Inferring Phenotypic Trait Evolution on Large Trees With Many Incomplete Measurements
| Autor: | Marc A. Suchard, Philippe Lemey, Max R. Tolkoff, Gabriel W. Hassler, Lam Si Tung Ho, William L. Allen |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Matrix-normal LIFE-HISTORY VARIATION Bayesian inference 01 natural sciences 010104 statistics & probability 2.5 Research design and methodologies (aetiology) Aetiology MAXIMUM-LIKELIHOOD TEMPERATURE Computation (stat.CO) 050205 econometrics HERITABILITY 05 social sciences Statistics FAST-SLOW CONTINUUM 1.4 Methodologies and measurements Phylogenetics stat.ME Physical Sciences Matrix normal distribution Statistics Probability and Uncertainty Statistics and Probability Missing data Statistics & Probability MODELS Bioengineering Biology Statistics - Computation Article CONJUGATE ANALYSIS Methodology (stat.ME) Underpinning research 0502 economics and business ALGORITHM Econometrics 0101 mathematics Statistics - Methodology Demography stat.CO Science & Technology Phenotypic trait DNA Taxon SIZE Evolutionary biology Generic health relevance Mathematics |
| Zdroj: | Journal of the American Statistical Association, vol 117, iss 538 J Am Stat Assoc |
| DOI: | 10.6084/m9.figshare.12851292 |
| Popis: | 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. Existing control techniques almost universally scale poorly as the number of taxa increases. An additional challenge arises as obtaining a full suite of measurements becomes increasingly difficult with increasing taxa. This typically necessitates data imputation or integration that further exacerbates scalability. 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 non-heritable 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. Comment: 29 pages, 7 figures, 2 tables, 3 supplementary sections |
| Databáze: | OpenAIRE |
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