Abstract
Several epidemiological studies have reported that polymorphisms in microRNA-196a2 (miR-196a2) were associated with various cancers. However, the results remained unverified and were inconsistent in different cancers. Therefore, we carried out an updated meta-analysis to elaborate the effects of rs11614913 polymorphism on cancer susceptibility. A total of 84 articles with 35,802 cases and 41,541 controls were included to evaluate the association between the miR-196a2 rs11614913 and cancer risk by pooled odds ratios (ORs) and 95% confidence intervals (CIs). The results showed that miR-196a2 rs11614913 polymorphism is associated with cancer susceptibility, especially in lung cancer (homozygote comparison, OR =0.840, 95% CI =0.734–0.961; recessive model, OR =0.858, 95% CI =0.771–0.955), hepatocellular carcinoma (allelic contrast, OR =0.894, 95% CI =0.800–0.998; homozygote comparison, OR =0.900, 95% CI =0.813–0.997; recessive model, OR =0.800, 95% CI =0.678–0.944), and head and neck cancer (allelic contrast, OR =1.076, 95% CI =1.006–1.152; homozygote comparison, OR =1.214, 95% CI =1.043–1.413). In addition, significant association was found among Asian populations (allele model, OR =0.847, 95% CI =0.899–0.997, P=0.038; homozygote model, OR =0.878, 95% CI =0.788–0.977, P=0.017; recessive model, OR =0.895, 95% CI =0.824–0.972, P=0.008) but not in Caucasians. The updated meta-analysis confirmed the previous results that miR-196a2 rs11614913 polymorphism may serve as a risk factor for patients with cancers.
Keywords: miR-196a2, polymorphisms, cancer risk, meta-analysis
Introduction
The rising morbidity and mortality of cancer has drawn extensive attention worldwide, and finding possible risk factors of tumorigenesis has been a priority task for researchers. Recently, an increasing number of studies have focused on associations between miRNA polymorphisms and cancer susceptibility, which indicated that accumulation of genetic variants may be involved in cancer development, including oral cancer,1 lung cancer,2,3 gastric cancer,4 breast cancer,5 glioma,6 non-small cell lung cancer,7 hepatocellular carcinoma,8,9 gallbladder cancer,10 and head and neck cancer (HNC).11 As the molecular mechanism of cancer remains unclear, further exploration of more accurate cancer treatments and prognosis would be of great importance.
MiRNAs are a class of small non-coding RNAs with 18–25 nucleotides in length, which play as oncogenes or anti-oncogenes in the pathogenesis of tumor by targeting multiple genes.12–14 Studies have shown that almost 10%–30% of all human gene expressions have been regulated by mature miRNAs.15 MiRNAs could modulate related genes implicated in cellular processes, including cell differentiation, growth, apoptosis, and immune response.16–18
Hsa-microRNA-196a2 (miR-196a2), initially discovered by Lagos-Quintana et al,19 has been proven to play important roles in various cancers.20,21 Single nucleotide polymorphisms (SNPs) provide new sources of genetic variation, which contribute to potential molecular mechanisms of cancer development.22 SNPs or mutations in miRNA sequence may transform miRNA expression and/or maturation, related to miRNA function by activating the transcription of the primary transcript, pri-miRNA and pre-miRNA processing, and miRNA–mRNA interactions.23 MiR-196a2 rs11614913, as a definitional miRNA polymorphism,24–26 is crucially associated with cancer risk.23,27 It is located in the 3′-untranslated region of the miR-196a2 precursor.28 Hoffman et al5 also showed that miR-196a2 rs11614913 not only influenced the transcription level of mature miR-196a, but also had a biological effect on target gene production. This updated meta-analysis was performed to explore the association between the hsa-miR-196a2 polymorphism and cancer risk and to further estimate the overall cancer risk by pooling all available data.
Materials and methods
Publication search
Two investigators (LYH, HAB) carried out a systematic review on PubMed, Cochrane Library, and Web of Science, by using (“microRNA-196a2” or “miR-196a2”, or “miR-196-a-2” or “miR-196-2” or “miR-196-a” or “rs11614913”), and (“cancer” or “tumor” or “carcinoma” or “neoplasm” or “malignancy”), and (“polymorphism” or “variation” or “susceptibility”) as the search terms in order to identify potentially eligible studies. We based our dates for literature retrieval from January 2008 to September 2017.
Inclusion and exclusion criteria
Relevant studies had to meet the following inclusion criteria: 1) full-text article; 2) evaluation of a link between miRNA polymorphisms and cancer risks; 3) sufficient data for estimating the odds ratio (OR) with 95% CI and a P-value. Studies containing two or more case-control groups were considered as two or more independent studies. Studies that were, 1) review, letters, and comment articles; 2) not for cancer risk; and 3) duplicate samples or publications, were excluded.
Assessment of study quality
The quality of the study was determined by the Newcastle–Ottawa Scale for cohort studies.
Data extraction
Data extraction from the eligible studies were performed independently by two authors (LYH, HAB), based on the inclusion and exclusion criteria. For each publication, the following data were recorded: first author, date of publication, country of origin, ethnicity, type of tumor, source of control groups, total numbers of cases and controls, and genotyping method.
Statistical analysis
The departure of frequencies of miR-196a2 rs11614913 polymorphisms was assessed under the Hardy–Weinberg equilibrium (HWE) for each publication by adopting the goodness-of-fit test (chi-square or Fisher exact test). The association between the miR-196a2 rs11614913 polymorphisms and the risk of cancer was evaluated by calculating pooled OR together with corresponding 95% CI based on the method published by Woolf.29 Also, a P-value<0.05 was considered statistically significant. In addition, we used stratified meta-regression analyses to explore major causes of heterogeneity among the articles. We respectively examined the association between genetic mutants and cancer risk in allelic contrast (T vs C), homozygote comparisons (TT vs CC), heterozygote comparisons (TC vs CC), recessive model (TT vs TC+CC), and dominant model (TT+TC vs CC). Subgroup analyses were performed by ethnicity (Asian and Caucasian), tumor types (if one tumor type contained less than three individual studies, it was combined into “other cancer” subgroups), and source of control (hospital based and population based).
Q tests30 and I2 tests31 were carried out to test the heterogeneity. I2 values describe the percentage of total variation across studies that are due to heterogeneity rather than chance. I2=0% prompts no heterogeneity observed, with 25% identified as low, 50% as moderate, and 75% as high. If I2 was ≥50% or if the P-value of heterogeneity was <0.05, indicating significant heterogeneity among these articles, a random-effect model was used;32 otherwise, a fixed-effect mode was used.33 Sensitivity analyses were conducted to estimate the stability of the meta-analysis result. We adopted Egger’s test to assess potential publication bias by visual inspection of the Funnel plot. A P-value <0.05 was regarded as an indication of potential publication bias.34 All statistical analyses were performed with the Stata software package version 12.0 (Stata Corporation, College Station, TX, USA).
Results
Study identification
Overall, 84 articles,1–11,26,27,35–100 which were relevant to the search terms, were selected based on the inclusion criteria from PubMed, Cochrane, and Web of Science (Figure 1). These studies with a total of 35,802 cases and 41,541 controls were subjected to further checking. In the present meta-analysis, we excluded 73 articles (36 articles were meta-analysis, 22 articles did not express concern about cancer risk, 11 articles lacked detailed allele frequency data or OR calculation, and four articles were incomplete text). The included study characteristics are provided in Table 1.
Table 1.
