrenia and a lower risk of psychosis in AD [28]. The most significant associations with LOAD were observed in the APOE region on chromosome 19 (p < 5 × 10−8). Outside the APOE region, the most significant association was found in CR1 gene on chromosome 1 (chr1:207611623, rs3818361, G/A, p = 6 × 10−3). This gene is one of the ten highest risk factors for developing AD. The CR1 gene encodes the complement receptor 1 (CR1), which is one of the regulators of complement activity. The CR1 might prevent excessive complement activation. The presence of CR1 on erythrocytes is noted as an important component in protecting tissues against immune-complex deposition and following disease, such as AD [29].
After correction for multiple testing (the Benjamini and Hochberg false discovery rate method), only the APOE region genetic markers were remained statistically significant. Interestingly, according to our data, the G allele frequency (28%) of rs2075650 at TOMM40 gene in our Russian sample sets is significantly higher than in other populations not only of European descent (13–16%), but also of the Asian population (10–12%). Salakhov et al. [30] found that TOMM40 gene rs2075650 was associated with low-density lipoprotein cholesterol levels in healthy men group of the Russian population (Kemerovo, Russia), and G allele frequency in the total group was 21%. It should be noted that, most of the genetic variants contributing to risk of developing AD are located in non-coding regions and, presumably, they may influence as a controller of the functional activity of closely located genes that have an activating effect on transcription [31]. We can suggest that there may be some additional genetic signal in this locus that is impact to risk AD. The study results supplement the previous data on the AD risk development genetic markers in the Russian population [32].
Within the chromosomal region 19q12-q13.33, there is an accumulation of SNPs that are probably critical for beginning and development of AD. A huge number of publications devoted to the role of this region in the progress of AD. In addition, although APOE was identified as a susceptibility factor for LOAD over 25 years ago [33], it is still not clear how the ε4 allele contributes to disease risk. Some observations indicate an independent effect of SNPs of the NECTIN2-TOMM40-APOE locus [34], while other studies showed that there are general effects of several SNPs [35]. Our data demonstrated that in both the cases and the controls rs157580 of TOMM40 gene is in high pairwise LD with the SNPs rs2075650 and rs769449 (D’ = 100). However, LOAD-affected individuals are characterized by higher pairwise LD between neighboring SNPs compared with unaffected individuals (Figure 1). Notably, the haplotype block structure for cases demonstrates that Block2 included rs429358 and rs7412 from the APOE gene, which are formed the APOE isoforms (ε2, ε3, ε4). Our study showed that the haplotypes that include the ε3 allele of APOE and the A allele of TOMM40 gene rs2075650 are associated with reduced risk for LOAD compared with haplotypes that include the ε4 allele of APOE and the G allele of rs2075650. In a recently published study on heritability of extreme longevity in human populations, it was found that the ε4 allele of APOE is associated with substantial reduction in the chance for extreme longevity [36].
Haplotypes that showed an association with a decrease in extreme longevity in Sebastiani et al. in our study demonstrated a significant association with an increased risk of developing LOAD. The possible combinations of loci that may reduce the risk of LOAD development were obtained by researching gene–gene interactions with GMDR. Approximately two-fold reduced risk for LOAD was observed for the combination of CLU rs1532278 “CC”, TCF4 rs17594526 “CC”, APOE rs429358 “TT” genotypes in the three-locus model. In the model combining TCF4 and APOE, genotype combination CC/TT could similarly be considered to protective to LOAD. Notably, our data presented intergenic interaction between the three loci (CLU, TCF4, APOE), demonstrating no significant associations in our replicative analysis. Transcription factor 4 gene (TCF4) plays an important role in nervous system development. TCF4, a basic helix–loop–helix transcription factor, is broadly expressed and is critical for normal brain development. The gene has been identified as the cause of Pitt-Hopkins syndrome (PTHS), and it has been implicated in various other neuropsychiatric diseases, including schizophrenia, autism and depression [37,38,39].
The STRING database aims to combine all known and predicted associations between proteins, including both functional associations and physical interactions. In the present study, we used this exploratory bioinformatic analysis to predict a possible PPI network between neurological and mental diseases with using GWAS database. The network shows possible PPIs between associated genes of AD, schizophrenia and cognitive performance. A protein–protein interaction network with three subnetworks was formed for 24 proteins. A more detailed analysis of subnetworks detected the traits of protein grouping for each of them. The first group (with the APOE) is characterized by the maximum number of edges (21). Proteins of this subnetwork are related by common biological processes, such as regulation (positive and negative) of various processes and molecule transport, as well as such common molecular function as binding. There were no significant GO associations for the other protein subgroup (the CACNA1C as a center node), but these proteins were connected through publication studies with psychiatric diseases, such as schizophrenia, bipolar disorder, depression, and others [40,41,42]. The grouping trait in the third subnetwork (with the CNTNAP2 in the center) is the biological processes related with the neuron development and differentiation, as well as with the regulation of these processes. In addition, all these three subsystems are connected together by the Reelin as a bridge. Reelin is a large signaling protein that is engaged in a cascade of cytoplasmic events that control the migration of neurons during brain development and it is essential for the correct development and plasticity of the cerebral cortex and regulate synaptic plasticity, neurotransmission and memory in the adult brain. Presumably, Reelin plays a significant role both in AD and in psychiatric disorders [43,44,45].
