A joint model for nonparametric functional mapping of longitudinal trajectory and time-to-event

2006

Joint Model for Mapping Longitudinal Traits and Time-to-Event Data

Sample size: 90 publication Evidence: moderate

Author Information

Author(s): Lin Min, Wu Rongling

Primary Institution: Department of Statistics, University of Florida; Department of Biostatistics and Bioinformatics, Duke University

Can a joint statistical model effectively map the relationship between longitudinal traits and time-to-event data?

The model allows for the detection of specific QTL that govern both longitudinal traits and developmental processes.

The model was validated using data from a forest tree study.
Significant correlations were found between the age to first flower and volume growth.
Three significant QTL were detected, indicating the model's effectiveness.

This study created a new way to look at how certain genes affect both growth over time and when plants start to flower.

The study used Legendre polynomials to model growth trajectories and a maximum likelihood approach to analyze the data.

The model was reduced to a one-QTL model due to limited sample size for sufficient estimates of two-QTL model parameters.

The study involved a hybrid population of Populus trees.

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