Literature DB >> 33640484

Different biases in meta-analyses of case-control and cohort studies: an example from genomics and precision medicine.

Sarah A Palumbo1, Janet D Robishaw2, Joanne Krasnoff2, Charles H Hennekens2.   

Abstract

PURPOSE: Meta-analyses of observational studies reduce the role of chance but also introduce bias because the individual component studies are not randomized. Further, it is plausible that the bias may be different in case-control and cohort studies. We explored these issues in meta-analyses of observational studies of Opioid Use Disorder (OUD).
METHODS: From a systematic literature review of 152 published meta-analyses, 11 fulfilled the initial inclusion criteria of observational studies of OUD. Of these, 9 were meta-analyses of case-control studies and 2 were meta-analyses of cohort studies but only 4 (3 case-control and 1 cohort) targeted more than one specific chromosomal location.
RESULTS: The meta-analyses of the 3 case-control studies, which included 13 individual studies, identified 12 different single nucleotide polymorphisms on 6 different genes on 5 different chromosomes. None was the same as the gene on Chromosome 15 identified from the meta-analysis of the cohort studies.
CONCLUSIONS: These data, from genetic studies, suggest biases are different in meta-analyses of case-control and cohort studies, perhaps due to greater selection bias in case-control studies. These observations have potential importance in the application of meta-analyses to many common and serious diseases, as well as genomics and precision medicine, including OUD.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Bias; Genomics; Meta-analysis; Precision medicine

Mesh:

Year:  2021        PMID: 33640484      PMCID: PMC8165016          DOI: 10.1016/j.annepidem.2021.02.013

Source DB:  PubMed          Journal:  Ann Epidemiol        ISSN: 1047-2797            Impact factor:   6.996


  15 in total

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Authors:  Charles H Hennekens; David Demets
Journal:  JAMA       Date:  2009-12-02       Impact factor: 56.272

2.  Statistical association and causation: contributions of different types of evidence.

Authors:  Charles H Hennekens; David DeMets
Journal:  JAMA       Date:  2011-03-16       Impact factor: 56.272

3.  Discrepancies between meta-analyses and subsequent large randomized, controlled trials.

Authors:  J LeLorier; G Grégoire; A Benhaddad; J Lapierre; F Derderian
Journal:  N Engl J Med       Date:  1997-08-21       Impact factor: 91.245

4.  Association of the OPRM1 Variant rs1799971 (A118G) with Non-Specific Liability to Substance Dependence in a Collaborative de novo Meta-Analysis of European-Ancestry Cohorts.

Authors:  Tae-Hwi Schwantes-An; Juan Zhang; Li-Shiun Chen; Sarah M Hartz; Robert C Culverhouse; Xiangning Chen; Hilary Coon; Josef Frank; Helen M Kamens; Bettina Konte; Leena Kovanen; Antti Latvala; Lisa N Legrand; Brion S Maher; Whitney E Melroy; Elliot C Nelson; Mark W Reid; Jason D Robinson; Pei-Hong Shen; Bao-Zhu Yang; Judy A Andrews; Paul Aveyard; Olga Beltcheva; Sandra A Brown; Dale S Cannon; Sven Cichon; Robin P Corley; Norbert Dahmen; Louisa Degenhardt; Tatiana Foroud; Wolfgang Gaebel; Ina Giegling; Stephen J Glatt; Richard A Grucza; Jill Hardin; Annette M Hartmann; Andrew C Heath; Stefan Herms; Colin A Hodgkinson; Per Hoffmann; Hyman Hops; David Huizinga; Marcus Ising; Eric O Johnson; Elaine Johnstone; Radka P Kaneva; Kenneth S Kendler; Falk Kiefer; Henry R Kranzler; Ken S Krauter; Orna Levran; Susanne Lucae; Michael T Lynskey; Wolfgang Maier; Karl Mann; Nicholas G Martin; Manuel Mattheisen; Grant W Montgomery; Bertram Müller-Myhsok; Michael F Murphy; Michael C Neale; Momchil A Nikolov; Denise Nishita; Markus M Nöthen; John Nurnberger; Timo Partonen; Michele L Pergadia; Maureen Reynolds; Monika Ridinger; Richard J Rose; Noora Rouvinen-Lagerström; Norbert Scherbaum; Christine Schmäl; Michael Soyka; Michael C Stallings; Michael Steffens; Jens Treutlein; Ming Tsuang; Tamara L Wall; Norbert Wodarz; Vadim Yuferov; Peter Zill; Andrew W Bergen; Jingchun Chen; Paul M Cinciripini; Howard J Edenberg; Marissa A Ehringer; Robert E Ferrell; Joel Gelernter; David Goldman; John K Hewitt; Christian J Hopfer; William G Iacono; Jaakko Kaprio; Mary Jeanne Kreek; Ivo M Kremensky; Pamela A F Madden; Matt McGue; Marcus R Munafò; Robert A Philibert; Marcella Rietschel; Alec Roy; Dan Rujescu; Sirkku T Saarikoski; Gary E Swan; Alexandre A Todorov; Michael M Vanyukov; Robert B Weiss; Laura J Bierut; Nancy L Saccone
Journal:  Behav Genet       Date:  2015-09-21       Impact factor: 2.805

