Evidence map›Paper›PMID 28143390›Full record

ArticleBMC evolutionary biology2017

Analysis of a mechanistic Markov model for gene duplicates evolving under subfunctionalization.

Tristan L Stark, David A Liberles, Barbara R Holland, Małgorzata M O'Reilly

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Article in BMC evolutionary biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

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10citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Tristan L StarkSchool of Physical Sciences, University of Tasmania, Churchill Ave, Hobart, 7001, Australia. Tristan.Stark@utas.edu.au.
David A LiberlesCenter for Computational Genetics and Genomics and Department of Biology, Temple University, Philadelphia, 19122, USA.
Barbara R HollandSchool of Physical Sciences, University of Tasmania, Churchill Ave, Hobart, 7001, Australia.
Małgorzata M O'ReillySchool of Physical Sciences, University of Tasmania, Churchill Ave, Hobart, 7001, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGene duplication has been identified as a key process driving functional change in many genomes. Several biological models exist for the evolution of a pair of duplicates after a duplication event, and it is believed that gene duplicates can evolve in different ways, according to one process, or a mix of processes. Subfunctionalization is one such process, under which the two duplicates can be preserved by dividing up the function of the original gene between them. Analysis of genomic data using subfunctionalization and related processes has thus far been relatively coarse-grained, with mathematical treatments usually focusing on the phenomenological features of gene duplicate evolution.

resultsHere, we develop and analyze a mathematical model using the mechanics of subfunctionalization and the assumption of Poisson rates of mutation. By making use of the results from the literature on the Phase-Type distribution, we are able to derive exact analytical results for the model. The main advantage of the mechanistic model is that it leads to testable predictions of the phenomenological behavior (instead of building this behavior into the model a priori), and allows for the estimation of biologically meaningful parameters. We fit the survival function implied by this model to real genome data (Homo sapiens, Mus musculus, Rattus norvegicus and Canis familiaris), and compare the fit against commonly used phenomenological survival functions. We estimate the number of regulatory regions, and rates of mutation (relative to silent site mutation) in the coding and regulatory regions. We find that for the four genomes tested the subfunctionalization model predicts that duplicates most-likely have just a few regulatory regions, and the rate of mutation in the coding region is around 5-10 times greater than the rate in the regulatory regions. This is the first model-based estimate of the number of regulatory regions in duplicates.

conclusionsStrong agreement between empirical results and the predictions of our model suggest that subfunctionalization provides a consistent explanation for the evolution of many gene duplicates.

Indexed as

Evolution, MolecularGene DuplicationModels, GeneticMutationAnimalsBiological EvolutionGenes, DuplicateGenes, RegulatorGenomeMarkov ChainsContinuous-time Markov chain (CTMC)Gene duplicationNeofunctionalizationNonfunctionalizationPhase-type distributionPseudogenizationSubfunctionalization

Identifiers

PMID28143390
PMCPMC5282866

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.