Complex segregation analysis of familial diseases with variable age of onset: comparison of different methods by a simulation study.
Standard methods of segregation analysis, such as originally developed in the unified model (UM) or the regressive logistic model (RLM) do not account for age of onset, and use instead age at examination. To take into account age of onset, models should be formulated using survival analysis concepts, as it was recently proposed with a model based on a logistic hazard function (LHM). A simulation study was conducted to compare the performances of the three methods (UM, RLM, and LHM) in analyzing generated familial data with variable age of onset. When the data were simulated under a polygenic hypothesis, all analysis models were robust with respect to the false conclusion of a major gene, if the tests of transmission probabilities were performed properly. When the data were generated under a major gene hypothesis, two main results were observed: 1) the use of the LHM markedly increased the power to detect a major gene, in particular when a genotype by age interaction was introduced in the model; and 2) in the situation of disease-specific mortality, the use of either UM (whether specific mortality was accounted for or not) or RLM led to both spurious conclusions and bias in parameter estimates. These latter results obtained with the UM and the RLM can be explained by the violation of one constraint of both models observed in a situation of disease-specific mortality, i.e., given all covariates, the probability of being affected and that of not being affected should sum to 1. The use of methods based on survival analysis concepts is recommended in the study of familial diseases with variable age of onset, especially in the case of a correlation between age of onset and age at examination which is induced by disease-specific mortality.
Abel
L
L
INSERM U.194, Hôpital Pitié-Salpêtrière, Paris, France.
Garcia
A
A
Demenais
F
F
D000328:Adult; D017668:Age of Onset; D000368:Aged; D000369:Aged, 80 and over; D030342:Genetic Diseases, Inborn / Q000453:epidemiology* / Q000235:genetics; D006801:Humans; D008875:Middle Aged; D008957:Models, Genetic; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D012044:Regression Analysis; D012307:Risk Factors; D016019:Survival Analysis