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. 2017 Mar 22;8(26):43506–43520. doi: 10.18632/oncotarget.16490

The associations between the MAPT polymorphisms and Alzheimer’s disease risk: a meta-analysis

Futao Zhou 1, Danli Wang 1
PMCID: PMC5522165  PMID: 28415654

Abstract

Published studies revealed that the microtubule-associated protein tau (MAPT) gene polymorphisms increased Alzheimer’s disease (AD) risk; the associations of 4 single nucleotide polymorphisms (SNPs, rs242557G/A, rs2471738C/T, rs3785883G/A and rs1467967A/G) of the MAPT gene with AD risk, however, remain inconclusive. Here, we conducted a meta-analysis to investigate the relationship between the MAPT SNPs and AD risk. A significant association of SNP rs242557 with AD risk was found in a dominant [odds ratio (OR) = 1.05, 95% confidence interval (CI) = 1.01, 1.10, P = 0.025] genetic model, and a suggestive association in an allelic (OR = 1.03, 95% CI = 1.00, 1.06, P = 0.078). When APOE epsilon 4 carrier status was included in stratified analysis, this association was even stronger (allelic model for the APOE epsilon 4 positive individuals: OR = 1.24, 95% CI = 1.08, 1.43, P = 0.003). Furthermore, a significant association of SNP rs2471738 with AD risk was found under all the four models (allelic: OR = 1.11, 95% CI = 1.01, 1.20, P = 0.021; dominant: OR = 1.10, 95% CI = 1.00, 1.21, P = 0.046; recessive: OR = 1.18, 95% CI = 1.05, 1.32, P = 0.004; additive: OR = 1.20, 95% CI = 1.07, 1.34, P = 0.002) models. However, pooled results suggest that the neither rs3785883 nor rs1467967 is associated with AD risk under all the four genetic models. In summary, our study provides further evidence of the associations of the MAPT SNPs with AD risk.

Keywords: Alzheimer’s disease, microtubule-associated protein tau, single nucleotide polymorphisms, meta-analysis

INTRODUCTION

One of the neuropathological hallmarks of Alzheimer's Disease (AD) is the neurofibrillary tangle, which contains paired helical filaments (PHFs) composed of hyperphosphorylated forms of the microtubule-associated protein tau (MAPT) by mechanism which is not illustrated [1]. Increasing attention has been paid to endogenous and exogenous factors, as well as genetic risk factors contributing to the incidence of AD [2], stimulating the disease progression of AD [3]. It was believed that the identification of key genetic determinants for AD might help further understand its underlying mechanism.

Human MAPT gene is located on chromosome 17q21. There have been conflicting results showing positive or negative findings on the association between the MAPT SNPs and AD risk. Some studies were showed that SNPs rs242557 [4, 5], rs3785883 [6] in US series, rs2471738 [6, 7] and rs1467967 [8] of the MAPT gene might been associated with increased AD risk. Some studies were, however, reported that rs242557 [8–10], rs3785883 [11–14], rs2471738 [11, 14, 15] and rs1467967 [7, 16] might not be associated with AD risk [10, 11, 13, 16, 17].

There are many factors leading to these different results about the association between the MAPT SNPs and AD risk. One of primary reasons is low statistical power and the limited sample size in each study. Therefore, we performed a meta-analysis on the association between the MAPT SNPs and AD risk by pooling all available published data. In this study, we evaluated the genetic heterogeneity of the studies included and then carried out a meta-analysis on the association between the MAPT SNPs (rs242557, rs2471738, rs3785883 and rs1467967) with AD risk to make a more accurate assessment of the relationship for greater power in detecting the disease associations.

RESULTS

Characteristic of eligible studies

The literature search was done on studies up to January 2017 and availability of an English-language abstract or paper for review; this yielded 208 hits (PubMed: 14, Google scholar: 194). 194 of these were excluded, including 16 duplicates, 67 non-AD case reports, 34 reviews, 34 irrelevant studies, 25 data not available, 10 abstracts, 5 non-English language papers (also non-Chinese) and 3 case reports. In total, 64 independent studies from 14 articles published from 2005 to 2016 providing data of the MAPT genotype, were included in the current meta-analysis (16 for rs242557, 14 for rs2471738, 14 for rs3785883 and 15 for rs1467967; Figure 1). We found that in all the studies included SNPs neither rs75721 (within exon 14) nor rs9468 (within exon 13) was significantly associated with increased AD risk (results not shown). So, we analyzed the associations between these SNPs (rs242557, rs2471738, rs3785883 and rs1467967) of the MAPT gene and AD risk involved in 14666/17532, 13812/17201, 14607/17883 and 15064/17687 cases/controls, respectively. The NOS results indicated that the methodological quality of these selected studies was generally good. The study characteristics were listed in Table 1.

Figure 1. Flow diagram of study selection.

Figure 1

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Table 1. Main characteristics of the studies included in this meta-analysis of the associations between these SNPs of the MAPT gene and AD risk.

