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Evolutionary algorithms for the selection of single nucleotide polymorphisms.

Hubley RM, Zitzler E, Roach JC - BMC Bioinformatics (2003)

Bottom Line: The choice of subset is influenced by many factors, including estimated or known reliability of the SNP, biochemical factors, intellectual property, cost, and effectiveness of the subset for mapping genes or identifying disease loci.They provide flexibility with respect to the problem formulation if a problem description evolves or changes.Results are produced as a trade-off front, allowing the user to make informed decisions when prioritizing factors.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Systems Biology, Seattle, WA, USA. rhubley@systemsbiology.org

ABSTRACT

Background: Large databases of single nucleotide polymorphisms (SNPs) are available for use in genomics studies. Typically, investigators must choose a subset of SNPs from these databases to employ in their studies. The choice of subset is influenced by many factors, including estimated or known reliability of the SNP, biochemical factors, intellectual property, cost, and effectiveness of the subset for mapping genes or identifying disease loci. We present an evolutionary algorithm for multiobjective SNP selection.

Results: We implemented a modified version of the Strength-Pareto Evolutionary Algorithm (SPEA2) in Java. Our implementation, Multiobjective Analyzer for Genetic Marker Acquisition (MAGMA), approximates the set of optimal trade-off solutions for large problems in minutes. This set is very useful for the design of large studies, including those oriented towards disease identification, genetic mapping, population studies, and haplotype-block elucidation.

Conclusion: Evolutionary algorithms are particularly suited for optimization problems that involve multiple objectives and a complex search space on which exact methods such as exhaustive enumeration cannot be applied. They provide flexibility with respect to the problem formulation if a problem description evolves or changes. Results are produced as a trade-off front, allowing the user to make informed decisions when prioritizing factors. MAGMA is open source and available at http://snp-magma.sourceforge.net. Evolutionary algorithms are well suited for many other applications in genomics.

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Genome Encoding. Alternative methods of encoding problem solutions as "genomes" include bit vectors (top) and arrays (bottom). In this cartoon, the solutions encoded by the two different methods are identical. For the problems we have studied, arrays are more compact and evolve more efficiently than bit vectors.
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Figure 1: Genome Encoding. Alternative methods of encoding problem solutions as "genomes" include bit vectors (top) and arrays (bottom). In this cartoon, the solutions encoded by the two different methods are identical. For the problems we have studied, arrays are more compact and evolve more efficiently than bit vectors.

Mentions: Evolutionary algorithms are excellent for exploring the set of all possible solutions, or solution space, of many multiobjective problems. In order to apply a multiobjective algorithm, solutions to a problem must be encodable as a genome (Figure 1). There may be more than one way to encode a solution; the choice of encodings can impact the performance of the algorithm. We illustrate this in our Results with an exploration of two possible encodings for our SNP selection problem. The first encoding represents solutions as a bit vector. The second encoding represents solutions as a variable-length list.


Evolutionary algorithms for the selection of single nucleotide polymorphisms.

Hubley RM, Zitzler E, Roach JC - BMC Bioinformatics (2003)

Genome Encoding. Alternative methods of encoding problem solutions as "genomes" include bit vectors (top) and arrays (bottom). In this cartoon, the solutions encoded by the two different methods are identical. For the problems we have studied, arrays are more compact and evolve more efficiently than bit vectors.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC183839&req=5

Figure 1: Genome Encoding. Alternative methods of encoding problem solutions as "genomes" include bit vectors (top) and arrays (bottom). In this cartoon, the solutions encoded by the two different methods are identical. For the problems we have studied, arrays are more compact and evolve more efficiently than bit vectors.
Mentions: Evolutionary algorithms are excellent for exploring the set of all possible solutions, or solution space, of many multiobjective problems. In order to apply a multiobjective algorithm, solutions to a problem must be encodable as a genome (Figure 1). There may be more than one way to encode a solution; the choice of encodings can impact the performance of the algorithm. We illustrate this in our Results with an exploration of two possible encodings for our SNP selection problem. The first encoding represents solutions as a bit vector. The second encoding represents solutions as a variable-length list.

Bottom Line: The choice of subset is influenced by many factors, including estimated or known reliability of the SNP, biochemical factors, intellectual property, cost, and effectiveness of the subset for mapping genes or identifying disease loci.They provide flexibility with respect to the problem formulation if a problem description evolves or changes.Results are produced as a trade-off front, allowing the user to make informed decisions when prioritizing factors.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Systems Biology, Seattle, WA, USA. rhubley@systemsbiology.org

ABSTRACT

Background: Large databases of single nucleotide polymorphisms (SNPs) are available for use in genomics studies. Typically, investigators must choose a subset of SNPs from these databases to employ in their studies. The choice of subset is influenced by many factors, including estimated or known reliability of the SNP, biochemical factors, intellectual property, cost, and effectiveness of the subset for mapping genes or identifying disease loci. We present an evolutionary algorithm for multiobjective SNP selection.

Results: We implemented a modified version of the Strength-Pareto Evolutionary Algorithm (SPEA2) in Java. Our implementation, Multiobjective Analyzer for Genetic Marker Acquisition (MAGMA), approximates the set of optimal trade-off solutions for large problems in minutes. This set is very useful for the design of large studies, including those oriented towards disease identification, genetic mapping, population studies, and haplotype-block elucidation.

Conclusion: Evolutionary algorithms are particularly suited for optimization problems that involve multiple objectives and a complex search space on which exact methods such as exhaustive enumeration cannot be applied. They provide flexibility with respect to the problem formulation if a problem description evolves or changes. Results are produced as a trade-off front, allowing the user to make informed decisions when prioritizing factors. MAGMA is open source and available at http://snp-magma.sourceforge.net. Evolutionary algorithms are well suited for many other applications in genomics.

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