Inferring Diversification Rates in Phylogenetic Trees Using Approximate Bayesian Computation and Markovian Binary Trees

核心概念

This paper presents a novel approach for inferring diversification rates in phylogenetic trees using Approximate Bayesian Computation (ABC) with Markovian Binary Trees (MBTs), offering a more flexible and potentially accurate alternative to traditional likelihood-based methods.

要約

Bibliographic Information:

He, M., Hautphenne, S., & Chan, Y. (2024). Approximate Bayesian computation for Markovian binary trees in phylogenetics. arXiv preprint arXiv:2309.00194v2.

Research Objective:

This paper aims to develop a new method for inferring diversification rates (speciation, extinction, and transition rates) from phylogenetic trees using Approximate Bayesian Computation (ABC) with Markovian Binary Trees (MBTs). The authors focus on scenarios where species can exist in one of two possible states (phases) and investigate both reducible (unidirectional transitions) and irreducible (bidirectional transitions) MBT models.

Methodology:

The researchers employ an ABC-PMC (Population Monte Carlo) algorithm to infer MBT parameters from phylogenetic trees. They develop and utilize a suite of summary statistics to compare observed and simulated trees, including average branch length, tree height, normalized lineage-through-time (nLTT) curve, Colless balance index, and novel phase-specific balance and transition statistics. The accuracy of their method is evaluated through simulation studies, comparing their results to those obtained using maximum likelihood estimation (MLE) with the BiSSE model. Finally, they apply their method to a real-world dataset of squamata (reptiles) to infer diversification rates associated with oviparity and viviparity.

Key Findings:

• The proposed ABC method accurately infers MBT parameters, particularly in the simpler reducible case.
• The method demonstrates higher accuracy compared to MLE using the BiSSE model, especially for transition rates.
• Inference accuracy improves with increasing tree size (number of leaves).
• Applying the method to the squamata dataset supports previous findings of higher speciation and extinction rates in viviparous species compared to oviparous species.
• Model selection using ABC SMC favors an irreducible model for the squamata dataset, suggesting transitions between oviparity and viviparity occur in both directions.

Main Conclusions:

The study demonstrates the potential of ABC and MBTs as a powerful and versatile approach for inferring diversification rates in phylogenetics. The authors argue that their method offers a more flexible and potentially more accurate alternative to traditional likelihood-based methods, particularly for complex evolutionary scenarios.

Significance:

This research contributes to the field of phylogenetics by introducing a novel and promising method for inferring diversification rates. The use of ABC with MBTs allows for greater flexibility in modeling evolutionary processes and may lead to more accurate estimations of diversification parameters.

Limitations and Future Research:

The study primarily focuses on MBT models with two phases and limited transition possibilities. Future research could explore the applicability of this method to MBT models with more phases and unrestricted transitions. Additionally, investigating the impact of incomplete lineage sorting and other confounding factors on the accuracy of the method would be beneficial.

統計

The observed dataset consists of 100 trees, each with 50 leaves.
The default parameters for the reducible case with equal death rates are (λ1, λ2, µ, q12) = (1.5, 0.51, 0.15, 0.5).
The default parameters for the reducible case with arbitrary death rates are (λ1, λ2, µ1, µ2, q12) = (1.5, 0.51, 0.7, 0.15, 0.5).
The default parameters for the irreducible case are (λ1, λ2, µ1, µ2, q12, q21) = (3, 2, 1, 0.5, 0.5, 0.25).
The squamata phylogenetic tree analyzed contains 3951 tips, with 3108 oviparous and 843 viviparous species.

引用

抽出されたキーインサイト

Approximate Bayesian computation for Markovian binary trees in phylogenetics

by Mingqi He, S... 場所 arxiv.org 10-04-2024

https://arxiv.org/pdf/2309.00194.pdf

深掘り質問

How might this ABC-based approach be extended to incorporate phylogenetic uncertainty, such as uncertainty in tree topology or branch lengths?

Incorporating phylogenetic uncertainty, stemming from uncertainties in tree topology or branch lengths, into the ABC-MBT framework presents a significant methodological challenge. Here are several potential strategies:
1. Bayesian Phylogenetic Inference within ABC:

Instead of using a single, fixed tree topology, one could integrate over a distribution of plausible trees obtained from Bayesian phylogenetic inference methods like MrBayes or BEAST.
For each ABC iteration:

Sample a tree from the posterior distribution of trees.
Simulate MBT data on this sampled tree.
Calculate summary statistics on the simulated data.

This approach explicitly accounts for uncertainty in the tree, allowing the ABC procedure to explore a wider range of evolutionary scenarios.
2. Posterior Predictive Simulation:

After obtaining the posterior distribution of MBT parameters using ABC on the inferred tree, perform posterior predictive simulations.
For each set of posterior MBT parameters:

Simulate a new phylogeny under the MBT model.
Compare the distribution of summary statistics from these simulated trees to the observed data.

