Relationship Between FERMT2, CELF1, COPI, CHRNA2, and ABCA7 Genetic Polymorphisms and Alzheimer’s Disease Risk in the Southern Chinese Population

Ding, Yanfei; Chen, Haijuan; Yan, Yi; Qiu, Yinghui; Zhao, Aonan; Li, Binyin; Xu, Wei; Deng, Yulei

doi:10.3233/ADR-230072

Relationship Between FERMT2, CELF1, COPI, CHRNA2, and ABCA7 Genetic Polymorphisms and Alzheimer’s Disease Risk in the Southern Chinese Population

Article type: Research Article

Authors: Ding, Yanfei^a | Chen, Haijuan^a | Yan, Yi^a | Qiu, Yinghui^a | Zhao, Aonan^a | Li, Binyin^a | Xu, Wei^a | Deng, Yulei^{a; b; *}

Affiliations: [a] Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China | [b] Department of Neurology, Ruijin Hospital, Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Correspondence: [*] Correspondence to: Yulei Deng, Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Tel.: +86 13651955717; E-mail: [email protected]; ORCID: 0000-0002-9564-2609

Keywords: Alzheimer’s disease, Apolipoprotein E, neurodegenerative disease, single nucleotide polymorphisms

DOI: 10.3233/ADR-230072

Journal: Journal of Alzheimer's Disease Reports, vol. 7, no. 1, pp. 1247-1257, 2023

Received 15 July 2023

Accepted 5 October 2023

Published: 9 November 2023

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Supplementary Materials:

Supplementary Material

Abstract

Background:

Alzheimer’s disease (AD) is a multi-gene inherited disease, and apolipoprotein E (APOE) ɛ4 is a strong risk factor. Other genetic factors are important but limited.

Objective:

This study aimed to investigate the relationship between 17 single-nucleotide polymorphisms (SNPs) and AD in the Southern Chinese populations.

Methods:

We recruited 242 AD patients and 208 controls. The SNaPshot technique was used to detect the SNPs.

Results:

Adjusted for sex and age, we found rs6572869 (FERMT2), rs11604680 (CELF1), and rs1317149 (CELF1) were associated with AD risk in the dominant (rs6572869: p = 0.022, OR = 1.55; rs11604680: p = 0.007, OR = 1.68; rs1317149: p = 0.033, OR = 1.50) and overdominant models (rs6572869: p = 0.001, OR = 1.96; rs11604680: p = 0.002, OR = 1.82; rs1317149: p = 0.003, OR = 1.80). rs9898218 (COPI) was associated with AD risk in the overdominant model (p = 0.004, OR = 1.81). Further, rs2741342 (CHRNA2) was associated with AD protection in the dominant (p = 0.002, OR = 0.5) and additive models (p = 0.002, OR = 0.64). Mutations in rs10742814 (CELF1), rs11039280 (CELF1), and rs3752242 (ABCA7) contributed to AD protection. Among them, rs10742814 (CELF1), rs3752242 (ABCA7), and rs11039280 (CELF1) were more significantly associated with AD carrying APOE ɛ4, whereas rs1317149 (CELF1) showed an opposite trend. Interestingly, rs4147912 (ABCA7) and rs2516049 (HLA-DRB1) were identified to be relevant with AD carrying APOE ɛ4. Using expression quantitative trait locus analysis, we found polymorphisms in CELF1 (rs10742814 and rs11039280), ABCA7 (rs4147912), HLA-DRB1 (rs2516049), and ADGRF4 (rs1109581) correlated with their corresponding gene expression in the brain.

Conclusions:

We identified four risk and four protective SNPs associated with AD in the Southern Chinese population, with different correlations between APOE ɛ4 carriers and non-carriers. rs4147912 (ABCA7) and rs2516049 (HLA-DRB1) were associated with AD carrying APOE ɛ4.

INTRODUCTION

Alzheimer’s disease (AD) is a common age-related neurodegenerative disease associated with genetic factors and accounts for most dementia in the elderly population worldwide [1]. Genetic factors explain 58–79% of AD heritability [2]. Therefore, the identification of AD-related loci can provide a reliable basis for a timely and reliable diagnosis. Apolipoprotein E (APOE) ɛ4 is a major risk factor for sporadic AD [3]. A meta-analysis in Caucasians showed that individuals with the ɛ4 allele were at increased risk of AD (ɛ2/ɛ4: odds ratio [OR] = 2.6; ɛ3/ɛ4: OR = 3.2, ɛ4/ɛ4: OR = 14.9) [4]. However, APOE accounts for only 10–20% of the risk of late-onset AD, suggesting that other genetic risk factors are also important in AD pathogenesis [5].

