Liping Hou1, Kai Wang, Christopher W Bartlett. 1. Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital, Columbus, Ohio 43205, USA.
Abstract
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.
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.
Authors: Sobha Puppala; Dawn K Coletta; Jennifer Schneider; Shirley L Hu; Vidya S Farook; Thomas D Dyer; Rector Arya; John Blangero; Ravindranath Duggirala; Ralph A DeFronzo; Christopher P Jenkinson Journal: Hum Hered Date: 2011-02-05 Impact factor: 0.444
Authors: Veronica J Vieland; Joachim Hallmayer; Yungui Huang; Alistair T Pagnamenta; Dalila Pinto; Hameed Khan; Anthony P Monaco; Andrew D Paterson; Stephen W Scherer; James S Sutcliffe; Peter Szatmari Journal: J Neurodev Disord Date: 2011-01-19 Impact factor: 4.025
Authors: Christopher W Bartlett; Liping Hou; Judy F Flax; Abby Hare; Soo Yeon Cheong; Zena Fermano; Barbie Zimmerman-Bier; Charles Cartwright; Marco A Azaro; Steven Buyske; Linda M Brzustowicz Journal: Am J Psychiatry Date: 2014-01 Impact factor: 18.112
Authors: Samuel L Wolock; Andrew Yates; Stephen A Petrill; Jason W Bohland; Clancy Blair; Ning Li; Raghu Machiraju; Kun Huang; Christopher W Bartlett Journal: J Child Psychol Psychiatry Date: 2013-08-02 Impact factor: 8.982
Authors: Christopher W Bartlett; Brett G Klamer; Steven Buyske; Stephen A Petrill; William C Ray Journal: Genes (Basel) Date: 2019-09-19 Impact factor: 4.096