KinCohort

KinCohort implements likelihood-based analysis of kin-cohort data to estimate penetrance of rare autosomal mutations.

Key Features:

• MATLAB implementation: Provided as a MATLAB software package for likelihood-based analysis of kin-cohort data.
• Marginal likelihood approach: KinCohort employs a marginal likelihood method that is flexible and computationally straightforward, offering greater flexibility than Wacholder et al. (1998) and greater robustness than the likelihood approach of Gail et al. (1999) in the presence of residual familial correlation.
• Robustness and efficiency: The approach balances robustness and computational efficiency, with performance characterized through simulation experiments.

Scientific Applications:

• Penetrance estimation: Estimation of penetrance for identified rare autosomal mutations using genotype data and detailed family history from selected participants.
• Genetic epidemiology studies: Analysis of familial transmission and disease risk in studies that leverage kin-cohort designs.
• Empirical demonstration: The methodology has been applied to data from the Washington Ashkenazi Study.

Methodology:

Uses a kin-cohort design with selection of participants providing genotype and family-history data and applies a marginal likelihood method; robustness and efficiency were evaluated by simulation experiments.