More than 30 AD-related susceptibility genes have been identified by genome-wide association study (GWAS) meta-analyses, including FERMT2, ABCA7, CR1, HLA-DRB, and CELF1 [6–9]. Furthermore, AD-related variants may affect the expression of nearby genes that influence the pathological processes of brain amyloidosis, tauopathy, and neurodegeneration, which provides valuable insights into the molecular mechanisms of AD pathogenesis [10]. A GWAS analysis of 1,126,563 individuals from European populations recently identified 3,915 variants across 38 independent loci associated with AD and proposed five new susceptibility loci (TNIP1, NTN5, AGRN, LILRB2, HAVCR2) [9]. The association between SNPs within ABCA7, EPHA1, and CD2AP and AD was not observed in an East Asian study, which was identified in Caucasians previously [11].

The genetic basis of race-specific AD susceptibility may be due to differences in minor allele frequency and the complexity of the underlying genetic structure inherent [11, 12]. Most GWAS and subsequent replication studies have been conducted almost exclusively in Caucasian populations, with relatively limited but significant AD inheritance in other populations. Replication of GWAS results in different ethnic populations may help identify SNPs associated with AD [13]. Therefore, in this study, we selected 17 SNP loci in 12 genes that have never been reported in Asian populations (FERMT2 rs6572869, ABCA7 rs4147912 and rs3752242, CELF1 rs10742814, rs1317149, rs11604680 and rs11039280, COPI rs9898218, CHRNA2 rs2741342, ADGRF4 rs1109581, HLA-DRB1 rs2516049, MS4A rs7116190 and rs667897, NRXN1 rs698842, CR1 rs4266886, CSMD1 rs7813641, and OR51I1 rs77336780), to investigate whether these SNPs are independently or in combination with APOE ɛ4 concerning AD risk in southern Chinese populations. In addition, we used expression quantitative trait loci (eQTL) datasets to assess the potential effects of these loci on gene expression, contributing to a better understanding of disease-related mechanisms and the discovery of new therapeutic pathways.

MATERIALS AND METHODS

Subjects

All enrolled subjects were evaluated by at least two experienced neurologists and underwent a standard series of examinations, including medical history, physical examination, systematic neuropsychological, such as standard Mini-Mental State Examination (MMSE) and clock drawing test (CDT) and neuroimaging tests such as magnetic resonance. Among them, 36 healthy controls and 52 AD patients received ¹⁸F-florbetapir positron emission tomogram examination. The results of AD patients met the criteria for probable AD defined by National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA criteria) [14]. The physicians assessed the healthy controls recruited to confirm that they had no cognitive impairment [15, 16]. All participants were Han Chinese living in southern China. We excluded participants with a history of other neurological disorders that could lead to dementia, such as Parkinson’s disease, stroke, multiple sclerosis, brain tumors, and major depressive disorder [17].

This study conformed with the Declaration of Helsinki and was approved by the Ethics Committee of Ruijin Hospital, affiliated with the Shanghai Jiao Tong University School of Medicine (2018-No.6). All participants and their caregivers were fully informed about the study and signed a written consent form.

Genotype analysis

Genomic DNA was obtained from the peripheral blood leukocytes of the participants using the phenol-chloroform-isopropyl alcohol method. We designed the extension primers and polymerase chain reaction (PCR) using Primer5 software (version 5.00) and used SNaPshot technology (Applied Biosystems, Foster City, CA, United States) to genotype SNPs. SNPs of APOE (rs429358 and rs7412) were detected by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP)[3]. The primer sequences are listed in Supplementary Table 1.

Gene expression analysis

Two databases, Braineac and Genotype-Tissue Expression (GTEx), were used to investigate the relationship between genotype and gene expression. The Braineac dataset included eQTL data for ten brain regions from 134 individuals of European descent without neurodegenerative disease. The GTEx database contained genotype and gene expression data of about 1000 individuals, mostly from Caucasian populations, covering 54 non-diseased tissues.

