RESUMO
BACKGROUND: Genetic heterogeneity and complex biologic mechanisms of blood pressure regulation pose significant challenges to the identification of susceptibility loci influencing hypertension. Previous linkage studies have reported regions of interest, but lack consistency across studies. Incorporation of covariates, in particular the interaction between two independent risk factors (gender and BMI) greatly improved our ability to detect linkage. RESULTS: We report a highly significant signal for linkage to chromosome 2p, a region that has been implicated in previous linkage studies, along with several suggestive linkage regions. CONCLUSION: We demonstrate the importance of including covariates in the linkage analysis when the phenotype is complex.
Assuntos
Índice de Massa Corporal , Ligação Genética/genética , Hipertensão/epidemiologia , Mapeamento Cromossômico/estatística & dados numéricos , Cromossomos Humanos Par 2/genética , Estudos de Coortes , Feminino , Predisposição Genética para Doença/epidemiologia , Predisposição Genética para Doença/genética , Humanos , Hipertensão/genética , Masculino , Fenótipo , Locos de Características Quantitativas/genética , Característica Quantitativa Herdável , Fatores Sexuais , Software/estatística & dados numéricosRESUMO
Three multivariate techniques used to derive principal components (PCs) from family data were compared for their ability to model family data and power to detect linkage. Using the simulated data from Genetic Analysis Workshop 12, the five quantitative traits were first adjusted for age, sex, and environmental factors 1 and 2. Then, standard PCs, PCs obtained from between-family covariance, and PCs obtained from within-family genetic covariance were derived and subjected to multivariate sib pair linkage analysis. The standard PCs obtained from the overall correlation matrix allowed identification of key features of the true genetic model more readily than did the other methods. For detection of linkage, standard PCs and PCs obtained from the between-family genetic covariance performed similarly in terms of both power and type 1 error, and both methods performed better than the PCs obtained from within-family genetic covariance.
