Objective: Non-random missing data can adversely affect family-based linkage detection through loss of power and possible introduction of bias depending on how censoring is modeled. We examined the statistical properties of a previously proposed quantitative trait threshold (QTT) model developed for when censored data can be reasonably inferred to be beyond an unknown threshold. Methods: The QTT model is a Bayesian model integration approach implemented in the PPL framework that requires neither specification of the threshold nor imputation of the missing data. This model was evaluated under a range of simulated data sets and compared to other methods with missing data imputed. Results: Across the simulated conditions, the addition of a threshold parameter did not change the PPL’s properties relative to quantitative trait analysis on non-censored data except for a slight reduction in the average PPL as a reflection of the lowered information content due to censoring. This remained the case for non-normally distributed data and extreme sampling of pedigrees. Conclusions: Overall, the QTT model showed the smallest loss of linkage information relative to alternative approaches and therefore provides a unique analysis tool that obviates the need for ad hoc imputation of censored data in gene mapping studies.
© 2012 S. Karger AG, Basel
- Quantitative trait threshold model
- Censored data
- PPL framework
- Bayesian model integration
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Assist. Prof. Christopher W. Bartlett, PhD
Battelle Center for Mathematical Medicine, The Research Institute
Nationwide Children’s Hospital and The Ohio State University
JW3926, 700 Children’s Drive, Columbus, OH 43205 (USA)
Received: September 1, 2011
Accepted after revision: August 15, 2012
Published online: September 26, 2012
Number of Print Pages : 11
Number of Figures : 5, Number of Tables : 1, Number of References : 33
Human Heredity (International Journal of Human and Medical Genetics)
Vol. 74, No. 1, Year 2012 (Cover Date: November 2012)
Journal Editor: Devoto M. (Philadelphia, Pa./Rome)
ISSN: 0001-5652 (Print), eISSN: 1423-0062 (Online)
For additional information: http://www.karger.com/HHE
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