PPubMed12 Feb 2024Cited 4×

Models for the retention of duplicate genes and their biological underpinnings [version 2; peer review: 2 approved]

Raquel Assis, Gavin Conant, Barbara Holland, David A. Liberles, Malgorzata M. O'Reilly, Amanda E. Wilson

Abstract

"Malgorzata M. O'Reilly" 'Amanda E. Wilson'] Gene content in genomes changes through several different processes, with gene duplication being an important contributor to such changes. Gene duplication occurs over a range of scales from individual genes to whole genomes, and the dynamics of this process can be context dependent. Still, there are rules by which genes are retained or lost from genomes after duplication, and probabilistic modeling has enabled characterization of these rules, including their context-dependence. Here, we describe the biology and corresponding mathematical models that are used to understand duplicate gene retention and its contribution to the set of biochemical functions encoded in a genome.

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The paper reviews biological mechanisms and probabilistic models that explain why duplicate genes are retained in genomes.

What’s new

i.Provides a comprehensive overview of biological factors influencing duplicate gene retention
ii.Classifies and summarizes a range of probabilistic models (Markov, QBD, hazard, etc.)
iii.Integrates biophysical subfunctionalization with dosage balance into a combined Markov model
iv.Introduces the concept of gene duplicability and its impact on retention
v.Highlights gaps in empirical testing and suggests future research directions

How they did it

The authors performed an extensive literature review of empirical studies and theoretical models, extracting assumptions and mathematical formulations. They organized models by biological building blocks and compared their predictions. Figures and tables were used to illustrate model structures and parameter relationships.

What they found

The probability of eventual subfunctionalization for two identical copies is 1−0.5^(f−1), where f is the number of functions
The review catalogs five integrated modeling frameworks (Biophysical+Dosage Markov, jPRIME, Maere et al., Phase‑Type, QBD)
Models with subfunctionalization fit age‑distribution data better than constant‑rate models

Caveats

✕Many models have not yet been applied to real genomic datasets
✕Assumptions such as modularity of regulatory units or exponential loss may not hold universally
✕Gene conversion events are difficult to incorporate into standard models
✕Predictions often rely on parameters that are hard to estimate empirically

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