Author | Year | Country | Ethnicity | Cancer type | Genotyping method | Source of control | Case
|
Control
|
HWE | ||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
TT | CT | CC | TT | CT | CC | ||||||||
Hu et al7 | 2008 | China | Asian | LC | PCR | PB | 152 | 264 | 140 | 32 | 52 | 23 | 0.827 |
Hu et al35 | 2009 | China | Asian | BRC | PCR-RFLP | PB | 287 | 483 | 239 | 358 | 517 | 218 | 0.207 |
Tian et al3 | 2009 | China | Asian | LC | PCR-RFLP | PB | 293 | 512 | 253 | 307 | 519 | 209 | 0.700 |
Hoffman et al5 | 2009 | USA | Caucasian | BRC | TaqMan | HB | 71 | 229 | 166 | 36 | 209 | 181 | 0.583 |
Catucci et al36 | 2010 | Italy | Caucasian | BRC | TaqMan | PB | 244 | 842 | 776 | 377 | 1,246 | 1,116 | 0.326 |
Wang et al38 | 2010 | China | Asian | ESCC | PCR | PB | 48 | 262 | 148 | 111 | 250 | 128 | 0.600 |
Okubo et al83 | 2010 | Japan | Asian | GC | Gel Pictures | HB | 166 | 281 | 105 | 372 | 592 | 216 | 0.466 |
Peng et al4 | 2010 | China | Asian | GC | PCR-RFLP | PB | 43 | 94 | 76 | 50 | 107 | 56 | 0.936 |
Srivastava et al10 | 2010 | India | Asian | GLC | PCR-RFLP | PB | 121 | 97 | 21 | 121 | 94 | 15 | 0.566 |
Dou et al6 | 2010 | China | Asian | Glioma | PCR-LDR | HB | 189 | 343 | 111 | 208 | 305 | 143 | 0.119 |
Li et al9 | 2010 | China | Asian | HCC | PCR-RFLP | HB | 82 | 150 | 78 | 78 | 102 | 42 | 0.402 |
Akkiz et al8 | 2010 | Turkey | Caucasian | HCC | PCR-RFLP | HB | 22 | 86 | 77 | 40 | 87 | 58 | 0.492 |
Liu et al11 | 2010 | USA | Caucasian | HNC | PCR-RFLP | PB | 194 | 565 | 350 | 202 | 545 | 383 | 0.737 |
Kim et al110 | 2010 | Korea | Asian | LC | PCR-RFLP | HB | 162 | 305 | 187 | 185 | 300 | 155 | 0.126 |
Catucci et al36 | 2010 | Germany | Caucasian | BRC | MassARRAY | PB | 216 | 696 | 584 | 157 | 512 | 432 | 0.711 |
Christensen et al37 | 2010 | USA | Caucasian | HNC | AppliedBiosystems | PB | 0 | 302 | 182 | 0 | 367 | 188 | NA |
Mittal et al41 | 2011 | India | Asian | BLC | PCR-RFLP | PB | 5 | 131 | 76 | 14 | 127 | 109 | 0.003 |
Jedlinski et al40 | 2011 | Australia | Caucasian | BRC | PCR | PB | 33 | 86 | 68 | 31 | 82 | 58 | 0.830 |
Zhan et al42 | 2011 | China | Asian | CRC | PCR-RFLP | HB | 56 | 128 | 68 | 163 | 267 | 113 | 0.849 |
Zhou et al43 | 2011 | China | Asian | CSCC | PCR-RFLP | PB | 57 | 123 | 46 | 82 | 169 | 58 | 0.077 |
Vinci et al111 | 2011 | Italy | Caucasian | LC | TaqMan | PB | 12 | 54 | 35 | 10 | 61 | 58 | 0.267 |
Hong et al2 | 2011 | Korea | Asian | LC | TaqMan | HB | 96 | 224 | 86 | 134 | 198 | 96 | 0.163 |
George et al39 | 2011 | Italy | Caucasian | PC | PCR-RFLP | PB | 3 | 101 | 55 | 10 | 114 | 106 | 0.002 |
Linhares et al45 | 2012 | Brazil | Mix | BRC | TaqMan | HB | 117 | 177 | 94 | 96 | 165 | 127 | 0.005 |
Chen et al44 | 2012 | China | Asian | CRC | PCR-LDR | HB | 35 | 64 | 27 | 107 | 206 | 94 | 0.788 |
Min et al24 | 2012 | Korea | Asian | CRC | PCR-RFLP | HB | 125 | 201 | 120 | 148 | 254 | 100 | 0.633 |
Zhu et al47 | 2012 | China | Asian | CRC | TaqMan | HB | 130 | 303 | 140 | 172 | 295 | 121 | 0.790 |
Hezova et al25 | 2012 | Czech | Caucasian | CRC | TaqMan | HB | 26 | 89 | 82 | 22 | 103 | 87 | 0.291 |
Zhang et al100 | 2012 | China | Asian | CRC | PCR-RFLP | PB | 172 | 204 | 79 | 185 | 197 | 81 | 0.026 |
Ahn et al48 | 2013 | Korea | Asian | GC | PCR-RFLP | PB | 119 | 242 | 100 | 128 | 232 | 87 | 0.322 |
Yoon et al46 | 2012 | Korea | Asian | LC | TaqMan | PB | 99 | 186 | 101 | 24 | 32 | 15 | 0.480 |
Zhang et al104 | 2012 | China | Asian | BRC | PCR-RFLP | PB | 133 | 93 | 17 | 148 | 89 | 11 | 0.893 |
Chu et al87 | 2012 | China | Asian | HNC | PCR-RFLP | HB | 136 | 277 | 57 | 132 | 206 | 87 | 0.690 |
Vinci et al113 | 2013 | Italy | Caucasian | CRC | HRMA | HB | 12 | 86 | 62 | 11 | 84 | 83 | 0.087 |
Lv et al51 | 2013 | China | Asian | CRC | PCR-RFLP | PB | 114 | 223 | 10 | 91 | 331 | 109 | 0.000 |
Umar et al112 | 2013 | India | Asian | ESCC | PCR-RFLP | HB | 22 | 121 | 146 | 16 | 122 | 171 | 0.330 |
Wei et al114 | 2013 | China | Asian | ESCC | SNPscanTM | HB | 106 | 196 | 65 | 113 | 170 | 87 | 0.141 |
Toraih et al98 | 2016 | Egypt | Caucasian | OSCC | PCR | PB | 32 | 93 | 84 | 10 | 35 | 55 | 0.221 |
Wang et al53 | 2013 | China | Asian | GC | TaqMan | HB | 226 | 371 | 152 | 232 | 448 | 220 | 0.898 |
Zhang et al55 | 2013 | China | Asian | HCC | MassARRAY | HB | 294 | 488 | 214 | 328 | 502 | 165 | 0.245 |
Han et al49 | 2013 | China | Asian | HCC | PCR | PB | 305 | 505 | 207 | 304 | 485 | 220 | 0.310 |
Tong et al65 | 2013 | China | Asian | ALL | TaqMan | HB | 159 | 308 | 103 | 237 | 307 | 129 | 0.434 |
Pavlakis et al93 | 2013 | Greece | Caucasian | PCC | PCR-RFLP | HB | 48 | 33 | 12 | 50 | 58 | 14 | 0.647 |
Pu et al84 | 2014 | China | Asian | GC | PCR-RFLP | HB | 25 | 95 | 39 | 86 | 324 | 101 | 0.000 |
Bansal et al56 | 2014 | India | Asian | BRC | PCR-RFLP | PB | 12 | 41 | 68 | 21 | 59 | 85 | 0.042 |
Kupcinskas et al62 | 2014 | Lithuania | Caucasian | CRC | PCR | HB | 27 | 87 | 79 | 54 | 174 | 199 | 0.104 |
Qu et al64 | 2014 | China | Asian | ESCC | PCR | PB | 48 | 207 | 126 | 82 | 211 | 133 | 0.918 |
Wang et al66 | 2014 | China | Asian | ESCC | PCR-LDR | PB | 162 | 307 | 128 | 154 | 298 | 145 | 0.970 |
Dikeakos et al58 | 2014 | Greece | Caucasian | GC | PCR-RFLP | HB | 15 | 46 | 102 | 172 | 229 | 79 | 0.850 |
Qi et al86 | 2014 | China | Asian | HCC | PCR | HB | 60 | 209 | 45 | 121 | 214 | 71 | 0.156 |
Chu et al57 | 2014 | China | Asian | HCC | PCR-RFLP | HB | 66 | 81 | 41 | 100 | 167 | 70 | 0.986 |
Parlayan et al115 | 2014 | Japan | Asian | LC | TaqMan | HB | 38 | 81 | 29 | 146 | 270 | 108 | 0.410 |
Li et al63 | 2014 | China | Asian | NPC | TaqMan | HB | 322 | 489 | 209 | 270 | 518 | 218 | 0.301 |
Du et al59,60 | 2014 | China | Asian | RCC | PCR | HB | 121 | 189 | 43 | 109 | 179 | 74 | 0.974 |
Omrani et al85 | 2014 | Iran | Asian | BRC | PCR-RFLP | PB | 0 | 25 | 78 | 0 | 18 | 218 | NA |
Kou et al91 | 2014 | China | Asian | HCC | PCR | HB | 37 | 150 | 84 | 103 | 304 | 125 | 0.001 |
Roy et al94 | 2014 | India | Asian | HNC | AppliedBiosystems | HB | 46 | 187 | 218 | 38 | 168 | 242 | 0.250 |
Li et al63 | 2014 | China | Asian | HNC | AppliedBiosystems | PB | 322 | 489 | 209 | 270 | 518 | 218 | 0.300 |
Deng et al67 | 2015 | China | Asian | BLC | PCR-RFLP | PB | 52 | 66 | 41 | 76 | 166 | 56 | 0.040 |
Qi et al72 | 2015 | China | Asian | BRC | PCR | PB | 168 | 119 | 34 | 185 | 88 | 17 | 0.141 |
Dikaiakos et al68 | 2015 | Greece | Caucasian | CRC | PCR-RFLP | PB | 69 | 69 | 19 | 117 | 149 | 33 | 0.156 |
