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      Assessing effectiveness of many-objective evolutionary algorithms for selection of tag SNPs

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      Author(s): , , * ,
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      Publication date (Electronic):
      Journal: PLOS ONE
      Publisher: Public Library of Science

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          Abstract

          Background

          Studies on genome-wide associations help to determine the cause of many genetic diseases. Genome-wide associations typically focus on associations between single-nucleotide polymorphisms (SNPs). Genotyping every SNP in a chromosomal region for identifying genetic variation is computationally very expensive. A representative subset of SNPs, called tag SNPs, can be used to identify genetic variation. Small tag SNPs save the computation time of genotyping platform, however, there could be missing data or genotyping errors in small tag SNPs. This study aims to solve Tag SNPs selection problem using many-objective evolutionary algorithms.

          Methods

          Tag SNPs selection can be viewed as an optimization problem with some trade-offs between objectives, e.g. minimizing the number of tag SNPs and maximizing tolerance for missing data. In this study, the tag SNPs selection problem is formulated as a many-objective problem. Nondominated Sorting based Genetic Algorithm (NSGA-III), and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), which are Many-Objective evolutionary algorithms, have been applied and investigated for optimal tag SNPs selection. This study also investigates different initialization methods like greedy and random initialization. optimization.

          Results

          The evaluation measures used for comparing results for different algorithms are Hypervolume, Range, SumMin, MinSum, Tolerance rate, and Average Hamming distance. Overall MOEA/D algorithm gives superior results as compared to other algorithms in most cases. NSGA-III outperforms NSGA-II and other compared algorithms on maximum tolerance rate, and SPEA2 outperforms all algorithms on average hamming distance.

          Conclusion

          Experimental results show that the performance of our proposed many-objective algorithms is much superior as compared to the results of existing methods. The outcomes show the advantages of greedy initialization over random initialization using NSGA-III, SPEA2, and MOEA/D to solve the tag SNPs selection as many-objective optimization problem.

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          Most cited references24

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          MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition

          Hui LI, Qingfu Zhang (2007)
          Article 0 comments Cited 487 times – based on 0 reviews     Review now
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            An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints

            Himanshu Jain, Kalyanmoy Deb (2014)
            Article 0 comments Cited 323 times – based on 0 reviews     Review now
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              Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium.

              Christopher Carlson, Michael A Eberle, Mark J. Rieder … (2004)
              Common genetic polymorphisms may explain a portion of the heritable risk for common diseases. Within candidate genes, the number of common polymorphisms is finite, but direct assay of all existing common polymorphism is inefficient, because genotypes at many of these sites are strongly correlated. Thus, it is not necessary to assay all common variants if the patterns of allelic association between common variants can be described. We have developed an algorithm to select the maximally informative set of common single-nucleotide polymorphisms (tagSNPs) to assay in candidate-gene association studies, such that all known common polymorphisms either are directly assayed or exceed a threshold level of association with a tagSNP. The algorithm is based on the r(2) linkage disequilibrium (LD) statistic, because r(2) is directly related to statistical power to detect disease associations with unassayed sites. We show that, at a relatively stringent r(2) threshold (r2>0.8), the LD-selected tagSNPs resolve >80% of all haplotypes across a set of 100 candidate genes, regardless of recombination, and tag specific haplotypes and clades of related haplotypes in nonrecombinant regions. Thus, if the patterns of common variation are described for a candidate gene, analysis of the tagSNP set can comprehensively interrogate for main effects from common functional variation. We demonstrate that, although common variation tends to be shared between populations, tagSNPs should be selected separately for populations with different ancestries.
              Article 0 comments Cited 305 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Rashad Moqa: Role: MethodologyRole: SoftwareRole: Writing – original draft
                Irfan Younas: Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Maryam Bashir:
                ORCID: https://orcid.org/0000-0001-6124-5317
                Role: Formal analysisRole: InvestigationRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Seyedali Mirjalili: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS One
                Journal ID (publisher-id): plos
                Title: PLOS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2022
                Publication date (Electronic): 8 December 2022
                Volume: 17
                Issue: 12
                Electronic Location Identifier: e0278560
                Affiliations
                [001] FAST School of Computing, National University of Computer and Emerging Sciences, Lahore, Pakistan
                Torrens University Australia, AUSTRALIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-6124-5317
                Article
                Publisher ID: PONE-D-21-31031
                DOI: 10.1371/journal.pone.0278560
                PMC ID: 9731481
                PubMed ID: 36480538
                SO-VID: 0ef0ce03-0f11-4928-80a9-185d7facad43
                Copyright © © 2022 Moqa et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 11 October 2021
                Date accepted : 19 November 2022
                Page count
                Figures: 10, Tables: 5, Pages: 24
                Funding
                The authors received no specific funding for this work.
                Categories
                Subject: Research Article
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Single Nucleotide Polymorphisms
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Heredity
                Subject: Genetic Mapping
                Subject: Haplotypes
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Applied Mathematics
                Subject: Algorithms
                Subject: Evolutionary Algorithms
                Subject: Research and Analysis Methods
                Subject: Simulation and Modeling
                Subject: Algorithms
                Subject: Evolutionary Algorithms
                Subject: Research and Analysis Methods
                Subject: Computational Techniques
                Subject: Evolutionary Computation
                Subject: Evolutionary Algorithms
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Applied Mathematics
                Subject: Algorithms
                Subject: Research and Analysis Methods
                Subject: Simulation and Modeling
                Subject: Algorithms
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Optimization
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Genomics
                Subject: Human Genomics
                Subject: Biology and Life Sciences
                Subject: Molecular Biology
                Subject: Molecular Biology Techniques
                Subject: Genotyping
                Subject: Research and Analysis Methods
                Subject: Molecular Biology Techniques
                Subject: Genotyping
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Applied Mathematics
                Subject: Algorithms
                Subject: Genetic Algorithms
                Subject: Research and Analysis Methods
                Subject: Simulation and Modeling
                Subject: Algorithms
                Subject: Genetic Algorithms
                Custom metadata
                Data Availability The data underlying the results presented in the study are available from https://www.science.org/doi/abs/10.1126/science.1105436.

                ScienceOpen disciplines: Uncategorized
                Data availability:
                ScienceOpen disciplines: Uncategorized

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