The PPI networks have limited clinical predictive value, but discover hypothetical new paths of interaction. The forecasted PPI network needs careful interpretation and warrants preclinical and clinical validations. Nevertheless, this network gives new facts that will be helpful to new studies of general molecular background between neurological and mental diseases.
5. Conclusions
In summary, genes and proteins affecting cognitive function are of special interest in the search of genetic composition of AD. The vast majority of cases of AD are viewed as sporadic results from the complex interaction of genetic risk factors and unknown environmental conditions. The present study confirmed the importance of the APOE-TOMM40 locus as the main risk locus of development and progress of LOAD. Association analysis and bioinformatics approaches detected interactions both at the association level of single SNPs and at the level of genes and proteins. Strong evidence for association with LOAD was seen for the APOE-4 polymorphism, as well as for rs429358, rs769449 and rs2075650, using various approaches: replicative association analysis, LD analysis, GMDR analysis and protein–protein interaction analysis.
Currently, priority approaches are aiming not only to search for genes, but also to establish their functional effect. This provides the possibility of using the progress made in identifying the biological effects of risk alleles to recognize new protective mechanisms against more rapid cognitive decline in AD. Identifying biological pathways that increase risk may be useful in the future for the treatment of cognitive and behavioral disorders. Nevertheless, the current findings are a step forward in in this direction. We believe that the identification of additional risk loci would be useful from the development of predictive approaches of AD.
A.B., K.V., A.M. and V.S. contributed conception and design of the study; N.Z., I.Z., O.M. and L.M. organized the database; A.B. performed the statistical analysis; A.B. wrote the first draft of the manuscript; V.S. reviewed and corrected draft of the manuscript. All authors have read and agreed to the published version of the manuscript.
This research was funded by the Russian Science Foundation, grant number 16-14-00020.
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Scientific Ethics Committee of the Research Institute of Medical Genetics (Tomsk National Research Medical Center of the Russian Academy of Sciences) (protocol number 2017/108).
Informed consent was obtained from all subjects involved in the study.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 1. The structure of linkage disequilibrium in the genomic region of the NECTIN2-TOMM40-APOE locus: (a) tThe structure of LD for the cases; (b) the structure of LD for the controls. Linkage disequilibrium was measured by the D’ statistic: black—a strong link (D’ = 1, LOD > 2), grey—a significant link (D’ < 1, LOD > 2), white—poor link (D’ < 1, LOD < 2). A D’ value of 100 indicates a complete LD between 2 markers.
Enlarge this image.Figure 2. Haplotype frequencies for the LOAD patients and the control group. * Statistically significant differences between the cases and the controls (p < 0.05).
Enlarge this image.Figure 3. Gene–gene interactions among TCF4 (rs17594526), APOE (rs429358) and CLU (rs1532278) loci in AD and healthy subjects: (a) two-factor model; (b) three-factor model. The score distribution of AD subjects (left black bar in boxes) and control subjects (right black bar in boxes) is shown for each genotype combination. High-risk genotype combinations are represented by dark gray shade cells, while light gray shade cells represent low-risk genotype combinations. Cells with no shading or white cells represent genotype combination for which no data is observed.
Enlarge this image.Figure 4. Protein–protein interaction network of studied genes. The nodes and edges represent the proteins (genes) and their interactions, respectively. The PPI network contained 24 nodes and 39 edges. The STRING indicated that the proteins formed three subnetworks of interconnected gene products. Each subnetwork had a central protein: APOE, CACNA1C, CTNNA2. All these three subsystems are connected together by RELN as a bridge.
Demographic parameters of the studied groups.
| Parameter | Patients with LOAD, |
Control Group, |
|---|---|---|
| Gender: Women | 120 (64.86%) | 200 (69.69%) |
| Gender: Men | 65 (35.14%) | 87 (30.31%) |
| Mean age | 72.15 ± 7.87 | 71.8 ± 5.70 |
| Race | Caucasoid | Caucasoid |
| Population | Russians | Russians |
Frequencies of the 58 SNPs analyzed for LOAD.
| Gene. | SNP ID | Allele a | Functional Consequence |
Position (GRCh38) | Minor Allele Frequency | MAF | |
|---|---|---|---|---|---|---|---|
| Cases | Controls | HapMap | |||||
| CHD6 | rs1010304 | G/A | intron variant | 20:41473007 | 0.06 | 0.05 | 0.06 |
| NCAPD3 | rs1031381 | T/C | intron variant | 11:134218788 | 0.40 | 0.43 | 0.42 |
| MPC2 | rs10489202 | T/G | intron variant | 1:167933841 | 0.21 | 0.20 | 0.22 |
| CSMD1 | rs10503253 | A/C | intron variant | 8:4323322 | 0.23 | 0.27 | 0.26 |
| CCDC60 | rs11064768 | G/A | intron variant | 12:119380704 | 0.08 | 0.07 | 0.07 |
| NT5C2 | rs11191580 | C/T | intron variant | 10:103146454 | 0.09 | 0.09 | 0.11 |
| LOC105378889-PRMT6 | rs12125971 | T/C | intergenic variant | 1:106921021 | 0.08 | 0.08 | 0.07 |
| LOC101928778-LOC105371627 | rs12140439 | A/C | intergenic variant | 1:177753772 | 0.29 | 0.30 | 0.34 |
| TENM4 | rs12290811 | A/T | intron variant | 11:79372576 | 0.12 | 0.12 | 0.14 |
| LUZP2 | rs12361953 | G/T | intron variant | 11:24589584 | 0.14 | 0.14 | 0.15 |
| CADM2 | rs12494658 | C/T | intron variant | 3:85825326 | 0.24 | 0.30 | 0.28 |
| SNX29 | rs12922317 | G/A | intron variant | 16:11983775 | 0.40 | 0.35 | 0.35 |
| LOC105373605 | rs12989701 | A/C | intron variant | 2:127130409 | 0.10 | 0.15 | 0.15 |
| CTNNA2 | rs13034462 | G/T | intron variant | 2:79892368 | 0.03 | 0.04 | 0.05 |
| BRD1 | rs138880 | C/A | intron variant | 22:49824963 | 0.21 | 0.21 | 0.23 |
| DCHS2 | rs1466662 | A/T | intron variant | 4:154426241 | 0.35 | 0.35 | 0.69 |
| CLU | rs1532278 | T/C | intron variant | 8:27608798 | 0.44 | 0.39 | 0.38 |
| TOMM40 | rs157580 | G/A | intron variant | 19:44892009 | 0.34 | 0.36 | 0.35 |
| LOC730100 | rs1606974 | A/G | intron variant | 2:51646461 | 0.08 | 0.06 | 0.07 |
| NKAPL | rs1635 | T/G | missense variant | 6:28259826 | 0.07 | 0.06 | 0.05 |
| LSM1 | rs16887244 | G/A | intron variant | 8:38173827 | 0.23 | 0.21 | 0.21 |
| POM121L2 | rs16897515 | A/C | missense variant | 6:27310241 | 0.09 | 0.11 | 0.12 |
| CNTN4 | rs17194490 | T/G | intron variant | 3:2506102 | 0.18 | 0.15 | 0.14 |
| ARHGAP31 | rs17203055 | G/A | intron variant | 3:119365484 | 0.11 | 0.10 | 0.12 |
| TCF4 | rs17512836 | C/T | intron variant | 18:55527730 | 0.008 | 0.02 | 0.05 |
| CADM2 | rs17518584 | C/T | intron variant | 3:85555773 | 0.34 | 0.32 | 0.30 |
| TCF4 | rs17594526 | T/C | intron variant | 18:55391007 | 0.01 | 0.02 | 0.05 |
|
GPR89P-
|
rs17693963 | C/A | intergenic variant | 6:27742386 | 0.06 | 0.06 | 0.08 |
| TOMM40 | rs2075650 | G/A | intron variant | 19:44892362 | 0.26 | 0.28 | 0.16 |
| CLU | rs2279590 | T/C | intron variant | 8:27598736 | 0.44 | 0.40 | 0.39 |
| ZNF365 | rs2393895 | C/A | intron variant | 10:62579087 | 0.23 | 0.23 | 0.20 |
| CSMD1 | rs2616984 | G/A | intron variant | 8:4625619 | 0.33 | 0.31 | 0.29 |
| DNAH11 | rs368331 | G/A | intron variant | 7:21703356 | 0.07 | 0.06 | 0.06 |
| FBXO40 | rs3772130 | G/A | intron variant | 3:121625293 | 0.20 | 0.25 | 0.25 |
| STK24 | rs3783006 | C/G | intron variant | 13:98458955 | 0.46 | 0.48 | 0.47 |
| CR1 | rs3818361 | A/G | intron variant | 1:207611623 | 0.33 | 0.25 | 0.25 |
| CD33 | rs3826656 | G/A | intron variant | 19:51223357 | 0.24 | 0.24 | 0.23 |
| APOE | rs429358 | C/T | missense variant | 19:44908684 | 0.22 | 0.10 | 0.13 |
| ACSM1 | rs433598 | T/C | intron variant | 16:20668884 | 0.35 | 0.37 | 0.34 |
| APOC1 | rs4420638 | G/A | 500B Downstream Variant | 19:44919689 | 0.18 | 0.14 | 0.18 |
| CNTN4 | rs4629318 | A/G | intron variant | 3:2851590 | 0.15 | 0.15 | 0.14 |
| CDON | rs472926 | G/A | intron variant | 11:126035363 | 0.16 | 0.17 | 0.17 |
| CACNA1C | rs4765905 | C/G | intron variant | 12:2240418 | 0.32 | 0.37 | 0.37 |
| TENM4 | rs530965 | T/C | intron variant | 11:79354056 | 0.49 | 0.47 | 0.49 |
