Next Article in Journal
Genetic Diversity of Common Olive (Olea europaea L.) Cultivars from Nikita Botanical Gardens Collection Revealed Using RAD-Seq Method
Next Article in Special Issue
Finding an Appropriate Mouse Model to Study the Impact of a Treatment for Friedreich Ataxia on the Behavioral Phenotype
Previous Article in Journal
Selection of Reliable Reference Genes for Gene Expression Normalization in Sagittaria trifolia
Previous Article in Special Issue
A Patient with Corticobasal Syndrome and Progressive Non-Fluent Aphasia (CBS-PNFA), with Variants in ATP7B, SETX, SORL1, and FOXP1 Genes
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 14
Issue 7
10.3390/genes14071322
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genome-Wide Epistasis Study of Cerebrospinal Fluid Hyperphosphorylated Tau in ADNI Cohort

by
Dandan Chen
1,2,†,
Jin Li
1,†,
Hongwei Liu
3,
Xiaolong Liu
1,
Chenghao Zhang
1,
Haoran Luo
1,
Yiming Wei
1,
Yang Xi
3,
Hong Liang
1,* and
Qiushi Zhang
3,*
1
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
2
School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China
3
School of Computer Science, Northeast Electric Power University, Jilin 132012, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2023, 14(7), 1322; https://doi.org/10.3390/genes14071322
Submission received: 1 June 2023 / Revised: 19 June 2023 / Accepted: 20 June 2023 / Published: 23 June 2023
(This article belongs to the Special Issue Study on Genotypes and Phenotypes of Neurodegenerative Diseases)
Browse Figures
Review Reports Versions Notes

Abstract

:
Alzheimer’s disease (AD) is the main cause of dementia worldwide, and the genetic mechanism of which is not yet fully understood. Much evidence has accumulated over the past decade to suggest that after the first large-scale genome-wide association studies (GWAS) were conducted, the problem of “missing heritability” in AD is still a great challenge. Epistasis has been considered as one of the main causes of “missing heritability” in AD, which has been largely ignored in human genetics. The focus of current genome-wide epistasis studies is usually on single nucleotide polymorphisms (SNPs) that have significant individual effects, and the amount of heritability explained by which was very low. Moreover, AD is characterized by progressive cognitive decline and neuronal damage, and some studies have suggested that hyperphosphorylated tau (P-tau) mediates neuronal death by inducing necroptosis and inflammation in AD. Therefore, this study focused on identifying epistasis between two-marker interactions at marginal main effects across the whole genome using cerebrospinal fluid (CSF) P-tau as quantitative trait (QT). We sought to detect interactions between SNPs in a multi-GPU based linear regression method by using age, gender, and clinical diagnostic status (cds) as covariates. We then used the STRING online tool to perform the PPI network and identify two-marker epistasis at the level of gene–gene interaction. A total of 758 SNP pairs were found to be statistically significant. Particularly, between the marginal main effect SNP pairs, highly significant SNP–SNP interactions were identified, which explained a relatively high variance at the P-tau level. In addition, 331 AD-related genes were identified, 10 gene–gene interaction pairs were replicated in the PPI network. The identified gene-gene interactions and genes showed associations with AD in terms of neuroinflammation and neurodegeneration, neuronal cells activation and brain development, thereby leading to cognitive decline in AD, which is indirectly associated with the P-tau pathological feature of AD and in turn supports the results of this study. Thus, the results of our study might be beneficial for explaining part of the “missing heritability” of AD.

1. Introduction

Alzheimer’s disease (AD) is an insidious neurodegenerative disorder. The currently available therapies do not slow disease progression, provides short-term symptomatic relief only [1,2]. Genome-wide association studies (GWAS) and related techniques are gradually discovering variants of causal gene variants that contribute to complex human diseases. However, after years of GWAS efforts by countless researchers, these findings can explain only a small fraction of the heritability and the genetic factors of many human diseases and traits failed to be discovered, the so-called “missing heritability” [3,4]. Many research suggest that “missing heritability” in AD remains extensive with an estimated 25% of phenotypic variance unexplained by known variants, which may be explained by epistasis [5,6,7]. A single nucleotide polymorphism (SNP) is defined as single nucleotide alteration in a DNA sequence among individuals [8,9]. As a major drawback, in common GWAS, the focus is usually on SNPs that have significant individual effects [10,11]. Epistasis is the phenomenon about the interaction alleles of different loci when expressing a certain phenotype, and it cannot be attributed to the additive combination of effects corresponding to the individual loci. If the effect of one variant affecting a complex trait depends on the genotype of a second variant affecting that trait, epistasis will occurs [12]. To be specfic, epistasis leads to complex phenotypic effects, in which the effect of one locus is masked by the effects on another locus or the joint effects of two SNPs may be significant whereas they are ineffective separately [13,14]. Therefore, epistasis detection is expected to explain the “missing heritability” of many complex diseases such as AD, diabetes, and hypertension [15,16,17].
As the number of interactions grows exponentially with the number of variants, computational limitation is a bottleneck [18]. Most methods of epistasis detection choose to refrain from the brute force search in the SNP–SNP interaction space and try to reduce computational burden using dimensionality reduction screening and priori knowledge [19,20]. However, using more subjective priori knowledge or random factors for dimension reduction search will lead to signal loss because the risk of epistatic interaction is unknown [21]. Moreover, most methods of epistasis detection have been designed for case-control tasks over the past decade, with few on quantitative traits (QT) [19]. Compared to case–control status, QT has increased statistical significance and could better track AD progression [22]. Therefore, mining more potential loci by QT, which are implicated in AD without dropping signals across the whole genome, is urgently needed.
Emerging data have suggested that the prevailing amyloid cascade hypothesis is insufficient to explain many aspects of AD pathogenesis, neuroinflammation also plays an important role in the pathogenesis of AD [2,23]. AD is characterized by extracellular amyloid- β (A β ) peptides in cortical A β plaques, intracellular phosphorylated tau protein as neurofibrillary tangles, and neuronal as well as axonal degeneration. These key hallmarks of AD can be measured in vivo with positron emission tomography (PET) imaging and biofluid markers including plasma and CSF assays [24]. CSF tau, P-tau, and A β 42 are established biomarkers for AD and have been widely used as QT for genetic analyse [25]. Furthermore, accumulated P-tau may be the primary contributor to neurodegeneration during AD, and neuroinflammation is a central mechanism involved in neurodegeneration as observed in AD and might play a critical role in inducing neurodegeneration [26,27,28]. Moreover, P-tau, one of the three candidate CSF QT, has been widely studied in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. For example, heterogeneity in p-tau species carries predictive power in the identification of disease severity in incipient AD [29]. P-tau might start neurodegenerative processes and are necessary for cognitive decline [30]. Tau pathology is an initiating factor in sporadic AD [31]. Increase of p181tau levels might predict preclinical AD in cognitively normal elderly [32].
Therefore, CSF P-tau was used as a QT to advance statistical power and biological interpretation in this study. Then, we performed genome-wide epistasis detection in ADNI cohort based on a multi-GPU method, which has a better detection power outperform other competitive approaches.

