Credibility assessment of meta-analysis evidence on genetic association study_Chinese Journal of Evidence-Based Medicine

Authors：

ZHAO Xiang ¹ ^# , QIU Chengfeng ^2,3 ^# , SHI Zhihua ³ , DENG Ziwei ³ , WENG Hong ⁴ , YANG Yihua ⁵ ,  TAN Liming ^2,3 ,  ZENG Xiantao ⁴

1. Department of General Practice, The First People’s Hospital of Huaihua City, Huaihua, 418000, P.R.China;
2. Department of Evidence-based Medicine and Clinical Center, The First People’s Hospital of Huaihua City, Huaihua, 418000, P.R.China;
3. Department of Pharmacy, The First People’s Hospital of Huaihua City, Huaihua, 418000, P.R.China;
4. Center for Evidence-Based and Translational Medicine, Zhongnan Hospital, Wuhan University, Wuhan, 430071, P.R.China;
5. School of Pharmacy, Xuzhou Medical University, Xuzhou, 210004, P.R.China;

Corresponding author：

TAN Liming, Email: limingtan@126.com; ZENG Xiantao, Email: zengxiantao1128@163.com

Keywords：

Genetic association; Meta-analysis; Venice criteria; Credibility

DOI：

10.7507/1672-2531.201804142

Video：

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Meta-analysis has become a common approach to summarize genetic association with the tremendous amount of published epidemiological evidence. Assessing the credibility of meta-analysis evidence on genetic association is a rapidly growing challenge. This paper illuminates how to assess the credibility of meta-analysis evidence by using Venice criteria. A semi-quantitative index assigns three levels for the amount of evidence, replication and protection from bias. At the end, three considerations are merged into a grading scheme, which generates three composite assessments: weak, moderate or strong. Credibility assessment is necessary to estimate whether a true genetic association exists. Such method provides indication for further study and is of clinical importance.

Citation： ZHAO Xiang, QIU Chengfeng, SHI Zhihua, DENG Ziwei, WENG Hong, YANG Yihua, TAN Liming, ZENG Xiantao. Credibility assessment of meta-analysis evidence on genetic association study. Chinese Journal of Evidence-Based Medicine, 2018, 18(8): 883-887. doi: 10.7507/1672-2531.201804142 Copy

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2.	Mocellin S, Verdi D, Pooley KA, et al. Genetic variation and gastric cancer risk: a field synopsis and meta-analysis. Gut, 2015, 64(8): 1209-1219.
3.	Qiu C, Zeng P, Li X, et al. What is the impact of PCSK9 rs505151 and rs11591147 polymorphisms on serum lipids level and cardiovascular risk: a meta-analysis. Lipids Health Dis, 2017, 16(1): 111.
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7.	Ioannidis JP. Non-replication and inconsistency in the genome-wide association setting. Hum Hered, 2007, 64(4): 203-213.
8.	Balding DJ. A tutorial on statistical methods for population association studies. Nat Rev Genet, 2006, 7(10): 781-791.
9.	Clayton DG, Walker NM, Smyth DJ, et al. Population structure, differential bias and genomic control in a large-scale, case-control association study. Nat Genet, 2005, 37(11): 1243-1246.
10.	Pompanon F, Bonin A, Bellemain E, et al. Genotyping errors: causes, consequences and solutions. Nat Rev Genet, 2005, 6(11): 847-859.
11.	Ioannidis JP, Patsopoulos NA, Evangelou E. Heterogeneity in meta-analyses of genome-wide association investigations. PLoS One, 2007, 2(9): e841.
12.	Ioannidis JP, Trikalinos TA, Zintzaras E. Extreme between-study homogeneity in meta-analyses could offer useful insights. J Clin Epidemiol, 2006, 59(10): 1023-1032.
13.	Skol AD, Scott LJ, Abecasis GR, et al. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet, 2006, 38(2): 209-213.
14.	Hattersley AT, McCarthy MI. What makes a good genetic association study? Lancet, 2005, 366(9493): 1315-1323.
15.	Wang Y, Localio R, Rebbeck TR. Evaluating bias due to population stratification in epidemiologic studies of gene-gene or gene-environment interactions. Cancer Epidemiol Biomarkers Prev, 2006, 15(1): 124-132.
16.	Cordell HJ, Clayton DG. Genetic association studies. Lancet, 2005, 366(9491): 1121-1131.
17.	Newton-Cheh C, Hirschhorn JN. Genetic association studies of complex traits: design and analysis issues. Mutat Res, 2005, 573(1-2): 54-69.
18.	Price AL, Patterson NJ, Plenge RM, et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet, 2006, 38(8): 904-909.
19.	Chan AW, Hróbjartsson A, Haahr MT, et al. Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. JAMA, 2004, 291(20): 2457-2465.
20.	Shields PG. Publication bias is a scientific problem with adverse ethical outcomes: the case for a section for null results. Cancer Epidemiol Biomarkers Prev, 2000, 9(8): 771-772.