| CSMD2 | rs544991 | T/C | intron variant | 1:33723829 | 0.31 | 0.27 | 0.31 |
|
PICALM-
|
rs561655 | G/A | intergenic variant | 11:86089237 | 0.30 | 0.35 | 0.33 |
| CHD6 | rs6129846 | T/C | intron variant | 20:41478674 | 0.06 | 0.05 | 0.06 |
| NRXN3 | rs6574433 | G/A | intron variant | 14:78319816 | 0.45 | 0.42 | 0.42 |
| CR1 | rs6656401 | A/G | intron variant | 1:207518704 | 0.31 | 0.23 | 0.24 |
| NECTIN2 | rs6857 | T/C | 3 Prime UTR Variant | 19:44888997 | 0.28 | 0.19 | 0.17 |
| NECTIN2 | rs6859 | A/G | intron variant | 19:44878777 | 0.50 | 0.42 | 0.44 |
| LOC105375630 | rs7004633 | G/A | intron variant | 8:88748082 | 0.21 | 0.19 | 0.20 |
| RELN | rs7341475 | A/G | intron variant | 7:103764368 | 0.16 | 0.19 | 0.16 |
| LOC105373605 | rs7561528 | A/G | intron variant | 2:127132061 | 0.31 | 0.31 | 0.34 |
| APOE | rs769449 | A/G | intron variant | 19:44906745 | 0.17 | 0.08 | 0.10 |
| KLHL1 | rs7984606 | C/A | intron variant | 13:69881529 | 0.003 | 0.004 | 0.05 |
| NKAIN2 | rs9491140 | T/C | intron variant | 6:124370091 | 0.32 | 0.32 | 0.31 |
| APOE | rs7412 | T/C | missense variant | 19:44908822 | 0.08 | 0.07 | 0.07 |
a Minor allele/major allele.
Association of statistically significant genetic markers with LOAD in the Russian population.
| Gene | Variant | Minor Allele | OR (95% CI) | Major Allele | OR (95% CI) | p Value | p Value Corrected |
|---|---|---|---|---|---|---|---|
| APOE4 * |
rs429358 |
ε4 | 2.88 (1.95–4.24) | ε3 | 0.35 (0.24–0.51) | 5 × 10−8 | 8.62 × 10−4 |
| APOE * | rs769449 | A | 2.44 (1.62–3.66) | G | 0.41 (0.27–0.62) | 1 × 10−5 | 2.5 × 10−3 |
| APOE * | rs429358 | C | 2.53 (1.75–3.67) | T | 0.39 (0.27–0.57) | 5 × 10−7 | 1.7 × 10−3 |
| TOMM40 * | rs2075650 | G | 1.67 (1.21–2.29) | A | 0.60 (0.44–0.82) | 2 × 10−3 | 3.4 × 10−3 |
| NECTIN2 * | rs6857 | T | 1.65 (1.21–2.26) | C | 0.61 (0.44–0.83) | 2 × 10−3 | 4.3 × 10−3 |
| CR1 | rs3818361 | A | 1.5 (1.12–2.00) | G | 0.67 (0.5–0.89) | 6 × 10−3 | 5.2 × 10−3 |
| CR1 | rs6656401 | A | 1.46 (1.09–1.96) | G | 0.68 (0.51–0.92) | 0.01 | 6 × 10−3 |
| NECTIN2 | rs6859 | A | 1.34 (1.03–1.74) | G | 0.75 (0.57–0.97) | 0.03 | 6.8 × 10−3 |
| FBXO40 | rs3772130 | G | 0.72 (0.52–0.99) | A | 1.39 (1.01–1.91) | 0.04 | 7.7 × 10−3 |
OR odds ratio, CI confidence interval, p values corrected based on Benjamini and Hochberg method, * Statistically significant results after multiple comparison correction.
Identical haplotypes identified in both groups.
| N | rs, Gene | Haplotype Frequencies (Cases/Controls) |
|||||||
|---|---|---|---|---|---|---|---|---|---|
| rs6859 |
rs6857 |
rs157580 |
rs2075650 |
rs769449 |
rs429358 |
rs7412 |
rs4420638 |
||
| 1 | G | C | G | A | G | T | C | A | 21.4/26.3 |
| 2 | A | C | A | A | G | T | C | A | 15.7/16.4 |
| 3 | G | C | A | A | G | T | C | A | 14.0/20.9 |
| 4 | A | T | A | G | A | C | C | G | 11.0/6.4 |
| 5 | A | T | A | G | G | T | C | A | 9.1/9.9 |
| 6 | A | C | G | A | G | T | C | A | 7.1/5.5 |
| 7 | G | C | G | A | G | T | T | A | 5.0/3.1 |
| 8 | G | C | A | A | G | T | T | A | 2.8/3.0 |
| 9 | A | C | A | A | G | T | C | G | 2.3/2.7 |
| 10 | G | T | A | G | A | C | C | G | 1.4/1.0 |
| 11 | G | T | A | A | G | C | C | G | 1.1/0.6 |
| 12 | G | C | A | A | A | C | C | G | 0.7/0.6 |
| 13 | A | T | A | A | G | C | C | G | 0.7/0.8 |
| 14 | G | C | A | A | G | T | C | G | 0.5/1.0 |
Gene–gene interaction models by GMDR analysis.
| Best Interaction Model | TBA # | CVC | p Value | OR # (95% CI) # |
|---|---|---|---|---|
| APOE | 0.59 | 6/10 | 0.0006 | 2.81 (1.54–5.13) |
| TCF4, APOE | 0.66 | 10/10 | <0.0001 | 4.04 (2.27–7.17) |
| CLU, TCF4, APOE | 0.66 | 10/10 | <0.0001 | 5.71 (3.03–10.78) |
CVC—cross validation consistency, TBA—testing balanced accuracy, # Values rounded up to 2 decimal places.