2. Materials and Methods

2.1. Genotyping Data and Subjects Processing

Data used in this study were obtained from the ADNI database. The ADNI is a longitudinal multi-center study designed to be used for the early detection and tracking of AD, which was founded in 2004 under the leadership of Dr. Michael W. Weine and supported by the Foundation for the National Institutes of Health ($27 million) and the National Institute on Aging ($40 million). The primary goal of ADNI was developing biomarkers as outcome measures for clinical trials, examining biomarkers in earlier stages of the disease, and developing biomarkers as predictors of cognitive decline, etc. The SNP data were collected from the Illumina 2.5M array and the Illumina OmniQuad array including ADNI-1, ADNI-GO, and ADNI-2 cohorts, which can be downloaded from the LONI website (https://adni.loni.usc.edu, accessed on 19 June 2023). A total of 687,414 SNPs were involved in the study. To get pure SNP data, genetic analysis tool PLINK v1.90 was used to filter the SNPs according to the following quality control (QC) criteria: (1) SNPs on chromosome 1–22; (2) minimum call rate for SNPs and subjects ≥ 95%; (3) minimum allele frequencies (MAF) ≥ 5%; (4) Hardy-Weinberg equilibrium (HWE) test p ≥ 10 − 6 . After the QC, a total of 563,980 SNPs participated in this study.
Subjects were checked by the following QC flow: (1) call rate per subjects ≥ 90%; (2) gender check; (3) identity check. Then, EIGENSTRAT was used to perform the population stratification analysis [33]. The population stratification analysis yielded 89 subjects who were non-Hispanic Caucasians. These 89 participants were excluded from the analysis. Finally, 1079 subjects passed the QC.
CSF P-tau phenotype was used as QT in this study. The QC criteria of phenotype was based on two principles: baseline consistency principle and normal distribution principle. Out of the 1079 subjects retained after the QC, 860 subjects had both genotype data and phenotype (CSF P-tau). These subjects (N = 860) including 201 cognitive normal cognition (CN), 84 significant memory impairment (SMC), 251 early mild cognitive impairment (EMCI), 209 late mild cognitive impairment (LMCI), and 115 AD subjects.
Overall, 860 valid P-tau of CSF subjects and 563,980 remained for the subsequent genome-wide SNP-SNP interaction analysis.

2.2. Genome-Wide SNP-SNP Interaction Analysis

In this study, including as covariates in the liner regression analysis such as age, gender and clinical diagnostic status (cds), we consider the linear regression model of additive main effect of two SNPs,
L 1 , 2 = α 0 + α 1 × S N P 1 + α 2 × S N P 2 + a g e + g e n d e r + c d s + ε i
where α 0 , α 1 and α 2 are regression coefficients; ε i is a residual that follows a normal distribution with mean zero and variance σ 2 . Therefore, the sum of both additive main effect and SNP 1 -SNP 2 interaction is then given by
L 1 , 2 S = α 0 + α 1 × S N P 1 + α 2 × S N P 2 + α 1 , 2 × S N P 1 × S N P 2 + a g e + g e n d e r + c d s + ε i
where α 0 , α 1 , α 2 and α 1 , 2 are regression coefficients. We set Y = Y 1 Y 2 … Y n , where n is the number of subjects in the sample, then the signal of SNPs is set in the form of S i = S 1 j S 2 j … S n j , where j = 1 or 2; S i j is the genotype of the allele on the SNP j of the ith subject; S i j = 0, 1 or 2. For each SNP-SNP interaction pair, the interaction effect was evaluated by two linear regression models according to the CSF P-tau quantitative trait. In practice, we test the significance of the interaction terms using an F test, then the p-value would be calculated.

2.3. Bioinformatics Analyses

To further explain the biological functions of SNP-SNP interaction pairs with significant interactions, all SNPs were mapped to the corresponding genes according to position based on the Homo sapiens genome assembly GRCh37 (hg19), and SNPs not located within the gene region were mapped to nearby genes by position offset of 100 kb. Moreover, the gene databases National Center for Biotechnology Information Phenotype-Genotype Integrator (NCBI PheGenI) was used to analyze the association of the candidate genes with phenotype trait, and Reactome 2022 was used to conduct pathway analysis for discover associated biological processes. For the gene-gene interaction pairs after mapping from the SNP level, functional enrichment processes were performed by PPI network enrichment analysis through the STRING database.

3. Results

3.1. SNP-SNP Interaction Results

In this study, a genome-wide SNP-SNP interaction detection using CSF P-tau as the intermediate quantitative phenotype was implemented. With the assistance of the GEEpiQt tool [34], we completely detected all SNP-SNP interaction pairs with significant interaction across the whole genome. According to the set p-value criteria, 758 SNP interaction pairs passed the significance requirement. After the GWAS analysis was conducted on the SNPs of 758 interaction pairs, all the results indicated that the statistical significance of the interaction effect was much higher than the main effect. The interaction effects and main effects of 758 significant SNP-SNP interaction pairs are shown in Figure 1.
To further confirm the association of SNP interactions with quantitative phenotypes, the explained variance of genetic epistasis of CSF P-tau was calculated by IBM SPSS 24.0. Two general linear models were used, with age, sex and disease diagnosis status added as covariates, and interaction terms were added to one model for calculating the main effect and interaction effect on phenotypic explanatory rate separately. The R square of the interaction terms and the additive terms are shown in Figure 2. The top 10 R-square of SNP-SNP interaction pairs and results of post hoc analysis on P-tau level are seen in Table 1. Age, gender, and cds accounted for 9.3% of variance on the P-tau level. Moreover, Table 1 gives the proportion of additional variance in P-tau level explained by the combined main effect and interaction effect of SNP 1 and SNP 2 after accounting for age, gender, cds, SNP 1 and SNP 2 . The percentages of each interaction pair are as follows. For rs2291948 (APOOP5)—rs2619171, the interaction term accounted for 5.6% of variance, and the main effects accounted for 0.1% of variance (5.7% combined). For rs17069204 (SEC63)—rs4983187 (LINC02588), the interaction term accounted for 5.5% of variance, and the main effects accounted for 0.8% of variance (6.3% combined). For rs6882813—rs17416058, the interaction term accounted for 5.5% of variance, and the main effects accounted for 0.7% of variance (6.2% combined). For rs129600 (PPARA)—rs6602151 (RSU1), the interaction term accounted for 5.4% of variance, and the main effects accounted for 0.5% of variance (5.9% combined). For rs6796502 (PRSS42P)—rs6999890 (SLC45A4), the interaction term accounted for 5.3% of variance, and the main effects accounted for 0.4% of variance (5.7% combined). For rs1412839 (PDPN)—rs2397718, the interaction term accounted for 5.3% of variance, and the main effects accounted for 0.5% of variance (5.8% combined). For rs2219872 (GRIP1)—rs2647911 (C12orf66), the interaction term accounted for 5.2% of variance, and the main effects accounted for 1.3% of variance (6.5% combined). For rs9320250 (OSTM1)—rs4983187 (LINC02588), the interaction term accounted for 5.2% of variance, and the main effects accounted for 1.1% of variance (6.3% combined). For rs10802434 (SCCPDH)—rs12470444 (NRP2), the interaction term accounted for 5.2% of variance, and the main effects accounted for 0.6% of variance (5.8% combined). For rs2487643 (PDPN)—rs2397718, the interaction term accounted for 5.2% of variance, and the main effects accounted for 0.6% of variance (5.8% combined).

3.2. Functional Annotations for Significant Interaction Pairs

A total of 1161 SNPs were mapped onto 578 genes. Then, gene set enrichment analysis was performed based on HDSigDB Human 2021 in Enrichr. The results of enrichment analysis indicate that 331 genes have been shown associated with AD, and the number of unconfirmed AD-related genes is 247. In order to make more sensible explanations at the gene level in subsequent study, these gene pairs were categorized according to the relationship of genes with AD on both sides. The interactions were classified into three categories: both genes in each pair are AD-related, only one gene in each pair is AD-related, and none gene in each pair is associated with AD, as shown in Figure 3.
Moreover, the gene databases National Center for Biotechnology Information Phenotype-Genotype Integrator (NCBI PhenGenI) and Reactome 2022 were used to analyze the association of the detected genes with CSF P-tau quantitative traits. Pathway enrichment analysis of genes mapped by the identified SNPs based on their enrichment adjusted p-values is presented in Figure 4. The Reactome enrichment analysis showed that three of the top ten pathways were significant, Cell-Cell Communication, Cell Junction Organization and Neuronal System pathways. Among them, there is evidence that “Cell-Cell Communication” is associated with the abnormal accumulation of phosphorylated tau protein in Alzheimer’s disease. In the Neuronal System pathway, tau protein is normally involved in stabilizing microtubules in neurons, but in Alzheimer’s disease, it can become hyperphosphorylated and form aggregates called neurofibrillary tangles. These tangles can disrupt the transport of nutrients and other substances within neurons, which can further damage the pathways of the neuronal system. The results of the PhenGenI Association enrichment analysis were found significant in several diseases, such as Platelet Function Tests, Alzheimer Disease, and Stroke.

3.3. Potential Interactions via Protein-Protein Interaction Analysis

To investigate and validate the potential interactions additionally, we submitted 174 gene-gene interaction pairs to the STRING database for PPI enrichment analysis. All the 174 gene-gene interaction pairs were selected from the 1st relationship shows in Figure 3a and the 2nd relationship as shows in Figure 3b. Notably, 10 of the gene interaction pairs overlapped with PPI networks in the database, as shown in Figure 5, including: SPSB1-EPHB1, HNRNPU-NEDD4L, MYT1L-NYAP2, GGCX-F13A1, LRP1B-PDE4D, RARB-NR3C1, CCL2-SEMA6D, ROBO1-TLE1, CSNK1A1-PTK7, MYO5B-PCDH15.

