Yin et al. (Bioscience Reports (2019) 39, BSR20180923) recently published a meta-analysis about the association between the K469E (rs5498) polymorphism and risk of coronary heart disease (CHD). Authors included 14 studies based on their inclusion criteria. They indicated that only studies which their genotyping data were in Hardy–Weinberg equilibrium (HWE) were included in their meta-analysis. They also tested HWE for these studies and found all the control groups in HWE. As their main finding, they concluded that ‘K469E polymorphism is associated with CHD risk and the K allele is a more significant risk factor for developing CHD amongst Chinese and Caucasians populations’. However, there seems to be presenting some mistakes in HWE test which strongly affects included studies and the final conclusion. Here we aim to comment on the issue.

Keywords:
Coronary artery disease, Comment, ICAM-1, Meta-Analysis, Polymorphism
Subjects:
Cardiovascular System & Vascular Biology, DNA, Chromosomes & Chromosomal Structure, Mutation

Dear Editor,

Unfortunately, based on our analysis, contrary to meta-analysis by Yin et al. [1], studies they included in their meta-analysis were not in Hardy–Weinberg equilibrium (HWE), and many included articles (seven articles) show deviation from HWE, even after adjustment. It seems that authors made some mistake in calculating HWE. In Table 1 we showed P-values for HWE test and ineligible studies, based on ‘HardyWeinberg’ package in R programming language (https://cran.rproject.org/web/packages/HardyWeinberg/HardyWeinberg.pdf). Our results were double checked with STATA (genhwi form of genhw, https://www.stata.com/users/mcleves/genhw/genhw.hlp), and also manually. In manual method, P-value of HWE test was calculated based on four following steps. (i) We calculated allele frequencies in control group: K = [(2 × KK) + KE]/(2 × total), so E should be E = 1 − K. (ii) We calculated expected genotypes based on allele frequencies: KK = K2 × total, KE = (2 × K × E) × total, and EE = EE2 × total. (iii) We carried out chi-square test between observed and expected genotypes (χ2 = Σ(Ob − Ex)2/Ex). (iv) Finally, results were interpreted based on chi-square routine distribution table (steps (i–iii) are shown in Table 2 and step (iv) in Table 3). Also regarding the study by Sarecka-Hujar et al. [2], the genotyping data were not correctly included in Table 1 of their meta-analysis, GG(EE) and AA(KK) genotypes and allele frequencies were displaced in both case and control groups. Correct data are shown in Table 1. Also, they [2] indicate that ‘the distribution of ICAM1 genotypes was not compatible with HWE’ which clearly violates inclusion criteria (iv) in Yin et al. [1] meta-analysis.

Table 1
Genotyping data and HWE results for studies in Yin et al. [1] meta-analysis
StudiesCase KKKEEEControl KKKEEEP-valueAdjusted P-valueDesign
Shang, Q. (2005) 48 50 24 29 33 35 0.002 0.005 Exclude 
Li, Y.J. (2010) 47 39 7 52 36 13 0.103 0.180 Include 
Lu, F.H. (2006) 61 69 30 45 65 59 0.003 0.008 Exclude 
Zhang, S.R. (2006) 111 52 10 69 59 13 0.940 0.973 Include 
Rao, D. (2005) 84 41 20 59 19 66 <0.001 <0.001 Exclude 
Wei, Y.S. (2006) 124 84 17 101 103 26 0.973 0.973 Include 
Zhou, Y.L. (2006) 38 45 20 102 62 33 <0.001 <0.001 Exclude 
Wang, M. (2005) 96 61 8 91 90 18 0.524 0.734 Include 
Jiang, H. (2002) 202 226 100 60 66 87 <0.001 <0.001 Exclude 
Milutinović, A. (2006) 47 72 33 65 109 41 0.695 0.811 Include 
Sarecka-Hujar, B. (2009) 61 118 12 73 122 8 <0.001 <0.001 Exclude 
Mohamed, A. (2010) 20 37 43 2 11 37 0.332 0.516 Include 
Luo, J.Y. (2014) 339 278 57 461 273 45 0.587 0.747 Include 
Yang, M. (2014) 305 251 48 266 160 42 0.015 0.029 Exclude 
StudiesCase KKKEEEControl KKKEEEP-valueAdjusted P-valueDesign
Shang, Q. (2005) 48 50 24 29 33 35 0.002 0.005 Exclude 
Li, Y.J. (2010) 47 39 7 52 36 13 0.103 0.180 Include 
Lu, F.H. (2006) 61 69 30 45 65 59 0.003 0.008 Exclude 
Zhang, S.R. (2006) 111 52 10 69 59 13 0.940 0.973 Include 
Rao, D. (2005) 84 41 20 59 19 66 <0.001 <0.001 Exclude 
Wei, Y.S. (2006) 124 84 17 101 103 26 0.973 0.973 Include 
Zhou, Y.L. (2006) 38 45 20 102 62 33 <0.001 <0.001 Exclude 
Wang, M. (2005) 96 61 8 91 90 18 0.524 0.734 Include 
Jiang, H. (2002) 202 226 100 60 66 87 <0.001 <0.001 Exclude 
Milutinović, A. (2006) 47 72 33 65 109 41 0.695 0.811 Include 
Sarecka-Hujar, B. (2009) 61 118 12 73 122 8 <0.001 <0.001 Exclude 
Mohamed, A. (2010) 20 37 43 2 11 37 0.332 0.516 Include 
Luo, J.Y. (2014) 339 278 57 461 273 45 0.587 0.747 Include 
Yang, M. (2014) 305 251 48 266 160 42 0.015 0.029 Exclude 

Finally included articles are shown in bold.

