A pattern-mixture model for haplotype analysis of longitudinal traits with non-ignorable dropout

Hongying Li, Rongling Wu

Research output: Contribution to journalReview article

2 Citations (Scopus)

Abstract

Current statistical methods allow the characterization of DNA sequence variants associated with interpersonal differences in a complex biological response. However, this process is significantly hindered when some subjects have to drop out early due to physiological side effects or limited duration. Here, we derive a pattern-mixture model for detecting functional nucleotide combinations (or haplotypes) responsible for longitudinal responses by making full use of information from those dropout data. The model was formulated within the maximum likelihood context, with the model parameters, haplotype frequencies, and haplotype effects estimated by implementing the EM and Newton-Raphson algorithms. One advantage of the model is to generate and address a number of clinically meaningful hypotheses about the genetic control mechanisms of longitudinal responses and time-to-event processes. By analyzing a pharmacogenomic data set, the model identified significant haplotype effects on heart rate increases in response to increasing doses of dobutamine. The statistical properties of the model and its usefulness and utilization were investigated through computer simulation. The new model can be used to unravel the genetic architecture of interpersonal variation in complex longitudinal responses with incomplete data and ultimately to materialize the idea of clinical genomics.

Original languageEnglish (US)
Pages (from-to)337-357
Number of pages21
JournalStatistica Sinica
Volume22
Issue number1
DOIs
StatePublished - Jan 1 2012

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Pattern-mixture Model
Haplotype
Drop out
Dobutamine
Model
Newton-Raphson Algorithm
Incomplete Data
Heart Rate
DNA Sequence
Statistical method
Statistical property
Maximum Likelihood
Genomics
Mixture model
Dose
Computer Simulation

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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abstract = "Current statistical methods allow the characterization of DNA sequence variants associated with interpersonal differences in a complex biological response. However, this process is significantly hindered when some subjects have to drop out early due to physiological side effects or limited duration. Here, we derive a pattern-mixture model for detecting functional nucleotide combinations (or haplotypes) responsible for longitudinal responses by making full use of information from those dropout data. The model was formulated within the maximum likelihood context, with the model parameters, haplotype frequencies, and haplotype effects estimated by implementing the EM and Newton-Raphson algorithms. One advantage of the model is to generate and address a number of clinically meaningful hypotheses about the genetic control mechanisms of longitudinal responses and time-to-event processes. By analyzing a pharmacogenomic data set, the model identified significant haplotype effects on heart rate increases in response to increasing doses of dobutamine. The statistical properties of the model and its usefulness and utilization were investigated through computer simulation. The new model can be used to unravel the genetic architecture of interpersonal variation in complex longitudinal responses with incomplete data and ultimately to materialize the idea of clinical genomics.",
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A pattern-mixture model for haplotype analysis of longitudinal traits with non-ignorable dropout. / Li, Hongying; Wu, Rongling.

In: Statistica Sinica, Vol. 22, No. 1, 01.01.2012, p. 337-357.

Research output: Contribution to journalReview article

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AU - Li, Hongying

AU - Wu, Rongling

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AB - Current statistical methods allow the characterization of DNA sequence variants associated with interpersonal differences in a complex biological response. However, this process is significantly hindered when some subjects have to drop out early due to physiological side effects or limited duration. Here, we derive a pattern-mixture model for detecting functional nucleotide combinations (or haplotypes) responsible for longitudinal responses by making full use of information from those dropout data. The model was formulated within the maximum likelihood context, with the model parameters, haplotype frequencies, and haplotype effects estimated by implementing the EM and Newton-Raphson algorithms. One advantage of the model is to generate and address a number of clinically meaningful hypotheses about the genetic control mechanisms of longitudinal responses and time-to-event processes. By analyzing a pharmacogenomic data set, the model identified significant haplotype effects on heart rate increases in response to increasing doses of dobutamine. The statistical properties of the model and its usefulness and utilization were investigated through computer simulation. The new model can be used to unravel the genetic architecture of interpersonal variation in complex longitudinal responses with incomplete data and ultimately to materialize the idea of clinical genomics.

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