Li et al69 | 2015 | China | Asian | HCC | PCR | HB | 51 | 131 | 84 | 30 | 123 | 113 | 0.689 |
Li et al69 | 2015 | China | Asian | NHL | PCR-RFLP | PB | 111 | 146 | 61 | 144 | 134 | 42 | 0.225 |
Nikolic et al71 | 2015 | Serbia | Caucasian | PC | PCR-RFLP | PB | 40 | 161 | 150 | 41 | 147 | 121 | 0.728 |
He et al90 | 2015 | China | Asian | BRC | MassARRAY | HB | 134 | 223 | 93 | 136 | 233 | 81 | 0.990 |
Sushma et al97 | 2015 | India | Asian | OSCC | PCR-RFLP | PB | 68 | 10 | 22 | 81 | 15 | 6 | 0.212 |
Sodhi et al95 | 2015 | India | Asian | LC | PCR-RFLP | PB | 19 | 161 | 70 | 8 | 146 | 101 | 0.000 |
Jiang et al26 | 2016 | China | Asian | GC | PCR | HB | 300 | 423 | 166 | 290 | 487 | 198 | 0.804 |
Dai et al74 | 2016 | China | Asian | BRC | MassARRAY | HB | 98 | 265 | 197 | 144 | 284 | 155 | 0.540 |
Zhao et al82 | 2016 | China | Asian | BRC | TaqMan | PB | 33 | 50 | 31 | 25 | 61 | 28 | 0.449 |
Song et al79 | 2016 | China | Asian | OC | PCR | PB | 111 | 247 | 121 | 142 | 203 | 86 | 0.385 |
Shen et al78 | 2016 | China | Asian | ESCC | SNaPshot | PB | 407 | 698 | 295 | 672 | 1,121 | 392 | 0.043 |
Li et al75 | 2016 | China | Asian | GC | PCR | HB | 75 | 83 | 24 | 92 | 79 | 11 | 0.265 |
Li et al76 | 2016 | China | Asian | HCC | PCR | HB | 20 | 64 | 25 | 35 | 52 | 18 | 0.861 |
Xu et al80 | 2016 | China | Asian | HCC | PCR-RFLP | HB | 56 | 128 | 68 | 163 | 267 | 113 | 0.849 |
Qiu and Liu77 | 2016 | China | Asian | HCC | PCR | PB | 61 | 141 | 68 | 70 | 121 | 46 | 0.626 |
Jiang et al26 | 2016 | China | Asian | HCC | TaqMan | PB | 159 | 308 | 103 | 237 | 307 | 129 | 0.099 |
Yin et al81 | 2016 | China | Asian | LC | TaqMan | PB | 149 | 298 | 128 | 178 | 297 | 133 | 0.664 |
Zhang et al99 | 2016 | China | Asian | HCC | PCR-RFLP | HB | 65 | 85 | 25 | 122 | 138 | 42 | 0.770 |
Sun et al96 | 2016 | China | Asian | OC | PCR | HB | 39 | 66 | 29 | 77 | 116 | 34 | 0.360 |
Toraih et al98 | 2016 | Egypt | Caucasian | HCC | PCR | PB | 11 | 31 | 23 | 17 | 53 | 80 | 0.082 |
Morales et al92 | 2016 | Chile | Mix | BRC | TaqMan | HB | 57 | 191 | 192 | 114 | 351 | 342 | 0.121 |
Gu and Tu88 | 2016 | China | Asian | GC | PCR | HB | 51 | 96 | 39 | 31 | 98 | 57 | 0.310 |
Hashemi et al89 | 2016 | Iran | Asian | GC | PCR-RFLP | PB | 17 | 88 | 64 | 12 | 93 | 77 | 0.021 |
Abbreviations: ALL, acute lymphoblastic leukemia; BLC, bladder cancer; BRC, breast cancer; CRC, colorectal cancer; CSCC, cervical cancer; ESCC, esophageal squamous cell carcinoma; GC, gastric cancer; GLC, gallbladder cancer; HB, hospital based; HCC, hepatocellular carcinoma; HNC, head and neck cancer; HRMA, high-resolution melting analysis; HWE, Hardy–Weinberg equilibrium of controls; LC, lung cancer; NHL, non-Hodgkin lymphoma; NPC, nasopharyngeal carcinoma; NA, not available; OC, ovarian cancer; OSCC, oral squamous cell carcinomas; PB, population based; PC, prostate cancer; PCC, pancreatic cancer; PCR, polymerase chain reaction; PCR-LDR, polymerase chain reaction-ligation detection reaction; PCR-RFLP, polymerase chain reaction restriction fragment length polymorphism; RCC, renal cell carcinoma.
In total, there were studies on hepatocellular carcinoma (n=14), breast cancer (n=14), colorectal cancer (n=10), gastric cancer (n=10), lung cancer (n=9), esophageal squamous cell carcinoma (ESCC; n=6), HNC (n=5), bladder cancer (n=2), prostate cancer (n=2), oral squamous cell carcinoma (n=2), epithelial ovarian cancer (n=2), renal cell cancer (n=1), glioma (n=1), pancreatic cancer (n=1), cervical cancer (n=1), nasopharyngeal carcinoma (n=1), gallbladder cancer (n=1), acute lymphoblastic leukemia (n=1), and non-Hodgkin lymphoma (n=1). There were 64 studies of Asians and 18 studies of Caucasians.
Among the genotyping methods used in these studies, 57 studies used polymerase chain reaction (including polymerase chain reaction restriction fragment length polymorphism and polymerase chain reaction-ligation detection reaction), 16 studies used Taqman SNP genotyping assay, and others used MassARRAY and DNA sequencing. The controls of 42 studies mainly came from a hospital-based healthy population matched for gender and age, and 42 studies had population-based controls (PB). The distribution of genotypes in the controls of all of the studies was in agreement with HWE (P>0.05).
Quantitative synthesis
In this meta-analysis, we analyzed the hsa-miR-196a2 rs11614913 polymorphism in 84 comparisons with 35,802 cases and 41,541 controls. All the studies were pooled into the meta-analysis, and the results showed that the hsa-miR-196a2 rs11614913 polymorphism was significantly associated with the risk of cancer in the following genetic models: TT vs CC: OR =0.900, 95% CI =0.813–0.987, P=0.043; TT vs TC+CC: OR =0.918, 95% CI =0.851–0.989, P=0.025.
Then, we performed the subgroup analysis of different specific cancer types, genotypes, control sources, and ethnicities (Table 2). In the different cancer types, close association between rs11614913 and cancer risk was found for lung cancer (homozygote comparison, OR =0.840, 95% CI =0.734–0.961, P=0.011; recessive model, OR =0.858, 95% CI =0.771–0.955, P=0.005), hepatocellular carcinoma (allelic contrast, OR =0.894, 95% CI =0.800–0.998, P=0.047; homozygote comparison, OR =0.900, 95% CI =0.813–0.997, P=0.039; recessive model, OR =0.800, 95% CI =0.678–0.944, P=0.008), and HNC (allelic contrast, OR =1.076, 95% CI =1.006–1.152, P=0.033; homozygote comparison, OR =1.214, 95% CI =1.043–1.413, P=0.012; Figures 2 and 3). However, the association between rs11614913 and breast cancer, ESCC, gastric cancer (GC), or colorectal cancer (CRC) is not statistically significant.
Table 2.
rs11614913 | na | Case/control | T vs C
|
TT vs CC
|
TC vs CC
|
|||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
OR (95% CI) | P-value | P–H | I2, % | OR (95% CI) | P-value | P–H | I2, % | OR (95% CI) | P-value | P–H | I2, % | |||
(A) | ||||||||||||||
Total | 84 | 35,802/41,541 | 0.958 (0.911–1.008) | 0.096 | 0.000 | 81.30 | 0.900 (0.813–0.987) | 0.043 | 0.000 | 78.80 | 1.005 (0.935–1.079) | 0.902 | 0.000 | 71.60 |
Genotyping method | ||||||||||||||
PCR | 57 | 19,301/22,204 | 0.939 (0.871–1.012) | 0.100 | 0.000 | 84.50 | 0.849 (0.732–0.986) | 0.032 | 0.000 | 81.70 | 0.987 (0.883–1.102) | 0.812 | 0.000 | 77.40 |
Taqman | 16 | 8,565/10,286 | 1.021 (0.940–1.110) | 0.618 | 0.000 | 67.40 | 1.059 (0.894–1.253) | 0.507 | 0.000 | 65.70 | 1.053 (0.977–1.134) | 0.174 | 0.410 | 3.70 |
Ethnicity | ||||||||||||||
