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. 2016 Jun 7;22:1924–1935. doi: 10.12659/MSM.895984

Polymorphisms of CHAT but not TFAM or VR22 are Associated with Alzheimer Disease Risk

Lili Gao 1,A,B,C, Yan Zhang 2,C,D,E, Jinghua Deng 3,B,C,F, Wenbing Yu 4,B,C,D, Yunxia Yu 5,A,E,G,✉
PMCID: PMC4917321  PMID: 27272392

Abstract

Background

Alzheimer disease (AD) is a chronic neurodegenerative disease that is one of the most prevalent health problems among seniors. The cause of AD has not yet been elucidated, but many risk factors have been identified that might contribute to the pathogenesis and prognosis of AD. We conducted a meta-analysis of studies involving CHAT, TFAM, and VR22 polymorphisms and AD susceptibility to further understand the pathogenesis of AD.

Material/Methods

PubMed/Medline, Embase, Web of Science, the Cochrane Library, and Google Scholar were searched for relevant articles. Rs1880676, rs2177369, rs3810950, and rs868750 of CHAT; rs1937 and rs2306604 of TFAM; and rs10997691 and rs7070570 of VR22 are studied in this meta-analysis.

Results

A total of 51 case-control studies with 16 446 cases and 16 057 controls were enrolled. For CHAT, rs2177369 (G>A) in whites and rs3810950 (G>A) in Asians were found to be associated with AD susceptibility. No association was detected between rs1880676 and rs868750 and AD risk. For TFAM and VR22, no significant association was detected in studied single-nucleotide polymorphisms (SNPs).

Conclusions

Rs2177369 and rs3810950 of CHAT are associated with AD susceptibility, but rs1880676 and rs868750 are not. Rs1937 and rs2306604 of TFAM, and rs10997691 and rs7070570 of VR22 are not significantly associated with AD risk.

MeSH Keywords: 1-Acylglycerophosphocholine O-Acyltransferase; Alzheimer Disease; Meta-Analysis as Topic; Polymorphism, Single Nucleotide; Genes, Mitochondrial

Background

Alzheimer disease (AD) is one of the most prevalent health problems among seniors. It is a chronic neurodegenerative disease characterized by progressive cognition impairment and short-term memory loss, which usually deteriorates with aging. Amyloid plaques and neurofibrillary tangles are identified as 2 hallmarks in the AD process [1].

The amyloid cascade hypothesis is one of the most influential hypotheses regarding AD pathogenesis. It suggests that the initial pathological event in AD is triggered by deposition of amyloid β (Aβ) in the brain, which further leads to the formation of tau-immunoreactive neurofibrillary tangles (NFT), extracellular senile plaques (SP), neuron dysfunction, and neuronal loss [2]. Aβ peptides are cut from amyloid precursor protein (APP) by secretases and aggregate to form oligomers. The malformation of oligomers or the dysfunction of oligomers further break down enzymes, leading to amyloid plaques and neurofibrillary tangles and triggering the process of AD. Tau as a microtubules-associated protein is also suspected to play an important part in the progression of AD, and was found to be the major constituent of neurofibrillary tangles. According to the amyloid cascade hypothesis, formation of the insoluble aggregates of tau is triggered by increased Aβ level via the induced hyperphosphorylation of tau [3]. In contrast, in the tau hypothesis it is the tau protein abnormality that is thought to trigger the disease [4]. Another important hypothesis regarding the pathogenesis of AD is the acetylcholine hypothesis; it is also the basis of most currently available AD drugs. According to this theory, AD is caused by reduced synthesis of the neurotransmitter acetylcholine (ACh) [5], and, by external supplementation of ACh, the symptoms of AD can be reduced.

Aside from cells, the mitochondrial cascade hypothesis indicates that critical changes in mitochondrial function initiate other pathologies characteristic of AD. Accumulation of amyloid-β (Aβ) causes mitochondrial dysfunction in AD, leading to decreased ATP levels and increased ROS generation. It can also enhance mitochondrial dysfunction and apoptosis, and inhibit protein import inside the mitochondria. Mitochondrial DNA mutations and mitochondrial DNA damage are also involved in the pathogenesis of AD. Phosphorylated tau and Aβ can lead to increased mitochondrial fission and neurodegeneration. Aβ and APP impair mitochondrial fusion/fission processes, mitophagy, and mitochondrial movement, and cause abnormal morphology [6].

In addition to the various AD hypotheses, many genes involved in the pathway are suspected to be risk factors of AD, including APP, APOE, CASS4, and CELF1 [7]. Although the association of AD with some genes has been verified by many studies, the contradictions between different studies make it difficult to form firm conclusions about such associations. Therefore, we performed a meta-analysis of published studies to investigate the correlation between suspected genes and AD susceptibly.

