Single nucleotide polymorphism network: a combinatorial paradigm for risk prediction
Risk prediction for a particular disease in a population through SNP genotyping exploits tests whose primary goal is to rank the SNPs on the basis of their disease association. This manuscript reveals a different approach of predicting the risk through network representation by using combined genoty...
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| creator | Das Roy, Puspita Sengupta, Dhriti Dasgupta, Anjan Kr Kundu, Sudip Chaudhuri, Utpal Thakur, Indranil Guha, Pradipta Majumder, Mousumi Roy, Roshni Roy, Bidyut |
| description | Risk prediction for a particular disease in a population through SNP genotyping exploits tests whose primary goal is to rank the SNPs on the basis of their disease association. This manuscript reveals a different approach of predicting the risk through network representation by using combined genotypic data (instead of a single allele/haplotype). The aim of this study is to classify diseased group and prediction of disease risk by identifying the responsible genotype. Genotypic combination is chosen from five independent loci present on platelet receptor genes P2RY1 and P2RY12. Genotype-sets constructed from combinations of genotypes served as a network input, the network architecture constituting super-nodes (e.g., case and control) and nodes representing individuals, each individual is described by a set of genotypes containing M markers (M = number of SNP). The analysis becomes further enriched when we consider a set of networks derived from the parent network. By maintaining the super-nodes identical, each network is carrying an independent combination of M-1 markers taken from M markers. For each of the network, the ratio of case specific and control specific connections vary and the ratio of super-node specific connection shows variability. This method of network has also been applied in another case-control study which includes oral cancer, precancer and control individuals to check whether it improves presentation and interpretation of data. The analyses reveal a perfect segregation between super-nodes, only a fraction of mixed state being connected to both the super-nodes (i.e. common genotype set). This kind of approach is favorable for a population to classify whether an individual with a particular genotypic combination can be in a risk group to develop disease. In addition with that we can identify the most important polymorphism whose presence or absence in a population can make a large difference in the number of case and control individuals. |
| doi_str_mv | 10.1371/journal.pone.0074067 |
| format | Article |
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This manuscript reveals a different approach of predicting the risk through network representation by using combined genotypic data (instead of a single allele/haplotype). The aim of this study is to classify diseased group and prediction of disease risk by identifying the responsible genotype. Genotypic combination is chosen from five independent loci present on platelet receptor genes P2RY1 and P2RY12. Genotype-sets constructed from combinations of genotypes served as a network input, the network architecture constituting super-nodes (e.g., case and control) and nodes representing individuals, each individual is described by a set of genotypes containing M markers (M = number of SNP). The analysis becomes further enriched when we consider a set of networks derived from the parent network. By maintaining the super-nodes identical, each network is carrying an independent combination of M-1 markers taken from M markers. For each of the network, the ratio of case specific and control specific connections vary and the ratio of super-node specific connection shows variability. This method of network has also been applied in another case-control study which includes oral cancer, precancer and control individuals to check whether it improves presentation and interpretation of data. The analyses reveal a perfect segregation between super-nodes, only a fraction of mixed state being connected to both the super-nodes (i.e. common genotype set). This kind of approach is favorable for a population to classify whether an individual with a particular genotypic combination can be in a risk group to develop disease. In addition with that we can identify the most important polymorphism whose presence or absence in a population can make a large difference in the number of case and control individuals.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0074067</identifier><identifier>PMID: 24040168</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Acute coronary syndromes ; Adenosine diphosphate ; Biochemistry ; Biophysics ; Blood clots ; Blood platelets ; Cancer ; Case-Control Studies ; Classification ; Combinatorial analysis ; Computer Simulation ; Data interpretation ; Data processing ; Disease ; DNA Mutational Analysis ; DNA repair ; Enzymes ; Gene Order ; Gene polymorphism ; Genes ; Genetic aspects ; Genetic Loci ; Genetic Predisposition to Disease ; Genetics ; Genomics ; Genotype ; Genotype & phenotype ; Genotypes ; Genotyping ; Group dynamics ; Haplotypes ; Health risks ; Heart attacks ; Humans ; Internal medicine ; Markers ; Models, Biological ; Neural Networks (Computer) ; Nodes ; Oncology, Experimental ; Oral cancer ; Patients ; Polymorphism ; Polymorphism, Single Nucleotide ; Predictions ; Receptors, Purinergic P2Y1 - genetics ; Receptors, Purinergic P2Y12 - genetics ; Retrospective Studies ; Risk ; Signal transduction ; Single nucleotide polymorphisms ; Single-nucleotide polymorphism ; Studies</subject><ispartof>PloS one, 2013-09, Vol.8 (9), p.e74067-e74067</ispartof><rights>COPYRIGHT 2013 Public Library of Science</rights><rights>2013 Das Roy et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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This manuscript reveals a different approach of predicting the risk through network representation by using combined genotypic data (instead of a single allele/haplotype). The aim of this study is to classify diseased group and prediction of disease risk by identifying the responsible genotype. Genotypic combination is chosen from five independent loci present on platelet receptor genes P2RY1 and P2RY12. Genotype-sets constructed from combinations of genotypes served as a network input, the network architecture constituting super-nodes (e.g., case and control) and nodes representing individuals, each individual is described by a set of genotypes containing M markers (M = number of SNP). The analysis becomes further enriched when we consider a set of networks derived from the parent network. By maintaining the super-nodes identical, each network is carrying an independent combination of M-1 markers taken from M markers. 