Homology in the Age of Developmental Genomics

2019

Systematic Biology

127

Modeling discrete phenotypic traits for either ancestral character state reconstruction or morphology-based phylogenetic inference suffers from ambiguities of character coding, homology assessment, dependencies, and selection of adequate models. These drawbacks occur because trait evolution is driven by two key processes—hierarchical and hidden—which are not accommodated simultaneously by the available phylogenetic methods. The hierarchical process refers to the dependencies between anatomical body parts, while the hidden process refers to the evolution of gene regulatory networks (GRNs) underlying trait development. Herein, I demonstrate that these processes can be efficiently modeled using structured Markov models (SMM) equipped with hidden states, which resolves the majority of the problems associated with discrete traits. Integration of SMM with anatomy ontologies can adequately incorporate the hierarchical dependencies, while the use of the hidden states accommodates hidden evolution of GRNs and substitution rate heterogeneity. I assess the new models using simulations and theoretical synthesis. The new approach solves the long-standing “tail color problem,” in which the trait is scored for species with tails of different colors or no tails. It also presents a previously unknown issue called the “two-scientist paradox,” in which the nature of coding the trait and the hidden processes driving the trait’s evolution are confounded; failing to account for the hidden process may result in a bias, which can be avoided by using hidden state models. All this provides a clear guideline for coding traits into characters. This article gives practical examples of using the new framework for phylogenetic inference and comparative analysis.

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Modeling Hidden Processes: Morphology Grns and Smm With Hidmentioning

confidence: 99%

Integration of Anatomy Ontologies and Evo-Devo Using Structured Markov Models Suggests a New Framework for Modeling Discrete Phenotypic Traits

2019

Systematic Biology

127

“…A lack of general consensus on what constitutes a character versus character state presents a major challenge to the coding of traits. One group of studies insist that the distinction between the character and character state is important (Pinna 1991;Sereno 2007;Wagner 2015), while another suggests that both concepts are the same (Nelson and Platnick, 1981;Patterson, 1982). Here, I provide the evidence in favor of the latter statement.…”

Section: Invariance: Character and Character States Are The Samementioning

confidence: 99%

“…8b) indicating that the trait is controlled by multiple genetic factors which are usually unknown to a researcher . Numerous evo-devo studies show that this scenario is a commonplace (Abouheif 1999;Hall 2003;Moczek 2008;McCune and Schimenti 2012;Wagner 2015). For instance, some males of Drosophila have a pigmented spot, located on the wing, that they use in a courtship display (Prud'Homme et al 2006).…”

Section: Overview Of Correspondence Between Grns and Morphologymentioning

confidence: 99%

Integration of anatomy ontologies and evo-devo using structured Markov models suggests a new framework for modeling discrete phenotypic traits

2017

Preprint

Modeling discrete phenotypic traits for either ancestral character state reconstruction or morphologybased phylogenetic inference suffers from ambiguities of character coding, homology assessment, dependencies, and selection of adequate models. These drawbacks occur because trait evolution is driven by two key processes -hierarchical and hidden -which are not accommodated simultaneously by the available phylogenetic methods. The hierarchical process refers to the dependencies between anatomical body parts, while the hidden process refers to the evolution of gene regulatory networks underlying trait development. Herein, I demonstrate that these processes can be efficiently modeled using structured Markov models equipped with hidden states, which resolves the majority of the problems associated with discrete traits. Integration of structured Markov models with anatomy ontologies can adequately incorporate the hierarchical dependencies, while the use of the hidden states accommodates hidden evolution of gene regulatory networks and substitution rate heterogeneity. I assess the new models using simulations and theoretical synthesis. The new approach solves the long-standing tail color problem (that aims at coding tail when it is absent) and presents a previously unknown issue called the "two-scientist paradox". The latter issue refers to the confounding nature of the coding of a trait and the hidden processes driving the trait's evolution; failing to account for the hidden process may result in a bias, which can be avoided by using hidden state models. All this provides a clear guideline for coding traits into characters. This paper gives practical examples of using the new framework for phylogenetic inference and comparative analysis. KEY WORDS:discrete trait, character, morphology, homology, anatomy ontology, structured Markov models, hidden Markov models, lumpability, gene regulatory networks Understanding the processes driving trait evolution is crucial for explaining evolutionary radiations (Price et al. 2010;Van Bocxlaer et al. 2010;Tobias et al. 2014), the origin of complexity and novelty (Moczek 2008;Ramirez and Michalik 2014), and for inferring phylogenies. For many of these analyses, we need to (1) discretize the trait (delimit the trait within a phenotype), (2) assess its primary homology (similarity), and finally (3) encode the trait (observations) into a character string or vector (see the definitions in Box 1, section A). This procedure, called character construction (Wiens 2001), is a basic stage of any analysis, and has a profound influence on all downstream stages. Despite the plethora of inference frameworks -be it parsimony, maximum likelihood or a Bayesian framework [reviewed in O'Meara (2012)] -the lack of repeatable and agreed-upon approaches for character construction generates considerable ambiguity. As a result, different hypotheses of discretization and different ways of coding the same hypothesis into a character may be proposed for the same trait (Hawkins et al. 1997;Strong and Lipscomb 19...

“…One group of the researchers suggest that both concepts are the same [3][4][5]. However, others argue that character and character state are essentially different [6][7][8][9][10][11][12]. A recent view supporting the distinction treats character states as mutually exclusive observations that have to be combined together into a character to perform an analysis [2].…”

Section: Introductionmentioning

confidence: 99%

The Invariant Nature of a Morphological Character and Character State: Insights from Gene Regulatory Networks

2019

Systematic Biology

What constitutes a discrete morphological character versus character state has been long discussed in the systematics literature but the consensus on this issue is still missing. Different methods of classifying organismal features into characters and character states (CCSs) can dramatically affect the results of phylogenetic analyses. Here, I show that, in the framework of Markov models, the modular structure of the gene regulatory network (GRN) underlying trait development, and the hierarchical nature of GRN evolution, essentially remove the distinction between morphological CCS, thus endowing the CCS with an invariant property with respect to each other. This property allows the states of one character to be represented as several individual characters and vice versa. In practice, this means that a phenotype can be encoded using a set of characters or just one complex character with numerous states. The representation of a phenotype using one complex character can be implemented in Markov models of trait evolution by properly structuring transition rate matrix.