Inferring Phenotypic Trait Evolution on Large Trees With Many Incomplete Measurements

• 成果类型: Article
• 署名作者: Hassler, Gabriel; Tolkoff, Max R.; Allen, William L.; Ho, Lam Si Tung; Lemey, Philippe; Suchard, Marc A.
• 署名单位: University of California System; University of California Los Angeles; University of California Los Angeles Medical Center; David Geffen School of Medicine at UCLA; University of California System; University of California Los Angeles; Swansea University; Dalhousie University; KU Leuven; University of California System; University of California Los Angeles; University of California Los Angeles Medical Center; David Geffen School of Medicine at UCLA; Takeda Pharmaceutical Company Ltd; Takeda Pharmaceuticals International, Inc.
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1799812
• 发表日期: 2022
• 页码: 678-692
• 关键词: life-history variation; fast-slow continuum; maximum-likelihood; conjugate analysis; heritability; models; dna; temperature; algorithm; size

摘要

Comparative biologists are often interested in inferring covariation between multiple biological traits sampled across numerous related taxa. To properly study these relationships, we must control for the shared evolutionary history of the taxa to avoid spurious inference. An additional challenge arises as obtaining a full suite of measurements becomes increasingly difficult with increasing taxa. This generally necessitates data imputation or integration, and existing control techniques typically scale poorly as the number of taxa increases. We propose an inference technique that integrates out missing measurements analytically and scales linearly with the number of taxa by using a post-order traversal algorithm under a multivariate Brownian diffusion (MBD) model to characterize trait evolution. We further exploit this technique to extend the MBD model to account for sampling error or nonheritable residual variance. We test these methods to examine mammalian life history traits, prokaryotic genomic and phenotypic traits, and HIV infection traits. We find computational efficiency increases that top two orders-of-magnitude over current best practices. While we focus on the utility of this algorithm in phylogenetic comparative methods, our approach generalizes to solve long-standing challenges in computing the likelihood for matrix-normal and multivariate normal distributions with missing data at scale.for this article are available online.