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Table 2
Results of steps (i–iii) of manual HWE test
StudiesOb = Observed genotypesAllele frequencyEx = Expected genotypesX2P-value
KKKEEETotalKEKKKEEE
Shang, Q. (2005) 29 33 35 97 0.47 0.53 21.3 48.3 27.3 9.75 0.002 
Li, Y.J. (2010) 52 36 13 101 0.69 0.31 48.5 43.0 9.5 2.66 0.103 
Lu, F.H. (2006) 45 65 59 169 0.46 0.54 35.5 83.9 49.5 8.59 0.003 
Zhang, S.R. (2006) 69 59 13 141 0.70 0.30 68.8 59.4 12.8 0.01 0.940 
Rao, D. (2005) 59 19 66 144 0.48 0.52 32.6 71.8 39.6 77.90 <0.001 
Wei, Y.S. (2006) 101 103 26 230 0.66 0.34 101.1 102.8 26.1 0.00 0.973 
Zhou, Y.L. (2006) 102 62 33 197 0.68 0.32 89.8 86.4 20.8 15.73 <0.001 
Wang, M. (2005) 91 90 18 199 0.68 0.32 92.9 86.1 19.9 0.41 0.524 
Jiang, H. (2002) 60 66 87 213 0.44 0.56 40.6 104.8 67.6 29.19 <0.001 
Milutinović, A. (2006) 65 109 41 215 0.56 0.44 66.4 106.2 42.4 0.15 0.695 
Sarecka-Hujar, B. (2009) 73 122 8 203 0.66 0.34 88.5 91.1 23.5 23.37 <0.001 
Mohamed, A. (2010) 2 11 37 50 0.15 0.85 1.1 12.8 36.1 0.94 0.332 
Luo, J.Y. (2014) 461 273 45 779 0.77 0.23 458.3 278.4 42.3 0.30 0.587 
Yang, M. (2014) 266 160 42 468 0.74 0.26 255.8 180.4 31.8 5.98 0.015 
StudiesOb = Observed genotypesAllele frequencyEx = Expected genotypesX2P-value
KKKEEETotalKEKKKEEE
Shang, Q. (2005) 29 33 35 97 0.47 0.53 21.3 48.3 27.3 9.75 0.002 
Li, Y.J. (2010) 52 36 13 101 0.69 0.31 48.5 43.0 9.5 2.66 0.103 
Lu, F.H. (2006) 45 65 59 169 0.46 0.54 35.5 83.9 49.5 8.59 0.003 
Zhang, S.R. (2006) 69 59 13 141 0.70 0.30 68.8 59.4 12.8 0.01 0.940 
Rao, D. (2005) 59 19 66 144 0.48 0.52 32.6 71.8 39.6 77.90 <0.001 
Wei, Y.S. (2006) 101 103 26 230 0.66 0.34 101.1 102.8 26.1 0.00 0.973 
Zhou, Y.L. (2006) 102 62 33 197 0.68 0.32 89.8 86.4 20.8 15.73 <0.001 
Wang, M. (2005) 91 90 18 199 0.68 0.32 92.9 86.1 19.9 0.41 0.524 
Jiang, H. (2002) 60 66 87 213 0.44 0.56 40.6 104.8 67.6 29.19 <0.001 
Milutinović, A. (2006) 65 109 41 215 0.56 0.44 66.4 106.2 42.4 0.15 0.695 
Sarecka-Hujar, B. (2009) 73 122 8 203 0.66 0.34 88.5 91.1 23.5 23.37 <0.001 
Mohamed, A. (2010) 2 11 37 50 0.15 0.85 1.1 12.8 36.1 0.94 0.332 
Luo, J.Y. (2014) 461 273 45 779 0.77 0.23 458.3 278.4 42.3 0.30 0.587 
Yang, M. (2014) 266 160 42 468 0.74 0.26 255.8 180.4 31.8 5.98 0.015 
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Table 3
Chi-square distribution table
P-valueχ2 (df = 1)
0.995 0.000 
0.975 0.000 
0.20 1.642 
0.10 2.706 
0.05 3.841 
0.025 5.024 
0.02 5.412 
0.01 6.635 
0.005 7.879 
0.002 9.550 
0.001 10.828 
P-valueχ2 (df = 1)
0.995 0.000 
0.975 0.000 
0.20 1.642 
0.10 2.706 
0.05 3.841 
0.025 5.024 
0.02 5.412 
0.01 6.635 
0.005 7.879 
0.002 9.550 
0.001 10.828 
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After deleting studies with deviation from HWE and meta-analysis of included articles, we found completely different results. Genotyping data related to seven finally included articles [2–8], involving 1582 coronary heart disease (CHD) cases and 1715 controls, are shown in Table 1 (shown in bold and black color), and meta-analysis results based on five different genetics models are presented in Table 4 and Figure 1. According to our observation, we did not find a significant result in different and overall ethnicity in any genetic model. Finally, in contrast with Yin et al. [1] study and based on meta-analysis of studies in HWE, it can be concluded that ICAM-1 gene polymorphism E469K may not be related to the risk of CHD. More studies could help us to get a definitive result.

