This is the PhD project at KCL about applying functional mixed-effect models in quantitative genetics.
In evolutionary biology, function-valued traits, such as growth trajectories, are phenotypes of living organisms whose value can be described by a function of some continuous index. These traits can be assessed for continuous genetic variation using a quantitative genetic approach where one of the primary aims is to decompose the genetic and environmental variations. While existing literature has explored mixed effects models for such traits, this research uniquely focuses on using modern functional data analysis methodologies in quantitative genetics. The primary objective is to extend functional mixed-effect models for quantitative genetics experiments. A key secondary objective is to conduct an in-depth theoretical analysis, specifically addressing time and phase variation within these models. This focuses on improving our understanding of how traits change over time, while also accounting for variations that may arise from genetic factors or environmental influences.