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. 2021 Jan 15;11(1):58. doi: 10.3390/life11010058

Table 3.

The results of Forest plot analysis of NSCL/P risk related to the ABCA4 rs560426 polymorphism using the five genetic models.

Genetic Model First Author, Publication Year NSCL/P Control Weight Odds Ratio
Events Total Events Total M-H, Random, 95%CI
G vs. A Pan, 2011 277 742 274 768 9.2% 1.07 [0.87, 1.32]
Mostowska, 2012 183 412 422 892 8.7% 0.89 [0.70, 1.13]
Huang, 2012 204 600 223 708 8.8% 1.12 [0.89, 1.41]
Fontoura, 2012 290 640 449 800 9.2% 0.65 [0.53, 0.80]
Zhong-wei, 2013 163 362 86 208 6.7% 1.16 [0.82, 1.64]
Bagordakis, 2013 310 598 342 767 9.1% 1.34 [1.08, 1.66]
Ludwig, 2014 139 286 295 658 7.9% 1.16 [0.88, 1.54]
Mi, 2015 164 444 195 648 8.3% 1.36 [1.05, 1.76]
do Rego Borges, 2015 282 586 369 704 9.0% 0.84 [0.68, 1.05]
Babu Gurramkonda, 2015 124 288 150 352 7.2% 1.02 [0.74, 1.39]
Velázquez-Aragón, 2016 120 264 273 518 7.5% 0.75 [0.56, 1.01]
Wu, 2018 174 496 193 560 8.3% 1.03 [0.80, 1.32]
Subtotal (95%CI) 5718 7583 100.0% 1.01 [0.88, 1.15]
Total Events 2430 3271
Heterogeneity: Tau2 = 0.04; Chi2 = 39.48, df = 11 (p < 0.0001); I2 = 72%; Test for overall effect: Z = 0.10 (p = 0.92)
GG vs. AA Pan, 2011 51 226 57 224 9.1% 0.85 [0.55, 1.32]
Mostowska, 2012 39 101 96 216 8.8% 0.79 [0.49, 1.27]
Huang, 2012 39 174 26 183 8.3% 1.74 [1.01, 3.01]
Fontoura, 2012 86 202 123 197 9.4% 0.45 [0.30, 0.67]
Zhong-wei, 2013 36 60 18 54 6.6% 3.00 [1.39, 6.45]
Bagordakis, 2013 85 159 85 212 9.3% 1.72 [1.13, 2.60]
Ludwig, 2014 33 70 66 166 8.1% 1.35 [0.77, 2.37]
Mi, 2015 30 118 29 187 8.1% 1.86 [1.05, 3.29]
do Rego Borges, 2015 65 141 91 165 9.0% 0.70 [0.44, 1.09]
Babu Gurramkonda, 2015 26 72 35 96 7.6% 0.99 [0.52, 1.86]
Velázquez-Aragón, 2016 32 76 68 122 8.0% 0.58 [0.32, 1.03]
Wu, 2018 29 132 24 135 7.8% 1.30 [0.71, 2.38]
Subtotal (95%CI) 1531 1957 100.0% 1.08 [0.79, 1.47]
Total EVENTS 551 718
Heterogeneity: Tau2 = 0.23; Chi2 = 47.66, df = 11 (p < 0.00001); I2 = 77%; Test for overall effect: Z = 0.47 (p = 0.64)
AG vs. AA Pan, 2011 175 320 160 327 9.3% 1.26 [0.92, 1.72]
Mostowska, 2012 105 167 230 350 8.6% 0.88 [0.60, 1.30]
Huang, 2012 126 261 171 328 9.2% 0.86 [0.62, 1.19]
Fontoura, 2012 118 234 203 277 8.7% 0.37 [0.26, 0.54]
Zhong-wei, 2013 91 145 50 86 6.9% 1.21 [0.70, 2.09]
Bagordakis, 2013 140 214 172 299 8.8% 1.40 [0.97, 2.01]
Ludwig, 2014 73 110 163 263 7.7% 1.21 [0.76, 1.93]
