Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

TL;DR

This paper introduces multivariate mixed survival models for genetic analysis of longevity traits, combining continuous and discrete time approaches with complex covariance structures for improved quantitative genetic studies.

Contribution

It presents a novel framework that integrates continuous and discrete time survival models with genetic variance decomposition, suitable for large, complex datasets in quantitative genetics.

Findings

Models enable variance decomposition into genetic and environmental components.

Framework allows joint analysis of continuous and discrete time survival data.

Methods are scalable for large genetic datasets.

Abstract

A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant. The discrete time models used are multivariate variants of the discrete relative risk models. These models allow for regular parametric likelihood-based inference by exploring a coincidence of their likelihood functions and the likelihood functions of suitably defined multivariate generalized linear mixed models. The models include a dispersion parameter, which is essential for obtaining a decomposition of the variance of the trait of interest as a sum of parcels representing the additive genetic effects, environmental effects and unspecified sources of variability; as required in quantitative genetic applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed.