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The impact of violating the independence assumption in meta-analysis on biomarker discovery

With rapid advancements in high-throughput sequencing technologies, massive amounts of “-omics” data are now available in almost every biomedical field. Due to variance in biological models and analytic methods, findings from clinical and biological studies are often not generalizable when tested in...

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Autores principales: Abbas-Aghababazadeh, Farnoosh, Xu, Wei, Haibe-Kains, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9885264/
https://www.ncbi.nlm.nih.gov/pubmed/36726714
http://dx.doi.org/10.3389/fgene.2022.1027345
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author Abbas-Aghababazadeh, Farnoosh
Xu, Wei
Haibe-Kains, Benjamin
author_facet Abbas-Aghababazadeh, Farnoosh
Xu, Wei
Haibe-Kains, Benjamin
author_sort Abbas-Aghababazadeh, Farnoosh
collection PubMed
description With rapid advancements in high-throughput sequencing technologies, massive amounts of “-omics” data are now available in almost every biomedical field. Due to variance in biological models and analytic methods, findings from clinical and biological studies are often not generalizable when tested in independent cohorts. Meta-analysis, a set of statistical tools to integrate independent studies addressing similar research questions, has been proposed to improve the accuracy and robustness of new biological insights. However, it is common practice among biomarker discovery studies using preclinical pharmacogenomic data to borrow molecular profiles of cancer cell lines from one study to another, creating dependence across studies. The impact of violating the independence assumption in meta-analyses is largely unknown. In this study, we review and compare different meta-analyses to estimate variations across studies along with biomarker discoveries using preclinical pharmacogenomics data. We further evaluate the performance of conventional meta-analysis where the dependence of the effects was ignored via simulation studies. Results show that, as the number of non-independent effects increased, relative mean squared error and lower coverage probability increased. Additionally, we also assess potential bias in the estimation of effects for established meta-analysis approaches when data are duplicated and the assumption of independence is violated. Using pharmacogenomics biomarker discovery, we find that treating dependent studies as independent can substantially increase the bias of meta-analyses. Importantly, we show that violating the independence assumption decreases the generalizability of the biomarker discovery process and increases false positive results, a key challenge in precision oncology.
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spelling pubmed-98852642023-01-31 The impact of violating the independence assumption in meta-analysis on biomarker discovery Abbas-Aghababazadeh, Farnoosh Xu, Wei Haibe-Kains, Benjamin Front Genet Genetics With rapid advancements in high-throughput sequencing technologies, massive amounts of “-omics” data are now available in almost every biomedical field. Due to variance in biological models and analytic methods, findings from clinical and biological studies are often not generalizable when tested in independent cohorts. Meta-analysis, a set of statistical tools to integrate independent studies addressing similar research questions, has been proposed to improve the accuracy and robustness of new biological insights. However, it is common practice among biomarker discovery studies using preclinical pharmacogenomic data to borrow molecular profiles of cancer cell lines from one study to another, creating dependence across studies. The impact of violating the independence assumption in meta-analyses is largely unknown. In this study, we review and compare different meta-analyses to estimate variations across studies along with biomarker discoveries using preclinical pharmacogenomics data. We further evaluate the performance of conventional meta-analysis where the dependence of the effects was ignored via simulation studies. Results show that, as the number of non-independent effects increased, relative mean squared error and lower coverage probability increased. Additionally, we also assess potential bias in the estimation of effects for established meta-analysis approaches when data are duplicated and the assumption of independence is violated. Using pharmacogenomics biomarker discovery, we find that treating dependent studies as independent can substantially increase the bias of meta-analyses. Importantly, we show that violating the independence assumption decreases the generalizability of the biomarker discovery process and increases false positive results, a key challenge in precision oncology. Frontiers Media S.A. 2023-01-04 /pmc/articles/PMC9885264/ /pubmed/36726714 http://dx.doi.org/10.3389/fgene.2022.1027345 Text en Copyright © 2023 Abbas-Aghababazadeh, Xu and Haibe-Kains. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Abbas-Aghababazadeh, Farnoosh
Xu, Wei
Haibe-Kains, Benjamin
The impact of violating the independence assumption in meta-analysis on biomarker discovery
title The impact of violating the independence assumption in meta-analysis on biomarker discovery
title_full The impact of violating the independence assumption in meta-analysis on biomarker discovery
title_fullStr The impact of violating the independence assumption in meta-analysis on biomarker discovery
title_full_unstemmed The impact of violating the independence assumption in meta-analysis on biomarker discovery
title_short The impact of violating the independence assumption in meta-analysis on biomarker discovery
title_sort impact of violating the independence assumption in meta-analysis on biomarker discovery
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9885264/
https://www.ncbi.nlm.nih.gov/pubmed/36726714
http://dx.doi.org/10.3389/fgene.2022.1027345
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