Cumulative Meta-Analysis for Genetic Association: When Is a New Study Worthwhile?

Abstract

Objectives: In this paper, we address the questions: how large a sample size would be required to show genome-wide significance between a single nucleotide polymorphism (SNP) and a genetic trait in a meta-analysis of a newly planned study together with the existing ones? Or alternatively: will a planned study of size n be able to provide evidence of a genetic association when this study is combined with a current meta-analysis? Methods: We examine the potential impact of a newly planned genetic study on an existing meta-analysis through the use of a simulation-based algorithm. The proposed approach provides an empirical estimate of the power of the updated meta-analysis to detect genome-wide significance (p < 5.0 × 10^-8) of a complex trait and each of a set of specific SNPs of interest or the expected p value of the updated meta-analysis including the current and proposed studies. Results: This technique is illustrated in the context of an updated meta-analysis of case-control studies in Paget's disease. A second example illustrates the impact of adding a newly planned study to a large meta-analysis of SNP associations with human height. Conclusions: The proposed algorithm is particularly useful for the design of studies to assess a selected set of high-priority SNP associations that are ‘nearly' significant in meta-analysis of existing studies. The results may help investigators decide whether an updated meta-analysis is likely to achieve genome-wide significance.

CYP1A1 Ile462Val Polymorphism Is Associated with Cervical Cancer Risk in Caucasians Not Asians: A Meta-Analysis

“…Sensitivity analysis was performed to investigate the influence of a single study on overall analysis, and to test the robustness of overall results. Cumulative meta-analysis was carried out to observe the change when with sample sizes were enlarged (Pabalan, 2010 ; Rotondi and Bull, 2012 ). Publication bias was assessed by visual inspection of funnel plots and Egger's test.…”

No association between XRCC1 gene Arg194Trp polymorphism and risk of lung cancer: evidence based on an updated cumulative meta-analysis

“…The influence of sample size on the overall risk estimation was carried out by cumulative meta-analysis [35], and the influence of single study on the overall risk estimation was determined through sensitivity analysis by omitting one study each time. The publication bias was detected by funnel plot analysis.…”

“…In order to obtain more comprehensive and precise results, we conducted cumulative meta-analysis [34, 35] to explore the truly association between Arg194Trp polymorphism and lung cancer risk based on 25 case–control studies. The meta-analysis is reported based on preferred reporting items for systematic reviews and meta-analyses (PRISMA) [36] statement.…”

Reappraisal of XRCC1 Arg194Trp polymorphism and glioma risk: a cumulative meta-analysis

“…Sensitivity analysis was applied by excluding each single study each time to explore the stability of overall results. The cumulative meta-analysis was carried out to observe the change when with sample sizes were enlarged [22, 23]. The publication bias was detected by funnel plot analysis and the Egger linear regression test [54].…”

“…Moreover, there are many relevant studies which are published after the previous 14 meta-analyses and also yield inconsistent results. Therefore, we performed this updated and cumulative meta-analysis [22, 23] to explore the more precise association between Arg194Trp polymorphism and glioma risk. Subgroup analyses were also performed according to Caucasian and Asian populations to investigate ethnicity-specific effects; the subgroup analyses based on the source of controls and the HWE for controls were conducted as well.…”