Unknown

Dataset Information

0

Genetic analysis of rare disorders: bayesian estimation of twin concordance rates.


ABSTRACT: Twin concordance rates provide insight into the possibility of a genetic background for a disease. These concordance rates are usually estimated within a frequentistic framework. Here we take a Bayesian approach. For rare diseases, estimation methods based on asymptotic theory cannot be applied due to very low cell probabilities. Moreover, a Bayesian approach allows a straightforward incorporation of prior information on disease prevalence coming from non-twin studies that is often available. An MCMC estimation procedure is tested using simulation and contrasted with frequentistic analyses. The Bayesian method is able to include prior information on both concordance rates and prevalence rates at the same time and is illustrated using twin data on cleft lip and rheumatoid arthritis.

SUBMITTER: van den Berg SM 

PROVIDER: S-EPMC3442174 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

altmetric image

Publications

Genetic analysis of rare disorders: bayesian estimation of twin concordance rates.

van den Berg Stéphanie M SM   Hjelmborg Jacob Vb JV  

Behavior genetics 20120619 5


Twin concordance rates provide insight into the possibility of a genetic background for a disease. These concordance rates are usually estimated within a frequentistic framework. Here we take a Bayesian approach. For rare diseases, estimation methods based on asymptotic theory cannot be applied due to very low cell probabilities. Moreover, a Bayesian approach allows a straightforward incorporation of prior information on disease prevalence coming from non-twin studies that is often available. An  ...[more]

Similar Datasets

| S-EPMC6656380 | biostudies-literature
| S-EPMC6520909 | biostudies-literature
2019-02-01 | PXD010691 | Pride
| S-EPMC5738153 | biostudies-literature
| S-EPMC3700233 | biostudies-literature
| S-EPMC4878778 | biostudies-literature
| S-EPMC3464018 | biostudies-literature
| S-EPMC7908701 | biostudies-literature
| S-EPMC7999138 | biostudies-literature
| S-EPMC5662129 | biostudies-literature