Literature DB >> 17403027

The use of meta-analysis risk estimates for candidate genes in combination to predict coronary heart disease risk.

F Drenos1, J C Whittaker, S E Humphries.   

Abstract

Although the risk for coronary heart disease (CHD) associated with single SNPs is modest it has been suggested that, in combination, several common risk-associated alleles could lead to a substantially better heart disease risk prediction. We have modelled this using 10 SNPs in ten candidate genes (APOB, NOS3, APOE, ACE, SERPINE1, MTHFR, ITGA2B, PON 1, LPL, and CETP) and their predicted summary risk estimates from meta-analysis. Based on published allele frequencies, approximately 29% of the general population would be expected to carry less than three risk alleles, approximately 55% would carry 3 or 4 risk alleles, 4% would have 6 and 1% 7 or more risk alleles. Compared to the mean of those with 3 or 4 risk associated genotypes, those with 6 and 7-or-more alleles have a significantly higher risk odds ratio (OR) of CHD (mean OR (95% Confidence Intervals), 1.70 (1.14 to 2.55); and 4.51 (2.89 to 7.04) respectively), while compared to those in the lowest decile of risk, those in the highest decile have a CHD odds ratio in the range of 3.05 (2.24 to 4.14). Taking into account age and the risk alleles carried, the mean 10 year probability for developing CHD for a 55 year old man was calculated to be 15% (8.6% to 24.8%), with nearly 1 in 5 having more than 20% risk. Whether this particular group of 10 SNPs will improve the accuracy of CHD predictions over the combination of classical risk factors in clinical use requires further experimental evidence.

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Year:  2007        PMID: 17403027     DOI: 10.1111/j.1469-1809.2007.00359.x

Source DB:  PubMed          Journal:  Ann Hum Genet        ISSN: 0003-4800            Impact factor:   1.670


  10 in total

1.  Evaluation of genetic risk scores for prediction of dichotomous outcomes.

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2.  'Over-the-counter' genetic testing: what does it really mean for primary care?

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Journal:  Br J Gen Pract       Date:  2009-04       Impact factor: 5.386

Review 3.  Genetic testing for atherosclerosis risk: inevitability or pipe dream?

Authors:  Matthew Lanktree; Jisun Oh; Robert A Hegele
Journal:  Can J Cardiol       Date:  2008-11       Impact factor: 5.223

4.  Evaluation of genetic tests for susceptibility to common complex diseases: why, when and how?

Authors:  Caroline Fiona Wright; Mark Kroese
Journal:  Hum Genet       Date:  2009-11-20       Impact factor: 4.132

5.  A composite scoring of genotypes discriminates coronary heart disease risk beyond conventional risk factors in the Boston Puerto Rican Health Study.

Authors:  M Junyent; K L Tucker; J Shen; Y-C Lee; C E Smith; J Mattei; C-Q Lai; L D Parnell; J M Ordovas
Journal:  Nutr Metab Cardiovasc Dis       Date:  2009-06-05       Impact factor: 4.222

6.  Similarity of polygenic profiles limits the potential for elite human physical performance.

Authors:  Alun G Williams; Jonathan P Folland
Journal:  J Physiol       Date:  2007-09-27       Impact factor: 5.182

Review 7.  Genetics of coronary artery disease and myocardial infarction.

Authors:  Xuming Dai; Szymon Wiernek; James P Evans; Marschall S Runge
Journal:  World J Cardiol       Date:  2016-01-26

8.  Gene Polymorphism and Total Genetic Score in Martial Arts Athletes with Different Athletic Qualifications.

Authors:  Anna Vostrikova; Victoria Pechenkina; Maria Danilova; Svetlana Boronnikova; Ruslan Kalendar
Journal:  Genes (Basel)       Date:  2022-09-19       Impact factor: 4.141

9.  Gene polymorphisms in association with emerging cardiovascular risk markers in adult women.

Authors:  Amy Z Fan; Ajay Yesupriya; Man-huei Chang; Meaghan House; Jing Fang; Renée Ned; Donald Hayes; Nicole F Dowling; Ali H Mokdad
Journal:  BMC Med Genet       Date:  2010-01-15       Impact factor: 2.103

10.  A Meta-Prediction of Methylenetetrahydrofolate-Reductase Polymorphisms and Air Pollution Increased the Risk of Ischemic Heart Diseases Worldwide.

Authors:  Zhao-Feng Chen; Lufei Young; Chong Ho Yu; S Pamela K Shiao
Journal:  Int J Environ Res Public Health       Date:  2018-07-10       Impact factor: 3.390

  10 in total

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