Multidimensional Stochastic Process Model and its Applications to Analysis of Longitudinal Data with Genetic Information

TL;DR

This paper introduces a multi-dimensional genetic stochastic process model and an R package for analyzing longitudinal biodemographic data, integrating genetic and physiological variables to better understand aging processes.

Contribution

The paper presents a novel multi-dimensional genetic stochastic process model and a dedicated R package for analyzing complex longitudinal data with genetic information.

Findings

Implementation of GenSPM software tool in R

Enhanced analysis of aging-related changes

Integration of genetic and physiological data

Abstract

Stochastic Process Model has many applications in analysis of longitudinal biodemographic data. Such data contain various physiological variables (sometimes known as covariates). It also can potentially contain genetic information available for all or a part of participants. Taking advantage from both genetic and non-genetic information can provide future insights into a broad range of processes describing aging-related changes in the organism. In this paper, we implemented a multi-dimensional Genetic Stochastic Process Model (GenSPM) in newly developed software tool, R-package stpm, which allows researchers performing such kind of analysis.