{"title":"基因组研究中的经验调整固定效应荟萃分析方法。","authors":"Wimarsha T Jayanetti, Sinjini Sikdar","doi":"10.1515/sagmb-2023-0041","DOIUrl":null,"url":null,"abstract":"<p><p>In recent years, meta-analyzing summary results from multiple studies has become a common practice in genomic research, leading to a significant improvement in the power of statistical detection compared to an individual genomic study. Meta analysis methods that combine statistical estimates across studies are known to be statistically more powerful than those combining statistical significance measures. An approach combining effect size estimates based on a fixed-effects model, called METAL, has gained extreme popularity to perform the former type of meta-analysis. In this article, we discuss the limitations of METAL due to its dependence on the theoretical null distribution, leading to incorrect significance testing results. Through various simulation studies and real genomic data application, we show how modifying the <i>z</i>-scores in METAL, using an empirical null distribution, can significantly improve the results, especially in presence of hidden confounders. For the estimation of the null distribution, we consider two different approaches, and we highlight the scenarios when one null estimation approach outperforms the other. This article will allow researchers to gain an insight into the importance of using an empirical null distribution in the fixed-effects meta-analysis as well as in choosing the appropriate empirical null distribution estimation approach.</p>","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"23 1","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Empirically adjusted fixed-effects meta-analysis methods in genomic studies.\",\"authors\":\"Wimarsha T Jayanetti, Sinjini Sikdar\",\"doi\":\"10.1515/sagmb-2023-0041\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In recent years, meta-analyzing summary results from multiple studies has become a common practice in genomic research, leading to a significant improvement in the power of statistical detection compared to an individual genomic study. Meta analysis methods that combine statistical estimates across studies are known to be statistically more powerful than those combining statistical significance measures. An approach combining effect size estimates based on a fixed-effects model, called METAL, has gained extreme popularity to perform the former type of meta-analysis. In this article, we discuss the limitations of METAL due to its dependence on the theoretical null distribution, leading to incorrect significance testing results. Through various simulation studies and real genomic data application, we show how modifying the <i>z</i>-scores in METAL, using an empirical null distribution, can significantly improve the results, especially in presence of hidden confounders. For the estimation of the null distribution, we consider two different approaches, and we highlight the scenarios when one null estimation approach outperforms the other. This article will allow researchers to gain an insight into the importance of using an empirical null distribution in the fixed-effects meta-analysis as well as in choosing the appropriate empirical null distribution estimation approach.</p>\",\"PeriodicalId\":49477,\"journal\":{\"name\":\"Statistical Applications in Genetics and Molecular Biology\",\"volume\":\"23 1\",\"pages\":\"\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistical Applications in Genetics and Molecular Biology\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1515/sagmb-2023-0041\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Applications in Genetics and Molecular Biology","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1515/sagmb-2023-0041","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0
摘要
近年来,对多项研究的汇总结果进行元分析已成为基因组研究中的一种常见做法,与单个基因组研究相比,元分析可显著提高统计检测能力。众所周知,结合各研究统计估计值的元分析方法比结合统计显著性度量的方法在统计上更强大。一种基于固定效应模型的效应大小估计值组合方法(称为 METAL)在进行前一种类型的元分析时极为流行。在本文中,我们将讨论 METAL 因依赖于理论空分布而导致显著性检验结果不正确的局限性。通过各种模拟研究和真实基因组数据的应用,我们展示了如何利用经验零分布修改 METAL 中的 z 分数,从而显著改善结果,尤其是在存在隐藏混杂因素的情况下。对于空分布的估计,我们考虑了两种不同的方法,并强调了一种空估计方法优于另一种方法的情况。本文将使研究人员深入了解在固定效应荟萃分析中使用经验零分布以及选择合适的经验零分布估计方法的重要性。
Empirically adjusted fixed-effects meta-analysis methods in genomic studies.
In recent years, meta-analyzing summary results from multiple studies has become a common practice in genomic research, leading to a significant improvement in the power of statistical detection compared to an individual genomic study. Meta analysis methods that combine statistical estimates across studies are known to be statistically more powerful than those combining statistical significance measures. An approach combining effect size estimates based on a fixed-effects model, called METAL, has gained extreme popularity to perform the former type of meta-analysis. In this article, we discuss the limitations of METAL due to its dependence on the theoretical null distribution, leading to incorrect significance testing results. Through various simulation studies and real genomic data application, we show how modifying the z-scores in METAL, using an empirical null distribution, can significantly improve the results, especially in presence of hidden confounders. For the estimation of the null distribution, we consider two different approaches, and we highlight the scenarios when one null estimation approach outperforms the other. This article will allow researchers to gain an insight into the importance of using an empirical null distribution in the fixed-effects meta-analysis as well as in choosing the appropriate empirical null distribution estimation approach.
期刊介绍:
Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.