5.  Cis-Expression Quantitative Trait Loci Mapping Reveals Replicable Associations with Heroin Addiction in OPRM1.

Authors:  Dana B Hancock; Joshua L Levy; Nathan C Gaddis; Cristie Glasheen; Nancy L Saccone; Grier P Page; Gary K Hulse; Dieter Wildenauer; Erin A Kelty; Sibylle G Schwab; Louisa Degenhardt; Nicholas G Martin; Grant W Montgomery; John Attia; Elizabeth G Holliday; Mark McEvoy; Rodney J Scott; Laura J Bierut; Elliot C Nelson; Alex H Kral; Eric O Johnson
Journal:  Biol Psychiatry       Date:  2015-01-29       Impact factor: 13.382

6.  Genome-wide meta-analyses identify multiple loci associated with smoking behavior.

Authors: 
Journal:  Nat Genet       Date:  2010-04-25       Impact factor: 38.330

7.  Endothelial nitric oxide synthase genotype and ischemic heart disease: meta-analysis of 26 studies involving 23028 subjects.

Authors:  Juan P Casas; Leonelo E Bautista; Steve E Humphries; Aroon D Hingorani
Journal:  Circulation       Date:  2004-03-08       Impact factor: 29.690

Review 8.  OPRM1 A118G gene variant and postoperative opioid requirement: a systematic review and meta-analysis.

Authors:  In Cheol Hwang; Ji-Young Park; Seung-Kwon Myung; Hong Yup Ahn; Ken-ichi Fukuda; Qin Liao
Journal:  Anesthesiology       Date:  2014-10       Impact factor: 7.892

9.  Genome-wide Association Study Identifies a Regulatory Variant of RGMA Associated With Opioid Dependence in European Americans.

Authors:  Zhongshan Cheng; Hang Zhou; Richard Sherva; Lindsay A Farrer; Henry R Kranzler; Joel Gelernter
Journal:  Biol Psychiatry       Date:  2018-01-11       Impact factor: 13.382

10.  Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes.

Authors:  Angli Xue; Yang Wu; Zhihong Zhu; Futao Zhang; Kathryn E Kemper; Zhili Zheng; Loic Yengo; Luke R Lloyd-Jones; Julia Sidorenko; Yeda Wu; Allan F McRae; Peter M Visscher; Jian Zeng; Jian Yang
Journal:  Nat Commun       Date:  2018-07-27       Impact factor: 14.919

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  1 in total

1.  What Is the Mechanism of Government Green Development Behavior Considering Multi-Agent Interaction? A Meta-Analysis.

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Journal:  Int J Environ Res Public Health       Date:  2022-07-06       Impact factor: 4.614

  1 in total

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