Literature DB >> 17293603

Genetic effects versus bias for candidate polymorphisms in myocardial infarction: case study and overview of large-scale evidence.

Evangelia E Ntzani1, Evangelos C Rizos, John P A Ioannidis.   

Abstract

Several genetic polymorphisms have been proposed to be associated with myocardial infarction (MI). The authors examined the evidence and biases underlying such associations using a case-study meta-analysis and an overview of large-scale data. In a meta-analysis of 27 studies addressing the association of the angiotensin type 1 receptor (AT1R)+1166A/C polymorphism with MI (10,180 cases, 17,129 controls), the *C allele conferred an increase in MI risk (odds ratio = 1.13 per allele, p = 0.005). However, there was large between-study heterogeneity; the largest study showed no effect, contradicting smaller studies; and studies with blinded genotyping showed no effect. The authors conducted an overview of meta-analyses of genetic associations for MI or coronary artery disease, including at least three studies and 3,000 subjects. In their latest meta-analysis, another 14 polymorphisms were found to have formally significant associations. If true, these associations would already explain 42% of the MI risk for Caucasian populations. Significant between-study heterogeneity was common. Across the 32 largest studies, only two found formally significant results (nine would be expected if each meta-analysis showed a true association). Even with large-scale evidence from meta-analyses, significant associations for MI may be subject to bias. Large-scale single studies and prospective consortia should be used for detecting and validating the genetic determinants of MI.

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Year:  2007        PMID: 17293603     DOI: 10.1093/aje/kwk085

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  20 in total

1.  Replication of past candidate loci for common diseases and phenotypes in 100 genome-wide association studies.

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Review 2.  Biomarker tests for risk assessment in coronary artery disease: will they change clinical practice?

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Journal:  Mol Diagn Ther       Date:  2014-02       Impact factor: 4.074

3.  Evaluation of the potential excess of statistically significant findings in published genetic association studies: application to Alzheimer's disease.

Authors:  Fotini K Kavvoura; Matthew B McQueen; Muin J Khoury; Rudolph E Tanzi; Lars Bertram; John P A Ioannidis
Journal:  Am J Epidemiol       Date:  2008-09-08       Impact factor: 4.897

Review 4.  Genetic testing and common disorders in a public health framework: how to assess relevance and possibilities. Background Document to the ESHG recommendations on genetic testing and common disorders.

Authors:  Frauke Becker; Carla G van El; Dolores Ibarreta; Eleni Zika; Stuart Hogarth; Pascal Borry; Anne Cambon-Thomsen; Jean Jacques Cassiman; Gerry Evers-Kiebooms; Shirley Hodgson; A Cécile J W Janssens; Helena Kaariainen; Michael Krawczak; Ulf Kristoffersson; Jan Lubinski; Christine Patch; Victor B Penchaszadeh; Andrew Read; Wolf Rogowski; Jorge Sequeiros; Lisbeth Tranebjaerg; Irene M van Langen; Helen Wallace; Ron Zimmern; Jörg Schmidtke; Martina C Cornel
Journal:  Eur J Hum Genet       Date:  2011-04       Impact factor: 4.246

5.  Circulating micro ribonucleic acids in cardiovascular disease: a look beyond myocardial injury.

Authors:  Johannes Mair
Journal:  Ann Transl Med       Date:  2016-10

6.  A genetic risk score is associated with incident cardiovascular disease and coronary artery calcium: the Framingham Heart Study.

Authors:  George Thanassoulis; Gina M Peloso; Michael J Pencina; Udo Hoffmann; Caroline S Fox; L Adrienne Cupples; Daniel Levy; Ralph B D'Agostino; Shih-Jen Hwang; Christopher J O'Donnell
Journal:  Circ Cardiovasc Genet       Date:  2012-01-10

Review 7.  Systems biology and functional genomics approaches for the identification of cellular responses to drug toxicity.

Authors:  Alison Hege Harrill; Ivan Rusyn
Journal:  Expert Opin Drug Metab Toxicol       Date:  2008-11       Impact factor: 4.481

8.  Genetic variation in angiotensin-converting enzyme-related pathways associated with sudden cardiac arrest risk.

Authors:  Nona Sotoodehnia; Guo Li; Catherine O Johnson; Rozenn N Lemaitre; Kenneth M Rice; Thomas D Rea; David S Siscovick
Journal:  Heart Rhythm       Date:  2009-06-09       Impact factor: 6.343

Review 9.  Methods for meta-analysis in genetic association studies: a review of their potential and pitfalls.

Authors:  Fotini K Kavvoura; John P A Ioannidis
Journal:  Hum Genet       Date:  2007-11-17       Impact factor: 4.132

10.  Causal relationship of susceptibility genes to ischemic stroke: comparison to ischemic heart disease and biochemical determinants.

Authors:  Paul Bentley; George Peck; Liam Smeeth; John Whittaker; Pankaj Sharma
Journal:  PLoS One       Date:  2010-02-09       Impact factor: 3.240

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