Statistical analyses

Statistical analyses were performed using SPSS software (version 26.0). A t-test and chi-square analysis were used to compare the demographic characteristics of the two groups. We performed a chi-square analysis to assess the distribution of allele and genotype and Hardy-Weinberg equilibrium (HWE) for each SNP. Four genetic models were applied to evaluate the risk of AD for each SNP after adjusting for age and sex. With M defined as the major allele and m as the minor allele, the models are: Dominant: 1 (mm + Mm) versus 0 (MM); Recessive: 1 (mm) versus 0 (MM + Mm); Additive: 0 (MM) versus 1 (Mm) versus 2 (mm); Overdominant: 1 (Mm) versus 0 (MM + mm). Statistical significance was set at p < 0.05. Bonferroni correction was performed.

RESULTS

Demographic characteristics of the study population

A total of 242 patients with AD and 208 healthy controls were recruited in this study. Table 1 shows the demographic and clinical information of the participants. There was no significant difference between the two groups in age and sex. Patients with AD had lower educational attainment than controls, which is consistent with previous studies [18].

Table 1

Characteristics of the study population

	Patients (N= 242)	Controls (N= 208)	p
Female, n (%)	157 (64.9%)	127 (61.1%)	0.40
Male, n (%)	85 (35.1%)	81 (38.9%)
Age at examination (y, mean±SD)	72.81±9.56	71.19±9.76	0.076
Education level (y, mean±SD)	8.32±4.97	11.95±3.23	<0.001
MMSE score (mean±SD)	12.08±5.46	29.08±0.76	<0.001
CDT score (mean±SD)	1.83±1.57	3.90±0.14	<0.001
APOE genotype (%)
ɛ2/ɛ2	2 (0.8%)	1 (0.5%)
ɛ2/ɛ3	13 (5.4%)	25 (12.0%)
ɛ2/ɛ4	7 (2.9%)	5 (2.4%)
ɛ3/ɛ3	96 (39.7%)	121 (58.2%)
ɛ3/ɛ4	102 (42.1%)	50 (24.0%)
ɛ4/ɛ4	22 (9.1%)	6 (2.9%)
APOE ɛ4 carriers	131 (54.1%)	61 (29.3%)	<0.001

MMSE, Mini-Mental State Examination; APOE, apolipoprotein E, APOE ɛ4 carriers have at least one copy of the ɛ4 allele; SD, standard deviation; Bolding indicates statistically significant values; CDT, clock drawing test.

Analysis of the association between SNPs and AD

All genotypes followed the HWE. The allele and genotype frequencies of the SNPs are listed in Supplementary Table 2. rs2741342 of CHRNA was associated with AD in allele and genotype frequencies (allele: p = 0.003, OR = 0.67, 95% confidence interval [CI] = 0.52–0.88; genotype: p = 0.005) (Table 2). Differences persisted after Bonferroni corrections for allele frequencies. The genotype or allele frequencies of rs6572869 in FERMT2, rs11604680, rs1317149, and rs11039280 in CELF1, rs9898218 in COPI, and rs3752242 in ABCA7 were also associated with AD (Table 2). However, these associations vanished after the Bonferroni correction.

Table 2

Association of SNP with AD risk

SNP	Gene	minor	P allele	P genotype	Dominant	Recessive	Additive	Overdominant
		allele	OR	p	p	p	p
			(95% CI)		OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
rs6572869	FERMT2	A	0.35	0.005	0.022	0.13	0.33	0.001
			1.15		1.55	0.63	1.15	1.96
			0.87–1.53		1.07–2.26	0.35–1.14	0.87–1.51	1.32–2.92
rs11604680	CELF1	G	0.078	0.007	0.007	0.53	0.065	0.002
			1.31		1.68	0.81	1.32	1.82
			0.98–1.75		1.15–2.44	0.41–1.56	0.98–1.78	1.24–2.67
rs1317149	CELF1	A	0.35	0.009	0.033	0.13	0.33	0.003
			1.15		1.50	0.6	1.16	1.80
			0.87–1.53		1.03–2.19	0.31–1.16	0.87–1.55	1.23–2.64
rs9898218	COPI	T	0.53	0.005	0.065	0.073	0.48	0.004
			1.11		1.43	0.51	1.11	1.81
			0.82–1.51		0.98–2.10	0.24–1.07	0.83–1.50	1.21–2.71
rs2741342	CHRNA2	A	0.003	0.005	0.002	0.068	0.002	0.16
			0.67		0.5	0.64	0.64	0.77
			0.52–0.88		0.33–0.77	0.4–1.03	0.48–0.85	0.53–1.11
rs10742814	CELF1	A	0.23	0.17	0.61	0.039	0.17	0.39
			0.84		0.91	0.55	0.83	1.18
			0.63–1.10		0.62–1.32	0.31–0.97	0.63–1.09	0.81–1.72
rs11039280	CELF1	A	0.046	0.064	0.26	0.016	0.048	0.63
			0.75		0.81	0.49	0.76	1.1
			0.57–0.99		0.56–1.17	0.28–0.88	0.58–0.99	0.75–1.61
rs3752242	ABCA7	A	0.037	0.10	0.047	0.26	0.049	0.23
			0.74		0.68	0.73	0.76	0.80
			0.57–0.98		0.47–0.99	0.43–1.26	0.58–1.00	0.55–1.16