4. Discussion

In this study, detection of genome-wide SNP-SNP interaction based on multi-GPU were performed. To our knowledge, this study is a highly comprehensive epistatic study of QT at the P-tau level. A total of 758 SNP-SNP pairs were found to be statistically significant, and highly significant SNP–SNP interactions were detected between the marginal main effect SNPs. In particular, the interaction effects were much higher than the main effects (Figure 1). As we expected, all identified interaction pairs explained a relatively high-level variance at the P-tau level (Figure 2 and Table 1), which could be helpful for explaining some part of the “missing heritability” of AD.
To identify potential genetic epistasis implicated in AD and obtain biologically meaningful explanations at gene-gene interaction level, 174 gene-gene interaction pairs, which from 1st relationship (Figure 3a) and 2nd relationship (Figure 3b) were submitted to the STRING database to perform the PPI enrichment analysis. As shown in Figure 5, the PPI sub-network containing 20 genes and 33 gene-gene interactions were identified. As a result, ten gene-gene interaction pairs overlapped with the PPI network need further discussion.
CSNK1A1 is a casein kinase which is involved in the phosphorylation state of tau [35]. Protein tyrosine kinase 7(PTK7) is a regulator of Wnt signaling pathways [36]. Wnt signaling is deregulated in AD, which could contribute to synapse degeneration and cognitive decline. This deficiency in Wnt signaling may further exacerbate tau hyperphosphorylation [37]. Therefore, the CSNK1A1 and PTK7 interaction shows strong associations with tau phosphorylation.
The SPRY domain-containing SOCS box protein 1 (SPSB1) is involved in the development of AD through nitric oxide (NO) pathways. To be specific, NO pathways contribute to pathogenesis of neurodegeneration in AD and other neurodegenerative dementias by involving in neuroinflammation, while SPSB1 negatively control NO production and limit cellular toxicity [38]. Ephrin type-B receptor 1 (EphB1) is upregulated in injured motor neurons, and then activated astrocytes [39]. Furthermore, activated astrocytes mediate neuroinflammation and neurodegeneration. Neuroinflammation induces neurodegeneration and the processes involved in neurodegeneration augments neuroinflammation [26].
Neurodegeneration is mediated by inflammatory and neurotoxic mediators such as chemokine (C-C motif) ligand 2 (CCL2), CCL5, tumor necrosis factor-alpha (TNF- α ) and interleukin-6 (IL-6) etc. The increased level of these mediators including CCL2 lead to neurodegeneration and neuronal death in neurodegenerative diseases [26,40]. These inflammatory and neurotoxic mediators directly or indirectly through glial cells and inflammatory cells affect neuronal survival and induce neurodegeneration [26]. Moreover, CCL2 is implicated in the pathways recruiting microglia and the development of P-tau pathology, and might be related to reducing neuroinflammation [41]. SEMA6D is a regulator of microglial phagocytosis and inflammatory cytokine (TNF α , IL-6 etc.) release in a TREM2-dependent manner [42,43]. Moreover, microglial phagocytosis is a disease-associated process emerging from AD genetics [44]. Excessive microglial phagocytosis of synapses can be observed in AD, leading to significant synapse loss and memory impairment [45]. And lack of microglial phagocytosis can exacerbate pathology of AD and induce memory impairment [46]. Microglia are also major players in neuroinflammation [23].
RP1B belonged to the low-density lipoprotein (LDL) receptor family, and several members of the LDL family have been implicated in cellular processes relevant to neurodegeneration, including tau uptake et al. Enhanced LRP1B activity can protect against the pathogenesis of AD and cognitive decline in old age. Inhibition of phosphodiesterase 4D (PDE4D) activity can enhance phosphorylation of tau.
MYT1L is a critical mediator of directly converting human brain vascular pericytes (HBVPs) into cholinergic neuronal cells, and the cholinergic deficit is thought to underlie progressed cognitive decline in AD. Neuronal tyrosine-phosphorylated phosphoinositide-3-kinase adapter 2 (NYAP2) is involved in remodeling of actin cytoskeleton [47]. Actin cytoskeleton has been described as an underlying factor of synaptic failure in AD, which could contribute to AD pathology [48].
Regulation of HNRNPU expression ameliorates impairments of learning and memory abilities in an AD rat model. NEDD4L is identified as potential nuclear enriched abundant transcript 1 (NEAT1) interaction proteins. upregulated NEAT1 can give rise to the amyloid accumulation and cognitive decline in AD.
In summary, CSNK1A1-PTK7 interaction and PDE4D gene shows strong associations with P-tau, which is directly associated with pathogenesis of AD. Two pairs of gene-gene interactions show strong associations with AD in terms of neuroinflammation and neurodegeneration: SPSB1-EPHB1, CCL2-SEMA6D. MYT1L gene, LRP1B gene and NEDD4L gene show strong associations with AD, and leading to cognitive decline in AD. LRP1B gene is also associated with pathogenesis of AD. HNRNPU gene exerts its effects on learning and memory abilities in AD. NYAP2 gene is involved in remodeling of actin cytoskeleton, which is indirectly associated with AD pathology. Two pairs of gene-gene interactions can affect the activation of neuronal cells and contribute to brain development: RARB-NR3C1, ROBO1-TLE1 [49,50]. MYO5B gene and GGCX gene show strong associations with schizophrenia [51]. Neuroinflammation is well established in a subset of schizophrenia patients [52]. In addition, two genes have not yet been associated with AD pathology: F13A1, PCDH15, which warrant further investigation.
To our knowledge, neurodegeneration appears to be the biological mechanism most proximate to cognitive decline in AD [53]. A β and tau pathologies interact synergistically in the preclinical stages of AD, which contributing to faster neurodegeneration and cognitive decline [54,55]. Therefore, the identified gene-gene interactions and genes in the PPI network might be related to neuroinflammation and neurodegeneration, thereby leading to cognitive decline in AD, which is indirectly proves that accumulated P-tau may be the primary contributor to neurodegeneration during AD [27], and in turn supports the results of this study.

5. Conclusions

Aimed at performing genome-wide epistasis detection in the ADNI cohort, we used CSF P-tau as a QT. 331 AD-related gene were replicated, which are previously confirmed AD risk genes. We also replicated 10 findings in the PPI network, which are SPSB1-EPHB1, HNRNPU-NEDD4L, MYT1L-NYAP2, GGCX-F13A1, LRP1B-PDE4D, RARB-NR3C1, CCL2-SEMA6D, ROBO1-TLE1, CSNK1A1- PTK7, MYO5B-PCDH15. Moreover, 3 gene-gene pairs of interaction showed strong association with AD: CSNK1A1-PTK7, SPSB1-EPHB1, CCL2-SEMA6D. Interactions between RARB and NR3C1, between ROBO1 and TLE1 can affect the activation of neuronal cells and contribute to brain development. Our study also revealed two genes have not yet been associated with AD pathology: F13A1, PCDH15, which warrant further investigation. In summary, our results can provide useful clues to the aspect of inducing neuroinflammation and neurodegeneration, and show strong association with AD in terms of cognitive decline. Therefore, this study might open new avenues to complement common GWAS. Furthermore, our results may be replicated by considering other quantitative traits using different databases and methods to complement the PPI network. Biological interpretation is also a direction of future research.

Author Contributions

D.C. designed the entire study, executed data analysis, and wrote the manuscript. J.L. performed data analysis and wrote the manuscript. Q.Z. conceived and designed the study, reviewed data analysis, and critically revised the manuscript for important intellectual content. H.L.(Hong Liang) performed data analysis and revised the manuscript. H.L. (Hongwei Liu) played a major role in data analysis and biological analysis and contributed preparing figures. Y.X., X.L., C.Z., H.L. (Haoran Luo) and Y.W. performed literature search and prepared the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This project was supported by the National Natural Science Foundation of China (grant number: 62206044).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used in this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The database is the (http://adni.loni.usc.edu/, accessed on 19 June 2023) of the Alzheimer’s disease neuroimaging database.

