First author/year | Country (ethnicity) | Genotyping methods | Case/Control | Cases | Controls | MAFs | HWE | NOS | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Genotypes | Allele | Genotypes | Allele | |||||||||||||
rs2306283 | Â | Â | Â | GG | GA | AA | G | A | GG | GA | AA | G | A | |||
Huang 2004 | Taiwan(Asian) | PCR-RFLP | 58/75 | 31 | 20 | 7 | 82 | 34 | 53 | 16 | 6 | 122 | 28 | 0.187 | 0.010 | 6 |
Tian 2007 | China(Asian) | PCR-RFLP | 96/101 | 56 | 29 | 11 | 141 | 51 | 75 | 21 | 5 | 171 | 31 | 0.153 | 0.044 | 7 |
Wong 2009 | Malaysia(Asian) | HRM | 65/110 | 38 | 19 | 8 | 95 | 35 | 55 | 47 | 8 | 157 | 63 | 0.286 | 0.634 | 5 |
Prachukthum 2009 | Thailand(Asian) | PCR-RFLP | 91/86 | 59 | 28 | 4 | 146 | 36 | 47 | 36 | 3 | 130 | 42 | 0.270 | 0.799 | 8 |
Watchko 2009 | USA(Caucasian) | PCR-RFLP | 153/298 | 118 | 33 | 2 | 269 | 37 | 228 | 65 | 5 | 521 | 75 | 0.126 | 0.882 | 7 |
Zhang 2010 | China(Asian) | PCR-RFLP | 220/200 | 127 | 75 | 18 | 329 | 111 | 102 | 77 | 21 | 281 | 119 | 0.298 | 0.264 | 8 |
Buyukkale 2011 | Turkey(Caucasian) | PCR-RFLP | 102/53 | 30 | 56 | 16 | 116 | 88 | 17 | 24 | 12 | 58 | 48 | 0.453 | 0.530 | 6 |
Jiang 2012 | China(Asian) | Sequencing | 163/63 | 95 | 52 | 16 | 242 | 84 | 53 | 8 | 2 | 114 | 12 | 0.095 | 0.036 | 5 |
de Azevedo 2012 | Brazil(Mixed) | TaqMan | 157/237 | 42 | 78 | 37 | 162 | 152 | 58 | 123 | 56 | 239 | 235 | 0.496 | 0.558 | 7 |
Liu 2013 | China(Asian) | PCR-RFLP | 183/192 | 107 | 59 | 17 | 273 | 93 | 139 | 43 | 10 | 321 | 63 | 0.164 | 0.011 | 6 |
D’Silva 2014 | India(Asian) | PCR-RFLP | 126/181 | 25 | 73 | 28 | 123 | 129 | 15 | 80 | 86 | 110 | 252 | 0.696 | 0.547 | 7 |
Yang 2015 | China(Asian) | HRM | 129/108 | 71 | 41 | 17 | 183 | 75 | 66 | 35 | 7 | 167 | 49 | 0.227 | 0.428 | 8 |
Lu 2015 | China(Asian) | PCR-RFLP | 121/87 | 73 | 39 | 9 | 185 | 57 | 56 | 25 | 6 | 137 | 37 | 0.213 | 0.185 | 6 |
Weng 2016 | Taiwan(Asian) | PCR-RFLP | 100/344 | 72 | 28 | 0 | 172 | 28 | 234 | 110 | 0 | 578 | 110 | 0.160 | ≤ 0.001 | 5 |
Zhou 2018 | China(Asian) | Sequencing | 286/250 | 159 | 112 | 15 | 430 | 142 | 146 | 84 | 20 | 376 | 124 | 0.248 | 0.116 | 9 |
Qizhi 2018 | China(Asian) | Sequencing | 279/178 | 177 | 81 | 21 | 435 | 123 | 109 | 61 | 8 | 279 | 77 | 0.216 | 0.884 | 8 |
Amandito 2019 | Indonesia(Asian) | PCR-RFLP | 41/47 | 26 | 12 | 3 | 64 | 18 | 34 | 11 | 2 | 79 | 15 | 0.160 | 0.382 | 5 |