The P allele and P genotype were examined using the Chi-squared test. The p value was adjusted for sex and age using binary logistic regression. Bold indicates statistically significant values.

Four risk SNPs and four protective SNPs associated with AD were identified. Adjusted for sex and age, rs6572869 of FERMT2 and rs11604680 and rs1317149 of CELF1 were associated with the risk of AD in the dominant (rs6572869: p = 0.022, OR = 1.55, 95 % CI = 1.07–2.26; rs11604680: p = 0.007, OR = 1.68, 95% CI = 1.15–2.44; rs1317149: p = 0.033, OR = 1.50, 95% CI = 1.03–2.19) and overdominant models (rs6572869: p = 0.001, OR = 1.96, 95% CI = 1.32–2.92; rs11604680: p = 0.002, OR = 1.82, 95% CI = 1.24–2.67; rs1317149: p = 0.003, OR = 1.80, 95% CI = 1.23–2.64) (Table 2). After the Bonferroni correction, these differences persisted in the overdominant model. In addition, rs9898218 of COPI is associated with AD risk in the overdominant model (p = 0.004, OR = 1.81, 95% CI = 1.21–2.71) (Table 2).

Further, rs2741342 of CHRNA2 was associated with AD protection in the dominant (p = 0.002, OR = 0.5, 95% CI = 0.33–0.77) and additive models (p = 0.002, OR = 0.64, 95% CI = 0.48–0.85), even after Bonferroni correction (Table 2). The recessive model results of rs10742814 and rs11039280 of CELF1 showed that they are protective SNPs associated with AD (rs10742814: p = 0.039, OR = 0.55, 95% CI = 0.31–0.97; rs11039280: p = 0.016, OR = 0.49, 95% CI = 0.28–0.88) (Table 2). These associations vanished after the Bonferroni correction. The results also showed that rs3752242 (ABCA7) was marginally associated with AD protection in the dominant and additive models with AD protection (Table 2).

Association analysis of subgroups stratified by APOE ɛ4 status

As expected, APOE ɛ4 increased susceptibility to AD (Table 1). Next, we stratified the participants according to their APOE ɛ4 carrier status. Other discrepancies were observed in the allele and genotype frequencies of the above risk and protective SNPs combined with APOE ɛ4 (Supplementary Tables 4 and 6).

Adjusted for sex and age, the association of risk SNPs (rs6572869 of FERMT2, rs11604680 of CELF1, rs9898218 of COPI) and protective SNPs (rs2741342 of CHRNA2) persisted in AD with and without APOE ɛ4, consistent with the results when unstratified (Table 3). Protective SNPs (rs10742814 of CELF1, rs3752242 of ABCA7, and rs11039280 of CELF1) were still associated with AD in APOE ɛ4 carriers, and the differences were more pronounced, but not in APOE ɛ4 non-carriers (Table 3). Nonetheless, the risk SNP rs1317149 of CELF1 was significantly associated with AD in APOE ɛ4 non-carriers but not in APOE ɛ4 carriers (Table 3).