Acknowledgments

The authors acknowledge the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhang, C.; Griciuc, A.; Hudry, E.; Wan, Y.; Quinti, L.; Ward, J.; Forte, A.M.; Shen, X.; Ran, C.; Elmaleh, D.R.; et al. Cromolyn reduces levels of the Alzheimer’s disease-associated amyloid β-protein by promoting microglial phagocytosis. Sci. Rep. 2018, 8, 1144. [Google Scholar] [CrossRef] [Green Version]
  2. Rusek, M.; Smith, J.; El-Khatib, K.; Aikins, K.; Czuczwar, S.J.; Pluta, R. The Role of the JAK/STAT Signaling Pathway in the Pathogenesis of Alzheimer’s Disease: New Potential Treatment Target. Int. J. Mol. Sci. 2023, 24, 864. [Google Scholar] [CrossRef] [PubMed]
  3. Bloom, J.S.; Ehrenreich, I.M.; Loo, W.T.; Lite, T.L.V.; Kruglyak, L. Finding the sources of missing heritability in a yeast cross. Nature 2013, 494, 234–237. [Google Scholar] [CrossRef] [Green Version]
  4. 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, 404–413. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Simmonds, E.; Leonenko, G.; Schmidt, K.M.; Hill, M.; Myers, A.; Shoai, M.; de Rojas, I.; Tesi, N.; Holstege, H.; van der Flier, W.; et al. What does heritability of Alzheimer’s disease represent? PLoS ONE 2023, 18, e0281440. [Google Scholar]
  6. Andrews, S.J.; Renton, A.E.; Fulton-Howard, B.; Podlesny-Drabiniok, A.; Marcora, E.; Goate, A.M. The complex genetic architecture of Alzheimer’s disease: Novel insights and future directions. eBiomedicine 2023, 90, 104511. [Google Scholar] [CrossRef] [PubMed]
  7. Ebbert, M.T.; Ridge, P.G.; Kauwe, J.S. Bridging the gap between statistical and biological epistasis in Alzheimer’s disease. BioMed Res. Int. 2015, 2015, 870123. [Google Scholar] [CrossRef]
  8. Min, A.; Thompson, E.; Basu, S. Comparing heritability estimators under alternative structures of linkage disequilibrium. G3 2022, 12, jkac134. [Google Scholar] [CrossRef]
  9. KaradaĞ, Ö.; Altun, G.; AktaŞ, S. Assessment of SNP-SNP interactions by using square contingency table analysis. An. Acad. Bras. CiÊNcias 2020, 92. [Google Scholar] [CrossRef]
  10. Lutz, M.W.; Sprague, D.; Chiba-Falek, O. Bioinformatics strategy to advance the interpretation of Alzheimer’s disease GWAS discoveries: The roads from association to causation. Alzheimer’s Dement. 2019, 15, 1048–1058. [Google Scholar] [CrossRef]
  11. Goudey, B.; Rawlinson, D.; Wang, Q.; Shi, F.; Ferra, H.; Campbell, R.M.; Stern, L.; Inouye, M.T.; Ong, C.S.; Kowalczyk, A. GWIS-model-free, fast and exhaustive search for epistatic interactions in case-control GWAS. BMC Genom. 2013, 14, S10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Mackay, T.F.; Moore, J.H. Why epistasis is important for tackling complex human disease genetics. Genome Med. 2014, 6, 42. [Google Scholar] [CrossRef] [Green Version]
  13. Mackay, T.F. Epistasis and quantitative traits: Using model organisms to study gene–gene interactions. Nat. Rev. Genet. 2014, 15, 22–33. [Google Scholar] [CrossRef] [Green Version]
  14. Moore, J.H. A global view of epistasis. Nat. Genet. 2005, 37, 13–14. [Google Scholar] [CrossRef]
  15. Chang, Y.C.; Wu, J.T.; Hong, M.Y.; Tung, Y.A.; Hsieh, P.H.; Yee, S.W.; Giacomini, K.M.; Oyang, Y.J.; Chen, C.Y. GenEpi: Gene-based epistasis discovery using machine learning. BMC Bioinform. 2020, 21, 68. [Google Scholar] [CrossRef]
  16. Yang, Y.; Yao, S.; Ding, J.M.; Chen, W.; Guo, Y. Enhancer-gene interaction analyses identified the epidermal growth factor receptor as a susceptibility gene for type 2 diabetes mellitus. Diabetes Metab. J. 2021, 45, 241–250. [Google Scholar] [CrossRef] [PubMed]
  17. Cordell, H.J. Detecting gene–gene interactions that underlie human diseases. Nat. Rev. Genet. 2009, 10, 392–404. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Wang, H.; Yue, T.; Yang, J.; Wu, W.; Xing, E.P. Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies. BMC Bioinform. 2019, 20, 656. [Google Scholar] [CrossRef] [PubMed]
  19. Russ, D.; Williams, J.A.; Cardoso, V.R.; Bravo-Merodio, L.; Pendleton, S.C.; Aziz, F.; Acharjee, A.; Gkoutos, G.V. Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models. PLoS ONE 2022, 17, e0263390. [Google Scholar] [CrossRef] [PubMed]
  20. Wang, Q.; Zhang, Q.; Meng, F.; Li, B. Objective-hierarchy based large-scale evolutionary algorithm for improving joint sparsity-compression of neural network. Inf. Sci. 2023, 640, 119095. [Google Scholar] [CrossRef]
  21. Yoshida, M.; Koike, A. SNPInterForest: A new method for detecting epistatic interactions. BMC Bioinform. 2011, 12, 469. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Wang, H.; Nie, F.; Huang, H.; Kim, S.; Nho, K.; Risacher, S.L.; Saykin, A.J.; Shen, L.; Initiative, A.D.N. Identifying quantitative trait loci via group-sparse multitask regression and feature selection: An imaging genetics study of the ADNI cohort. Bioinformatics 2012, 28, 229–237. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Leng, F.; Edison, P. Neuroinflammation and microglial activation in Alzheimer disease: Where do we go from here? Nat. Rev. Neurol. 2021, 17, 157–172. [Google Scholar] [CrossRef] [PubMed]