Asian | 64 | 28,337/31,932 | 0.847 (0.889–0.997) | 0.038 | 0.000 | 77.00 | 0.878 (0.788–0.977) | 0.017 | 0.000 | 76.00 | 1.012 (0.936–1.095) | 0.759 | 0.000 | 66.90 |
Caucasian | 18 | 7,321/8,414 | 0.997 (0.842–1.181) | 0.971 | 0.000 | 90.30 | 0.974 (0.714–1.329) | 0.870 | 0.000 | 86.10 | 0.963 (0.785–1.180) | 0.714 | 0.000 | 83.90 |
Cancer type | ||||||||||||||
BRC | 14 | 7,760/8,811 | 0.972 (0.869–1.088) | 0.626 | 0.000 | 79.70 | 0.972 (0.869–1.088) | 0.341 | 0.000 | 72.80 | 0.979 (0.854–1.121) | 0.754 | 0.001 | 61.50 |
CRC | 10 | 2,906/4,150 | 1.051 (0.867–1.276) | 0.611 | 0.000 | 86.50 | 1.051 (0.867–1.276) | 0.431 | 0.000 | 87.60 | 1.121 (0.832–1.510) | 0.454 | 0.000 | 81.10 |
ESCC | 6 | 3,492/4,376 | 0.944 (0.816–1.091) | 0.435 | 0.001 | 76.80 | 0.944 (0.816–1.091) | 0.385 | 0.000 | 82.40 | 1.050 (0.878–1.255) | 0.594 | 0.040 | 57.20 |
GC | 10 | 3,723/5,256 | 0.857 (0.663–1.109) | 0.241 | 0.000 | 93.80 | 0.857 (0.663–1.109) | 0.276 | 0.000 | 91.50 | 0.778 (0.552–1.098) | 0.153 | 0.000 | 88.70 |
HCC | 14 | 4,988/5,962 | 0.894 (0.800–0.998) | 0.047 | 0.000 | 72.60 | 0.900 (0.813–0.997) | 0.039 | 0.000 | 70.50 | 0.981 (0.838–1.149) | 0.816 | 0.005 | 56.30 |
HNC | 5 | 3,534/3,564 | 1.076 (1.006–1.152) | 0.033 | 0.285 | 20.40 | 1.214 (1.043–1.413) | 0.012 | 0.380 | 2.50 | 1.157 (0.922–1.451) | 0.209 | 0.003 | 75.00 |
LC | 9 | 2,786/3,191 | 0.95 (0.854–1.058) | 0.354 | 0.022 | 55.30 | 0.840 (0.734–0.961) | 0.011 | 0.025 | 48.10 | 0.997 (0.889–1.118) | 0.961 | 0.056 | 47.20 |
Design | ||||||||||||||
PB | 42 | 20,691/21,533 | 0.968 (0.907–1.033) | 0.324 | 0.000 | 77.20 | 0.899 (0.777–1.017) | 0.087 | 0.000 | 74.70 | 1.018 (0.928–1.117) | 0.703 | 0.000 | 66.60 |
HB | 42 | 15,111/20,008 | 0.945 (0.873–1.024) | 0.167 | 0.000 | 84.50 | 0.906 (0.813–0.997) | 0.211 | 0.000 | 81.90 | 0.987 (0.882–1.104) | 0.822 | 0.000 | 75.90 |
rs11614913 | na | TT vs TC+CC
|
TT+TC vs CC
|
||||||
---|---|---|---|---|---|---|---|---|---|
OR (95% CI) | P-value | P–H | I2, % | OR (95% CI) | P-value | P–H | I2, % | ||
(B) | |||||||||
Total | 84 | 0.918 (0.851–0.989) | 0.025 | 0.000 | 75.80 | 0.974 (0.901–1.052) | 0.498 | 0.000 | 78.40 |
Genotyping method | |||||||||
PCR | 57 | 0.880 (0.800–0.9690) | 0.009 | 0.000 | 73.20 | 0.949 (0.842–1.069) | 0.386 | 0.000 | 82.80 |
Taqman | 16 | 1.000 (0.858–1.166) | 0.996 | 0.000 | 71.90 | 1.063 (0.969–1.165) | 0.195 | 0.095 | 34.10 |
Ethnicity | |||||||||
Asian | 64 | 0.895 (0.824–0.972) | 0.008 | 0.000 | 76.50 | 0.972 (0.8396–1.005) | 0.493 | 0.000 | 72.90 |
Caucasian | 17 | 1.015 (0.820–1.256) | 0.894 | 0.000 | 75.30 | 0.966 (0.766–1.219) | 0.772 | 0.000 | 89.30 |
Cancer type | |||||||||
BRC | 14 | 0.943 (0.815–1.091) | 0.429 | 0.001 | 64.40 | 0.967 (0.830–1.126) | 0.663 | 0.000 | 73.30 |
CRC | 10 | 1.066 (0.823–1.381) | 0.628 | 0.000 | 79.00 | 1.130 (0.826–1.546) | 0.444 | 0.000 | 84.70 |
ESCC | 6 | 0.813 (0.610–1.085) | 0.160 | 0.000 | 81.30 | 1.000 (0.822–1.216) | 0.997 | 0.008 | 67.80 |
GC | 10 | 0.910 (0.697–1.189) | 0.489 | 0.000 | 83.90 | 0.763 (0.507–1.148) | 0.194 | 0.000 | 92.90 |
HCC | 14 | 0.800 (0.678–0.944) | 0.008 | 0.000 | 67.40 | 0.919 (0.776–1.089) | 0.332 | 0.000 | 66.20 |
HNC | 5 | 1.205 (0.799–1.817) | 0.375 | 0.000 | 90.10 | 1.156 (0.950–1.406) | 0.148 | 0.011 | 69.10 |
LC | 9 | 0.858 (0.771–0.955) | 0.005 | 0.158 | 32.50 | 0.997 (0.834–1.191) | 0.973 | 0.019 | 56.20 |
Design | |||||||||
PB | 42 | 0.924 (0.826–1.034) | 0.170 | 0.000 | 78.10 | 0.988 (0.897–1.087) | 0.800 | 0.000 | 72.40 |
HB | 42 | 0.912 (0.823–1.010) | 0.078 | 0.000 | 73.90 | 0.955 (0.843–1.081) | 0.465 | 0.000 | 82.70 |
Notes: Random-effects model was used when P-value of Q-test for heterogeneity test (P–H) is <0.05; otherwise, fixed-effect model was used. I2: 0%–25%, no heterogeneity; 25%–50%, modest heterogeneity; ≥50%, high heterogeneity.
Number of studies involved. Bold figures indicate statistically significant (P<0.05).
Abbreviations: BRC, breast cancer; CRC, colorectal cancer; ESCC, esophageal squamous cell carcinoma; GC, gastric cancer; HB, hospital based; HCC, hepatocellular carcinoma; HNC, head and neck cancer; LC, lung cancer; OR, odds ratio; PB, population based; PCR, polymerase chain reaction; P–H, P-value of heterogeneity test.
In ethnic subgroup analysis, a strong association was found between rs11614913 and cancer risk in the allelic contrast (T vs C: OR =0.847, 95% CI =0.899–0.997, P=0.038), the homozygote comparison (TT vs CC: OR =0.878, 95% CI =0.788–0.977, P=0.017), and the recessive model (OR =0.895, 95% CI =0.824–0.972, P=0.008) among Asians, whereas negative results were obtained for Caucasians in all genetic models. Additionally, decreased risk was observed in the polymerase chain reaction (PCR) method for the homozygote comparison (TT vs CC: OR =0.849, 95% CI =0.732–0.986, P=0.032) and the recessive model (TT vs TC+CC: OR =0.880, 95% CI =0.800–0.969, P=0.009), and no significant association of cancer risk was found in Taqman and other methods.
Test of heterogeneity
Among the studies of rs11614913, we found heterogeneity in overall comparisons and subgroup analysis. Moreover, the heterogeneity we evaluated for all genetic models by ethnicity, cancer type, source of controls, as well HWE status was significant. However, we found that heterogeneity could not be explained by the variable ethnicity, cancer type, source of controls, and HWE status (data not shown).
Sensitivity analysis
Sensitivity analysis was conducted to assess the effect by excluding a single study in turn. Sensitivity analysis of the rs11614913 polymorphism in an allelic comparison is presented in Table S1. Overall, we found that no individual study had an influence on the pooled OR. The results demonstrated that the pooled ORs were not materially altered, suggesting the stability of our meta-analysis.
Publication bias
The publication bias of the present meta-analysis was assessed by Begg’s funnel plot and Egger’s test. The funnel plot for the rs11614913 polymorphism in the allelic comparison is presented in Table S2. No evidence of publication bias was noted in Begg’s funnel plot (T vs C [P-value for Begg’s test =0.660], TT vs CC [P-value for Begg’s test =0.971, Figure 4], TC vs CC [P-value for Begg’s test =0.951], TT vs TC+CC [P-value for Begg’s test =0.908, Figure 4], TC+TT vs CC [P-value for Begg’s test =0.592]) and Egger’s test (allele contrast [P=0.923], homozygous model [P=0.822], heterozygous model [P=0.761], recessive model [P=0.899], and dominant model [P=0.401]). The quality of included studies is presented in Table 3.
Table 3.