Discrepancies between the simulated and observed summary statistic distributions might indicate poor model fit due to unaccounted phylogenetic uncertainty.
3. Summary Statistic Design:

Develop summary statistics that are robust to small changes in tree topology or branch lengths.
For example, instead of using precise branching times, one could use statistics based on the relative order of branching events or clade sizes.
Challenges and Considerations:

Computational Cost: Incorporating phylogenetic uncertainty significantly increases the computational burden of ABC, as it requires simulating data on numerous trees.
Summary Statistic Choice: The choice of summary statistics becomes even more crucial when accounting for phylogenetic uncertainty. Ideally, these statistics should be informative about the MBT parameters while being relatively insensitive to small topological variations.
Prior Sensitivity: The influence of the prior distribution on phylogenetic inference should be carefully assessed, as it can propagate to the ABC results.

Could the reliance on summary statistics in ABC limit the method's ability to capture subtle but important patterns in the data compared to full likelihood methods?

Yes, the reliance on summary statistics in ABC can potentially limit its ability to capture subtle patterns in the data compared to full likelihood methods. Here's why:
Information Loss:

Summary statistics, by definition, condense the information present in the full data. This condensation inevitably leads to some loss of information.
Subtle patterns that might be detectable by examining the complete data might be obscured or averaged out when summarized.
Statistic Choice:

The effectiveness of ABC hinges critically on the choice of summary statistics.
If the chosen statistics are not sufficiently informative about the underlying process or parameters of interest, ABC may fail to capture important signals in the data.
Finding a set of statistics that adequately captures all relevant aspects of complex evolutionary models can be challenging.
Likelihood Methods:

In contrast, full likelihood methods directly utilize the complete data and do not rely on data reduction through summary statistics.
This allows them, in principle, to extract more information from the data and potentially detect subtle patterns that might be missed by ABC.
When ABC Might Still Be Preferable:

Computational Tractability: ABC can be computationally more feasible than full likelihood methods, especially for complex models where calculating the likelihood is intractable or very time-consuming.
Model Complexity: ABC provides a viable approach for parameter estimation and model selection in situations where likelihood-based methods are not computationally feasible due to model complexity.
Mitigating Information Loss in ABC:

Careful Statistic Selection: Invest significant effort in selecting summary statistics that are highly informative about the parameters and processes of interest.
Multiple Statistics: Use a diverse set of summary statistics to capture different aspects of the data.
Model Checking: Employ rigorous model checking procedures, such as posterior predictive checks, to assess whether the chosen statistics and model adequately capture the observed patterns in the data.

If we view the evolution of language as a branching process, what "phases" might be relevant for modeling its diversification, and how could this ABC-MBT framework be applied to study linguistic evolution?

Viewing language evolution through the lens of a branching process using the ABC-MBT framework offers a compelling way to study linguistic diversification. Here's how we can approach this:
Relevant "Phases" in Language Evolution:

Geographical Location: Languages spoken in different geographical regions often experience distinct evolutionary pressures (e.g., contact with other languages, environmental influences). Geographical location could act as a "phase" influencing diversification rates.

Sociolinguistic Factors:

Speaker Population Size: Languages with larger speaker populations might diversify more slowly due to greater inertia and standardization pressures.
Social Prestige: Languages with higher social prestige might spread more rapidly, leading to different diversification dynamics.
Contact Intensity: Languages in frequent contact with others might experience accelerated rates of borrowing and change.

Structural Features:

Morphological Complexity: Languages with simpler morphological structures might diversify more rapidly, as changes are easier to implement and propagate.
Phonological Inventory Size: Languages with larger phoneme inventories might be more resistant to change due to a greater number of contrastive elements.

Applying ABC-MBT to Linguistic Evolution:

Data:

Phylogenetic trees of language families, often constructed using lexical and grammatical data.
Information on language traits (phases) for extant languages.

Model:

Define an MBT model where "phases" represent the linguistic features described above.
Specify prior distributions for the birth (language emergence), death (language extinction), and transition rates between phases.

Summary Statistics:

Develop summary statistics sensitive to language diversification patterns, such as:

Rates of language emergence and extinction within different phases.
Phylogenetic tree imbalance, reflecting differences in diversification rates.
Distribution of language traits across the phylogeny.

ABC Inference:

Use ABC-MBT to estimate the posterior distributions of model parameters, providing insights into:

How different linguistic features influence diversification rates.
The relative importance of geographical, sociolinguistic, and structural factors in language evolution.

Challenges and Considerations:

Data Availability: Obtaining reliable phylogenetic trees and trait data for a wide range of languages can be challenging.
Model Complexity: Defining a realistic MBT model that captures the complexities of language evolution is not trivial.
Interpreting Phases: The meaning and boundaries of linguistic "phases" can be fluid and context-dependent, requiring careful consideration.
Despite these challenges, the ABC-MBT framework provides a promising avenue for exploring the dynamics of language diversification and testing hypotheses about the factors driving linguistic evolution.