  24. Cruchaga, C.; Kauwe, J.S.; Harari, O.; Jin, S.C.; Cai, Y.; Karch, C.M.; Benitez, B.A.; Jeng, A.T.; Skorupa, T.; Carrell, D.; et al. GWAS of cerebrospinal fluid tau levels identifies risk variants for Alzheimer’s disease. Neuron 2013, 78, 256–268. [Google Scholar] [CrossRef] [Green Version]
  25. Guo, T.; Korman, D.; La Joie, R.; Shaw, L.M.; Trojanowski, J.Q.; Jagust, W.J.; Landau, S.M. Normalization of CSF pTau measurement by Aβ40 improves its performance as a biomarker of Alzheimer’s disease. Alzheimer’s Res. Ther. 2020, 12, 97. [Google Scholar] [CrossRef]
  26. Kempuraj, D.; Thangavel, R.; Natteru, P.; Selvakumar, G.; Saeed, D.; Zahoor, H.; Zaheer, S.; Iyer, S.; Zaheer, A. Neuroinflammation induces neurodegeneration. J. Neurol. Neurosurg. Spine 2016, 1, 1003. [Google Scholar]
  27. Dong, Y.; Yu, H.; Li, X.; Bian, K.; Zheng, Y.; Dai, M.; Feng, X.; Sun, Y.; He, Y.; Yu, B.; et al. Hyperphosphorylated tau mediates neuronal death by inducing necroptosis and inflammation in Alzheimer’s disease. J. Neuroinflamm. 2022, 19, 205. [Google Scholar] [CrossRef]
  28. Parhizkar, S.; Holtzman, D.M. APOE Mediated Neuroinflammation and Neurodegeneration in Alzheimer’s Disease. In Seminars in Immunology; Elsevier: Amsterdam, The Netherlands, 2022; p. 101594. [Google Scholar]
  29. Servaes, S.; Lussier, F.Z.; Therriault, J.; Tissot, C.; Bezgin, G.; Kang, M.S.; Wang, Y.T.; Stevenson, J.; Rahmouni, N.; Arias, J.F.; et al. pTau heterogeneity as a measure for disease severity in incipient Alzheimer’s disease. Alzheimer’s Dement. 2022, 18, e063749. [Google Scholar] [CrossRef]
  30. Penke, B.; Szűcs, M.; Bogár, F. New Pathways Identify Novel Drug Targets for the Prevention and Treatment of Alzheimer’s Disease. Int. J. Mol. Sci. 2023, 24, 5383. [Google Scholar] [CrossRef]
  31. Arnsten, A.F.; Datta, D.; Del Tredici, K.; Braak, H. Hypothesis: Tau pathology is an initiating factor in sporadic Alzheimer’s disease. Alzheimer’s Dement. 2021, 17, 115–124. [Google Scholar] [CrossRef]
  32. Meredith, J.E., Jr.; Sankaranarayanan, S.; Guss, V.; Lanzetti, A.J.; Berisha, F.; Neely, R.J.; Slemmon, J.R.; Portelius, E.; Zetterberg, H.; Blennow, K.; et al. Characterization of novel CSF Tau and ptau biomarkers for Alzheimer’s disease. PLoS ONE 2013, 8, e76523. [Google Scholar] [CrossRef] [PubMed]
  33. Price, A.L.; Patterson, N.J.; Plenge, R.M.; Weinblatt, M.E.; Shadick, N.A.; Reich, D. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 2006, 38, 904–909. [Google Scholar] [CrossRef] [PubMed]
  34. Zhang, Q.; Liu, H.; Ao, L.; Liang, H.; Chen, D. A GPU-based approach for detecting genome-wide SNP-SNP interactions of quantitative trait in ADNI cohorts. In Proceedings of the 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Las Vegas, NV, USA, 6–8 December 2022; pp. 2564–2570. [Google Scholar]
  35. Cheng, Y.; Pereira, M.; Raukar, N.; Reagan, J.L.; Queseneberry, M.; Goldberg, L.; Borgovan, T.; LaFrance Jr, W.C.; Dooner, M.; Deregibus, M.; et al. Potential biomarkers to detect traumatic brain injury by the profiling of salivary extracellular vesicles. J. Cell. Physiol. 2019, 234, 14377–14388. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Berger, H.; Wodarz, A.; Borchers, A. PTK7 faces the Wnt in development and disease. Front. Cell Dev. Biol. 2017, 5, 31. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Palomer, E.; Buechler, J.; Salinas, P.C. Wnt signaling deregulation in the aging and Alzheimer’s brain. Front. Cell. Neurosci. 2019, 13, 227. [Google Scholar] [CrossRef] [Green Version]
  38. Lee, E.; Giovanello, K.S.; Saykin, A.J.; Xie, F.; Kong, D.; Wang, Y.; Yang, L.; Ibrahim, J.G.; Doraiswamy, P.M.; Zhu, H.; et al. Single-nucleotide polymorphisms are associated with cognitive decline at Alzheimer’s disease conversion within mild cognitive impairment patients. Alzheimer’s Dementia Diagn. Assess. Dis. Monit. 2017, 8, 86–95. [Google Scholar] [CrossRef] [Green Version]
  39. Tyzack, G.E.; Hall, C.E.; Sibley, C.R.; Cymes, T.; Forostyak, S.; Carlino, G.; Meyer, I.F.; Schiavo, G.; Zhang, S.C.; Gibbons, G.M.; et al. A neuroprotective astrocyte state is induced by neuronal signal EphB1 but fails in ALS models. Nat. Commun. 2017, 8, 1164. [Google Scholar] [CrossRef] [Green Version]
  40. Conductier, G.; Blondeau, N.; Guyon, A.; Nahon, J.L.; Rovère, C. The role of monocyte chemoattractant protein MCP1/CCL2 in neuroinflammatory diseases. J. Neuroimmunol. 2010, 224, 93–100. [Google Scholar] [CrossRef]
  41. Cherry, J.D.; Meng, G.; Daley, S.; Xia, W.; Svirsky, S.; Alvarez, V.E.; Nicks, R.; Pothast, M.; Kelley, H.; Huber, B.; et al. CCL2 is associated with microglia and macrophage recruitment in chronic traumatic encephalopathy. J. Neuroinflamm. 2020, 17, 370. [Google Scholar] [CrossRef]
  42. Finan, G.M.; Kwak, C.; Hyun-Woo, R.; Kim, T.W. Sema6D Promotes TREM2-dependent Phagocytosis in Human iPSC-derived Microglia. Alzheimer’s Dement. 2022, 18, e062563. [Google Scholar] [CrossRef]
  43. Albanus, R.D.; Finan, G.M.; Brase, L.; Chen, S.; Guo, Q.; Kannan, A.; Acquarone, M.; You, S.F.; Novotny, B.C.; Ribeiro Pereira, P.M.; et al. Systematic characterization of brain cellular crosstalk reveals a novel role for SEMA6D in TREM2-associated microglial function in Alzheimer’s disease. bioRxiv 2022. [Google Scholar] [CrossRef]