SNP loci Allele Ref no. First author Year Country Case (n) CTR (n) Case genotype Control genotype HWEct NOS
AA AB BB AA AB BB
rs242557
Exon 1		 G > A 11 Abraham, R. 2009 UK 979 1139 144 456 379 143 563 433 0.054 8
13 Allen, M.(Mayo Cohort) 2014 USA 1802 3133 260 838 704 500 1373 1260 0.0001 8
13 Allen, M.(ADGC Cohort) 2014 USA 6705 6702 865 3082 2758 849 3081 2772 0.88 8
13 Allen, M.(JS) 2014 USA 828 932 124 386 318 141 404 387 0.04 8
13 Allen, M.(RS) 2014 USA 460 2201 60 215 185 359 969 873 0.001 8
8 Feulner, T. M. 2010 Germany 491 479 68 246 177 81 220 178 0.36 9
9 Huin, V. 2016 France 35 19 5 16 14 3 9 7 0.97 7
5 Laws, S. M. 2007 Germany 434 279 64 205 165 28 120 131 0.99 8
4 Liu, Q. Y. 2013 China 796 796 146 394 256 134 356 306 0.08 8
10 Mateo, I.(a) 2008 Spain 300 360 30 127 143 36 153 171 0.84 8
16 Mukherjee, O. 2007 USA 361 358 47 166 148 49 167 142 0.99 6
6 Myers, A. J.(US) 2005 USA 181 131 26 85 70 12 55 64 0.97 8
6 Myers, A. J.(UK) 2005 UK 179 121 32 87 60 15 55 51 0.98 8
6 Myers, A. J. (US) 2007 USA 296 128 36 135 125 17 60 51 0.92 8
6 Myers, A. J. (US/UK) 2007 UK 655 380 94 309 252 44 171 165 0.98 8
12 Seto-Salvia, N. 2011 Spain 164 374 19 74 71 38 163 173 0.97 8
Total 14666 17532
APOE(+) 4 Liu, Q. Y. 2013 China 200 122 44 96 60 18 52 52 0.403 8
6 Myers, A. J. (US/UK) 2005 USA 360 252 55 171 134 34 105 113 0.229 8
11 Abraham, R. 2009 UK 597 271 83 271 243 21 139 111 0.012 8
APOE(−) 4 Liu, Q. Y. 2013 China 596 674 102 298 196 116 304 254 0.1291 8
6 Myers, A. J. (US/UK) 2005 USA 360 252 64 175 121 26 109 117 0.9342 8
Total 2113 1571
rs2471738
Intron 9		 C > T 11 Abraham, R. 2009 UK 970 1125 50 333 587 61 388 676 0.59 8
13 Allen, M.(Mayo Cohort) 2014 USA 1980 3302 106 671 1203 135 1112 2055 0.31 8
13 Allen, M.(ADGC Cohort) 2014 USA 6942 7239 292 2265 4385 287 2315 4637 0.93 8
13 Allen, M.(JS) 2014 USA 851 947 39 297 515 28 334 585 0.02 8
13 Allen, M.(RS) 2014 USA 585 2355 33 194 358 107 778 1470 0.75 8
14 Chang, C. W. 2014 China 109 108 6 38 65 7 40 61 0.9 7
16 Mukherjee, O. 2007 USA 361 358 13 111 237 16 119 223 0.98 6
15 Mateo, I.(b) 2008 Spain 293 396 8 84 201 9 110 277 0.62 8
6 Myers, A. J.(US) 2005 USA 181 131 12 70 99 4 39 88 0.9 8
6 Myers, A. J.(UK) 2005 UK 179 121 10 65 104 4 36 81 1 8
6 Myers, A. J. (US) 2007 USA 296 128 14 102 180 2 31 95 0.77 8
6 Myers, A. J. (US/UK) 2007 UK 655 380 38 239 378 10 102 268 0.94 8
12 Seto-Salvia, N. 2011 Spain 164 374 3 41 120 12 109 253 0.95 8
19 Vazquez-Higuera, J. L. 2009 Spain 246 237 9 64 173 5 62 170 0.81 8
Total 13812 17201
rs3785883
Intron 3		 G > A 11 Abraham, R. 2009 UK 967 1139 29 272 666 33 332 774 0.72 8
13 Allen, M.(Mayo Cohort) 2014 USA 1954 3293 66 581 1307 110 982 2201 0.97 8
13 Allen, M.(ADGC Cohort) 2014 USA 7397 7790 254 2135 5008 235 2203 5352 0.65 8
13 Allen, M.(JS) 2014 USA 841 943 30 238 573 26 267 650 0.82 8
13 Allen, M.(RS) 2014 USA 578 2350 23 176 379 84 715 1551 0.89 8
14 Chang, C. W. 2014 China 108 108 3 31 74 2 26 80 0.95 7
8 Feulner, T. M. 2010 Germany 491 479 28 148 315 21 133 325 0.12 9
5 Laws, S. M. 2007 Germany 433 279 11 118 304 11 88 180 0.97 8
16 Mukherjee, O. 2007 USA 361 358 14 116 231 14 115 229 0.93 6
6 Myers, A. J.(US) 2005 USA 181 131 5 51 125 6 45 80 0.92 8
6 Myers, A. J.(UK) 2005 UK 181 131 3 41 137 3 33 95 0.95 8
6 Myers, A. J. (US) 2007 USA 296 128 12 95 189 2 27 99 0.92 8
6 Myers, A. J.(US/UK) 2007 UK 655 380 19 185 451 11 107 262 0.98 8
12 Seto-Salvia, N. 2011 Spain 164 374 6 51 107 17 124 233 0.92 8
Total 14607 17883
rs1467967
Exon 1		 A > G 11 Abraham, R. 2009 UK 982 1153 88 417 477 93 509 551 0.1 8
13 Allen, M.(Mayo Cohort) 2014 USA 1868 3118 220 812 836 340 1376 1402 0.93 8
13 Allen, M.(ADGC Cohort) 2014 USA 7110 7255 765 3151 3194 752 3232 3271 0.26 8
13 Allen, M.(JS) 2014 USA 831 905 91 372 368 85 408 412 0.27 8
13 Allen, M.(RS) 2014 USA 536 2213 70 241 225 255 968 990 0.43 8
14 Chang, C. W. 2014 China 108 108 17 52 39 14 50 44 0.97 7
21 Elias-Sonnenschein, L. S. 2013 Finnish 869 685 104 391 374 89 308 288 0.64 8
8 Feulner, T. M. 2010 Germany 491 479 56 228 207 47 191 241 0.31 9
5 Laws, S. M. 2007 Germany 433 279 47 192 194 39 131 109 0.97 8
6 Myers, A. J.(US) 2005 USA 181 131 18 79 84 19 62 50 0.98 8
6 Myers, A. J.(UK) 2005 UK 179 121 18 78 83 13 54 54 0.93 8
6 Myers, A. J. (US) 2007 USA 296 128 32 131 133 15 57 56 0.93 8
6 Myers, A. J.(US/UK) 2007 UK 655 380 71 290 294 47 173 160 0.98 8
16 Mukherjee, O. 2007 USA 361 358 42 162 157 46 165 147 0.98 6
12 Seto-Salvia, N. 2011 Spain 164 374 14 67 83 26 145 203 0.99 8
Total 15064 17687
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Abbreviations: Ref no: reference number; NOS, the Newcastle-Ottawa Scale; CTR, control; HWEct, Hardy-Weinberg Equilibrium in controls;