1. Ioannidis JP, Boffetta P, Little J, et al. Assessment of cumulative evidence on genetic associations: interim guidelines. Int J Epidemiol, 2008, 37(1): 120-132.
2. Mocellin S, Verdi D, Pooley KA, et al. Genetic variation and gastric cancer risk: a field synopsis and meta-analysis. Gut, 2015, 64(8): 1209-1219.
3. Qiu C, Zeng P, Li X, et al. What is the impact of PCSK9 rs505151 and rs11591147 polymorphisms on serum lipids level and cardiovascular risk: a meta-analysis. Lipids Health Dis, 2017, 16(1): 111.
4. Zehetmayer S, Graf AC, Posch M. Sample size reassessment for a two-stage design controlling the false discovery rate. Stat Appl Genet Mol Biol, 2015, 14(5): 429-442.
5. Ioannidis JP, Trikalinos TA, Ntzani EE, et al. Genetic associations in large versus small studies: an empirical assessment. Lancet, 2003, 361(9357): 567-571.
6. NCI-NHGRI Working Group on Replication in Association Studies, Chanock SJ, Manolio T, et al. Replicating genotype-phenotype associations. Nature, 2007, 447(7145): 655-660.
7. Ioannidis JP. Non-replication and inconsistency in the genome-wide association setting. Hum Hered, 2007, 64(4): 203-213.
8. Balding DJ. A tutorial on statistical methods for population association studies. Nat Rev Genet, 2006, 7(10): 781-791.
9. Clayton DG, Walker NM, Smyth DJ, et al. Population structure, differential bias and genomic control in a large-scale, case-control association study. Nat Genet, 2005, 37(11): 1243-1246.
10. Pompanon F, Bonin A, Bellemain E, et al. Genotyping errors: causes, consequences and solutions. Nat Rev Genet, 2005, 6(11): 847-859.
11. Ioannidis JP, Patsopoulos NA, Evangelou E. Heterogeneity in meta-analyses of genome-wide association investigations. PLoS One, 2007, 2(9): e841.
12. Ioannidis JP, Trikalinos TA, Zintzaras E. Extreme between-study homogeneity in meta-analyses could offer useful insights. J Clin Epidemiol, 2006, 59(10): 1023-1032.
13. Skol AD, Scott LJ, Abecasis GR, et al. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet, 2006, 38(2): 209-213.
14. Hattersley AT, McCarthy MI. What makes a good genetic association study? Lancet, 2005, 366(9493): 1315-1323.
15. Wang Y, Localio R, Rebbeck TR. Evaluating bias due to population stratification in epidemiologic studies of gene-gene or gene-environment interactions. Cancer Epidemiol Biomarkers Prev, 2006, 15(1): 124-132.
16. Cordell HJ, Clayton DG. Genetic association studies. Lancet, 2005, 366(9491): 1121-1131.
17. Newton-Cheh C, Hirschhorn JN. Genetic association studies of complex traits: design and analysis issues. Mutat Res, 2005, 573(1-2): 54-69.
18. Price AL, Patterson NJ, Plenge RM, et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet, 2006, 38(8): 904-909.
19. Chan AW, Hróbjartsson A, Haahr MT, et al. Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. JAMA, 2004, 291(20): 2457-2465.
20. Shields PG. Publication bias is a scientific problem with adverse ethical outcomes: the case for a section for null results. Cancer Epidemiol Biomarkers Prev, 2000, 9(8): 771-772.

Chinese Journal of Evidence-Based Medicine

Credibility assessment of meta-analysis evidence on genetic association study

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