Table 3

Association of SNPs of candidate genes with AD risk stratified by APOE ɛ4 status

SNP	Gene	MAF	APOE ɛ4	Dominant model (adjusted)			Recessive model (adjusted)
				p	OR	95% CI	p	OR	95% CI
rs6572869	FERMT2	A	+	0.009	2.33	1.23–4.40	0.2	2.32	0.64–8.39
			–	0.43	1.22	0.74–2.01	0.014	0.33	0.14–0.80
rs11604680	CELF1	G	+	0.016	2.20	1.16–4.17	0.95	1.03	0.34–3.15
			–	0.028	1.76	1.06–2.92	0.38	0.65	0.25–1.68
rs9898218	COPI	T	+	0.45	1.28	0.68–2.40	0.023	0.23	0.065–0.81
			–	0.066	1.61	0.97–2.66	0.82	0.90	0.35–2.29
rs1317149	CELF1	A	+	0.058	1.85	0.98–3.47	0.95	1.03	0.34–3.15
			–	0.038	1.72	1.03–2.86	0.062	0.4	0.15–1.05
rs2741342	CHRNA2	A	+	0.028	0.43	0.20–0.91	0.15	0.56	0.25–1.24
			–	0.017	0.51	0.29–0.89	0.41	0.77	0.42–1.43
rs10742814	CELF1	A	+	0.39	0.76	0.40–1.43	0.004	0.30	0.13–0.68
			–	0.77	0.93	0.57–1.52	0.35	0.66	0.28–1.58
rs3752242	ABCA7	A	+	0.042	0.52	0.28–0.98	0.035	0.40	0.17–0.94
			–	0.69	0.90	0.54–1.50	0.81	1.09	0.53–2.25
rs11039280	CELF1	A	+	0.24	0.68	0.36–1.29	0.004	0.30	0.13–0.68
			–	0.31	0.77	0.47–1.27	0.16	0.53	0.22–1.28
rs4147912	ABCA7	A	+	0.034	2.28	1.06–4.87	0.68	1.60	0.17–14.74
			–	0.94	1.02	0.61–1.72	0.45	2.53	0.22–28.51
rs2516049	HLA-DRB1	G	+	0.69	1.14	0.60–2.16	0.033	0.30	0.10–0.91
			–	0.61	0.87	0.53–1.45	0.69	1.30	0.36–4.67
rs1109581	ADGRF4	A	+	0.33	0.72	0.37–1.40	0.81	0.83	0.19–3.66
			–	0.04	1.85	1.03–3.32	0.40	0.49	0.092–2.58
SNP	Gene	MAF	APOE ɛ4	Additive model (adjusted)			Overdominant model (adjusted)
				p	OR	95% CI	p	OR	95% CI
rs6572869	FERMT2	A	+	0.01	1.99	1.17–3.36	0.058	1.88	0.98–3.61
			–	0.53	0.89	0.62–1.28	0.011	1.97	1.17–3.33
rs11604680	CELF1	G	+	0.058	1.64	0.98–2.74	0.015	2.32	1.18–4.56
			–	0.18	1.31	0.88–1.94	0.007	1.99	1.20–3.30
rs9898218	COPI	T	+	0.72	0.91	0.56–1.50	0.044	2.02	1.02–4.02
			–	0.18	1.30	0.88–1.92	0.04	1.74	1.03–2.94
rs1317149	CELF1	A	+	0.14	1.47	0.89–2.43	0.056	1.90	0.98–3.69
			–	0.46	1.16	0.79–1.70	0.002	2.29	1.37–3.82
rs2741342	CHRNA2	A	+	0.021	0.57	0.35–0.92	0.33	0.73	0.39–1.38
			–	0.044	0.69	0.48–0.99	0.14	0.69	0.42–1.14
rs10742814	CELF1	A	+	0.036	0.63	0.41–0.97	0.19	1.55	0.81–2.95
			–	0.52	0.88	0.60–1.29	0.80	1.07	0.65–1.76
rs3752242	ABCA7	A	+	0.015	0.59	0.38–0.90	0.57	0.83	0.43–1.59
			–	0.87	0.97	0.67–1.4	0.59	0.87	0.53–1.43
rs11039280	CELF1	A	+	0.022	0.60	0.39–0.93	0.32	1.39	0.73–2.64
			–	0.16	0.76	0.52–1.11	0.87	0.96	0.58–1.59
rs4147912	ABCA7	A	+	0.046	2.03	1.01–4.07	0.043	2.24	1.02–4.91
			–	0.81	1.06	0.65–1.74	0.92	0.97	0.58–1.65
rs2516049	HLA-DRB1	G	+	0.52	0.86	0.53–1.38	0.084	1.89	0.92–3.88
			–	0.76	0.93	0.60–1.45	0.49	0.83	0.49–1.40
rs1109581	ADGRF4	A	+	0.38	0.78	0.46–1.35	0.36	0.72	0.36–1.46
			–	0.14	1.45	0.88–2.39	0.013	2.18	1.18–4.04

p-values were adjusted for sex and age using binary logistic regression. Bold indicates statistically significant values.