Author | Adequacy of case definition | Representativeness of the cases | Selection of controls | Definition of controls | Comparability of cases/controls | Ascertainment of exposure | Same method of ascertainment | Non-response rate |
---|---|---|---|---|---|---|---|---|
Hu et al7 | * | * | * | * | ** | * | * | NA |
Hu et al35 | * | * | NA | * | ** | * | * | NA |
Tian et al3 | * | * | NA | * | * | * | * | NA |
Hoffman et al5 | * | * | * | * | * | * | * | NA |
Catucci et al36 | * | * | NA | * | ** | NA | * | NA |
Wang et al38 | * | * | NA | * | ** | * | * | NA |
Okubo et al83 | * | * | * | * | ** | * | * | NA |
Peng et al4 | * | * | NA | * | ** | NA | * | NA |
Srivastava et al10 | * | * | NA | * | ** | * | * | NA |
Dou et al6 | * | * | NA | NA | * | NA | * | NA |
Li et al9 | * | * | * | * | ** | NA | * | NA |
Akkiz et al8 | * | * | NA | * | ** | NA | * | NA |
Liu et al11 | * | * | NA | * | * | * | * | NA |
Kim et al110 | * | * | NA | NA | * | * | * | NA |
Catucci et al36 | * | * | * | * | ** | * | * | NA |
Christensen et al37 | * | * | NA | * | ** | * | * | NA |
Mittal et al41 | * | * | NA | * | ** | * | * | NA |
Jedlinski et al40 | * | * | * | * | ** | NA | * | NA |
Zhan et al42 | * | * | NA | * | * | NA | * | NA |
Zhou et al43 | * | * | NA | * | ** | NA | * | NA |
Vinci et al111 | * | * | NA | * | ** | * | * | NA |
Hong et al2 | * | * | NA | * | * | * | * | NA |
George et al39 | * | * | NA | * | ** | * | * | NA |
Linhares et al45 | * | * | NA | * | ** | * | * | NA |
Chen et al44 | * | * | NA | * | ** | NA | * | NA |
Min et al24 | * | * | NA | * | ** | * | * | NA |
Zhu et al47 | * | * | NA | * | ** | * | * | NA |
Hezova et al25 | * | * | NA | * | ** | NA | * | NA |
Zhang et al100 | * | * | * | * | ** | * | * | NA |
Ahn et al48 | * | * | NA | * | ** | * | * | NA |
Yoon et al46 | * | * | NA | * | ** | * | * | NA |
Zhang et al104 | * | * | * | * | ** | NA | * | NA |
Chu et al87 | * | * | NA | * | ** | NA | * | NA |
Vinci et al113 | * | * | * | * | ** | NA | * | NA |
Lv et al51 | * | * | * | * | ** | NA | * | NA |
Umar et al112 | * | * | NA | NA | ** | * | * | NA |
Wei et al114 | * | * | NA | * | ** | * | * | NA |
Toraih et al98 | * | * | NA | * | ** | * | * | NA |
Wang et al53 | * | * | NA | * | ** | NA | * | NA |
Zhang et al55 | * | * | NA | NA | ** | NA | * | NA |
Han et al49 | * | * | * | * | ** | * | * | NA |
Tong et al65 | * | * | NA | * | ** | * | * | NA |
Pavlakis et al93 | * | * | NA | * | ** | * | * | NA |
Pu et al84 | * | * | * | * | ** | NA | * | NA |
Bansal et al56 | * | * | NA | * | ** | * | * | NA |
Kupcinskas et al62 | * | * | * | * | ** | * | * | NA |
Qu et al64 | * | * | NA | NA | ** | * | * | NA |
Wang et al66 | * | * | NA | * | ** | * | * | NA |
Dikeakos et al58 | * | * | NA | * | ** | * | * | NA |
Qi et al86 | * | * | NA | * | ** | NA | * | NA |
Chu et al57 | * | * | * | * | * | * | * | NA |
Parlayan et al115 | * | * | * | * | ** | * | * | NA |
Li et al63 | * | * | NA | * | ** | * | * | NA |
Du et al59,60 | * | * | NA | * | * | NA | * | NA |
Omrani et al85 | * | * | NA | * | ** | * | * | NA |
Kou et al91 | * | * | * | * | ** | * | * | NA |
Roy et al94 | * | * | NA | * | ** | * | * | NA |
Li et al63 | * | * | NA | * | ** | NA | * | NA |
Deng et al67 | * | * | * | * | ** | NA | * | NA |
Qi et al72 | * | * | NA | * | ** | NA | * | NA |
Dikaiakos et al68 | * | * | * | * | * | * | * | NA |
Li et al69 | * | * | NA | NA | ** | * | * | NA |
Li et al69 | * | * | NA | NA | ** | * | * | NA |
Nikolic et al71 | * | * | * | * | ** | * | * | NA |
He et al90 | * | * | NA | NA | ** | NA | * | NA |
Sushma et al97 | * | * | NA | * | ** | * | * | NA |
Sodhi et al95 | * | * | * | * | ** | * | * | NA |
Jiang et al26 | * | * | NA | * | ** | * | * | NA |
Dai et al74 | * | * | NA | * | ** | NA | * | NA |
Zhao et al82 | * | * | NA | * | ** | * | * | NA |
Song et al79 | * | * | * | * | * | NA | * | NA |
Shen et al78 | * | * | NA | * | ** | * | * | NA |
Li et al75 | * | * | NA | * | ** | NA | * | NA |
Li et al76 | * | * | NA | * | * | * | * | NA |
Xu et al80 | * | * | NA | NA | * | * | * | NA |
Qiu and Liu77 | * | * | * | * | * | * | * | NA |
Jiang et al26 | * | * | * | * | ** | * | * | NA |
Yin et al81 | * | * | NA | * | * | * | * | NA |
Zhang et al99 | * | * | * | * | ** | NA | * | NA |
Sun et al96 | * | * | * | * | * | * | * | NA |
Toraih et al98 | * | * | NA | * | ** | NA | * | NA |
Morales et al92 | * | * | NA | * | ** | * | * | NA |
Gu and Tu88 | * | * | NA | * | * | * | * | NA |
Hashemi et al89 | * | * | NA | * | ** | * | * | NA |
Notes: This table identified “high”quality choices with a “*”. A study can be awarded a maximum of one “*” for each numbered item within the selection and exposure categories. A maximum of two “**” can be given for comparability.
Abbreviation: NA, not available.
Discussion
MiRNAs are reported as critical posttranscriptional regulators in gene expression and are involved in various diseases. The associations between miR-196a2 rs11614913 polymorphism and susceptibility to different cancers are widely explored. Guo et al101 found that the C allele had the effect of increasing cancer risk in gastric cancer, and Ma et al102 found that TT could decrease the risk of colorectal cancer. Moreover, Wang et al103 and Zhang et al104 showed that the rs11614913 polymorphism has no association with the risk of hepatocellular carcinoma. However, the regulatory effects of miRNA in carcinogenesis remain unclear. Therefore, we performed this updated meta-analysis to explore the molecular mechanisms of the genetic associations between miRNA and SNPs with cancer risk.
MiR-196a2 is composed of two distinct mature miRNAs (miR-196a-3P and miR-196a-5P), which are processed from the same stem loop;105 thus, the potential targets of miR-196a could be influenced by its altered expression patterns. SNPs in miRNAs could potentially affect the processing or target selection of miRNAs,106,107 which is identified as a key factor in oncogenesis, and contributes to regulate the translation or degradation of messenger RNA (mRNA).23 Hoffman et al5 found that the expression of mature miR-196a2 was increased 9.3-fold in cells transfected with pre-miR-196a2-C but upregulated only by 4.4-fold with pre-miR-196a2-T, and that the C allele of rs11614913 increased mature miR-196a2 levels in lung cancer7 and CRC42 tissues. Xu et al108 have shown that miR-196a2 rs11614913 CC is associated with significantly increased expression of mature miR-196a (lower cycle threshold corresponding to a higher expression) in cardiac tissue specimens of congenital heart disease, and the increased miR-196a expression could further decrease mRNA target of HOXB8. These results indicated that the rs11614913 polymorphism may affect the processing of the pre-miRNA to its mature form.
Several meta-analyses have been performed to analyse the SNP of this miRNA that is associated with the cancer risk.104,109 In our present work, we screened out all the studies published to date and included more papers and cancer types than the previously published meta-analyses. For example, Kang et al109 conducted a meta-analysis encompassing the rs11614913 polymorphism in miR-196a2 and cancer risks, which suggested that the rs11614913 polymorphism may contribute to decreased susceptibility to liver cancer (allele model, homozygous model, dominant model, and heterozygous model) and lung cancer (allele model, homozygous model, and recessive model); however, this was not duplicated in our meta-analysis. In this study, we concluded that the rs11614913 polymorphism conferred a decreased susceptibility to lung cancer (homozygote comparison, recessive model) and hepatocellular carcinoma (allelic contrast, homozygote comparison, recessive model) or an increased susceptibility to HNC (allelic contrast, homozygote comparison). Our study had a larger sample size than the previous ones, which might influence the results. In addition, the previous meta-analyses did not evaluate the quality of the included studies.
According to the procedure of seeking for the source of heterogeneity, we performed subgroup studies according to cancer type, ethnicity, and source of control. A strong association was found between rs11614913 and cancer risk in lung cancers, hepatocellular carcinoma, and HNC, but not in breast cancer, gastric cancer, ESCC, or CRC, which was not similar to the findings of previous studies.101–103,109 The present meta-analysis showed that homozygote TT had the effect of decreasing the risk of lung cancer or hepatocellular carcinoma compared with that of CC homozygote or C allele carriers. We conducted another subgroup analysis by population to determine the association between these miRNA polymorphisms and tumorigenesis. The results suggested that individuals with alterative T allele could decrease cancer susceptibility in Asians but not in Caucasians, indicating that the difference of ethnic background and the living environment may also be a risk factor.
To determine the hsa-miR-196a2 rs11614913 polymorphism, PCR, Taqman, and other methods have been adopted. We found that the hsa-miR-196a2 rs11614913 polymorphism significantly decreased cancer risk in homozygous models and the recessive model when using the PCR method, but this result was not shown when selecting Taqman and other methods. Therefore, more effort may be necessary for further progress in SNP analysis. We found sources of heterogeneity in the studies from cancer type and ethnicity suggesting cancer and population playing important roles. When detecting the source of control, we observed significant associations in population-based and hospital-based controls. This may be due to the included studies matching age, gender, and residential area to control selection bias.
Nevertheless, several defects of this meta-analysis should be emphasized. Firstly, although we strictly screened articles and precisely extracted the data, the differences in the selection of subjects could not be eliminated. Secondly, in our meta-analysis, only Asian and Caucasian ethnicities were included, and the impact of the differences in racial descent should not be ignored. Thirdly, potential language bias could not be avoided due to limitation of studies published in English or Chinese. Therefore, it is not possible to avoid potential publication bias in this meta-analysis.
In summary, miR-196a2 rs11614913 polymorphism may contribute to the development of cancer, especially in lung cancer, hepatocellular carcinoma, and HNC. It might be useful as a candidate marker for the diagnosis of these cancers, and could also be a potential protective factor for cancer risks in Asians. Furthermore, more significant studies and investigations with larger populations focusing on cancer types or ethnicities should be performed to confirm the results.
Supplementary materials
Table S1.
