|Title||Semiparametric inference on the penetrances of rare genetic mutations based on a case-family design.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Zhang, Hong, Donglin Zeng, Sylviane Olschwang, and Kai Yu|
|Journal||J Stat Plan Inference|
|Date Published||2013 Feb|
A formal semiparametric statistical inference framework is proposed for the evaluation of the age-dependent penetrance of a rare genetic mutation, using family data generated under a case-family design, where phenotype and genotype information are collected from first-degree relatives of case probands carrying the targeted mutation. The proposed approach allows for unobserved risk factors that are correlated among family members. Some rigorous large sample properties are established, which show that the proposed estimators were asymptotically semi-parametric efficient. A simulation study is conducted to evaluate the performance of the new approach, which shows the robustness of the proposed semiparamteric approach and its advantage over the corresponding parametric approach. As an illustration, the proposed approach is applied to estimating the age-dependent cancer risk among carriers of the MSH2 or MLH1 mutation.
|Alternate Journal||J Stat Plan Inference|
|Original Publication||Semiparametric inference on the penetrances of rare genetic mutations based on a case-family design.|
|PubMed Central ID||PMC3544474|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA082659 / CA / NCI NIH HHS / United States
R37 GM047845 / GM / NIGMS NIH HHS / United States
ZIA CP010181-08 / / Intramural NIH HHS / United States
Semiparametric inference on the penetrances of rare genetic mutations based on a case-family design.