Figure 1
Forest plot of CHD risk associated with the K469E polymorphism for K/E + K/K versus E/E genotype (A). Funnel plot (B) and forest plot (C) related to publication bias and sensitivity analysis.
View largeDownload slide
CHD risk associated with the K469E polymorphism for K/E + K/K versus E/E genotype

Forest plot of CHD risk associated with the K469E polymorphism for K/E + K/K versus E/E genotype (A). Funnel plot (B) and forest plot (C) related to publication bias and sensitivity analysis.

Figure 1
Forest plot of CHD risk associated with the K469E polymorphism for K/E + K/K versus E/E genotype (A). Funnel plot (B) and forest plot (C) related to publication bias and sensitivity analysis.
View largeDownload slide
CHD risk associated with the K469E polymorphism for K/E + K/K versus E/E genotype

Forest plot of CHD risk associated with the K469E polymorphism for K/E + K/K versus E/E genotype (A). Funnel plot (B) and forest plot (C) related to publication bias and sensitivity analysis.

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Table 4
Meta-analysis of CHD risk associated with the K469E polymorphism based on different genetics models
ClassificationAllelic (K vs. E) OR [95% CI]Q test P-valueK/E + K/K vs. E/E OR [95% CI]Q test P-valueKK vs. K/E + E/E OR [95% CI]Q test P-valueK/E vs. K/K + E/E OR [95% CI]Q test P-value
Chinese 1.23 [0.84–1.78] 0.01 1.32 [0.79–2.22] 0.03 1.25 [0.79–1.98] 0.01 0.89 [0.63–1.26] 0.01 
Caucasian 1.79 [0.50–6.44] 0.01 1.75 [0.41–7.52] 0.01 2.14 [0.39–11.7] 0.03 1.26 [0.55–2.93] 0.06 
Overall 1.33 [0.95–1.85] 0.01 1.44 [0.89–2.33] 0.01 1.32 [0.89–1.96] 0.01 0.95 [0.71–1.27] 0.01 
ClassificationAllelic (K vs. E) OR [95% CI]Q test P-valueK/E + K/K vs. E/E OR [95% CI]Q test P-valueKK vs. K/E + E/E OR [95% CI]Q test P-valueK/E vs. K/K + E/E OR [95% CI]Q test P-value
Chinese 1.23 [0.84–1.78] 0.01 1.32 [0.79–2.22] 0.03 1.25 [0.79–1.98] 0.01 0.89 [0.63–1.26] 0.01 
Caucasian 1.79 [0.50–6.44] 0.01 1.75 [0.41–7.52] 0.01 2.14 [0.39–11.7] 0.03 1.26 [0.55–2.93] 0.06 
Overall 1.33 [0.95–1.85] 0.01 1.44 [0.89–2.33] 0.01 1.32 [0.89–1.96] 0.01 0.95 [0.71–1.27] 0.01 
ClassificationK/K vs. E/E OR [95% CI]Q test P-valueK/K vs. K/E OR [95% CI]Q test P-valueK/E vs. E/E OR [95% CI]Q test P-value
Chinese 1.47 [0.75–2.88] 0.01 1.20 [0.78–1.83] 0.01 1.06 [0.78–1.43] 0.40   
Caucasian 2.48 [0.27–22.49] 0.01 1.19 [0.75–1.88] 0.24 1.49 [0.43–5.10] 0.01   
Overall 1.57 [0.88–2.80] 0.01 1.22 [0.86–1.74] 0.03 1.11 [0.86–1.42] 0.01   
ClassificationK/K vs. E/E OR [95% CI]Q test P-valueK/K vs. K/E OR [95% CI]Q test P-valueK/E vs. E/E OR [95% CI]Q test P-value
Chinese 1.47 [0.75–2.88] 0.01 1.20 [0.78–1.83] 0.01 1.06 [0.78–1.43] 0.40   
Caucasian 2.48 [0.27–22.49] 0.01 1.19 [0.75–1.88] 0.24 1.49 [0.43–5.10] 0.01   
Overall 1.57 [0.88–2.80] 0.01 1.22 [0.86–1.74] 0.03 1.11 [0.86–1.42] 0.01   
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The authors declare that there are no competing interests associated with the manuscript.

CHD

Coronary heart disease

HWE

Hardy–Weinberg equilibrium

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© 2019 The Author(s).
2019
This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).