Abstract
The most powerful and comprehensive approach of study in modern biology is to understand the whole process of development and all events of importance to development which occur in the process. As a consequence, joint modeling of developmental processes and events has become one of the most demanding tasks in statistical research. Here, we propose a joint modeling framework for functional mapping of specific quantitative trait loci (QTLs) which controls developmental processes and the timing of development and their causal correlation over time. The joint model contains two submodels, one for a developmental process, known as a longitudinal trait, and the other for a developmental event, known as the time to event, which are connected through a QTL mapping framework. A nonparametric approach is used to model the mean and covariance function of the longitudinal trait while the traditional Cox proportional hazard (PH) model is used to model the event time. The joint model is applied to map QTLs that control whole-plant vegetative biomass growth and time to first flower in soybeans. Results show that this model should be broadly useful for detecting genes controlling physiological and pathological processes and other events of interest in biomedicine.
Highlights
To study biology, a classic approach is dimension reduction in which a biological phenomenon or process is dissected into several discrete features over time and space
Functional mapping has proven to be powerful for elucidating the dynamic genetic architecture of complex phenotypic traits by identifying when specific genes involved turn on and turn off and how long they are expressed in a time course
Taking advantage of event models, such as semiparametric Cox proportional hazard model, Weibull model, accelerated failure time (AFT) model, we here propose a sophisticated model for joint modeling of longitudinal trait and time to event to locate the QTLs which control the event via a dynamic trait
Summary
A classic approach is dimension reduction in which a biological phenomenon or process is dissected into several discrete features over time and space. By estimating the correlation between longitudinal traits and event time, Lin and Wu [19] developed a first model that connects these two aspects within functional mapping. Taking advantage of event models, such as semiparametric Cox proportional hazard model, Weibull model, accelerated failure time (AFT) model, we here propose a sophisticated model for joint modeling of longitudinal trait and time to event to locate the QTLs which control the event via a dynamic trait The detection of those QTLs that are common to these types of traits may help to prevent or accelerate the outcome by genetic approaches. The statistical properties of the model applied to estimate QTL temporal effects in this example and its practical usefulness are investigated by simulation studies
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