Mi, 2015 104 192 137 295 8.8% 1.36 [0.95, 1.96]
Babu Gurramkonda, 2015 72 118 80 141 7.4% 1.19 [0.73, 1.96]
do Rego Borges, 2015 152 228 187 261 8.6% 0.79 [0.54, 1.16]
Velázquez-Aragón, 2016 56 100 137 191 7.3% 0.50 [0.30, 0.83]
Wu, 2018 116 219 145 256 8.8% 0.86 [0.60, 1.24]
Subtotal (95% CI) 2308 3074 100.0% 0.93 [0.73, 1.17]
Total Events 1328 1835
Heterogeneity: Tau2 = 0.13; Chi2 = 46.53, df = 11 (p < 0.00001); I2 = 76%; Test for overall effect: Z = 0.63 (p = 0.53)
AG + GG vs. AA Pan, 2011 226 371 217 384 9.1% 1.20 [0.90, 1.60]
Fontoura, 2012 204 320 326 400 8.7% 0.40 [0.28, 0.56]
Huang, 2012 165 300 197 354 8.9% 0.97 [0.71, 1.33]
Mostowska, 2012 144 206 326 446 8.5% 0.85 [0.59, 1.23]
Zhong-wei, 2013 127 181 68 104 7.2% 1.25 [0.74, 2.08]
Bagordakis, 2013 225 299 257 384 8.7% 1.50 [1.07, 2.11]
Ludwig, 2014 106 143 229 329 7.8% 1.25 [0.80, 1.95]
Mi, 2015 134 222 166 324 8.6% 1.45 [1.03, 2.05]
do Rego Borges, 2015 217 293 278 352 8.5% 0.76 [0.53, 1.10]
Babu Gurramkonda, 2015 68 144 115 176 7.7% 0.47 [0.30, 0.75]
Velázquez-Aragón, 2016 88 132 205 259 7.6% 0.53 [0.33, 0.84]
Wu, 2018 145 248 169 280 8.6% 0.92 [0.65, 1.31]
Subtotal (95%CI) 2859 3792 100.0% 0.89 [0.70, 1.14]
Total Events 1849 2553
Heterogeneity: Tau2 = 0.15; Chi2 = 59.28, df = 11 (p < 0.00001); I2 = 81%; Test for overall effect: Z = 0.90 (p = 0.37)
GG vs. AA + AG Pan, 2011 51 371 57 384 9.6% 0.91 [0.61, 1.37]
Fontoura, 2012 86 320 123 400 12.2% 0.83 [0.60, 1.15]
Huang, 2012 39 300 26 354 6.9% 1.89 [1.12, 3.18]
Mostowska, 2012 39 206 96 446 9.4% 0.85 [0.56, 1.29]
Bagordakis, 2013 85 299 85 384 11.4% 1.40 [0.99, 1.98]
Zhong-wei, 2013 36 181 18 104 5.3% 1.19 [0.63, 2.22]
Ludwig, 2014 33 143 66 329 7.9% 1.20 [0.74, 1.92]
do Rego Borges, 2015 65 293 91 352 10.9% 0.82 [0.57, 1.18]
Mi, 2015 30 222 29 324 6.6% 1.59 [0.92, 2.73]
Babu Gurramkonda, 2015 26 144 35 176 6.2% 0.89 [0.51, 1.56]
Velázquez-Aragón, 2016 32 132 68 259 7.7% 0.90 [0.55, 1.46]
Wu, 2018 29 248 24 280 6.1% 1.41 [0.80, 2.50]
Subtotal (95%CI) 2859 3792 100.0% 1.08 [0.91, 1.26]
Total Events 551 718
Heterogeneity: Tau2 = 0.03; Chi2 = 17.14, df = 11 (p = 0.10); I2 = 36%; Test for overall effect: Z = 0.87 (p = 0.38)

Abbreviations: NSCL/P, non-syndromic cleft lip with or without a cleft palate; CI, confidence interval. All models were analyzed based on random-effects model except for “GG vs. AA + AG” that was based on fixed-effects model