CHAT (choline O-acetyltransferase) gene encodes an enzyme that catalyzes the biosynthesis of ACh. The enzyme is also characteristic of cholinergic neurons, and changes in these neurons may contribute to some AD symptoms. The A allele of CHAT c.2384G>A polymorphism was also associated with earlier onset and possibly accelerated progression of AD [8]. CHAT was considered as a suspected gene in this meta-analysis.

TFAM (transcription factor A, mitochondrial) gene encodes a key mitochondrial transcription factor that functions in mitochondrial DNA replication and repair. Impaired expression of TFAM may influence the function of mitochondria and thus lead to AD.

Another suspected gene in this study is VR22 (also known as CTNNA3, catenin [cadherin-associated protein], alpha 3). The encoded protein plays a role in cell-cell adhesion. The association between VR22 and AD was first reported in several linkage studies [9–12]. Further studies also provided evidence of significant interaction between APOE-4 and VR22 SNPs [13], indicating that VR22 or a nearby gene may influence susceptibility to AD.

We conducted a meta-analysis of studies concerning CHAT, TFAM, and VR22 polymorphisms and AD susceptibility to further understand the pathogenesis of AD.

Material and Methods

Search strategy

In the current study, PubMed/Medline, Embase, Web of Science, the Cochrane Library, and Google Scholar were searched with related terms (details shown in Supplemenatry Table 1). Articles published prior to August 2015 were searched for potential SNP targets. References of retrieved articles were manually checked for other relevant publications.

Study selection and data extraction

The following criteria had to satisfied by eligible studies: (a) case-control studies covering the association between SNPs on CHAT, TFAM, or VR22 genes and susceptibility to AD; (b) sufficient requirements for estimating odds ratios (ORs) and their 95% confidence interval (CIs) must have been satisfied; (c) the diagnosis of AD was confirmed by the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria [14] published by the American Psychiatric Association, or the National Institute of Neurological and Communicative Disorders and Stroke (NINCDS) – the Alzheimer’s Disease and Related Disorders Association (ADRDA) Alzheimer’s Criteria [15]. Studies were excluded if they were: (a) not a case-control study; (b) had insufficient data provided; (c) were cited by a previous meta-analysis of same subject. The name of first author, publication year, country of origin, ethnicities of subjects, studied SNPs and genes, number of subjects, frequencies of allele and genotype, and indication of Hardy-Weinberg equilibrium (HWE) in the controls were documented for each study. Ethnicity was categorized as white or Asian. No study was conducted in African populations. Four SNPs for CHAT gene (rs1880676, rs2177369, rs3810950, and rs868750); 2 SNPs for TFAM gene (rs1937 and rs2306604); and 2 SNPs for VR22 gene (rs10997691 and rs7070570) were included in this meta-analysis. Data from retrieved studies were independently extracted by 2 reviewers. In cases of conflicting evaluations, 2 of the authors discussed the issues to reach a consensus; if no agreement could be reached, a third author would decide.

Statistical analysis

The strength of associations between the studied SNPs and susceptibility to AD were assessed by OR corresponding to 95% CI. Four genetic models (the allele, the dominant, the recessive, and the homozygous) were examined. A 2-sided P<0.05 in the Z test was considered as statistically significant. Subgroup analyses were performed by ethnicity (Asian/white). Heterogeneities were tested with Cochran’s Q-statistic [16] with a Ph>0.05 indicating lack of heterogeneity. Mantel-Haenszel (M-H) method for the fixed-effects model [17] was used to calculate the pooled OR estimate of studies without heterogeneity; otherwise, the DerSimonian and Laird (D-L) method [18] was used for the random-effects model. A funnel plot was used to detect publication bias. The standard error of log (OR) of each study was plotted against its log (OR) in the plot. Possible funnel plot asymmetry was evaluated by Egger’s linear regression test on the natural logarithm scale of OR [19]. All statistical analyses were performed with STATA version 12.0 software (Stata Corp, College Station, TX), using 2-sided P-values.

Results

Study characteristics

In the search for CHAT gene polymorphisms and AD association, we retrieved 26 articles [8,20–44] from PubMed/Medline, Embase, Web of Science, the Cochrane Library, and Google Scholar, with 28 studies related to rs1880676, rs2177369, rs3810950, and rs868750. For TFAM gene polymorphisms, 10 articles and 11 studies were enrolled. For VR22, 4 articles and 12 studies were enrolled. A total of 51 case-control studies were included in our meta-analysis, with 16 446 cases and 16 057 controls. The details of methodological and characteristics qualities of the eligible studies are compiled in Supplementary Table 2.