Interestingly, rs4147912 of ABCA7 and rs2516049 of HLA-DRB1 were identified to be relevant with AD in APOE ɛ4 carriers (dominant model of rs4147912: p = 0.034, OR = 2.28, 95% CI = 1.06–4.87; additive model of rs4147912: p = 0.046, OR = 2.03, 95% CI = 1.01–4.07; overdominant model of rs4147912: p = 0.043, OR = 2.24, 95% CI = 1.02–4.91; recessive model of rs2516049: p = 0.033, OR = 0.30, 95% CI = 0.10–0.91) (Table 3). Moreover, in non-APOE ɛ4 carriers, rs1109581 of ADGRF4 in the dominant and overdominant models was associated with AD (dominant model: p = 0.04, OR = 1.85, 95% CI = 1.03–3.32; overdominant model: p = 0.013, OR = 2.18, 95% CI = 1.18–4.04) (Table 3). However, these correlations were not available after Bonferroni correction (Table 3).

Analysis of the association between SNPs and related gene expression

We applied eQTL analysis to investigate the correlation between genotype and gene expression of the above 11 candidate loci to understand further the relationship between the loci and the pathogenesis of AD. According to GTEx, we found that the TT genotype at rs2516049 was associated with the high expression level of HLA-DRB1 in multiple brain regions, such as the anterior cingulate cortex (BA24), cortex, frontal cortex (BA9), putamen (basal ganglia), amygdala, cerebellum, caudate (basal ganglia), nucleus accumbens (basal ganglia), cerebellar hemisphere, substantia nigra, hypothalamus, and hippocampus (Table 4). In addition, the TT genotype at rs1109581 was associated with a higher level of ADGRF4 in the cerebellum (Table 4). According to the Braineac database, the A allele of rs4147912 was positively correlated with ABCA7 expression in the frontal cortex (p = 0.00012, probe set 3815488). Both A alleles of rs10742814 and rs11039280 increased CELF1 expression in the medulla oblongata (rs10742814: p = 0.00034, probe set 3372267; rs11039280: p = 0.00032, probe set 3372267) (Fig. 1). In the GTEx and Braineac databases, no other genotypes of SNPs were found to be significantly correlated with the expression levels of related genes in the brain.

Fig. 1

Effect of rs4147912, rs10742814, and rs11039280 on ABCA7 and CELF1 expression in normal human brain regions in Braineac. a) The A allele of rs4147912 increased ABCA7 expression in the frontal cortex with probe set 3815488 (p = 0.00012). b) The A allele of rs10742814 increased CELF1 expression in the medulla oblongata with probe set 3372267 (p = 0.00034). c) The A allele of rs11039280 increased CELF1 expression in the medulla oblongata with probe set 3372267 (p = 0.00032).

Table 4

Effects of rs2516049 and rs1109581 on HLA-DRB1 and ADGRF4 gene expression in different regions of the normal human brain in GTEx

SNP	Variant ID	Tissue	Samples	Genotype			p	NES
				TT	TC	CC
rs2516049	chr6_32602623_T_C_b38	Brain - Anterior cingulate cortex (BA24)	147	72	58	17	1.00E-16	–0.59
		Brain - Cortex	205	115	69	21	8.40E-13	–0.46
		Brain - Frontal Cortex (BA9)	175	92	66	17	2.30E-11	–0.44
		Brain - Putamen (basal ganglia)	170	89	69	12	3.80E-11	–0.51
		Brain - Amygdala	129	58	59	12	2.40E-10	–0.34
		Brain - Cerebellum	209	105	85	19	1.10E-09	–0.44
		Brain - Caudate (basal ganglia)	193	103	73	17	1.20E-09	–0.34
		Brain - Nucleus accumbens (basal ganglia)	202	102	81	19	2.30E-09	–0.37
		Brain - Cerebellar Hemisphere	175	88	68	19	2.80E-09	–0.44
		Brain - Substantia nigra	114	58	47	9	7.90E-08	–0.35
		Brain - Hypothalamus	170	90	66	14	4.00E-07	–0.28
		Brain - Hippocampus	165	81	68	16	0.0000015	–0.25
SNP	Variant ID	Tissue	Samples	Genotype			p	NES
				CC	CT	TT
rs1109581	chr6_47710446_C_T_b38	Brain - Cerebellum	209	114	83	12	0.000082	0.35

Samples: Number of RNA-seq samples with genotype, p-value: from a t-test that compares observed NES from single-tissue eQTL analysis to a null NES of 0, NES: normalized effect size, the slope of the linear regression of normalized expression data versus the three genotype categories using single-tissue eQTL analysis representing the eQTL effect size.

