Genetic random effects model for family data with long-term survivors: analysis of diabetic nephropathy in type 1 diabetes

✍ Scribed by Janne Pitkäniemi; Elena Moltchanova; Laura Haapala; Valma Harjutsalo; Jaakko Tuomilehto; Timo Hakulinen

• Publisher: John Wiley and Sons
• Year: 2007
• Tongue: English
• Weight: 197 KB
• Volume: 31
• Category: Article
• ISSN: 0741-0395
• DOI: 10.1002/gepi.20234

✦ Synopsis

Abstract

A shared and additive genetic variance component‐long‐term survivor (LTS) model for familial aggregation studies of complex diseases with variable age‐at‐onset phenotype and non‐susceptible subjects in the study cohort is proposed. LTS has been used from the early 1970s, especially in epidemiological studies of cancer. The LTS model utilizes information on the age at onset (survival) distribution to make inference on partially latent susceptibility. Bayesian modeling with uninformative priors is used and estimates of the posterior distribution of age at onset and susceptibility parameters of interest have been obtained using Bayesian Markov chain Monte Carlo (MCMC) methods with OpenBugs program. A simulation study confirms that we obtain posterior estimates of the model parameters on shared and genetic variance components of age at onset and susceptibility with good coverage rates. Further, we analyze familial aggregation of diabetic nephropathy (DN) in large Finnish cohort of 528 sibships with type 1 diabetes (T1D). According to the variance components estimated a substantial familial variation in the susceptibility to DN exist among families, while time to DN is less influenced by shared familial factors. Genet. Epidemiol. 2007. © 2007 Wiley‐Liss, Inc.