CHAT gene polymorphisms correlated with AD risk

Among the studied SNPs, rs2177369 (G>A) and rs3810950 (G>A) were found to be associated with AD susceptibility, but no association was detected between rs1880676 and rs868750 and AD risk (Figures 1, 2A). As shown in Table 1, rs2177369 (G>A) was a risk factor for AD onset (OR=1.61, 95% CI=1.07–2.43, P=0.022). For rs3810950 (G>A), a mutation is a risk factor for AD (OR=1.79, 95% CI=1.12–2.86, P=0.016, Figure 1). In subgroup analysis by ethnicity, the association was confirmed in Asians (Figure 2B), but not in whites (allele model: OR=1.23, 95%CI=1.01–1.48; homozygous model: OR=2.19, 95%CI=1.17–4.09; recessive model: OR=2.14, 95%CI=1.20–3.84, Table 1).

Figure 1.

Figure 1

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Forest plots showed the relationship of the 4 SNPs – rs1880676, rs2177369, rs3810950, and rs868750 – in CHAT gene and the risk of AD. The odds ratio from each study is represented by a square and the confidence interval is indicated by error bars. The subtotal and overall odds ratio is signified by a rhombus.

Figure 2.

Figure 2

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The forest plots of (A) CHAT rs1880676 and (B) CHAT rs3810950 by ethnicity. The odds ratio from each study is represented by a square and the confidence interval is indicated by error bars. The subtotal and overall odds ratio is signified by a rhombus.

Table 1.

Meta-analysis of four polymorphisms in ChAT gene and AD susceptibility.

Gene SNP Genetic model OR (95% CI) Podds ratio Tau2 I2 Pheterogeneity Ethnicity Publication bias
Caucasians Asians PBegg PEgger
ChAT rs1880676 A vs. G 1.01 (0.88–1.15) 0.896 0.017 51.6% 0.044 0.97 (0.86–1.11) 1.33 (0.96–1.83) 0.386 0.165
AA+GA vs. GG 0.97 (0.85–1.11) 0.687 0.010 30.4% 0.185 0.93 (0.83–1.03) 1.35 (0.95–1.94) 0.536 0.239
AA vs. GG 1.14 (0.74–1.74) 0.551 0.170 57.0% 0.023 1.08 (0.70–1.66) 2.74 (0.55–13.76) 0.536 0.095
AA vs. GG+GA 1.16 (0.77–1.75) 0.474 0.151 55.1% 0.029 1.11 (0.73–1.69) 2.54 (0.51–12.67) 0.536 0.104
rs2177369 A vs. G 1.13 (0.83–1.54) 0.439 0.133 88.6% <0.0001 1.13 (0.83–1.54) – 0.348 0.178
AA+GA vs. GG 1.14 (0.76–1.67) 0.531 0.198 82.6% <0.0001 1.14 (0.76–1.69) – 0.452 0.220
AA vs. GG 1.61 (1.07–2.43) 0.022 0.185 72.6% 0.003 1.61 (1.07–2.43) – 1.000 0.831
AA vs. GG+GA 1.53 (1.17–2.00) 0.002 0.063 57.0% 0.040 1.53 (1.17–2.00) – 0.707 0.659
rs3810950 A vs. G 1.23 (1.02–1.48) 0.033 0.060 77.2% <0.0001 1.18 (0.90–1.55) 1.23 (1.01–1.48) 0.592 0.214
AA+GA vs. GG 1.16 (0.97–1.38) 0.105 0.042 61.5% 0.008 1.09 (0.85–1.39) 1.20 (0.10–1.44) 0.592 0.292
AA vs. GG 1.79 (1.12–2.86) 0.016 0.346 72.5% <0.0001 1.44 (0.83–2.52) 2.19 (1.17–4.09) 0.858 0.325
AA vs. GG+GA 1.76 (1.14–2.70) 0.010 0.273 68.5% 0.001 1.45 (0.87–2.41) 2.14 (1.20–3.84) 1.000 0.355
rs868750 A vs. G 1.21 (0.96–1.52) 0.113 0.027 49.3% 0.116 1.21 (0.96–1.52) – 0.308 0.689
AA+GA vs. GG 1.19 (0.95–1.47) 0.125 0.014 27.5% 0.247 1.19 (0.95–1.47) – 0.308 0.628
AA vs. GG 1.78 (0.86–3.70) 0.123 0.229 41.1% 0.165 1.78 (0.86–3.71) – 0.734 0.858
AA vs. GG+GA 1.72 (0.87–3.37) 0.117 0.161 33.1% 0.213 1.72 (0.87–3.37) – 0.734 0.919
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OR – odds ratio; CI – confidence intervals; In genetic model, the bold one means mutation allele.

No association observed between SNPs of TFAM and VR22 and AD

A total of 3353 cases and 3089 controls from 11 studies were involved in the meta-analysis concerning rs1937 and rs2306604 of TFAM. No significant association was detected between the 2 SNPs and the risk of AD by the allele, the dominant, the recessive, or the homozygous model (Figure 3, Table 2). In subgroup analysis, 9 of the studies were in whites and only 2 were in Asians. No clear correlation could be identified in the stratification by ethnicity (Figure 4A, 4B).

Figure 3.

Figure 3

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Forest plots showed the association of rs1937 and rs2306604 in TFAM gene with the risk of AD. The odds ratio from each study is represented by a square and the confidence interval is indicated by error bars. The subtotal and overall odds ratio is signified by a rhombus.

Table 2.

Meta-analysis of two polymorphisms in TFAM gene and AD susceptibility.

Gene SNP Genetic model OR (95% CI) Podds ratio Tau2 I2 Pheterogeneity Ethnicity Publication bias
Caucasians Asians PBegg PEgger
TFAM rs1937 C vs. G 0.90 (0.90–1.17) 0.432 0.066 63.5% 0.018 0.94 (0.68–1.32) 0.76 (0.59–0.99) 1.000 0.395
CC+GC vs. GG 0.88 (0.69–1.13) 0.310 0.045 49.2% 0.080 0.91 (0.66–1.26) 0.78 (0.58–1.05) 1.000 0.496
CC vs. GG 0.86 (0.32–2.30) 0.759 0.817 57.3% 0.039 1.20 (0.43–3.34) 0.26 (0.09–0.80) 0.452 0.330
CC vs. GG+GC 0.87 (0.33–2.30) 0.783 0.759 55.6% 0.046 1.22 (0.45–3.32) 0.28 (0.09–0.84) 0.452 0.335
rs2306604 G vs. A 0.90 (0.79–1.0) 0.084 0.006 28.0% 0.235 0.87 (0.75–1.01) 0.98 (0.80–1.20) 0.462 0.308
GG+AG vs. AA 0.85 (0.70–1.02) 0.074 0.010 22.7% 0.270 0.78 (0.65–0.94) 1.04 (0.76–1.44) 0.462 0.387
GG vs. AA 0.82 (0.65–1.04) 0.107 0.014 19.1% 0.293 0.79 (0.59–1.06) 0.96 (0.64–1.43) 0.806 0.192
GG vs. AA+AG 0.89 (0.74–1.07) 0.200 0.000 0.0% 0.504 0.89 (0.70–1.11) 0.91 (0.66–1.26) 0.462 0.103
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OR – odds ratio; CI – confidence intervals; In genetic model, the bold one means mutation allele.

Figure 4.

Figure 4

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The forest plots of (A) TFAM rs1937, (B) TFAM rs2306604, and (C) VR22 rs7070570 by ethnicity. The odds ratio from each study is represented by a square and the confidence interval is indicated by error bars. The subtotal and overall odds ratio is signified by a rhombus.

The association of rs10997691 and rs7070570 polymorphism of VR22 and AD risk was investigated in 12 studies. No statistically significant correlation with AD was observed in the 4 models (Figure 5, Table 3). Nevertheless, increased or decreased AD susceptibility was not observed in subgroup analysis by ethnicity in the studies of rs7070570 polymorphism (Figure 4C).

Figure 5.

Figure 5

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Forest plots showed the association of rs10997691 and rs7070570 in VR22 gene with the risk of AD. The odds ratio from each study is represented by a square and the confidence interval is indicated by error bars. The subtotal and overall odds ratio is signified by a rhombus.

Table 3.

Meta-analysis of two polymorphisms in VR22 gene and AD susceptibility.

Gene SNP Genetic model OR (95% CI) Podds ratio Tau2 I2 Pheterogeneity Ethnicity Publication bias
Caucasians Asians PBegg PEgger
VR22 rs10997691 C vs. T 1.22 (0.96–1.54) 0.106 0.0000 0.0% 0.436 1.22 (0.96–1.54) – 0.308 0.211
CC+TC vs. TT 1.18 (0.91–1.54) 0.212 0.0000 0.0% 0.579 1.18 (0.91–1.54) – 0.308 0.098
CC vs. TT 1.87 (0.72–4.87) 0.200 0.1895 19.1% 0.295 1.87 (0.72–4.87) – 0.308 0.183
CC vs. TT+TC 1.82 (0.69–4.82) 0.229 0.2212 21.8% 0.280 1.82 (0.69–4.82) – 0.308 0.203
rs7070570 C vs. T 0.99 (0.90–1.10) 0.903 0.0000 0.0% 0.959 1.00 (0.89–1.11) 0.99 (0.78–1.25) 0.902 0.930
CC+TC vs. TT 1.02 (0.89–1.17) 0.802 0.0000 0.0% 0.740 0.99 (0.86–1.14) 1.58 (0.91–2.75) 0.386 0.269
CC vs. TT 1.07 (0.83–1.37) 0.617 0.0000 0.0% 0.780 1.00 (0.75–1.32) 1.41 (0.80–2.50) 0.536 0.710
CC vs. TT+TC 0.94 (0.76–1.14) 0.513 0.0000 0.0% 0.828 1.00 (0.77–1.32) 0.86 (0.63–1.16) 0.711 0.326
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OR – odds ratio; CI – confidence intervals; In genetic model, the bold one means mutation allele.

Publication bias

Publication biases of the articles were assessed by Begg’s funnel plot and Egger’s linear regression test on the metadata. The distribution of different studies on the funnel plot of each SNP appeared to be symmetrical, and no statistically significant asymmetry was detected by Egger’s test. Hence, no evidence of publication bias for the correlation between the SNPs and AD susceptibility was found (Tables 1–3).

Discussion

We performed a systematic meta-analysis of case-control association studies for susceptibility to AD. We screened 3 candidate genes – CHAT, TFAM, and VR22 – and their major polymorphisms. In the end, 51 studies of 16 446 cases and 16 057 controls were involved in the analysis. Our results showed that 2 SNPs of CHAT (rs2177369 and rs3810950) were significantly associated with AD susceptibility. We also observed ethnic differences for rs3810950 of CHAT, with A allele of rs3810950 in Asians as risk factors for AD, whereas rs1880676 and rs868750 of CHAT, rs1937 and rs2306604 of TFAM, and rs10997691 and rs7070570 of VR22 did not contribute to AD risk.

CHAT encodes the enzyme responsible for the biosynthesis of ACh. CHAT protein is a marker used in evaluating the function of basal forebrain cholinergic cells and dementia severity in AD [45,46]. Previous studies indicated that basal forebrain cholinergic neuron abnormalities are present very early in the course of AD, with altered expression of CHAT [47,48]. Mutations or polymorphisms of CHAT are also suspected to be related to AD and its treatment [49]. In agreement with previous results, we identified 2 SNPs of CHAT that contribute to the onset on AD.

TFAM locates in mitochondrial deoxyribonucleic acid (MTDNA) and encodes a key mitochondrial transcription factor that functions in mitochondrial DNA replication and repair. Mutations on TFAM can affect the function of mitochondria and contribute to the pathogenesis of AD. In accordance with the mitochondrial cascade hypothesis, the synergistic interactions between TFAM rs1937 and APOE4 status have been reported to influence AD risk [50], and rs2306604 A allele of TFAM was also found to be a moderate risk factor for AD [22]. However, in the present study, we failed to confirm the results of Belin et al. and Zhang et al. [22,44].

VR22, also known as CTNNA3, plays a role in cell-cell adhesion. VR22 can bind directly to b-catenin, whereas b-catenin forms a complex with presenilin 1 (PSEN1) [51], mutations of which cause familial cases of early-onset AD [52]. Nonetheless, the 2 SNPs we enrolled in this meta-analysis failed to show significant associations with AD.

The principal results of the present study suggest that TFAM and VR22 gene polymorphisms are not associated with risk of AD. All eligible case-control studies were included in this meta-analysis, including the most recent ones. However, there remain certain issues that need to be addressed in interpreting our results. Firstly, most of the subjects covered in our study were white (81.6% in cases and 76.0% in controls), which limits the general application of the results. As we have already observed, the association of AD with some SNPs can only be observed in certain ethnic groups. Further studies with more Asian and African subjects are recommended. Secondly, although it is statistically sufficient, the overall sample size for each SNP is still relatively small. Furthermore, individual genetic factors, the biological characteristics of tumors, and their interaction with the environment may influence cancer susceptibility and carcinogenesis. Because the diagnosis of most of the AD cases enrolled in the studies were based on diagnostic criteria rather than pathological examination, we cannot exclude that some cases might have been misdiagnosed, which further influences the results of this meta-analysis, and further work is required to minimize this effect.

Conclusions

Rs2177369 and rs3810950 of CHAT are associated with AD susceptibility, but rs1880676 and rs868750 are not. Rs1937 and rs2306604 of TFAM and rs10997691 and rs7070570 of VR22 are not significantly associated with AD risk.

Supplementary materials

Supplementary Table 1.

Research terms.

AD Alzheimer Disease[Mesh] OR Alzheimer Disease[tiab] OR Alzheimer Sclerosis[tiab] OR Alzheimer Syndrome[tiab] OR Alzheimer Type Senile Dementia[tiab] OR Alzheimer-Type Dementia[tiab] OR Alzheimer Type Dementia[tiab] OR Alzheimer Type Dementia[tiab] OR Senile Dementia[tiab] OR Primary Senile Degenerative Dementia[tiab] OR Alzheimer Dementia[tiab] OR Alzheimer’s Disease[tiab] OR Acute Confusional Senile Dementia[tiab] OR Presenile Dementia[tiab] OR Late Onset Alzheimer Disease[tiab] OR Focal Onset Alzheimer’s Disease[tiab] OR Familial Alzheimer Disease[tiab] OR Presenile Alzheimer Dementia[tiab] OR Early Onset Alzheimer Disease[tiab] OR AD
SNP Polymorphism, Genetic[Mesh] OR Polymorphisms, Genetic[tiab] OR Genetic Polymorphism[tiab] OR Polymorphism[tiab] OR Genetic Polymorphisms[tiab] OR Polymorphism, Single Nucleotide[Mesh] OR Nucleotide Polymorphism, Single[tiab] OR Nucleotide Polymorphisms, Single[tiab] OR Single Nucleotide Polymorphisms[tiab] OR SNPs[tiab] OR Single Nucleotide Polymorphism[tiab] OR Polymorphisms, Single Nucleotide[tiab]
CHAT CHAT[Mesh] OR CHAT[tiab] OR CHOACTASE[tiab] OR Choline O-Acetyltransferase[tiab] OR Choline Acetylase[tiab] OR Choline Acetyltransferase[tiab] OR rs868750[tiab] OR rs3810950[tiab] OR rs2177369[tiab] OR rs1880676[tiab]
TFAM TFAM[Mesh] OR TFAM[tiab] TCF6[tiab] OR MTTF1[tiab] OR MTTFA[tiab] OR transcription factor A, mitochondrial[tiab] OR rs1937[tiab] OR rs2306604[tiab]
VR22 CTNNA3[Mesh] OR CTNNA3[tiab] OR VR22[tiab] OR ARVD13[tiab] OR rs10997691[tiab] OR rs7070570[tiab]
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Supplementary Table 2.

Main characteristics of studies selected in the meta-analysis.

Gene SNP First Author Year Country Ethnicity Case Control Case Control
WW MW MM WW MW MM
ChAT rs1880676 Ahn Jo 2006 Korea Asian 316 264 211 99 6 193 69 2
G>A Giedraitis 2009 Sweden Caucasians 84 384 54 29 1 222 144 18
Harold 2003 UK Caucasians 68 85 34 25 9 49 33 3
Harold 2003 UK Caucasians 135 135 71 56 8 64 62 9
Harold 2003 UK Caucasians 194 209 105 77 12 127 79 3
Li 2008 Canada Caucasians 690 681 386 256 48 364 275 42
Ozturk 2005 USA Caucasians 1001 705 563 376 62 369 292 44
Reiman 2007 USA Caucasians 853 550 478 329 46 303 206 41
rs2177369 Cook 2014 UK Caucasians 381 370 158 207 105 162 164 55
G>A Cook 2005 UK Caucasians 202 295 95 124 76 88 85 29
Cook 2005 UK Caucasians 202 295 29 85 88 76 124 95
Cook 2005 UK Caucasians 179 175 26 79 74 29 83 63
Piccardi 2007 Italy Caucasians 158 118 44 75 39 40 57 21
Scacchi 2008 Italy Caucasians 442 218 167 200 75 61 117 40
rs3810950 Ahn Jo 2006 Korea Asian 316 264 211 99 6 192 70 2
G>A Cook 2005 UK Caucasians 210 315 112 76 22 161 128 26
Gruenblatt 2008 Austria Caucasians 120 456 63 45 12 268 164 24
Harold 2003 UK Caucasians 131 118 69 51 11 65 47 6
Kim 2004 Korea Asian 246 561 171 61 14 419 133 9
Lee 2012 Korea Asian 736 1386 505 205 26 1023 342 21
Mubumbila 2002 Germany & French Caucasians 122 112 48 32 42 64 34 14
Ozturk 2005 USA Caucasians 999 708 562 377 60 363 296 49
Schwarz 2003 Germany Caucasians 242 143 139 94 9 83 52 8
Tang 2008 China Asian 273 271 190 75 8 179 83 9
rs868750 Harold 2003 UK Caucasians 119 116 72 39 8 83 31 2
G>A Harold 2003 UK Caucasians 135 131 88 42 5 95 33 3
Harold 2003 UK Caucasians 209 222 129 75 5 130 84 8
Ozturk 2005 USA Caucasians 989 706 628 322 39 476 217 13
TFAM rs1937 Alvarez 2008 Spain Caucasians 300 183 277 23 0 158 23 2
G>C Belin 2007 Swedish Caucasians 423 313 339 78 6 251 55 7
Blomqvist 2005 Scotland Caucasians 122 152 95 21 6 123 27 2
Blomqvist 2005 Sweden Caucasians 204 174 156 43 5 143 30 1
Gunther 2004 – Caucasians 372 295 301 67 4 221 71 3
Zhang 2011 China Asian 394 390 274 116 4 250 126 14
rs2306604 Alvarez 2008 Spain Caucasians 300 183 93 151 56 50 99 34
A>G Belin 2007 Swedish Caucasians 406 318 164 169 73 100 152 66
Giedraitis 2009 Sweden Caucasians 85 400 29 41 15 146 200 54
Gunther 2004 – Caucasians 353 291 123 163 67 84 136 71
Zhang 2012 China Asian 394 390 98 204 92 100 192 98
VR22 rs10997691 Busby 2004 UK(I) Caucasians 133 110 94 35 4 81 24 5
T>C Busby 2004 UK(II) Caucasians 108 104 79 25 4 85 19 0
Busby 2004 USA(I) Caucasians 265 448 214 45 6 362 82 4
Busby 2004 USA(II) Caucasians 94 90 68 23 3 71 18 1
rs7070570 Blomqvist 2004 Scotland Caucasians 119 151 53 54 12 75 66 10
T>C Blomqvist 2004 Sweden Caucasians 534 173 277 214 43 89 73 11
Busby 2004 UK(I) Caucasians 145 121 72 63 10 54 58 9
Busby 2004 UK(II) Caucasians 107 106 56 41 10 53 41 12
Busby 2004 USA(I) Caucasians 266 423 141 110 15 226 172 25
Busby 2004 USA(II) Caucasians 422 381 222 169 31 195 159 27
Cellini 2005 Italy Caucasians 302 164 168 116 18 94 56 14
Kuwano 2006 Japan Asian 348 328 23 155 170 33 122 173
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W – wild allele; M – mutation allele.

Footnotes

Source of support: Departmental sources

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 1.

Research terms.

AD Alzheimer Disease[Mesh] OR Alzheimer Disease[tiab] OR Alzheimer Sclerosis[tiab] OR Alzheimer Syndrome[tiab] OR Alzheimer Type Senile Dementia[tiab] OR Alzheimer-Type Dementia[tiab] OR Alzheimer Type Dementia[tiab] OR Alzheimer Type Dementia[tiab] OR Senile Dementia[tiab] OR Primary Senile Degenerative Dementia[tiab] OR Alzheimer Dementia[tiab] OR Alzheimer’s Disease[tiab] OR Acute Confusional Senile Dementia[tiab] OR Presenile Dementia[tiab] OR Late Onset Alzheimer Disease[tiab] OR Focal Onset Alzheimer’s Disease[tiab] OR Familial Alzheimer Disease[tiab] OR Presenile Alzheimer Dementia[tiab] OR Early Onset Alzheimer Disease[tiab] OR AD
SNP Polymorphism, Genetic[Mesh] OR Polymorphisms, Genetic[tiab] OR Genetic Polymorphism[tiab] OR Polymorphism[tiab] OR Genetic Polymorphisms[tiab] OR Polymorphism, Single Nucleotide[Mesh] OR Nucleotide Polymorphism, Single[tiab] OR Nucleotide Polymorphisms, Single[tiab] OR Single Nucleotide Polymorphisms[tiab] OR SNPs[tiab] OR Single Nucleotide Polymorphism[tiab] OR Polymorphisms, Single Nucleotide[tiab]
CHAT CHAT[Mesh] OR CHAT[tiab] OR CHOACTASE[tiab] OR Choline O-Acetyltransferase[tiab] OR Choline Acetylase[tiab] OR Choline Acetyltransferase[tiab] OR rs868750[tiab] OR rs3810950[tiab] OR rs2177369[tiab] OR rs1880676[tiab]
TFAM TFAM[Mesh] OR TFAM[tiab] TCF6[tiab] OR MTTF1[tiab] OR MTTFA[tiab] OR transcription factor A, mitochondrial[tiab] OR rs1937[tiab] OR rs2306604[tiab]
VR22 CTNNA3[Mesh] OR CTNNA3[tiab] OR VR22[tiab] OR ARVD13[tiab] OR rs10997691[tiab] OR rs7070570[tiab]
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Supplementary Table 2.

Main characteristics of studies selected in the meta-analysis.

Gene SNP First Author Year Country Ethnicity Case Control Case Control
WW MW MM WW MW MM
ChAT rs1880676 Ahn Jo 2006 Korea Asian 316 264 211 99 6 193 69 2
G>A Giedraitis 2009 Sweden Caucasians 84 384 54 29 1 222 144 18
Harold 2003 UK Caucasians 68 85 34 25 9 49 33 3
Harold 2003 UK Caucasians 135 135 71 56 8 64 62 9
Harold 2003 UK Caucasians 194 209 105 77 12 127 79 3
Li 2008 Canada Caucasians 690 681 386 256 48 364 275 42
Ozturk 2005 USA Caucasians 1001 705 563 376 62 369 292 44
Reiman 2007 USA Caucasians 853 550 478 329 46 303 206 41
rs2177369 Cook 2014 UK Caucasians 381 370 158 207 105 162 164 55
G>A Cook 2005 UK Caucasians 202 295 95 124 76 88 85 29
Cook 2005 UK Caucasians 202 295 29 85 88 76 124 95
Cook 2005 UK Caucasians 179 175 26 79 74 29 83 63
Piccardi 2007 Italy Caucasians 158 118 44 75 39 40 57 21
Scacchi 2008 Italy Caucasians 442 218 167 200 75 61 117 40
rs3810950 Ahn Jo 2006 Korea Asian 316 264 211 99 6 192 70 2
G>A Cook 2005 UK Caucasians 210 315 112 76 22 161 128 26
Gruenblatt 2008 Austria Caucasians 120 456 63 45 12 268 164 24
Harold 2003 UK Caucasians 131 118 69 51 11 65 47 6
Kim 2004 Korea Asian 246 561 171 61 14 419 133 9
Lee 2012 Korea Asian 736 1386 505 205 26 1023 342 21
Mubumbila 2002 Germany & French Caucasians 122 112 48 32 42 64 34 14
Ozturk 2005 USA Caucasians 999 708 562 377 60 363 296 49
Schwarz 2003 Germany Caucasians 242 143 139 94 9 83 52 8
Tang 2008 China Asian 273 271 190 75 8 179 83 9
rs868750 Harold 2003 UK Caucasians 119 116 72 39 8 83 31 2
G>A Harold 2003 UK Caucasians 135 131 88 42 5 95 33 3
Harold 2003 UK Caucasians 209 222 129 75 5 130 84 8
Ozturk 2005 USA Caucasians 989 706 628 322 39 476 217 13
TFAM rs1937 Alvarez 2008 Spain Caucasians 300 183 277 23 0 158 23 2
G>C Belin 2007 Swedish Caucasians 423 313 339 78 6 251 55 7
Blomqvist 2005 Scotland Caucasians 122 152 95 21 6 123 27 2
Blomqvist 2005 Sweden Caucasians 204 174 156 43 5 143 30 1
Gunther 2004 – Caucasians 372 295 301 67 4 221 71 3
Zhang 2011 China Asian 394 390 274 116 4 250 126 14
rs2306604 Alvarez 2008 Spain Caucasians 300 183 93 151 56 50 99 34
A>G Belin 2007 Swedish Caucasians 406 318 164 169 73 100 152 66
Giedraitis 2009 Sweden Caucasians 85 400 29 41 15 146 200 54
Gunther 2004 – Caucasians 353 291 123 163 67 84 136 71
Zhang 2012 China Asian 394 390 98 204 92 100 192 98
VR22 rs10997691 Busby 2004 UK(I) Caucasians 133 110 94 35 4 81 24 5
T>C Busby 2004 UK(II) Caucasians 108 104 79 25 4 85 19 0
Busby 2004 USA(I) Caucasians 265 448 214 45 6 362 82 4
Busby 2004 USA(II) Caucasians 94 90 68 23 3 71 18 1
rs7070570 Blomqvist 2004 Scotland Caucasians 119 151 53 54 12 75 66 10
T>C Blomqvist 2004 Sweden Caucasians 534 173 277 214 43 89 73 11
Busby 2004 UK(I) Caucasians 145 121 72 63 10 54 58 9
Busby 2004 UK(II) Caucasians 107 106 56 41 10 53 41 12
Busby 2004 USA(I) Caucasians 266 423 141 110 15 226 172 25
Busby 2004 USA(II) Caucasians 422 381 222 169 31 195 159 27
Cellini 2005 Italy Caucasians 302 164 168 116 18 94 56 14
Kuwano 2006 Japan Asian 348 328 23 155 170 33 122 173
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W – wild allele; M – mutation allele.

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