Comparison | Study omitted | Estimate | (95% Conf Interval)
|
|
---|---|---|---|---|
Lower CI | Upper CI | |||
TT vs CC | Hu et al7 | 0.902 | 0.814 | 0.999 |
Hu et al35 | 0.904 | 0.815 | 1.002 | |
Tian et al3 | 0.902 | 0.814 | 1.001 | |
Hoffman et al5 | 0.890 | 0.805 | 0.985 | |
Catucci et al36 | 0.900 | 0.811 | 1.000 | |
Wang et al38 | 0.911 | 0.824 | 1.008 | |
Okubo et al83 | 0.900 | 0.812 | 0.998 | |
Peng et al4 | 0.904 | 0.816 | 1.002 | |
Srivastava et al10 | 0.903 | 0.815 | 1.000 | |
Dou et al6 | 0.897 | 0.809 | 0.994 | |
Li et al9 | 0.906 | 0.818 | 1.003 | |
Akkiz et al8 | 0.908 | 0.820 | 1.005 | |
Liu et al11 | 0.898 | 0.810 | 0.997 | |
Kim et al101 | 0.904 | 0.815 | 1.002 | |
Catucci et al36 | 0.899 | 0.810 | 0.997 | |
Christensen et al37 | 0.900 | 0.813 | 0.997 | |
Mittal et al41 | 0.904 | 0.816 | 1.001 | |
Jedlinski et al40 | 0.900 | 0.813 | 0.998 | |
Zhan et al42 | 0.906 | 0.818 | 1.004 | |
Zhou et al43 | 0.901 | 0.813 | 0.998 | |
Vinci et al102 | 0.895 | 0.809 | 0.992 | |
Hong et al2 | 0.902 | 0.814 | 1.000 | |
George et al39 | 0.902 | 0.815 | 0.999 | |
Linhares et al45 | 0.893 | 0.806 | 0.988 | |
Chen et al44 | 0.898 | 0.811 | 0.995 | |
Min et al24 | 0.904 | 0.815 | 1.002 | |
Zhu et al47 | 0.905 | 0.816 | 1.003 | |
Hezova et al25 | 0.897 | 0.810 | 0.994 | |
Zhang et al100 | 0.900 | 0.812 | 0.998 | |
Yoon et al46 | 0.904 | 0.816 | 1.001 | |
Zhang et al99 | 0.904 | 0.816 | 1.001 | |
Chu et al87 | 0.894 | 0.807 | 0.990 | |
Vinci et al105 | 0.897 | 0.810 | 0.994 | |
Ahn et al103 | 0.902 | 0.814 | 1.000 | |
Lv et al51 | 0.878 | 0.798 | 0.965 | |
Umar et al104 | 0.895 | 0.808 | 0.992 | |
Wei et al106 | 0.896 | 0.809 | 0.993 | |
Wang et al53 | 0.894 | 0.807 | 0.990 | |
Zhang et al55 | 0.904 | 0.816 | 1.003 | |
Han et al49 | 0.898 | 0.810 | 0.996 | |
Pavlakis et al93 | 0.899 | 0.812 | 0.996 | |
Tong et al65 | 0.901 | 0.813 | 1.000 | |
Pu et al84 | 0.902 | 0.814 | 1.000 | |
Bansal et al56 | 0.902 | 0.815 | 1.000 | |
Kupcinskas et al62 | 0.897 | 0.809 | 0.994 | |
Qu et al64 | 0.905 | 0.817 | 1.003 | |
Wang et al66 | 0.897 | 0.809 | 0.994 | |
Dikeakos et al58 | 0.925 | 0.843 | 1.015 | |
Qi et al86 | 0.902 | 0.814 | 1.000 | |
Chu et al57 | 0.898 | 0.810 | 0.995 | |
Parlayan et al107 | 0.900 | 0.812 | 0.997 | |
Li et al63 | 0.896 | 0.808 | 0.993 | |
Du et al59 | 0.892 | 0.806 | 0.987 | |
Omrani et al85 | 0.900 | 0.813 | 0.997 | |
Kou et al91 | 0.907 | 0.819 | 1.004 | |
Roy et al94 | 0.896 | 0.809 | 0.993 | |
Li et al63 | 0.896 | 0.808 | 0.993 | |
Deng et al67 | 0.900 | 0.812 | 0.997 | |
Qi et al72 | 0.907 | 0.819 | 1.005 | |
Dikaiakos et al68 | 0.899 | 0.812 | 0.996 | |
Li et al69 | 0.890 | 0.805 | 0.985 | |
Li et al69 | 0.907 | 0.819 | 1.004 | |
Nikolic et al71 | 0.902 | 0.814 | 1.000 | |
He et al90 | 0.901 | 0.813 | 0.999 | |
Sushma et al97 | 0.909 | 0.821 | 1.006 | |
Sodhi et al95 | 0.891 | 0.806 | 0.986 | |
Jiang et al26 | 0.896 | 0.808 | 0.993 | |
Toraih et al98 | 0.894 | 0.807 | 0.990 | |
Dai et al74 | 0.908 | 0.820 | 1.005 | |
Zhao et al82 | 0.898 | 0.811 | 0.995 | |
Song et al79 | 0.907 | 0.819 | 1.004 | |
Shen et al78 | 0.902 | 0.813 | 1.002 | |
Li et al75 | 0.907 | 0.820 | 1.005 | |
Li et al76 | 0.906 | 0.819 | 1.004 | |
Xu et al80 | 0.906 | 0.818 | 1.004 | |
Qiu et al77 | 0.905 | 0.817 | 1.003 | |
Jiang et al26 | 0.901 | 0.813 | 1.000 | |
Yin et al81 | 0.901 | 0.813 | 0.999 | |
Zhang et al99 | 0.901 | 0.813 | 0.998 | |
Sun et al96 | 0.904 | 0.817 | 1.002 | |
Toraih et al98 | 0.894 | 0.808 | 0.990 | |
Morales et al92 | 0.901 | 0.812 | 0.999 | |
Gu et al88 | 0.891 | 0.805 | 0.986 | |
Hashemi et al89 | 0.896 | 0.809 | 0.992 | |
Combined2–10,25,26,35–107 | 0.900 | 0.813 | 0.997 | |
TT vs TC+CC | Hu et al7 | 0.918 | 0.851 | 0.991 |
Hu et al35 | 0.920 | 0.852 | 0.993 | |
Tian et al3 | 0.918 | 0.850 | 0.991 | |
Hoffman et al5 | 0.910 | 0.844 | 0.980 | |
Catucci et al36 | 0.917 | 0.849 | 0.991 | |
Wang et al38 | 0.928 | 0.862 | 0.999 | |
Okubo et al83 | 0.917 | 0.850 | 0.991 | |
Peng et al4 | 0.919 | 0.852 | 0.991 | |
Srivastava et al10 | 0.918 | 0.850 | 0.990 | |
Dou et al6 | 0.918 | 0.850 | 0.991 | |
Li et al9 | 0.922 | 0.854 | 0.994 | |
Akkiz et al8 | 0.923 | 0.856 | 0.995 | |
Liu et al11 | 0.917 | 0.849 | 0.990 | |
Kim et al101 | 0.920 | 0.852 | 0.992 | |
Catucci et al36 | 0.916 | 0.849 | 0.989 | |
Christensen et al37 | 0.918 | 0.851 | 0.989 | |
Mittal et al41 | 0.921 | 0.854 | 0.993 | |
Jedlinski et al40 | 0.917 | 0.850 | 0.989 | |
Zhan et al42 | 0.922 | 0.854 | 0.994 | |
Zhou et al43 | 0.918 | 0.850 | 0.990 | |
Vinci et al102 | 0.915 | 0.849 | 0.987 | |
Hong et al2 | 0.922 | 0.854 | 0.994 | |
George et al39 | 0.920 | 0.853 | 0.992 | |
Linhares et al45 | 0.913 | 0.847 | 0.985 | |
Chen et al44 | 0.916 | 0.849 | 0.988 | |
Min et al24 | 0.918 | 0.850 | 0.990 | |
Zhu et al47 | 0.921 | 0.854 | 0.994 | |
Hezova et al25 | 0.915 | 0.848 | 0.987 | |
Zhang et al100 | 0.918 | 0.850 | 0.991 | |
Yoon et al46 | 0.920 | 0.853 | 0.993 | |
Zhang et al99 | 0.919 | 0.852 | 0.992 | |
Chu et al87 | 0.918 | 0.851 | 0.991 | |
Vinci et al105 | 0.919 | 0.851 | 0.991 | |
Ahn et al103 | 0.916 | 0.850 | 0.988 | |
Lv et al51 | 0.905 | 0.842 | 0.974 | |
Umar et al104 | 0.914 | 0.848 | 0.986 | |
Wei et al106 | 0.918 | 0.850 | 0.990 | |
Wang et al53 | 0.913 | 0.846 | 0.985 | |
Zhang et al55 | 0.919 | 0.851 | 0.992 | |
Han et al49 | 0.917 | 0.849 | 0.990 | |
Pavlakis et al93 | 0.921 | 0.854 | 0.994 | |
Tong et al65 | 0.913 | 0.847 | 0.985 | |
Pu et al84 | 0.918 | 0.851 | 0.990 | |
Bansal et al56 | 0.919 | 0.852 | 0.991 | |
Kupcinskas et al62 | 0.916 | 0.849 | 0.988 | |
Qu et al64 | 0.923 | 0.855 | 0.995 | |
Wang et al66 | 0.916 | 0.848 | 0.988 | |
Dikeakos et al58 | 0.931 | 0.866 | 1.001 | |
Qi et al86 | 0.924 | 0.857 | 0.996 | |
Chu et al57 | 0.914 | 0.847 | 0.986 | |
Parlayan et al107 | 0.918 | 0.851 | 0.990 | |
Li et al63 | 0.913 | 0.846 | 0.985 | |
Du et al59 | 0.914 | 0.847 | 0.986 | |
Omrani et al85 | 0.918 | 0.851 | 0.989 | |
Kou et al91 | 0.921 | 0.854 | 0.994 | |
Roy et al94 | 0.915 | 0.848 | 0.987 | |
Li et al63 | 0.906 | 0.845 | 0.971 | |
Deng et al67 | 0.913 | 0.847 | 0.985 | |
Qi et al72 | 0.923 | 0.856 | 0.995 | |
Dikaiakos et al68 | 0.914 | 0.848 | 0.987 | |
Li et al69 | 0.911 | 0.845 | 0.982 | |
Li et al69 | 0.922 | 0.855 | 0.995 | |
Nikolic et al71 | 0.919 | 0.852 | 0.991 | |
He et al90 | 0.917 | 0.850 | 0.990 | |
Sushma et al97 | 0.921 | 0.855 | 0.994 | |
Sodhi et al95 | 0.913 | 0.847 | 0.984 | |
Jiang et al26 | 0.914 | 0.847 | 0.986 | |
Toraih et al98 | 0.914 | 0.848 | 0.986 | |
Dai et al74 | 0.922 | 0.855 | 0.995 | |
Zhao et al82 | 0.914 | 0.848 | 0.986 | |
Song et al79 | 0.923 | 0.856 | 0.995 | |
Shen et al78 | 0.918 | 0.849 | 0.992 | |
Li et al75 | 0.921 | 0.854 | 0.993 | |
Li et al76 | 0.923 | 0.856 | 0.995 | |
Xu et al80 | 0.922 | 0.854 | 0.994 | |
Qiu et al77 | 0.921 | 0.854 | 0.993 | |
Jiang et al26 | 0.921 | 0.854 | 0.994 | |
Yin et al81 | 0.919 | 0.851 | 0.992 | |
Zhang et al99 | 0.918 | 0.851 | 0.991 | |
Sun et al96 | 0.919 | 0.852 | 0.992 | |
Toraih et al98 | 0.915 | 0.848 | 0.986 | |
Morales et al92 | 0.918 | 0.851 | 0.991 | |
Gu et al88 | 0.911 | 0.845 | 0.982 | |
Hashemi et al89 | 0.915 | 0.848 | 0.986 | |
Combined2–10,25,26,35–107 | 0.918 | 0.851 | 0.989 |
Table S2.