Significantly enriched pathways of studied genes.
| Pathway ID | Pathway Description | Observed Gene Count | False Discovery Rate | Matching Proteins in Your Network (Labels) |
|---|---|---|---|---|
| GO:0022008 | neurogenesis | 15 | 0.00028 | APOE,CDON,CLU,CNTN4,CNTNAP2,CTNNA2,KLHL1,LSM1,NRXN3,RELN,SORL1,STK24,TCF4,TENM4,ZNF365 |
| GO:0007155 | cell adhesion | 11 | 0.00036 | CADM2,CD33,CDON,CNTN4,CNTNAP2,CTNNA2,DCHS2,NRXN3,PVRL2,RELN,TENM4 |
| GO:0007399 | nervous system development | 17 | 0.00036 | APOE,CDON,CLU,CNTN4,CNTNAP2,CTNNA2,DNAH11,GRIN2B,KLHL1,LSM1,NRXN3,RELN,SORL1,STK24,TCF4,TENM4,ZNF365 |
| GO:0048699 | generation of neurons | 14 | 0.00036 | APOE,CDON,CNTN4,CNTNAP2,CTNNA2,KLHL1,LSM1,NRXN3,RELN,SORL1,STK24,TCF4,TENM4,ZNF365 |
| GO:0032989 | cellular component morphogenesis | 10 | 0.00044 | CLU,CNTN4,CNTNAP2,CTNNA2,NRXN3,PVRL2,RELN,STK24,TENM4,ZNF365 |
| GO:0050767 | regulation of neurogenesis | 10 | 0.00044 | APOE,CDON,CNTN4,LSM1,RELN,SORL1,STK24,TCF4,TENM4,ZNF365 |
| GO:0048666 | neuron development | 10 | 0.00045 | APOE,CNTN4,CNTNAP2,CTNNA2,KLHL1,NRXN3,RELN,STK24,TENM4,ZNF365 |
| GO:0031175 | neuron projection development | 9 | 0.00058 | APOE,CNTN4,CNTNAP2,CTNNA2,KLHL1,NRXN3,RELN,STK24,ZNF365 |
| GO:0000902 | cell morphogenesis | 9 | 0.00061 | CLU,CNTN4,CNTNAP2,CTNNA2,NRXN3,RELN,STK24,TENM4,ZNF365 |
| GO:0032990 | cell part morphogenesis | 8 | 0.00064 | CNTN4,CNTNAP2,CTNNA2,NRXN3,PVRL2,RELN,STK24,ZNF365 |
| GO:0007417 | central nervous system development | 10 | 0.00084 | CDON,CLU,CNTN4,CNTNAP2,CTNNA2,GRIN2B,KLHL1,RELN,TENM4,ZNF365 |
| GO:0007611 | learning or memory | 6 | 0.00089 | APOE,CNTNAP2,DNAH11,GRIN2B,NRXN3,RELN |
References
1. Alzheimer’s Association Report. Alzheimer’s disease facts and figures. Alzheimer’s Dement.; 2019; 15, pp. 321-387. Available online: https://alz-journals.onlinelibrary.wiley.com/doi/abs/10.1016/j.jalz.2019.01.010 (accessed on 6 August 2021).
2. Patterson, C. World Alzheimer Report. The State of the Art of Dementia Research: New Frontiers; Alzheimer’s Disease International: London, UK, 2018; Available online: https://www.alz.co.uk/research/WorldAlzheimerReport2018.pdf (accessed on 21 September 2018).
3. Sosa-Ortiz, A.L.; Acosta-Castillo, I.; Prince, M.J. Epidemiology of dementias and Alzheimer’s disease. Arch. Med. Res.; 2012; 43, pp. 600-608. [DOI: https://dx.doi.org/10.1016/j.arcmed.2012.11.003]
4. Alzheimer’s Association Report. Alzheimer’s disease facts and figures. Alzheimer’s Dement.; 2012; 8, pp. 131-168. Available online: https://www.sciencedirect.com/science/article/abs/pii/S1552526012000325 (accessed on 6 August 2021).
