Efficient Maximum Likelihood Estimation for Pedigree Data with the Sum-Product Algorithm.
Hum Hered
; 82(1-2): 1-15, 2016.
Article
em En
| MEDLINE
| ID: mdl-28728147
ABSTRACT
OBJECTIVE:
We analyze data sets consisting of pedigrees with age at onset of colorectal cancer (CRC) as phenotype. The occurrence of familial clusters of CRC suggests the existence of a latent, inheritable risk factor. We aimed to compute the probability of a family possessing this risk factor as well as the hazard rate increase for these risk factor carriers. Due to the inheritability of this risk factor, the estimation necessitates a costly marginalization of the likelihood.METHODS:
We propose an improved EM algorithm by applying factor graphs and the sum-product algorithm in the E-step. This reduces the computational complexity from exponential to linear in the number of family members.RESULTS:
Our algorithm is as precise as a direct likelihood maximization in a simulation study and a real family study on CRC risk. For 250 simulated families of size 19 and 21, the runtime of our algorithm is faster by a factor of 4 and 29, respectively. On the largest family (23 members) in the real data, our algorithm is 6 times faster.CONCLUSION:
We introduce a flexible and runtime-efficient tool for statistical inference in biomedical event data with latent variables that opens the door for advanced analyses of pedigree data.
Texto completo:
1
Bases de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Hum Hered
Ano de publicação:
2016
Tipo de documento:
Article
País de afiliação:
Alemanha