遗传关联性Meta分析证据可信度评价
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摘要
遗传关联性Meta分析将多个研究的数据整合,通过增大样本量以提高统计效能,成为探求真实遗传关联性的有效途径。Meta分析为遗传关联性证据的产生带来机遇,但同时也给此类证据的利用带来挑战。因此,合理评价证据的可信度确有必要。本文主要介绍如何使用Venice标准从分子流行病学角度评价遗传关联性Meta分析证据的可信度。评估指标包括证据量、重复性及偏倚控制三方面,最后综合三方面的分级结果,得出"强"、"中等"、"弱"三个等级结果。通过对遗传关联性Meta分析证据可信度的评估,为进一步的研究及证据的临床转化提供明确信息。
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.
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.
引文
1Ioannidis JP,Boffetta P,Little J,et al.Assessment of cumulative evidence on genetic associations:interim guidelines.Int J Epidemiol,2008,37(1):120-132.
2Mocellin S,Verdi D,Pooley KA,et al.Genetic variation and gastriccancer risk:a field synopsis and meta-analysis.Gut,2015,64(8):1209-1219.
3Qiu C,Zeng P,Li X,et al.What is the impact of PCSK9 rs505151and 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 App Genet Mol Biol,2015,14(5):429-442.
5Ioannidis 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 genotypephenotype associations.Nature,2007,447(7145):655-660.
7Ioannidis JP.Non-replication and inconsistency in the genomewide 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.
9Clayton 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.
11Ioannidis JP,Patsopoulos NA,Evangelou E.Heterogeneity in meta-analyses of genome-wide association investigations.PLo S One,2007,2(9):e841.
12 Ioannidis JP,Trikalinos TA,Zintzaras E.Extreme between-studyhomogeneity in meta-analyses could offer useful insights.J Clin Epidemiol,2006,59(10):1023-1032.
13Skol 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,Mc Carthy MI.What makes a good genetic association study?Lancet,2005,366(9493):1315-1323.
15Wang Y,Localio R,Rebbeck TR.Evaluating bias due to population stratification in epidemiologic studies of gene-gene or geneenvironment interactions.Cancer Epidemiol Biomarkers Prev,2006,15(1):124-132.
16 Cordell HJ,Clayton DG.Genetic association studies.Lancet,2005,366(9491):1121-1131.
17Newton-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.
19Chan 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.
2Mocellin S,Verdi D,Pooley KA,et al.Genetic variation and gastriccancer risk:a field synopsis and meta-analysis.Gut,2015,64(8):1209-1219.
3Qiu C,Zeng P,Li X,et al.What is the impact of PCSK9 rs505151and 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 App Genet Mol Biol,2015,14(5):429-442.
5Ioannidis 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 genotypephenotype associations.Nature,2007,447(7145):655-660.
7Ioannidis JP.Non-replication and inconsistency in the genomewide 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.
9Clayton 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.
11Ioannidis JP,Patsopoulos NA,Evangelou E.Heterogeneity in meta-analyses of genome-wide association investigations.PLo S One,2007,2(9):e841.
12 Ioannidis JP,Trikalinos TA,Zintzaras E.Extreme between-studyhomogeneity in meta-analyses could offer useful insights.J Clin Epidemiol,2006,59(10):1023-1032.
13Skol 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,Mc Carthy MI.What makes a good genetic association study?Lancet,2005,366(9493):1315-1323.
15Wang Y,Localio R,Rebbeck TR.Evaluating bias due to population stratification in epidemiologic studies of gene-gene or geneenvironment interactions.Cancer Epidemiol Biomarkers Prev,2006,15(1):124-132.
16 Cordell HJ,Clayton DG.Genetic association studies.Lancet,2005,366(9491):1121-1131.
17Newton-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.
19Chan 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.