5. Gatz, M.; Reynolds, C.A.; Fratiglioni, L.; Johansson, B.; Mortimer, J.A.; Berg, S.; Fiske, A.; Pedersen, N.L. Role of genes and environments for explaining Alzheimer disease. Arch. Gen. Psychiatry; 2006; 63, pp. 168-174. [DOI: https://dx.doi.org/10.1001/archpsyc.63.2.168]
6. Prestia, A.; Boccardi, M.; Galluzzi, S.; Cavedo, E.; Adorni, A.; Soricelli, A.; Bonetti, M.; Geroldi, C.; Giannakopoulos, P.; Thompson, P. et al. Hippocampal and amygdalar volume changes in elderly patients with Alzheimer’s disease and schizophrenia. Psychiatry Res.; 2011; 192, pp. 77-83. [DOI: https://dx.doi.org/10.1016/j.pscychresns.2010.12.015] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21458960]
7. Horesh, Y.; Katsel, P.; Haroutunian, V.; Domany, E. Gene expression signature is shared by patients with Alzheimer’s disease and schizophrenia at the superior temporal gyrus. Eur. J. Neurol.; 2011; 18, pp. 410-424. [DOI: https://dx.doi.org/10.1111/j.1468-1331.2010.03166.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20695885]
8. Lambert, J.C.; Heath, S.; Even, G.; Campion, D.; Sleegers, K.; Hiltunen, M.; Combarros, O.; Zelenika, D.; Bullido, M.J.; Tavernier, B. et al. Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat. Genet.; 2009; 41, pp. 1094-1099. [DOI: https://dx.doi.org/10.1038/ng.439] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19734903]
9. Harold, D.; Abraham, R.; Hollingworth, P.; Sims, R.; Gerrish, A.; Hamshere, M.L.; Pahwa, J.S.; Moskvina, V.; Dowzell, K.; Williams, A. et al. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s diseases. Nat. Genet.; 2009; 41, pp. 1088-1093. [DOI: https://dx.doi.org/10.1038/ng.440]
10. Logue, M.W.; Schu, M.; Vardarajan, B.N.; Buros, J.; Green, R.C.; Go, R.C.P.; Griffith, P.; Obisesan, T.O.; Shatz, R.; Borenstein, A. et al. A comprehensive genetic association study of Alzheimer disease in African Americans. Arch. Neurol.; 2011; 68, pp. 1569-1579. [DOI: https://dx.doi.org/10.1001/archneurol.2011.646] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22159054]
11. Cruchaga, C.; Haller, G.; Chakraverty, S.; Mayo, K.; Vallania, F.L.; Mitra, R.D.; Faber, K.; Williamson, J.; Bird, T.; Diaz-Arrastia, R. et al. Rare variants in APP, PSEN1 and PSEN2 increase risk for AD in late-onset Alzheimer’s disease families. PLoS ONE; 2012; 7, e31039. [DOI: https://dx.doi.org/10.1371/annotation/c92e16da-7733-421d-b063-1db19488daa6]
12. Holmans, P.; Green, E.K.; Pahwa, J.S.; Ferreira, M.A.; Purcell, S.M.; Sklar, P.; Owen, M.J.; O’Donovan, M.C.; Craddock, N. Wellcome Trust Case-Control Consortium. Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder. Am. J. Hum. Genet.; 2009; 85, pp. 13-24. [DOI: https://dx.doi.org/10.1016/j.ajhg.2009.05.011]
13. Kanehisa, M.; Goto, S.; Hattori, M.; Aoki-Kinoshita, K.F.; Itoh, M.; Kawashima, S.; Katayama, T.; Araki, M.; Hirakawa, M. From genomics to chemical genomics: New developments in KEGG. Nucleic Acids Res.; 2006; 34, pp. D354-D357. [DOI: https://dx.doi.org/10.1093/nar/gkj102] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16381885]
14. Harris, M.A.; Clark, J.; Ireland, A.; Lomax, J.; Ashburner, M.; Foulger, R.; Eilbeck, K.; Lewis, S.; Marshall, B.; Mungall, C. et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res.; 2004; 32, pp. D258-D261. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/14681407]
15. Szklarczyk, D.; Franceschini, A.; Wyder, S.; Forslund, K.; Heller, D.; Huerta-Cepas, J.; Simonovic, M.; Roth, A.; Santos, A.; Tsafou, K.P. et al. STRING v10: Protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res.; 2015; 43, pp. 447-452. [DOI: https://dx.doi.org/10.1093/nar/gku1003]
16. Warde-Farley, D.; Donaldson, S.L.; Comes, O.; Zuberi, K.; Badrawi, R.; Chao, P.; Franz, M.; Grouios, C.; Kazi, F.; Lopes, C.T. et al. The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res.; 2010; 38, pp. W214-W220. [DOI: https://dx.doi.org/10.1093/nar/gkq537]
17. Bocharova, A.V.; Stepanov, V.A. Genetic diversity of populations in North Eurasia by genetic markers associated with diseases impairing human cognitive functions. Russ. J. Genet.; 2021; 57, pp. 1082-1091. [DOI: https://dx.doi.org/10.1134/S1022795421080020]
18. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders; 4th ed. American Psychiatric Association: Washington, DC, USA, 2010.
19. McKhann, G.; Drachman, D.; Folstein, M.; Katzman, R.; Price, D.; Stadlan, E.M. Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology; 1984; 34, pp. 939-944. [DOI: https://dx.doi.org/10.1212/WNL.34.7.939]
20. Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B; 1995; 57, pp. 289-300. [DOI: https://dx.doi.org/10.1111/j.2517-6161.1995.tb02031.x]
21. Barrett, J.C.; Fry, B.; Maller, J.; Daly, M.J. Haploview: Analysis and visualization of LD and haplotype maps. Bioinformatics.; 2005; 21, pp. 263-265. [DOI: https://dx.doi.org/10.1093/bioinformatics/bth457] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15297300]
22. Chen, G.B.; Xu, Y.; Xu, H.M.; Li, M.D.; Zhu, J.; Lou, X.Y. Practical and theoretical considerations in study design for detecting gene-gene interactions using MDR and GMDR approaches. PLoS ONE; 2011; 6, e16981. [DOI: https://dx.doi.org/10.1371/journal.pone.0016981] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21386969]
23. Lipnicki, D.M.; Crawford, J.; Kochan, N.A.; Trollor, J.N.; Draper, B.; Reppermund, S.; Maston, K.; Mather, K.A.; Brodaty, H.; Sachdev, P.S. et al. Risk factors for mild cognitive impairment, dementia and mortality: The Sydney Memory and Ageing Study. J. Am. Med. Dir. Assoc.; 2017; 18, pp. 388-395. [DOI: https://dx.doi.org/10.1016/j.jamda.2016.10.014] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28043804]
24. Martínez-Martínez, A.B.; Torres-Perez, E.; Devanney, N.; Del Moral, R.; Johnson, L.A.; Arbones-Mainar, J.M. Beyond the CNS: The many peripheral roles of APOE. Neurobiol. Dis.; 2020; 138, 104809. [DOI: https://dx.doi.org/10.1016/j.nbd.2020.104809] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32087284]
25. Cai, C.; Wen, Z.; Li, L. The relationship between ApoE gene polymorphism and the efficacy of statins controlling hyperlipidemia. Am. J. Transl. Res.; 2021; 13, pp. 6772-6777. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34306425]
26. Liu, G.; Bao, X.; Jiang, Y.; Liao, M.; Jiang, Q.; Feng, R.; Zhang, L.; Ma, G.; Chen, Z.; Wang, G. et al. Identifying the Association Between Alzheimer’s Disease and Parkinson’s Disease Using Genome-Wide Association Studies and Protein-Protein Interaction Network. Mol. Neurobiol.; 2015; 52, pp. 1629-1636. [DOI: https://dx.doi.org/10.1007/s12035-014-8946-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25370933]
27. Wei, C.J.; Cui, P.; Li, H.; Lang, W.J.; Liu, G.Y.; Ma, X.F. Shared genes between Alzheimer’s disease and ischemic stroke. CNS Neurosci. Ther.; 2019; 25, pp. 855-864. [DOI: https://dx.doi.org/10.1111/cns.13117] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30859738]
28. DeMichele-Sweet, M.A.A.; Weamer, E.A.; Klei, L.; Vrana, D.T.; Hollingshead, D.J.; Seltman, H.J.; Sims, R.; Foroud, T.; Hernandez, I.; Moreno-Grau, S. et al. Genetic risk for schizophrenia and psychosis in Alzheimer disease. Mol. Psychiatry; 2018; 23, pp. 963-972. [DOI: https://dx.doi.org/10.1038/mp.2017.81]
29. Johansson, J.U.; Brubaker, W.D.; Javitz, H.; Bergen, A.W.; Nishita, D.; Trigunaite, A.; Crane, A.; Ceballos, J.; Mastroeni, D.; Tenner, A.J. et al. Peripheral complement interactions with amyloid β peptide in Alzheimer’s disease: Polymorphisms, structure, and function of complement receptor 1. Alzheimers Dement.; 2018; 14, pp. 1438-1449. [DOI: https://dx.doi.org/10.1016/j.jalz.2018.04.003]
30. Salakhov, R.R.; Goncharova, I.A.; Makeeva, O.A.; Golubenko, M.V.; Kulish, E.V.; Kashtalap, V.V.; Barbarash, O.L.; Puzyrev, V.P. TOMM40 Gene Polymorphisms Association with Lipid Profile. Genetika; 2014; 50, pp. 222-229. [DOI: https://dx.doi.org/10.1134/S1022795413120090]
31. Jansen, I.E.; Savage, J.E.; Watanabe, K.; Bryois, J.; Williams, D.M.; Steinberg, S.; Sealock, J.; Karlsson, I.K.; Hägg, S.; Athanasiu, L. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet.; 2019; 51, pp. 404-413. [DOI: https://dx.doi.org/10.1038/s41588-018-0311-9]
32. Stepanov, V.A.; Bocharova, A.V.; Marusin, A.V.; Zhukova, N.G.; Alifirova, V.M.; Zhukova, I.A. Replicative association analysis of genetic markers of cognitive traits with Alzheimer’s disease in the Russian population. Mol. Biol.; 2014; 48, pp. 952-962. [DOI: https://dx.doi.org/10.1134/S0026893314060168]