Polymorphism | Comparison | Subgroup | Begg’s test (P>z) |
Egger’s test (P>t) |
---|---|---|---|---|
rs11614913 | T vs C | Overall | 0.660 | 0.923 |
Taqman | 0.368 | 0.723 | ||
PCR | 0.640 | 0.859 | ||
Asian | 0.946 | 0.854 | ||
Caucasian | 0.147 | 0.969 | ||
HB | 0.509 | 0.386 | ||
PB | 0.251 | 0.579 | ||
TT vs CC | Overall | 0.971 | 0.822 | |
Taqman | 0.719 | 0.606 | ||
PCR | 0.832 | 0.762 | ||
Asian | 0.578 | 0.758 | ||
Caucasian | 0.163 | 0.971 | ||
HB | 0.721 | 0.489 | ||
PB | 0.666 | 0.880 | ||
TC vs CC | Overall | 0.951 | 0.761 | |
Taqman | 0.418 | 0.289 | ||
PCR | 0.839 | 0.933 | ||
Asian | 0.991 | 0.546 | ||
Caucasian | 0.902 | 0.767 | ||
HB | 0.721 | 0.601 | ||
PB | 0.965 | 0.453 | ||
TT+TC vs CC | Overall | 0.592 | 0.401 | |
Taqman | 0.418 | 0.613 | ||
PCR | 0.734 | 0.598 | ||
Asian | 0.986 | 0.185 | ||
Caucasian | 0.300 | 0.770 | ||
HB | 0.737 | 0.543 | ||
PB | 0.584 | 0.593 | ||
TT vs TC+CC | Overall | 0.908 | 0.899 | |
Taqman | 0.719 | 0.440 | ||
PCR | 0.912 | 0.917 | ||
Asian | 0.795 | 0.688 | ||
Caucasian | 0.537 | 0.857 | ||
HB | 0.673 | 0.503 | ||
PB | 0.914 | 0.508 |
Abbreviations: HB, hospital based; PB, population based; PCR, polymerase chain reaction.
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Acknowledgments
This review was supported by Health Care 3F Project of Shenzhen (Peking University First Hospital-The Second People’s Hospital of Shenzhen, Academician Yinglu Guo’s Team), the Shenzhen Key Medical Discipline Fund, Special Support Funds of Shenzhen for Introduced High-Level Medical Team, Shenzhen Foundation of Science and Technology (JCYJ20150330102720182), and Shenzhen Health and Family Planning Commission Scientific Research Project (201506026, 201601025, and 201606019).
Footnotes
Disclosure
The authors report no conflicts of interest in this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1.
Comparison | Study omitted | Estimate | (95% Conf Interval)
|
|
---|---|---|---|---|
Lower CI | Upper CI | |||
TT vs CC | Hu et al7 | 0.902 | 0.814 | 0.999 |
Hu et al35 | 0.904 | 0.815 | 1.002 | |
Tian et al3 | 0.902 | 0.814 | 1.001 | |
Hoffman et al5 | 0.890 | 0.805 | 0.985 | |
Catucci et al36 | 0.900 | 0.811 | 1.000 | |
Wang et al38 | 0.911 | 0.824 | 1.008 | |
Okubo et al83 | 0.900 | 0.812 | 0.998 | |
Peng et al4 | 0.904 | 0.816 | 1.002 | |
Srivastava et al10 | 0.903 | 0.815 | 1.000 | |
Dou et al6 | 0.897 | 0.809 | 0.994 | |
Li et al9 | 0.906 | 0.818 | 1.003 | |
Akkiz et al8 | 0.908 | 0.820 | 1.005 | |
Liu et al11 | 0.898 | 0.810 | 0.997 | |
Kim et al101 | 0.904 | 0.815 | 1.002 | |
Catucci et al36 | 0.899 | 0.810 | 0.997 | |
Christensen et al37 | 0.900 | 0.813 | 0.997 | |
Mittal et al41 | 0.904 | 0.816 | 1.001 | |
Jedlinski et al40 | 0.900 | 0.813 | 0.998 | |
Zhan et al42 | 0.906 | 0.818 | 1.004 | |
Zhou et al43 | 0.901 | 0.813 | 0.998 | |
Vinci et al102 | 0.895 | 0.809 | 0.992 | |
Hong et al2 | 0.902 | 0.814 | 1.000 | |
George et al39 | 0.902 | 0.815 | 0.999 | |
Linhares et al45 | 0.893 | 0.806 | 0.988 | |
Chen et al44 | 0.898 | 0.811 | 0.995 | |
Min et al24 | 0.904 | 0.815 | 1.002 | |
Zhu et al47 | 0.905 | 0.816 | 1.003 | |
Hezova et al25 | 0.897 | 0.810 | 0.994 | |
Zhang et al100 | 0.900 | 0.812 | 0.998 | |
Yoon et al46 | 0.904 | 0.816 | 1.001 | |
Zhang et al99 | 0.904 | 0.816 | 1.001 | |
Chu et al87 | 0.894 | 0.807 | 0.990 | |
Vinci et al105 | 0.897 | 0.810 | 0.994 | |
Ahn et al103 | 0.902 | 0.814 | 1.000 | |
Lv et al51 | 0.878 | 0.798 | 0.965 | |
Umar et al104 | 0.895 | 0.808 | 0.992 | |
Wei et al106 | 0.896 | 0.809 | 0.993 | |
Wang et al53 | 0.894 | 0.807 | 0.990 | |
Zhang et al55 | 0.904 | 0.816 | 1.003 | |
Han et al49 | 0.898 | 0.810 | 0.996 | |
Pavlakis et al93 | 0.899 | 0.812 | 0.996 | |
Tong et al65 | 0.901 | 0.813 | 1.000 | |
Pu et al84 | 0.902 | 0.814 | 1.000 | |
Bansal et al56 | 0.902 | 0.815 | 1.000 | |
Kupcinskas et al62 | 0.897 | 0.809 | 0.994 | |
Qu et al64 | 0.905 | 0.817 | 1.003 | |
Wang et al66 | 0.897 | 0.809 | 0.994 | |
Dikeakos et al58 | 0.925 | 0.843 | 1.015 | |
Qi et al86 | 0.902 | 0.814 | 1.000 | |
Chu et al57 | 0.898 | 0.810 | 0.995 | |
Parlayan et al107 | 0.900 | 0.812 | 0.997 | |
Li et al63 | 0.896 | 0.808 | 0.993 | |
Du et al59 | 0.892 | 0.806 | 0.987 | |
Omrani et al85 | 0.900 | 0.813 | 0.997 | |
Kou et al91 | 0.907 | 0.819 | 1.004 | |
Roy et al94 | 0.896 | 0.809 | 0.993 | |
Li et al63 | 0.896 | 0.808 | 0.993 | |
Deng et al67 | 0.900 | 0.812 | 0.997 | |
Qi et al72 | 0.907 | 0.819 | 1.005 | |
Dikaiakos et al68 | 0.899 | 0.812 | 0.996 | |
Li et al69 | 0.890 | 0.805 | 0.985 | |
Li et al69 | 0.907 | 0.819 | 1.004 | |
Nikolic et al71 | 0.902 | 0.814 | 1.000 | |
He et al90 | 0.901 | 0.813 | 0.999 | |
Sushma et al97 | 0.909 | 0.821 | 1.006 | |
Sodhi et al95 | 0.891 | 0.806 | 0.986 | |
Jiang et al26 | 0.896 | 0.808 | 0.993 | |
Toraih et al98 | 0.894 | 0.807 | 0.990 | |
Dai et al74 | 0.908 | 0.820 | 1.005 | |
Zhao et al82 | 0.898 | 0.811 | 0.995 | |
Song et al79 | 0.907 | 0.819 | 1.004 | |
Shen et al78 | 0.902 | 0.813 | 1.002 | |
Li et al75 | 0.907 | 0.820 | 1.005 | |
Li et al76 | 0.906 | 0.819 | 1.004 | |
Xu et al80 | 0.906 | 0.818 | 1.004 | |
Qiu et al77 | 0.905 | 0.817 | 1.003 | |