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In addition with that we can identify the most important polymorphism whose presence or absence in a population can make a large difference in the number of case and control individuals.</description><subject>Acute coronary syndromes</subject><subject>Adenosine diphosphate</subject><subject>Biochemistry</subject><subject>Biophysics</subject><subject>Blood clots</subject><subject>Blood platelets</subject><subject>Cancer</subject><subject>Case-Control Studies</subject><subject>Classification</subject><subject>Combinatorial analysis</subject><subject>Computer Simulation</subject><subject>Data interpretation</subject><subject>Data processing</subject><subject>Disease</subject><subject>DNA Mutational Analysis</subject><subject>DNA repair</subject><subject>Enzymes</subject><subject>Gene Order</subject><subject>Gene polymorphism</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Genetic Loci</subject><subject>Genetic Predisposition to 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nucleotide polymorphism network: a combinatorial paradigm for risk prediction</title><author>Das Roy, Puspita ; Sengupta, Dhriti ; Dasgupta, Anjan Kr ; Kundu, Sudip ; Chaudhuri, Utpal ; Thakur, Indranil ; Guha, Pradipta ; Majumder, Mousumi ; Roy, Roshni ; Roy, Bidyut</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-5ebe61da906a1e6404e87133dfa692b58e64ef54d5e2e5646e7cfba2a5b8c0073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Acute coronary syndromes</topic><topic>Adenosine diphosphate</topic><topic>Biochemistry</topic><topic>Biophysics</topic><topic>Blood clots</topic><topic>Blood platelets</topic><topic>Cancer</topic><topic>Case-Control Studies</topic><topic>Classification</topic><topic>Combinatorial analysis</topic><topic>Computer Simulation</topic><topic>Data interpretation</topic><topic>Data processing</topic><topic>Disease</topic><topic>DNA Mutational 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Utpal</au><au>Thakur, Indranil</au><au>Guha, Pradipta</au><au>Majumder, Mousumi</au><au>Roy, Roshni</au><au>Roy, Bidyut</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Single nucleotide polymorphism network: a combinatorial paradigm for risk prediction</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2013-09-11</date><risdate>2013</risdate><volume>8</volume><issue>9</issue><spage>e74067</spage><epage>e74067</epage><pages>e74067-e74067</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Risk prediction for a particular disease in a population through SNP genotyping exploits tests whose primary goal is to rank the SNPs on the basis of their disease association. This manuscript reveals a different approach of predicting the risk through network representation by using combined genotypic data (instead of a single allele/haplotype). The aim of this study is to classify diseased group and prediction of disease risk by identifying the responsible genotype. Genotypic combination is chosen from five independent loci present on platelet receptor genes P2RY1 and P2RY12. Genotype-sets constructed from combinations of genotypes served as a network input, the network architecture constituting super-nodes (e.g., case and control) and nodes representing individuals, each individual is described by a set of genotypes containing M markers (M = number of SNP). The analysis becomes further enriched when we consider a set of networks derived from the parent network. By maintaining the super-nodes identical, each network is carrying an independent combination of M-1 markers taken from M markers. For each of the network, the ratio of case specific and control specific connections vary and the ratio of super-node specific connection shows variability. This method of network has also been applied in another case-control study which includes oral cancer, precancer and control individuals to check whether it improves presentation and interpretation of data. The analyses reveal a perfect segregation between super-nodes, only a fraction of mixed state being connected to both the super-nodes (i.e. common genotype set). This kind of approach is favorable for a population to classify whether an individual with a particular genotypic combination can be in a risk group to develop disease. In addition with that we can identify the most important polymorphism whose presence or absence in a population can make a large difference in the number of case and control individuals.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>24040168</pmid><doi>10.1371/journal.pone.0074067</doi><tpages>e74067</tpages><oa>free_for_read</oa></addata></record> |
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| recordid | cdi_plos_journals_1431991296 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Acute coronary syndromes Adenosine diphosphate Biochemistry Biophysics Blood clots Blood platelets Cancer Case-Control Studies Classification Combinatorial analysis Computer Simulation Data interpretation Data processing Disease DNA Mutational Analysis DNA repair Enzymes Gene Order Gene polymorphism Genes Genetic aspects Genetic Loci Genetic Predisposition to Disease Genetics Genomics Genotype Genotype & phenotype Genotypes Genotyping Group dynamics Haplotypes Health risks Heart attacks Humans Internal medicine Markers Models, Biological Neural Networks (Computer) Nodes Oncology, Experimental Oral cancer Patients Polymorphism Polymorphism, Single Nucleotide Predictions Receptors, Purinergic P2Y1 - genetics Receptors, Purinergic P2Y12 - genetics Retrospective Studies Risk Signal transduction Single nucleotide polymorphisms Single-nucleotide polymorphism Studies |
| title | Single nucleotide polymorphism network: a combinatorial paradigm for risk prediction |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-15T06%3A20%3A15IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Single%20nucleotide%20polymorphism%20network:%20a%20combinatorial%20paradigm%20for%20risk%20prediction&rft.jtitle=PloS%20one&rft.au=Das%20Roy,%20Puspita&rft.date=2013-09-11&rft.volume=8&rft.issue=9&rft.spage=e74067&rft.epage=e74067&rft.pages=e74067-e74067&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0074067&rft_dat=%3Cgale_plos_%3EA478321177%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1431991296&rft_id=info:pmid/24040168&rft_galeid=A478321177&rft_doaj_id=oai_doaj_org_article_c06d516041c24134a4bd11943b5e6882&rfr_iscdi=true |