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Rampant False Detection of Adaptive Phenotypic Optimization by ParTI-Based Pareto Front Inference
1Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.
Molecular Biology and Evolution
|December 21, 2020
Summary
The ParTI program may overestimate evolutionary optimization by incorrectly identifying Pareto fronts and archetypes. This study reveals high false-positive rates due to population structure and flexible analysis, questioning previous findings.
Area of Science:
- Evolutionary biology
- Quantitative genetics
- Bioinformatics
Background:
- Organisms often face trade-offs when performing multiple tasks, influencing their fitness.
- Identifying optimal phenotypes (Pareto fronts) and task-specific traits (archetypes) is crucial for understanding evolutionary constraints and adaptations.
- The ParTI program was developed to identify Pareto fronts from high-dimensional phenotypic data, including molecular phenotypes.
Purpose of the Study:
- To assess the reliability of the ParTI program in identifying Pareto fronts and archetypes.
- To investigate the causes of potential errors in ParTI's analyses.
- To evaluate the generalizability of ParTI's findings in evolutionary biology.
Main Methods:
- Analysis of real and simulated phenotypic datasets lacking evolutionary optimization.
- Application of the ParTI program to these datasets.
- Statistical evaluation of false-positive rates and identification of error sources.
Main Results:
- The ParTI program exhibits extremely high false-positive rates in identifying Pareto fronts and archetypes.
- Phylogenetic relationships and population structures significantly contribute to these errors.
- The analytical flexibility of ParTI is comparable to p-hacking, inflating reliability concerns.
Conclusions:
- Current findings cast doubt on the validity of most ParTI-based results.
- Reliably identifying Pareto fronts and archetypes from high-dimensional phenotypic data remains a significant challenge.
- The study highlights the need for caution when interpreting results from methods like ParTI, especially in the presence of population structure.
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