DISCUSSION

In this study, the associations between 17 SNPs in 12 genes and the risk of AD were validated. To our knowledge, these SNPs have been investigated for the first time in the Southern Chinese population. Four risk SNPs and four protective SNPs were identified. Mutations in rs6572869 (FERMT2), rs11604680 (CELF1), rs1317149 (CELF1), and rs9898218 (COPI) were associated with a higher susceptibility to AD, whereas rs2741342 (CHRNA2), rs10742814 (CELF1), rs11039280 (CELF1), and rs3752242 (ABCA7) were protective against AD pathogenesis. In addition, the correlation between the above-mentioned related SNPs and AD is different when participants are divided into two groups according to their APOE ɛ4 carrier status. Furthermore, individuals with the A allele at rs4147912 had a higher AD susceptibility in APOE ɛ4 carriers, while homozygous GG at rs2516049 reduced AD morbidity. Heterozygote AG at rs1109581 of ADGRF4 in APOE ɛ4 non-carriers increased the risk of AD. The associations of the remaining six SNPs with AD were not replicated in the Southern Chinese group (Supplementary Tables 3, 5, and 7). Similarly, various populations may carry different AD-risk genetic variants.

CELF1 is an essential gene associated with AD genetic factors [19]. Aret, the homologue of CELF1 in fly, was associated with AD by regulating tau protein aggregation [20]. Furthermore, previous studies have found a significant correlation between CELF1 and the establishment of hippocampal white matter integrity [21]. Moreover, the CELF1 variants may correlate with higher levels of Aβ₄₂ in cerebrospinal fluid, thus associated with the AD pathology. In the Caucasian population, when rs11604680, rs1317149, rs10742814, and rs11039280 were aggregated with other SNPs, they were associated with cognition, but the association between a single SNP and AD has not been explored [22]. No studies have investigated the association between rs11604680, rs1317149, rs10742814, and rs11039280 of the CELF1 gene and AD risk in Asian populations. We provide evidence that heterozygous AG at rs11604680 and rs1317149 increased AD susceptibility, while homozygous AA at rs10742814 and rs11039280 decreased AD risk. Moreover, the correlations between these SNPs in APOE ɛ4 carriers and non-carriers were significantly inconsistent. For the first time, we hypothesized that they might interact with APOE and participate in the progression of AD, which needs to be confirmed by future studies.

ABCA7 encodes an ATP-binding cassette transporter that facilitates lipid efflux across membranes [23, 24]. ABCA7 is second only to APOE ɛ4 concerning amyloid deposition, and its loss-of-function increases β-secretase cleavage of the amyloid-β protein precursor (AβPP) [25]. ABCA7 knockout in mice carrying mutant human AβPPs increased insoluble Aβ deposition and amyloid plaques [26]. Two variants of ABCA7 (rs4147912 and rs3752242) were identified to be associated with cerebral amyloidosis and susceptibility to AD, but not with brain atrophy [25]. In this study, we discovered that the A allele at rs3752242 might be protective against AD, particularly in APOE ɛ4 carriers. Moreover, the A allele at rs4147912 is a risk factor for AD susceptibility in APOE ɛ4 carriers. We speculated that ABCA7 might interact with APOE ɛ4 and be associated with AD. APOE ɛ4 may promote the transport of oxidized lipids by ABCA7 through the production of reactive oxygen species (ROS). Studies have shown that APOE ɛ4 in astrocytes and neurons induce greater cPLA2 activation through p38 MAPK pathway, leading to more LTB4, iNOS, and ROS production, increased oxidative stress and neuroinflammation [27]. ROS induces neuronal lipid synthesis, leading to lipid peroxidation isolation in glial lipid droplets to delay neurotoxicity, and ABCA7 in neurons is involved in this process [28]. In addition, studies have shown that APOE ɛ4 is impaired in lipid transport and promotes neurodegeneration [29], which may have a synergistic effect with ABCA7 in this process.

FERMT2 is expressed in the brain, and its corresponding protein targets cell-matrix adhesion structures [30]. Reduction of FERMT2 in human neurons leads to reduced extracellular Aβ₄₀ and Aβ₄₂ levels and reduced total and phosphorylated tau, demonstrating the link between FERMT2 and AD pathology [31]. A recent study revealed that FERMT2 directly regulates AβPP metabolism and that inadequate expression of FERMT2 affects synaptic connectivity and axonal growth in an AβPP-dependent way [32]. GWAS results based on International Genomics of Alzheimer’s Project showed that rs6572869 of FERMT2 were significantly correlated with the incidence of AD [13, 33]. Our study confirmed that heterozygous AG of rs6572869 might be an influential genetic risk factor in AD pathophysiology.