Heterogeneity test

The strength of the association was estimated in the allelic, dominant, recessive and additive models. The heterogeneity among studies was tested with Q statistic and further quantified by I2 statistic. As measured by the I2 (Table 2), in this meta-analysis no significant heterogeneity existed between studies under all the genetic models tested for rs242557 (the range of I2 values from 0 to 33.1%), rs3785883 (the range of I2 values from 0 to 29.1%), and rs1467967 (the range of I2 values from 0 to 17.5%). Therefore, the fixed-effect model (Mantel-Haenszel method) was used to calculate the pooled ORs. However, for rs2471738 there was significant heterogeneity observed between studies under the allelic and dominant models (I2 = 62.0 and 57.1 for the allelic and dominant genetic models, respectively). Therefore, the random-effect model (Inverse Variance method) was used to calculate the pooled ORs under allelic and dominant models (fixed-effect model for the recessive and additive genetic models).

Table 2. The genetic heterogeneity test.

Genetic model X2 p I2 (%)
rs242557 NO stratification Allelic A vs. G 22.42 0.097 33.1
Dominant AA+AG vs. GG 18.94 0.216 20.8
Recessive AA vs. AG+GG 18.50 0.237 18.9
Additive AA vs. GG 21.35 0.126 29.8
Stratified by APOE ε4 allele Positive Allelic A vs. G 1.87 0.393 0
negative Allelic A vs. G 5.4 0.02 81.5
rs2471738 Allelic T vs. C 34.21 0.001 62.0
Dominant TT+TC vs. CC 30.32 0.004 57.1
Recessive TT vs. TC+CC 14.68 0.328 11.5
Additive TT vs. CC 18.39 0.143 29.3
rs3785883 Allelic A vs. G 18.33 0.146 29.1
Dominant AA+AG vs. GG 16.29 0.234 20.2
Recessive AA vs. AG+GG 6.17 0.94 0
Additive AA vs. GG 7.97 0.846 0
rs1467967 Allelic G vs. A 16.96 0.258 17.5
Dominant GG+AG vs. AA 15.12 0.37 7.4
Recessive GG vs. AG+AA 8.7 0.85 0
Additive GG vs. AA 13.19 0.512 0
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Meta-analysis results of the association between SNP rs242557 and AD risk

For rs242557 when the 16 studies were pooled into the meta-analysis using the fixed-effect model, a significant association was observed under the dominant (OR = 1.05, 95% CI = 1.01, 1.10, P = 0.025, Figure 3) model, and there was a trend under the allelic (OR = 1.03, 95% CI = 1.00, 1.06, P = 0.078, Figure 2) model. However, no significant association was found under the recessive (OR = 1.06, 95% CI = 0.95, 1.08, P = 0.766) and additive models (OR = 1.04, 95% CI = 0.97, 1.12, P = 0.223).

Figure 3. Forest plot for the meta-analysis of the association of SNP rs242557 and AD risk under the dominant model (AA + AG vs. GG).

Figure 3

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Figure 2. Forest plot for the meta-analysis of the association of SNP rs242557 and AD risk under the allelic model (A vs. G).

Figure 2

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When stratified by APOE ε4 carrier status, the association between the rs242557 SNP and AD risk was observed to be stronger in the individuals with APOE ε4-positive genotype (with no heterogeneity, I2 = 0, OR = 1.24, 95% CI = 1.08, 1.43, P = 0.003) than without stratification (OR = 1.03, 95% CI = 1.00, 1.06, P = 0.078) under the allelic model. But for the individuals with APOE ε4-negative genotype (APOE ε4-), there was large heterogeneity (I2 = 81.5, Table 2) under the allelic model, and no significant association between the rs242557 SNP with AD risk (OR = 1.29, 95% CI = 0.93, 1.80, P = 0.132, Table 3, Figure 4).

Table 3. The pooled results of the associations between these SNPs and AD risk as well as publication bias evaluation of the studies included.

SNP locus Genetic model Effect model Pz Pooled OR 95% CI Publication bias (p value)
Begg’s Egger’s
rs242557 Allelic A vs. G Fixed 0.078 1.03 1.00-1.06 0.753 0.982
Dominant AA+AG vs. GG Fixed 0.025 1.05 1.01-1.10 0.753 0.933
Recessive AA vs. AG+GG Fixed 0.766 1.06 0.95-1.08 0.558 0.341
Additive AA vs. GG Fixed 0.223 1.04 0.97-1.12 0.558 0.337
APOE (+) Allelic A vs. G Fixed 0.003 1.24 1.08-1.43 0.296 0.371
APOE (−) Allelic A vs. G Random 0.132 1.29 0.93-1.80 1.0 -
rs2471738 Allelic T vs. C Random 0.021 1.11 1.01-1.20 0.827 0.493
Dominant TT+TC vs. CC Random 0.046 1.10 1.00-1.21 0.101 0.667
Recessive TT vs. TC+CC Fixed 0.004 1.18 1.05-1.32 0.869 0.589
Additive TT vs. CC Fixed 0.002 1.20 1.07-1.34 0.189 0.469
rs3785883 Allelic A vs. G Fixed 0.179 1.03 0.99-1.07 0.324 0.543
Dominant AA+AG vs. GG Fixed 0.32 1.02 0.98-1.07 0.189 0.067
Recessive AA vs. AG+GG Fixed 0.144 1.10 0.97-1.24 0.274 0.732
Additive AA vs. GG Fixed 0.126 1.10 0.97-1.25 0.101 0.051
rs1467967 Allelic G vs. A Fixed 0.447 1.01 0.98-1.05 0.767 0.830
Dominant GG+AG vs. AA Fixed 0.737 1.01 0.96-1.05 0.921 0.804
Recessive GG vs. AG+AA Fixed 0.276 1.04 0.97-1.12 0.553 0.572
Additive GG vs. AA Fixed 0.301 1.04 0.97-1.12 0.692 0.383
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Figure 4. Forest plot for the meta-analysis of the association of SNP rs242557 and AD risk stratified by APOE ε4 allele status.

Figure 4

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Meta-analysis results of the association between SNP rs2471738 and AD risk

A significant association between SNP rs2471738 and AD risk was identified under the allelic (random-effect, OR = 1.11, 95% CI = 1.01, 1.20, P = 0.021, Figure 5 and Table 3) and dominant (OR = 1.10, 95% CI = 1.00, 1.21, P = 0.046, Figure 6 and Table 3) models. A significant association between SNP rs2471738 and AD risk was also identified under the recessive (fixed-effect, OR = 1.18, 95% CI = 1.05, 1.32, P = 0.004, Figure 7 and Table 3) and additive (OR = 1.20, 95% CI = 1.07, 1.34, P = 0.002, Figure 8 and Table 3) models.

Figure 5. Forest plot for the meta-analysis of the association of SNP rs2471738 and AD risk under the allelic model (T vs. C).

Figure 5

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Figure 6. Forest plot for the meta-analysis of the association of SNP rs2471738 and AD risk under the dominant model (TT + TC vs. CC).

Figure 6

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Figure 7. Forest plot for the meta-analysis of the association of SNP rs2471738 and AD risk under the recessive model (TT vs. CC + TC).

Figure 7

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Figure 8. Forest plot for the meta-analysis of the association of SNP rs2471738 and AD risk under the additive model (TT vs. CC).

Figure 8

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Meta-analysis results of the associations between SNPs rs3785883 and rs1467967 and AD risk

Using fixed-effect model, no significant association between SNP rs3785883 and AD risk was observed under all the four models (allelic: OR = 1.03, 95% CI = 0.99, 1.07, P = 0.179, Figure 9; dominant: OR = 1.02, 95% CI = 0.98, 1.07, P = 0.32; recessive: OR = 1.10, 95% CI = 0.97, 1.24, P = 0.144; additive: OR = 1.10, 95% CI = 0.97, 1.25, P = 0.126, Table 3).

Figure 9. Forest plot for the meta-analysis of the association of SNP rs3785883 and AD risk under the allelic model (A vs. G).

Figure 9

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Similarly, no significant association between SNP rs1467967 and AD risk was found under all the four models (fixed-effect, allelic: OR = 1.01, 95% CI = 0.98, 1.05, P = 0.449, Figure 10; dominant: OR = 1.01, 95% CI = 0.96, 1.05, P = 0.737; recessive: OR = 1.04, 95% CI = 0.97, 1.12, P = 0.276; additive: OR = 1.04, 95% CI = 0.97, 1.12, P = 0.301, Table 3).

Figure 10. Forest plot for the meta-analysis of the association of SNP rs1467967 and AD risk under the allelic model (G vs. A).

Figure 10

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Sensitivity analysis and evaluation of publication bias

Due to large heterogeneity between studies for rs2471738, we performed a sensitivity analysis by excluding a study [Allen, M. (JS), 2014; see Table 1] with departure from Hardy-Weinberg Equilibrium (HWE) in controls, we did not observe increased homogeneity across the rest studies (data not shown), suggesting that HWE deviation was not a source of between-study heterogeneity. The sensitivity analysis showed that for rs242557 and rs2471738 none of the studies included significantly changed the results under the allelic model (Figure 11A and 11B, respectively). The same results were observed for rs3785883/rs1467967 (Figure 12 A and 12B, respectively). Begg's and Egger's test were used to estimate the severity of publication bias with a P-value < 0.05 being considered statistically significant. No evidence of publication bias was found in any genetic model (Table 3).

Figure 11. Sensitivity analysis for rs242557 A. and rs2471738 B. under the allelic model.

Figure 11

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Figure 12. Sensitivity analysis for rs3785883 A. and rs1467967 B. under the allelic model.

Figure 12

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DISCUSSION

Tau protein is specifically expressed in neurons, directly interacts with tubulin and mediates its assembly [18]. It was found that the MAPT rs242557 (within exon 1) SNP was significantly associated with late-onset AD in 1592 Han Chinese subjects [4], in the German population [5] and in the US series [7]. However, it was reported that this SNP was not significantly associated with AD risk [10, 11] in the UK series [7]. For the rs2471738 (within intron 9) SNP, study findings revealed that there was significant association in the US series [6, 7] and US/UK series [6], or no [19] in 293 AD patients and 396 healthy controls [15], in 361 AD patients and 358 controls [16]. For the rs3785883 (within intron 3) SNP, it was found that there was significant association [13], or no [5, 14, 20]. For the rs1467967 (within exon 1) SNP, it was showed that there was significant association [5], or no [7, 11, 21]. There were consistent results on the association between the rs7521 [6, 7, 11, 22] and rs9468 [11] (too little data) SNPs and AD risk. Thus, these four SNPs of the MAPT gene were a matter of controversy.

Therefore, we conducted this meta-analysis to explore the association between the MAPT SNPs and AD risk. In summary, results from this meta-analysis suggest that of these SNPs tested, rs242557 is significantly associated with increased AD risk under the dominant genetic model, and the rs2471738 SNP is significantly associated with increased AD risk under all the four genetic model. In the stratified analysis by APOE ε4 allele status, APOE ε4 allele carriers, but not APOE ε4 allele non-carriers, were showed to be significantly associated with increased AD risk. This result indicates that there appears to be a gene-gene interaction between the APOE and the MAPT genes, which could increase susceptibility to AD. More studies should, however, be conducted to assess the interaction.

Because of the moderate heterogeneity, we conducted sensitivity analyses to evaluate the effects of each study on the combined ORs by sequential removal of each eligible study. The sensitivity analysis showed that none of these studies changed the significance of the combined ORs under the allelic model. It was showed that Allele A of rs242557 with the H1p promoter variant had 2.7-fold greater transcriptional activity than allele G with the H1p promoter variant and 4.2-fold greater than allele G with the H2p promoter variant. The H1 haplotype increases the expression of total MAPT transcript [6]; allele A (AA + AG) of rs242557 was associated with CSF total tau levels elevated levels compared to non-carriers (GG) [5], indicating that SNP rs242557 might be associated with the increased expression levels of tau protein. Trabzuni, D. et al [23]. found that the H1c haplotype (tagged by rs242557) was not significantly associated with increased mRNA expression of the MAPT, suggesting that there are other things about possible consequence of this SNP on the MAPT, which is needed for further investigations. In the current meta-analysis, SNP rs3785883 was found not to be associated with AD risk under all the genetic models; in AD cases, however, there was higher levels of Total tau mRNA in those individuals who carry rs3785883 minor allele (AA or AG) than those with non-carriers (GG) with evidence of beta-amyloid deposition [24], suggesting that SNP rs3785883, which changes the expression of the marker protein of AD, but is not associated with AD risk, might be an complicated SNP of the MAPT gene.

There are some limitations to this meta-analysis. First, the total number of studies was not large enough for such analyses to give meaningful interpretation, and only published studies were included in the meta-analysis. To be made, however, this approach requires the authors of all of the studies to share their data. Second, there was evidence of moderate heterogeneity between studies, in particularly for rs2471738. Third, the present meta-analysis failed to consider the possibility of gene-gene or SNP-SNP interactions in which further investigations are needed. So it is quite important to have more studies and sample in the future so that more precise conclusion about the association between the SNPs of the MAPT gene and AD risk could be achieved.

In conclusion, our meta-analysis confirmed the following: SNPs rs242557 and rs2471738 might be associated with increased AD risk, but rs3785883 and rs1467967 not. More well-conducted studies with larger sample size are needed to confirm our conclusion.

MATERIALS AND METHODS

Search strategies

All of the potential eligible studies were screened based on the electronic databases (PubMed and Google Scholar) up to 1st Jun. 2017. Systematic searching was performed using the combination of “Alzheimer*”, “rs242557 OR rs3785883 OR rs2471738 OR rs1467967 OR rs75721 OR rs9468”.

Inclusion and exclusion criteria

Only studies published as full-length articles in peer-reviewed journals were considered in the analysis. The eligible studies must satisfy the following inclusion criteria: i) concerning the association between the MAPT gene (including SNPs rs242557, rs3785883, rs2471738, rs1467967, rs75721 and rs9468) and AD risk; ii) case-control study design; iii) sufficient information accessible (e.g. sample size for each study, allele or genotype frequencies of these SNPs); iv) cases meeting the clinical criteria for AD. The exclusion criteria include: a duplicated publication; a review; a case report; not reported the genotype frequencies; non-AD cases, a review; an irrelevant study; datum not available; an abstract; in neither English nor Chinese; inconsistent with most studies in major allele size.

Data extraction

Data extracted from the included studies were as follows: first author, year of publication, country, sample size of cases and controls, numbers of case and control genotypes, p-value for HWE in controls and Newcastle-Ottawa Scale (NOS) Quality Assessment Scale. The inclusion/exclusion criteria were applied by 2 (ZFT and WDL) independent reviewers. We used the NOS to assess the quality of the included studies. A quality score was calculated based on three major components. Each component of the criteria scored 1 if present or 0 if absent. The scores were summed and a higher score represents better methodological quality.

Meta-analysis

All statistical analyses were performed using Stata software (College Station, TX). The association between the MAPT SNPs and AD risk was evaluated by pooled ORs and corresponding 95% CIs. Four genetic models, including allelic (G vs. A), dominant (AA + AG vs. GG), recessive (AA vs. AG + GG) and additive (AA vs. GG), were used to estimate this association. Sensitivity analyses were performed to determine whether undue influence of a single study was present. The possibility of publication bias was assessed by Begg's and Egger's test (P < 0.05 was considered as representative of statistically significant publication bias).

Acknowledgments

The research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY15H090013 and Project of Public Welfare Technology and Applications of Lishui City under Grant No. 2016GYX26.

Footnotes

CONFLICTS OF INTEREST

The authors declare that they have no conflict of interests.

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