title: Function-valued traits in evolution.
abstract: Many biological characteristics of evolutionary interest are not scalar variables but continuous functions. Given a dataset of function-valued traits generated by evolution, we develop a practical, statistical approach to infer ancestral function-valued traits, and estimate the generative evolutionary process. We do this by combining dimension reduction and phylogenetic Gaussian process regression, a non-parametric procedure that explicitly accounts for known phylogenetic relationships. We test the performance of methods on simulated, function-valued data generated from a stochastic evolutionary model. The methods are applied assuming that only the phylogeny, and the function-valued traits of taxa at its tips are known. Our method is robust and applicable to a wide range of function-valued data, and also offers a phylogenetically aware method for estimating the autocorrelation of function-valued traits.
authors: Department of Statistics, University of Warwick, Coventry CV4 7AL, UK. p.z.hadjipantelis@warwick.ac.uk
authors: Pantelis Z
authors: PZ
authors: Hadjipantelis
authors: Nick S
authors: NS
authors: Jones
authors: John
authors: J
authors: Moriarty
authors: David A
authors: DA
authors: Springate
authors: Christopher G
authors: CG
authors: Knight
mesh_terms: D000818:Animals; D019143:Evolution, Molecular; D006801:Humans; D008957:Models, Genetic; D016011:Normal Distribution; D010802:Phylogeny; D040641:Quantitative Trait Loci / Q000502:physiology*; D013269:Stochastic Processes