Cell trafficking and recycling are risk mechanisms for AD [34]. COPI-dependent trafficking alters subcellular localization and cell surface expression of AβPP. COPI-dependent trafficking is significantly associated with the reduction of amyloid plaques in the neurocortex and hippocampus of the AD mouse [34]. A large meta-analysis examining transcriptional changes in ageing and AD found that COPI subunit zeta 2 (COPZ2) was upregulated in AD [35]. Analysis of an AD GWAS dataset of European ancestry demonstrated that rs9898218 of COPI is significantly associated with AD risk [36]. Consistent with these results, we provided the first supporting evidence for the association between rs9898218 and AD risk in the Southern Chinese population.

It remains unknown whether rs2741342 of CHRNA2 is relevant to AD risk in the Chinese population. CHRNA2 encodes the alpha-2 subunit of neuronal acetylcholine receptor (nAChR), which is expressed in the hippocampal CA1 region and influences hippocampal-dependent memory and the plasticity of CA1 hippocampal synapses [37, 38]. CHRNA2 is currently one of the targets of AD clinical drug research [39]. rs2741342 has been confirmed to be associated with AD risk in people with Caucasian ancestry [13]. However, our study observed that the A allele of rs2741342 reduces susceptibility to AD, which may be due to race specificity and requires further validation.

HLA-DRB1, a genetic factor in AD pathogenesis, has shown a nominal association with the diagnosis of pathological AD, and methylation in this locus is associated with Aβ loading correlated with tau entanglement density [6, 40]. Analysis of GWAS from North America and Europe found that rs2516049 was significantly associated with neurofibrillary tangle pathology and correlated with AD progression [41]. rs1109581 is in an intron of ADGRF4, which is an adhesion G-protein-coupled receptor [42]. And GWAS results based on IGAP showed that rs1109581 of ADGRF4 were significantly correlated with the incidence of AD [13, 33]. Our results showed that, in the Southern Chinese population, homozygosity for the G allele of rs2516049 (HLA-DRB1) protects APOE ɛ4 carriers from AD susceptibility, whereas mutation of rs1109581 (ADGRF4) may increase the risk of AD in APOE ɛ4 non-carriers. The association of rs2516049 and rs1109581 with HLA-DRB1 and ADGRF4 expression in the brain suggested that mutations in these two loci might be involved in AD pathogenesis by affecting gene expression and interacting with APOE, which requires confirmation in future studies.

The associations between the other six SNPs and AD risk could not be replicated in this study, which may be due to heterogeneity in ethnic origin and/or small sample size [12, 43]. eQTL analysis can establish a link between genotype and gene expression patterns and help identify disease-causing genes. Many eQTLs affect the expression of local transcripts and distal genes [43]. Based on eQTL analysis, we further found that polymorphisms in HLA-DRB1 (rs2516049), ADGRF4 (rs1109581), CELF1 (rs10742814 and rs11039280), and ABCA7 (rs4147912) correlated with their corresponding gene expression in the brain, which demonstrated that polymorphisms in susceptibility loci might affect gene expression in different regions of the brain and thus affect AD pathogenesis. Meanwhile, positive eQTL association analysis could support the validity of this study and contribute to understanding the role of genetic variation in AD-related mechanisms.

In conclusion, our study identified several risk SNPs and protective SNPs associated with AD that have never been reported in Asian populations. Different SNP variants are either a risk factor or protective factor for AD and are associated with APOE ɛ4 carrier status, suggesting that the polymorphism may interact with APOE. Genetic polymorphisms remain insufficiently understood in the development of AD. AD pathogenesis is highly correlated with genetic factors, and the identification of AD-associated loci could provide a reliable basis for timely and reliable diagnosis. Our findings provide new insights into the relationship between risk loci and AD development in a southern Chinese population and contribute to the general understanding of the genetic basis of AD risk. Given the association between these loci and AD risk, future studies must investigate the role of risk genes in the pathogenesis of AD, as demonstrated here, to help further inform future AD-targeted therapies.

ACKNOWLEDGMENTS

We thank all the patients and healthy controls who participated in this study.

FUNDING

This study was supported by grants from the National Natural Science Foundation of China (82171400) and the Natural Science Foundation of Shanghai (19ZR1432500).

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

The data supporting the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

SUPPLEMENTARY MATERIAL

[1] The supplementary material is available in the electronic version of this article: https://dx.doi.org/10.3233/ADR-230072.

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