Jiang et al26 | 0.901 | 0.813 | 1.000 | |
Yin et al81 | 0.901 | 0.813 | 0.999 | |
Zhang et al99 | 0.901 | 0.813 | 0.998 | |
Sun et al96 | 0.904 | 0.817 | 1.002 | |
Toraih et al98 | 0.894 | 0.808 | 0.990 | |
Morales et al92 | 0.901 | 0.812 | 0.999 | |
Gu et al88 | 0.891 | 0.805 | 0.986 | |
Hashemi et al89 | 0.896 | 0.809 | 0.992 | |
Combined2–10,25,26,35–107 | 0.900 | 0.813 | 0.997 | |
TT vs TC+CC | Hu et al7 | 0.918 | 0.851 | 0.991 |
Hu et al35 | 0.920 | 0.852 | 0.993 | |
Tian et al3 | 0.918 | 0.850 | 0.991 | |
Hoffman et al5 | 0.910 | 0.844 | 0.980 | |
Catucci et al36 | 0.917 | 0.849 | 0.991 | |
Wang et al38 | 0.928 | 0.862 | 0.999 | |
Okubo et al83 | 0.917 | 0.850 | 0.991 | |
Peng et al4 | 0.919 | 0.852 | 0.991 | |
Srivastava et al10 | 0.918 | 0.850 | 0.990 | |
Dou et al6 | 0.918 | 0.850 | 0.991 | |
Li et al9 | 0.922 | 0.854 | 0.994 | |
Akkiz et al8 | 0.923 | 0.856 | 0.995 | |
Liu et al11 | 0.917 | 0.849 | 0.990 | |
Kim et al101 | 0.920 | 0.852 | 0.992 | |
Catucci et al36 | 0.916 | 0.849 | 0.989 | |
Christensen et al37 | 0.918 | 0.851 | 0.989 | |
Mittal et al41 | 0.921 | 0.854 | 0.993 | |
Jedlinski et al40 | 0.917 | 0.850 | 0.989 | |
Zhan et al42 | 0.922 | 0.854 | 0.994 | |
Zhou et al43 | 0.918 | 0.850 | 0.990 | |
Vinci et al102 | 0.915 | 0.849 | 0.987 | |
Hong et al2 | 0.922 | 0.854 | 0.994 | |
George et al39 | 0.920 | 0.853 | 0.992 | |
Linhares et al45 | 0.913 | 0.847 | 0.985 | |
Chen et al44 | 0.916 | 0.849 | 0.988 | |
Min et al24 | 0.918 | 0.850 | 0.990 | |
Zhu et al47 | 0.921 | 0.854 | 0.994 | |
Hezova et al25 | 0.915 | 0.848 | 0.987 | |
Zhang et al100 | 0.918 | 0.850 | 0.991 | |
Yoon et al46 | 0.920 | 0.853 | 0.993 | |
Zhang et al99 | 0.919 | 0.852 | 0.992 | |
Chu et al87 | 0.918 | 0.851 | 0.991 | |
Vinci et al105 | 0.919 | 0.851 | 0.991 | |
Ahn et al103 | 0.916 | 0.850 | 0.988 | |
Lv et al51 | 0.905 | 0.842 | 0.974 | |
Umar et al104 | 0.914 | 0.848 | 0.986 | |
Wei et al106 | 0.918 | 0.850 | 0.990 | |
Wang et al53 | 0.913 | 0.846 | 0.985 | |
Zhang et al55 | 0.919 | 0.851 | 0.992 | |
Han et al49 | 0.917 | 0.849 | 0.990 | |
Pavlakis et al93 | 0.921 | 0.854 | 0.994 | |
Tong et al65 | 0.913 | 0.847 | 0.985 | |
Pu et al84 | 0.918 | 0.851 | 0.990 | |
Bansal et al56 | 0.919 | 0.852 | 0.991 | |
Kupcinskas et al62 | 0.916 | 0.849 | 0.988 | |
Qu et al64 | 0.923 | 0.855 | 0.995 | |
Wang et al66 | 0.916 | 0.848 | 0.988 | |
Dikeakos et al58 | 0.931 | 0.866 | 1.001 | |
Qi et al86 | 0.924 | 0.857 | 0.996 | |
Chu et al57 | 0.914 | 0.847 | 0.986 | |
Parlayan et al107 | 0.918 | 0.851 | 0.990 | |
Li et al63 | 0.913 | 0.846 | 0.985 | |
Du et al59 | 0.914 | 0.847 | 0.986 | |
Omrani et al85 | 0.918 | 0.851 | 0.989 | |
Kou et al91 | 0.921 | 0.854 | 0.994 | |
Roy et al94 | 0.915 | 0.848 | 0.987 | |
Li et al63 | 0.906 | 0.845 | 0.971 | |
Deng et al67 | 0.913 | 0.847 | 0.985 | |
Qi et al72 | 0.923 | 0.856 | 0.995 | |
Dikaiakos et al68 | 0.914 | 0.848 | 0.987 | |
Li et al69 | 0.911 | 0.845 | 0.982 | |
Li et al69 | 0.922 | 0.855 | 0.995 | |
Nikolic et al71 | 0.919 | 0.852 | 0.991 | |
He et al90 | 0.917 | 0.850 | 0.990 | |
Sushma et al97 | 0.921 | 0.855 | 0.994 | |
Sodhi et al95 | 0.913 | 0.847 | 0.984 | |
Jiang et al26 | 0.914 | 0.847 | 0.986 | |
Toraih et al98 | 0.914 | 0.848 | 0.986 | |
Dai et al74 | 0.922 | 0.855 | 0.995 | |
Zhao et al82 | 0.914 | 0.848 | 0.986 | |
Song et al79 | 0.923 | 0.856 | 0.995 | |
Shen et al78 | 0.918 | 0.849 | 0.992 | |
Li et al75 | 0.921 | 0.854 | 0.993 | |
Li et al76 | 0.923 | 0.856 | 0.995 | |
Xu et al80 | 0.922 | 0.854 | 0.994 | |
Qiu et al77 | 0.921 | 0.854 | 0.993 | |
Jiang et al26 | 0.921 | 0.854 | 0.994 | |
Yin et al81 | 0.919 | 0.851 | 0.992 | |
Zhang et al99 | 0.918 | 0.851 | 0.991 | |
Sun et al96 | 0.919 | 0.852 | 0.992 | |
Toraih et al98 | 0.915 | 0.848 | 0.986 | |
Morales et al92 | 0.918 | 0.851 | 0.991 | |
Gu et al88 | 0.911 | 0.845 | 0.982 | |
Hashemi et al89 | 0.915 | 0.848 | 0.986 | |
Combined2–10,25,26,35–107 | 0.918 | 0.851 | 0.989 |
Table S2.
Polymorphism | Comparison | Subgroup | Begg’s test (P>z) |
Egger’s test (P>t) |
---|---|---|---|---|
rs11614913 | T vs C | Overall | 0.660 | 0.923 |
Taqman | 0.368 | 0.723 | ||
PCR | 0.640 | 0.859 | ||
Asian | 0.946 | 0.854 | ||
Caucasian | 0.147 | 0.969 | ||
HB | 0.509 | 0.386 | ||
PB | 0.251 | 0.579 | ||
TT vs CC | Overall | 0.971 | 0.822 | |
Taqman | 0.719 | 0.606 | ||
PCR | 0.832 | 0.762 | ||
Asian | 0.578 | 0.758 | ||
Caucasian | 0.163 | 0.971 | ||
HB | 0.721 | 0.489 | ||
PB | 0.666 | 0.880 | ||
TC vs CC | Overall | 0.951 | 0.761 | |
Taqman | 0.418 | 0.289 | ||
PCR | 0.839 | 0.933 | ||
Asian | 0.991 | 0.546 | ||
Caucasian | 0.902 | 0.767 | ||
HB | 0.721 | 0.601 | ||
PB | 0.965 | 0.453 | ||
TT+TC vs CC | Overall | 0.592 | 0.401 | |
Taqman | 0.418 | 0.613 | ||
PCR | 0.734 | 0.598 | ||
Asian | 0.986 | 0.185 | ||
Caucasian | 0.300 | 0.770 | ||
HB | 0.737 | 0.543 | ||
PB | 0.584 | 0.593 | ||
TT vs TC+CC | Overall | 0.908 | 0.899 | |
Taqman | 0.719 | 0.440 | ||
PCR | 0.912 | 0.917 | ||
Asian | 0.795 | 0.688 | ||
Caucasian | 0.537 | 0.857 | ||
HB | 0.673 | 0.503 | ||
PB | 0.914 | 0.508 |
Abbreviations: HB, hospital based; PB, population based; PCR, polymerase chain reaction.