Evidence map›Paper›PMID 34341836›Full record

ArticleJournal of molecular evolution2021

Detecting Selection on Segregating Gene Duplicates in a Population.

Tristan L Stark, Rebecca S Kaufman, Maria A Maltepes, Peter B Chi, David A Liberles

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Article in Journal of molecular evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Tristan L StarkDepartment of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, 19122, USA. tristan.stark@utas.edu.au.
Rebecca S KaufmanDepartment of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, 19122, USA.
Maria A MaltepesDepartment of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, 19122, USA.
Peter B ChiDepartment of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, 19122, USA.
David A LiberlesDepartment of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, 19122, USA. daliberles@temple.edu.ORCID 0000-0003-3487-8826

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene duplication is a fundamental process that has the potential to drive phenotypic differences between populations and species. While evolutionarily neutral changes have the potential to affect phenotypes, detecting selection acting on gene duplicates can uncover cases of adaptive diversification. Existing methods to detect selection on duplicates work mostly inter-specifically and are based upon selection on coding sequence changes, here we present a method to detect selection directly on a copy number variant segregating in a population. The method relies upon expected relationships between allele (new duplication) age and frequency in the population dependent upon the effective population size. Using both a haploid and a diploid population with a Moran Model under several population sizes, the neutral baseline for copy number variants is established. The ability of the method to reject neutrality for duplicates with known age (measured in pairwise dS value) and frequency in the population is established through mathematical analysis and through simulations. Power is particularly good in the diploid case and with larger effective population sizes, as expected. With extension of this method to larger population sizes, this is a tool to analyze selection on copy number variants in any natural or experimentally evolving population. We have made an R package available at https://github.com/peterbchi/CNVSelectR/ which implements the method introduced here.

Indexed as

DiploidyGene DuplicationAllelesPhenotypeSelection, Genetic

Identifiers

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Registered trials

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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.