  44. Podleśny-Drabiniok, A.; Marcora, E.; Goate, A.M. Microglial phagocytosis: A disease-associated process emerging from Alzheimer’s disease genetics. Trends Neurosci. 2020, 43, 965–979. [Google Scholar] [CrossRef] [PubMed]
  45. Wang, K.; Li, J.; Zhang, Y.; Huang, Y.; Chen, D.; Shi, Z.; Smith, A.D.; Li, W.; Gao, Y. Central nervous system diseases related to pathological microglial phagocytosis. Cns Neurosci. Ther. 2021, 27, 528–539. [Google Scholar] [CrossRef] [PubMed]
  46. Noda, M.; Suzumura, A. Sweepers in the CNS: Microglial migration and phagocytosis in the Alzheimer disease pathogenesis. Int. J. Alzheimer’S Dis. 2012, 2012, 891087. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Yokoyama, K.; Tezuka, T.; Kotani, M.; Nakazawa, T.; Hoshina, N.; Shimoda, Y.; Kakuta, S.; Sudo, K.; Watanabe, K.; Iwakura, Y.; et al. NYAP: A phosphoprotein family that links PI3K to WAVE1 signalling in neurons. Embo J. 2011, 30, 4739–4754. [Google Scholar] [CrossRef] [Green Version]
  48. Pelucchi, S.; Stringhi, R.; Marcello, E. Dendritic spines in Alzheimer’s disease: How the actin cytoskeleton contributes to synaptic failure. Int. J. Mol. Sci. 2020, 21, 908. [Google Scholar] [CrossRef] [Green Version]
  49. Tiwari, N.K.; Sathyanesan, M.; Schweinle, W.; Newton, S.S. Carbamoylated erythropoietin induces a neurotrophic gene profile in neuronal cells. Prog. NeuroPsychopharmacol. Biol. Psychiatry 2019, 88, 132–141. [Google Scholar] [CrossRef]
  50. Hettige, N.C.; Ernst, C. FOXG1 dose in brain development. Front. Pediatr. 2019, 7, 482. [Google Scholar] [CrossRef] [Green Version]
  51. Chen, Y.; Tian, L.; Zhang, F.; Liu, C.; Lu, T.; Ruan, Y.; Wang, L.; Yan, H.; Yan, J.; Liu, Q.; et al. Myosin Vb gene is associated with schizophrenia in Chinese Han population. Psychiatry Res. 2013, 207, 13–18. [Google Scholar] [CrossRef]
  52. Murphy, C.E.; Walker, A.K.; Weickert, C.S. Neuroinflammation in schizophrenia: The role of nuclear factor kappa B. Transl. Psychiatry 2021, 11, 528. [Google Scholar] [CrossRef]
  53. Merluzzi, A.P.; Vogt, N.M.; Norton, D.; Jonaitis, E.; Clark, L.R.; Carlsson, C.M.; Johnson, S.C.; Asthana, S.; Blennow, K.; Zetterberg, H.; et al. Differential effects of neurodegeneration biomarkers on subclinical cognitive decline. Alzheimer’s Dementia Transl. Res. Clin. Interv. 2019, 5, 129–138. [Google Scholar] [CrossRef] [PubMed]
  54. Rodriguez-Vieitez, E.; Montal, V.; Sepulcre, J.; Lois, C.; Hanseeuw, B.; Vilaplana, E.; Schultz, A.P.; Properzi, M.J.; Scott, M.R.; Amariglio, R.; et al. Association of cortical microstructure with amyloid-β and tau: Impact on cognitive decline, neurodegeneration, and clinical progression in older adults. Mol. Psychiatry 2021, 26, 7813–7822. [Google Scholar] [CrossRef] [PubMed]
  55. Selkoe, D.J.; Hardy, J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol. Med. 2016, 8, 595–608. [Google Scholar] [CrossRef] [PubMed]
Genes 14 01322 g001 550
Figure 1. (a) The 3D waterfall plot reveals interaction effects and main effects of each SNP with −log 10 (p-values). The number of significant SNP-SNP pairs was 758. For each interaction pair, the blue waterfall represents main effects of SNP 1 ; the green waterfall represents main effects of SNP 2 ; and the orange waterfall represents the interaction effects of SNP 1 –SNP 2 pairs. (b) The violin plot shows the distribution state and probability density of SNP-SNP interaction effect, main effect of SNP 1 and SNP 2 .
Figure 1. (a) The 3D waterfall plot reveals interaction effects and main effects of each SNP with −log 10 (p-values). The number of significant SNP-SNP pairs was 758. For each interaction pair, the blue waterfall represents main effects of SNP 1 ; the green waterfall represents main effects of SNP 2 ; and the orange waterfall represents the interaction effects of SNP 1 –SNP 2 pairs. (b) The violin plot shows the distribution state and probability density of SNP-SNP interaction effect, main effect of SNP 1 and SNP 2 .
Genes 14 01322 g001
Genes 14 01322 g002 550
Figure 2. (a) The 3D waterfall plot reveals interaction and additive terms with R square in the linear regression model. The orange area represents the variance explained by interaction term on P-tau. The green area represents the variance explained by additive term on P-tau. (b) The violin plot shows the distribution state and probability density of interaction R square, additive terms of R square the two SNPs.
Figure 2. (a) The 3D waterfall plot reveals interaction and additive terms with R square in the linear regression model. The orange area represents the variance explained by interaction term on P-tau. The green area represents the variance explained by additive term on P-tau. (b) The violin plot shows the distribution state and probability density of interaction R square, additive terms of R square the two SNPs.
Genes 14 01322 g002
Genes 14 01322 g003 550