33. Corder, E.H.; Saunders, A.M.; Strittmatter, W.J.; Schmechel, D.E.; Gaskell, P.C.; Small, G.W.; Roses, A.D.; Haines, J.L.; Pericak-Vance, M.A. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science; 1993; 261, pp. 921-923. [DOI: https://dx.doi.org/10.1126/science.8346443]
34. Arpawong, T.E.; Pendleton, N.; Mekli, K.; McArdle, J.J.; Gatz, M.; Armoskus, C.; Knowles, J.A.; Prescott, C.A. Genetic variants specific to aging-related verbal memory: Insights from GWASs in a population-based cohort. PLoS ONE; 2017; 12, e0182448. [DOI: https://dx.doi.org/10.1371/journal.pone.0182448]
35. Roses, A.D.; Lutz, M.W.; Amrine-Madsen, H.; Saunders, A.M.; Crenshaw, D.G.; Sundseth, S.S.; Huentelman, M.J.; Welsh-Bohmer, K.A.; Reiman, E.M. A TOMM40 variable-length polymorphism predicts the age of late-onset Alzheimer’s disease. Pharmacogenomics J.; 2010; 10, pp. 375-384. [DOI: https://dx.doi.org/10.1038/tpj.2009.69] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20029386]
36. Sebastiani, P.; Gurinovich, A.; Nygaard, M.; Sasaki, T.; Sweigart, B.; Bae, H.; Andersen, S.L.; Villa, F.; Atzmon, G.; Christensen, K. et al. APOE Alleles and Extreme Human Longevity. J. Gerontol. Ser. A Biol. Sci. Med. Sci.; 2019; 74, pp. 44-51. [DOI: https://dx.doi.org/10.1093/gerona/gly174]
37. Forrest, M.; Chapman, R.M.; Doyle, A.M.; Tinsley, C.L.; Waite, A.; Blake, D.J. Functional analysis of TCF4 missense mutations that cause Pitt–Hopkins syndrome. Hum. Mutat.; 2012; 33, pp. 1676-1686. [DOI: https://dx.doi.org/10.1002/humu.22160] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22777675]
38. Stefansson, H.; Ophoff, R.A.; Steinberg, S.; Andreassen, O.A.; Cichon, S.; Rujescu, D.; Werge, T.; Pietiläinen, O.P.; Mors, O.; Mortensen, P.B. et al. Common variants conferring risk of schizophrenia. Nature; 2009; 460, pp. 744-747. [DOI: https://dx.doi.org/10.1038/nature08186]
39. Wirgenes, K.V.; Sønderby, I.E.; Haukvik, U.K.; Mattingsdal, M.; Tesli, M.; Athanasiu, L.; Sundet, K.; Røssberg, J.I.; Dale, A.M.; Brown, A.A. et al. TCF4 sequence variants and mRNA levels are associated with neurodevelopmental characteristics in psychotic disorders. Transl. Psychiatry; 2012; 2, e112. [DOI: https://dx.doi.org/10.1038/tp.2012.39] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22832956]
40. Eckart, N.; Song, Q.; Yang, R.; Wang, R.; Zhu, H.; McCallion, A.S.; Avramopoulos, D. Functional Characterization of Schizophrenia-Associated Variation in CACNA1C. PLoS ONE; 2016; 11, e0157086. [DOI: https://dx.doi.org/10.1371/journal.pone.0157086]
41. Green, E.K.; Grozeva, D.; Jones, I.; Jones, L.; Kirov, G.; Caesar, S.; Gordon-Smith, K.; Fraser, C.; Forty, L.; Russell, E. et al. The bipolar disorder risk allele at CACNA1C also confers risk of recurrent major depression and of schizophrenia. Mol. Psychiatry; 2010; 15, pp. 1016-1022. [DOI: https://dx.doi.org/10.1038/mp.2009.49]
42. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: A genome-wide analysis. Lancet; 2013; 381, pp. 1371-1379. [DOI: https://dx.doi.org/10.1016/S0140-6736(12)62129-1]
43. Ishii, K.; Kubo, K.I.; Nakajima, K. Reelin and neuropsychiatric disorders. Front. Cell. Neurosci.; 2016; 10, 229. [DOI: https://dx.doi.org/10.3389/fncel.2016.00229] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27803648]
44. Ovadia, G.; Shifman, S. The genetic variation of RELN expression in schizophrenia and bipolar disorder. PLoS ONE; 2011; 6, e19955. [DOI: https://dx.doi.org/10.1371/journal.pone.0019955]
45. Bufill, E.; Roura-Poch, P.; Sala-Matavera, I.; Antón, S.; Lleó, A.; Sánchez-Saudinós, B.; Tomàs-Abadal, L.; Puig, T.; Abós, J.; Bernades, S. et al. Reelin signaling pathway genotypes and Alzheimer disease in a Spanish population. Alzheimer Dis. Assoc. Disord.; 2015; 29, pp. 169-172. [DOI: https://dx.doi.org/10.1097/WAD.0000000000000002]
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; Zhukova, Natalia 2 ; Zhukova, Irina 2 ; Minaycheva, Larisa 1 ; Makeeva, Oksana 3 ; Stepanov, Vadim 1 1 Tomsk National Research Medical Center, Research Institute of Medical Genetics, 634050 Tomsk, Russia;
2 Department of Neurology and Neurosurgery, Faculty of General Medicine, Siberian State Medical University, 634050 Tomsk, Russia;
3 Nebbiolo Center for Clinical Trials, 634009 Tomsk, Russia;