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Heuristic exploitation of genetic structure in marker-assisted gene pyramiding problems

Overview of attention for article published in BMC Genetics, January 2015
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Title
Heuristic exploitation of genetic structure in marker-assisted gene pyramiding problems
Published in
BMC Genetics, January 2015
DOI 10.1186/s12863-014-0154-z
Pubmed ID
Authors

Herman de Beukelaer, Geert de Meyer, Veerle Fack

Abstract

BackgroundOver the last decade genetic marker-based plant breeding strategies have gained increasing attention because genotyping technologies are no longer limiting. Now the challenge is to optimally use genetic markers in practical breeding schemes. For simple traits such as some disease resistances it is possible to target a fixed multi-locus allele configuration at a small number of causal or linked loci. Efficiently obtaining this genetic ideotype from a given set of parental genotypes is known as the marker-assisted gene pyramiding problem. Previous methods either imposed strong restrictions or used black box integer programming solutions, while this paper explores the power of an explicit heuristic approach that exploits the underlying genetic structure to prune the search space.ResultsGene Stacker is introduced as a novel approach to marker-assisted gene pyramiding, combining an explicit directed acyclic graph model with a pruned generation algorithm inspired by a simple exhaustive search. Both exact and heuristic pruning criteria are applied to reduce the number of generated schedules. It is shown that this approach can effectively be used to obtain good solutions for stacking problems of varying complexity. For more complex problems, the heuristics allow to obtain valuable approximations. For smaller problems, fewer heuristics can be applied, resulting in an interesting quality-runtime tradeoff. Gene Stacker is competitive with previous methods and often finds better and/or additional solutions within reasonable time, because of the powerful heuristics.ConclusionsThe proposed approach was confirmed to be feasible in combination with heuristics to cope with realistic, complex stacking problems. The inherent flexibility of this approach allows to easily address important breeding constraints so that the obtained schedules can be widely used in practice without major modifications. In addition, the ideas applied for Gene Stacker can be incorporated in and extended for a plant breeding context that e.g. also addresses complex quantitative traits or conservation of genetic background.AvailabilityGene Stacker is freely available as open source software at http://genestacker.ugent.be. The website also provides documentation and examples of how to use Gene Stacker.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Indonesia 1 6%
Netherlands 1 6%
France 1 6%
Unknown 14 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 29%
Researcher 4 24%
Other 2 12%
Student > Bachelor 1 6%
Professor 1 6%
Other 3 18%
Unknown 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 53%
Computer Science 2 12%
Biochemistry, Genetics and Molecular Biology 1 6%
Nursing and Health Professions 1 6%
Mathematics 1 6%
Other 2 12%
Unknown 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 February 2015.
All research outputs
#9,507,990
of 12,372,276 outputs
Outputs from BMC Genetics
#514
of 828 outputs
Outputs of similar age
#167,146
of 265,243 outputs
Outputs of similar age from BMC Genetics
#16
of 22 outputs
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So far Altmetric has tracked 828 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
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