Atasilp 2022 | Thailand(Asian) | TaqMan | 67/70 | 45 | 21 | 1 | 111 | 23 | 39 | 27 | 4 | 105 | 35 | 0.250 | 0.811 | 6 |
Boskabadi 2022 | Iran(Asian) | PCR-RFLP | 100/100 | 8 | 55 | 37 | 71 | 129 | 12 | 56 | 32 | 80 | 120 | 0.600 | 0.095 | 7 |
Yin 2022 | China(Asian) | MLPA-NGS | 65/52 | 28 | 33 | 4 | 89 | 41 | 27 | 19 | 6 | 73 | 31 | 0.298 | 0.360 | 8 |
rs4149056 | Â | Â | Â | TT | TC | CC | T | C | TT | TC | CC | T | C | Â | Â | Â |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Huang 2004 | Taiwan(Asian) | PCR-RFLP | 42/73 | 31 | 9 | 2 | 71 | 13 | 53 | 19 | 1 | 125 | 21 | 0.144 | 0.627 | 6 |
Wong 2009 | Malaysia(Asian) | HRM | 65/110 | 47 | 18 | 0 | 112 | 18 | 81 | 29 | 0 | 191 | 29 | 0.132 | 0.111 | 5 |
Watchko 2009 | USA(Caucasian) | PCR-RFLP | 153/298 | 58 | 69 | 26 | 185 | 121 | 135 | 117 | 46 | 387 | 209 | 0.351 | 0.017 | 7 |
Zhang 2010 | China(Asian) | PCR-RFLP | 220/200 | 185 | 34 | 1 | 404 | 36 | 147 | 50 | 3 | 344 | 56 | 0.140 | 0.588 | 8 |
de Azevedo 2012 | Brazil(Mixed) | TaqMan | 157/237 | 122 | 34 | 1 | 278 | 36 | 179 | 55 | 3 | 413 | 61 | 0.129 | 0.592 | 6 |
Liu 2013 | China(Asian) | PCR-RFLP | 183/192 | 137 | 35 | 11 | 309 | 57 | 141 | 42 | 9 | 324 | 60 | 0.156 | 0.018 | 7 |
D’Silva 2014 | India(Asian) | PCR-RFLP | 126/181 | 95 | 29 | 2 | 219 | 33 | 178 | 3 | 0 | 359 | 3 | 0.008 | 0.910 | 8 |
Yang 2015 | China(Asian) | HRM | 129/107 | 98 | 29 | 2 | 225 | 33 | 76 | 23 | 8 | 175 | 39 | 0.182 | 0.003 | 6 |
Lu 2015 | China(Asian) | PCR-RFLP | 121/87 | 91 | 30 | 0 | 212 | 30 | 63 | 23 | 1 | 149 | 25 | 0.144 | 0.487 | 9 |
Zhou 2018 | China(Asian) | Sequencing | 286/250 | 230 | 34 | 22 | 494 | 78 | 211 | 26 | 13 | 448 | 52 | 0.104 | ≤ 0.001 | 8 |
Qizhi 2018 | China(Asian) | Sequencing | 279/178 | 227 | 51 | 1 | 505 | 53 | 152 | 25 | 1 | 329 | 27 | 0.076 | 0.979 | 7 |
Li 2019 | China(Asian) | Sequencing | 447/544 | 285 | 106 | 56 | 676 | 218 | 398 | 140 | 6 | 936 | 152 | 0.140 | 0.099 | 8 |
Atasilp 2022 | Thailand(Asian) | TaqMan | 67/70 | 57 | 9 | 1 | 123 | 11 | 48 | 20 | 2 | 116 | 24 | 0.171 | 0.961 | 5 |
Boskabadi 2022 | Iran(Asian) | PCR-RFLP | 100/100 | 80 | 16 | 4 | 176 | 24 | 75 | 24 | 1 | 174 | 26 | 0.130 | 0.541 | 6 |
Yin 2022 | China(Asian) | MLPA-NGS | 65/52 | 48 | 17 | 0 | 113 | 17 | 41 | 11 | 0 | 93 | 11 | 0.106 | 0.393 | 9 |
Fan 2023 | China(Asian) | PCR | 144/50 | 118 | 25 | 1 | 261 | 27 | 41 | 9 | 0 | 91 | 9 | 0.090 | 0.484 | 7 |