BACKGROUND: Advances in high-throughput genomics facilitate the identification of novel genetic susceptibility variants for coronary heart disease (CHD). This may improve CHD risk prediction. The aim of the present simulation study was to investigate to what degree CHD risk can be predicted by testing multiple genetic variants (genetic profiling). METHODS: We simulated genetic profiles for a population of 100,000 individuals with a 10-year CHD incidence of 10%. For each combination of model parameters (number of variants, genotype frequency and odds ratio [OR]), we calculated the area under the receiver operating characteristic curve (AUC) to indicate the discrimination between individuals who will and will not develop CHD. RESULTS: The AUC of genetic profiles could rise to 0.90 when 100 hypothetical variants with ORs of 1.5 and genotype frequencies of 50% were simulated. The AUC of a genetic profile consisting of 10 established variants, with ORs ranging from 1.13 to 1.42, was 0.59. When 2, 5, and 10 times as many identical variants would be identified, the AUCs were 0.63, 0.69, and 0.76. CONCLUSION: To obtain AUCs similar to those of conventional CHD risk predictors, a considerable number of additional common genetic variants need to be identified with preferably strong effects.
BACKGROUND: Advances in high-throughput genomics facilitate the identification of novel genetic susceptibility variants for coronary heart disease (CHD). This may improve CHD risk prediction. The aim of the present simulation study was to investigate to what degree CHD risk can be predicted by testing multiple genetic variants (genetic profiling). METHODS: We simulated genetic profiles for a population of 100,000 individuals with a 10-year CHD incidence of 10%. For each combination of model parameters (number of variants, genotype frequency and odds ratio [OR]), we calculated the area under the receiver operating characteristic curve (AUC) to indicate the discrimination between individuals who will and will not develop CHD. RESULTS: The AUC of genetic profiles could rise to 0.90 when 100 hypothetical variants with ORs of 1.5 and genotype frequencies of 50% were simulated. The AUC of a genetic profile consisting of 10 established variants, with ORs ranging from 1.13 to 1.42, was 0.59. When 2, 5, and 10 times as many identical variants would be identified, the AUCs were 0.63, 0.69, and 0.76. CONCLUSION: To obtain AUCs similar to those of conventional CHD risk predictors, a considerable number of additional common genetic variants need to be identified with preferably strong effects.
Authors: Thomas G Schulze; Nirmala Akula; René Breuer; Jo Steele; Michael A Nalls; Andrew B Singleton; Franziska A Degenhardt; Markus M Nöthen; Sven Cichon; Marcella Rietschel; Francis J McMahon Journal: World J Biol Psychiatry Date: 2012-03-09 Impact factor: 4.132
Authors: Wolf H Rogowski; Scott D Grosse; Jürgen John; Helena Kääriäinen; Alastair Kent; Ulf Kristofferson; Jörg Schmidtke Journal: J Community Genet Date: 2010-10-16
Authors: Sebastian Okser; Terho Lehtimäki; Laura L Elo; Nina Mononen; Nina Peltonen; Mika Kähönen; Markus Juonala; Yue-Mei Fan; Jussi A Hernesniemi; Tomi Laitinen; Leo-Pekka Lyytikäinen; Riikka Rontu; Carita Eklund; Nina Hutri-Kähönen; Leena Taittonen; Mikko Hurme; Jorma S A Viikari; Olli T Raitakari; Tero Aittokallio Journal: PLoS Genet Date: 2010-09-30 Impact factor: 5.917
Authors: Zhi Wei; Kai Wang; Hui-Qi Qu; Haitao Zhang; Jonathan Bradfield; Cecilia Kim; Edward Frackleton; Cuiping Hou; Joseph T Glessner; Rosetta Chiavacci; Charles Stanley; Dimitri Monos; Struan F A Grant; Constantin Polychronakos; Hakon Hakonarson Journal: PLoS Genet Date: 2009-10-09 Impact factor: 5.917