Statistical inference on the penetrances of rare genetic mutations based on a case.family design

We propose a formal statistical inference framework for the evaluation of the penetrance of a rare genetic mutation using family data generated under a kin.cohort type of design, where phenotype and genotype information from first-degree relatives (sibs and/or offspring) of case probands carrying the targeted mutation are collected.Our approach is built upon a likelihood model with some minor assumptions, and it can be used for age-dependent penetrance estimation that permits adjustment for covariates.Furthermore, the derived likelihood allows unobserved risk factors that are correlated within family members.The validity of the approach is confirmed by simulation studies.We apply the proposed approach to estimating the age-dependent cancer risk among carriers of the MSH2 or MLH1 mutation.