Figure 3. Three categories of different relationships with AD correlation in a gene-gene interaction pair. (a) The 1st relationship, include 95 gene pairs, both genes in each pair are AD-related. (b) The 2nd relationship, includes 79 gene pairs, only one gene in each pair is AD-related. (c) The 3rd relationship, includes 25 gene pairs, none gene in each pair is associated with AD. The red spots represent AD-related genes. The blue spots represent AD-unrelated genes. Multiple lines between two points represent multiple pairs of SNP mapped ont to the same pair of genes.
Figure 3. Three categories of different relationships with AD correlation in a gene-gene interaction pair. (a) The 1st relationship, include 95 gene pairs, both genes in each pair are AD-related. (b) The 2nd relationship, includes 79 gene pairs, only one gene in each pair is AD-related. (c) The 3rd relationship, includes 25 gene pairs, none gene in each pair is associated with AD. The red spots represent AD-related genes. The blue spots represent AD-unrelated genes. Multiple lines between two points represent multiple pairs of SNP mapped ont to the same pair of genes.
Genes 14 01322 g003
Genes 14 01322 g004 550
Figure 4. Significantly enriched pathways of studied genes. x-axis indicates the number of overlapped genes of related pathway, y-axis indicates significant pathways. The gradient of the color represents the level of significance. The red bars represent high significance. (a) Top 20 pathways identified by PhenGenI Association 2021 enrichment, −log10 adjusted p-value. (b) Top 10 pathways identified by Reactome 2022, adjusted p-value.
Figure 4. Significantly enriched pathways of studied genes. x-axis indicates the number of overlapped genes of related pathway, y-axis indicates significant pathways. The gradient of the color represents the level of significance. The red bars represent high significance. (a) Top 20 pathways identified by PhenGenI Association 2021 enrichment, −log10 adjusted p-value. (b) Top 10 pathways identified by Reactome 2022, adjusted p-value.
Genes 14 01322 g004
Genes 14 01322 g005 550
Figure 5. The PPI subnetwork of studied genes. The nodes and edges represent the proteins (genes) and their interactions, respectively. The PPI subnetwork contained 20 nodes and 33 edges. The light pink connections represent ten overlapped gene-gene interaction pairs with the PPI network.
Figure 5. The PPI subnetwork of studied genes. The nodes and edges represent the proteins (genes) and their interactions, respectively. The PPI subnetwork contained 20 nodes and 33 edges. The light pink connections represent ten overlapped gene-gene interaction pairs with the PPI network.
Genes 14 01322 g005
Table
Table 1. Top10 R Square Of Snp-Snp Interaction Pairs.
Table 1. Top10 R Square Of Snp-Snp Interaction Pairs.
NOSNP 1 × SNP 2 GENECHRp-ValueExplained Variance (R Square)
GWASInteractionAge + Gender + cdsr 1 SNP 1 + SNP 2 2 SNP 1 × SNP 2 3
1rs2291948APOOP5160.9635361.70 × 10 − 9 0.0930.0010.056
rs2619171-150.911948
2rs17069204SEC6360.02228846.73 × 10 − 11 0.0930.0080.055
rs4983187LINC02588140.592583
3rs6882813-50.4206132.86 × 10 − 10 0.0930.0070.055
rs17416058-110.212032
4rs129600PPARA220.6351384.92 × 10 − 10 0.0930.0050.054
rs6602151RSU1100.870792
5rs6796502PRSS42P30.6765284.80 × 10 − 11 0.0930.0040.053
rs6999890SLC45A480.454329
6rs1412839PDPN10.2516936.00 × 10 − 10 0.0930.0050.053
rs2397718-50.785923
7rs2219872GRIP1120.0164833.52 × 10 − 12 0.0930.0130.052
rs2647911C12orf66120.292968
8rs9320250OSTM160.02696614.37 × 10 − 10 0.0930.0110.052
rs4983187LINC02588140.592583
9rs10802434SCCPDH10.8739338.49 × 10 − 10 0.0930.0060.052
rs12470444NRP220.291239
10rs2487643PDPN10.2314681.31 × 10 − 9 0.0930.0060.052
rs2397718-50.785923
1 Age + gender + cds: percent of variance in P-tau level explained by age, gender and cds. 2 SNP 1 + SNP 2 : percent of additional variance in P-tau level explained by the combined main effect of SNP 1 and SNP 2 after accounting for age, gender and diagnosis. 3 SNP 1 × SNP 2 : percent of additional variance in P-tau level explained by the interaction effect of SNP 1 and SNP 2 after accounting for age, gender, diagnosis, SNP 1 and SNP 2 . The genes with bold italics in the table are AD-related genes.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chen, D.; Li, J.; Liu, H.; Liu, X.; Zhang, C.; Luo, H.; Wei, Y.; Xi, Y.; Liang, H.; Zhang, Q. Genome-Wide Epistasis Study of Cerebrospinal Fluid Hyperphosphorylated Tau in ADNI Cohort. Genes 2023, 14, 1322. https://doi.org/10.3390/genes14071322

AMA Style

Chen D, Li J, Liu H, Liu X, Zhang C, Luo H, Wei Y, Xi Y, Liang H, Zhang Q. Genome-Wide Epistasis Study of Cerebrospinal Fluid Hyperphosphorylated Tau in ADNI Cohort. Genes. 2023; 14(7):1322. https://doi.org/10.3390/genes14071322

Chicago/Turabian Style

Chen, Dandan, Jin Li, Hongwei Liu, Xiaolong Liu, Chenghao Zhang, Haoran Luo, Yiming Wei, Yang Xi, Hong Liang, and Qiushi Zhang. 2023. "Genome-Wide Epistasis Study of Cerebrospinal Fluid Hyperphosphorylated Tau in ADNI Cohort" Genes 14, no. 7: 1322. https://doi.org/10.3390/genes14071322

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop