BACKGROUND/AIMS: Population-based studies have successfully identified genes affecting common diseases, but have not provided a molecular mechanism. We describe an approach for alcohol dependence connecting a mechanistic model at the molecular level with disease risk at the population level, and investigate how this model implies statistical gene-gene interactions that affect disease risk. METHODS: We develop a pharmacokinetic model describing how genetic variations in ADH1B, ADH1C, ADH7, ALDH2, and TAS2R38 affect consumption behavior, and alcohol and acetaldehyde levels over time in various tissues of individuals with a particular genotype to predict their susceptibility to alcohol dependence. RESULTS: We show that there is good agreement between the observed genotype/haplotype frequencies and those predicted by the model among cases and controls. Based on this framework, we show that we expect to observe statistical interactions among these genes for a reasonably large sample size when logistic regression models are used to relate genotype effects and disease risk. CONCLUSION: Our model exemplifies mechanistic modeling of how genes interact to influence an individual's susceptibility to alcohol dependence. We anticipate that this general approach could also be applied to study other diseases at the molecular level.
BACKGROUND/AIMS: Population-based studies have successfully identified genes affecting common diseases, but have not provided a molecular mechanism. We describe an approach for alcohol dependence connecting a mechanistic model at the molecular level with disease risk at the population level, and investigate how this model implies statistical gene-gene interactions that affect disease risk. METHODS: We develop a pharmacokinetic model describing how genetic variations in ADH1B, ADH1C, ADH7, ALDH2, and TAS2R38 affect consumption behavior, and alcohol and acetaldehyde levels over time in various tissues of individuals with a particular genotype to predict their susceptibility to alcohol dependence. RESULTS: We show that there is good agreement between the observed genotype/haplotype frequencies and those predicted by the model among cases and controls. Based on this framework, we show that we expect to observe statistical interactions among these genes for a reasonably large sample size when logistic regression models are used to relate genotype effects and disease risk. CONCLUSION: Our model exemplifies mechanistic modeling of how genes interact to influence an individual's susceptibility to alcohol dependence. We anticipate that this general approach could also be applied to study other diseases at the molecular level.
Authors: M Osier; A J Pakstis; J R Kidd; J F Lee; S J Yin; H C Ko; H J Edenberg; R B Lu; K K Kidd Journal: Am J Hum Genet Date: 1999-04 Impact factor: 11.025
Authors: H R Thomasson; H J Edenberg; D W Crabb; X L Mai; R E Jerome; T K Li; S P Wang; Y T Lin; R B Lu; S J Yin Journal: Am J Hum Genet Date: 1991-04 Impact factor: 11.025
Authors: Y Wang; C Tong; Z Wang; Z Wang; D Mauger; K G Tantisira; E Israel; S J Szefler; V M Chinchilli; H A Boushey; S C Lazarus; R F Lemanske; R Wu Journal: Pharmacogenomics J Date: 2015-01-20 Impact factor: 3.550