Genotype Matrix Mapping: Searching for Quantitative Trait Loci Interactions in Genetic Variation in Complex Traits
Abstract In order to reveal quantitative trait loci (QTL) interactions and the relationship between various interactions in complex traits, we have developed a new QTL mapping approach, named genotype matrix mapping (GMM), which searches for QTL interactions in genetic variation. The central approac...
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| Veröffentlicht in: | DNA research 2007-10, Vol.14 (5), p.217-225 |
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| creator | Isobe, Sachiko Nakaya, Akihiro Tabata, Satoshi |
| description | Abstract
In order to reveal quantitative trait loci (QTL) interactions and the relationship between various interactions in complex traits, we have developed a new QTL mapping approach, named genotype matrix mapping (GMM), which searches for QTL interactions in genetic variation. The central approach in GMM is the following. (1) Each tested marker is given a virtual matrix, named a genotype matrix (GM), containing intersecting lines and rows equal to the total allele number for that marker in the population analyzed. (2) QTL interactions are then estimated and compared through virtual networks among the GMs. To evaluate the contribution of marker combinations to a quantitative phenotype, the GMM method divides the samples into two non-overlapping subclasses, S
0 and S
1; the former contains the samples that have a specific genotype pattern to be evaluated, and the latter contains samples that do not. Based on this division, the F-measure is calculated as an index of significance. With the GMM method, we extracted significant marker combinations consisting of one to three interacting markers. The results indicated there were multiple QTL interactions affecting the phenotype (flowering date). GMM will be a valuable approach to identify QTL interactions in genetic variation of a complex trait within a variety of organisms. |
| doi_str_mv | 10.1093/dnares/dsm020 |
| format | Article |
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In order to reveal quantitative trait loci (QTL) interactions and the relationship between various interactions in complex traits, we have developed a new QTL mapping approach, named genotype matrix mapping (GMM), which searches for QTL interactions in genetic variation. The central approach in GMM is the following. (1) Each tested marker is given a virtual matrix, named a genotype matrix (GM), containing intersecting lines and rows equal to the total allele number for that marker in the population analyzed. (2) QTL interactions are then estimated and compared through virtual networks among the GMs. To evaluate the contribution of marker combinations to a quantitative phenotype, the GMM method divides the samples into two non-overlapping subclasses, S
0 and S
1; the former contains the samples that have a specific genotype pattern to be evaluated, and the latter contains samples that do not. Based on this division, the F-measure is calculated as an index of significance. With the GMM method, we extracted significant marker combinations consisting of one to three interacting markers. The results indicated there were multiple QTL interactions affecting the phenotype (flowering date). GMM will be a valuable approach to identify QTL interactions in genetic variation of a complex trait within a variety of organisms.</description><identifier>ISSN: 1340-2838</identifier><identifier>EISSN: 1756-1663</identifier><identifier>DOI: 10.1093/dnares/dsm020</identifier><identifier>PMID: 18000014</identifier><language>eng</language><publisher>Oxford: Oxford University Press</publisher><subject>Algorithms ; Alleles ; Biological and medical sciences ; Chromosome Mapping - methods ; Chromosome Mapping - statistics & numerical data ; Databases, Genetic ; Flowers - growth & development ; Fundamental and applied biological sciences. Psychology ; Genes. Genome ; Genetic Markers ; Genetic Variation ; Genetics of eukaryotes. Biological and molecular evolution ; Genotype ; Models, Genetic ; Molecular and cellular biology ; Molecular genetics ; Phenotype ; Quantitative Trait Loci ; Trifolium - genetics ; Trifolium - growth & development</subject><ispartof>DNA research, 2007-10, Vol.14 (5), p.217-225</ispartof><rights>The Author 2007. Kazusa DNA Research Institute. 2007</rights><rights>2008 INIST-CNRS</rights><rights>The Author 2007. Kazusa DNA Research Institute.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c479t-2e3fd7f2d2cd197fff1441c13b52f78584e853ada43f7a2216b56af0f85ef42a3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779902/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779902/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,1598,27901,27902,53766,53768</link.rule.ids><linktorsrc>$$Uhttps://dx.doi.org/10.1093/dnares/dsm020$$EView_record_in_Oxford_University_Press$$FView_record_in_$$GOxford_University_Press</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=19913340$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18000014$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Yano, M</contributor><creatorcontrib>Isobe, Sachiko</creatorcontrib><creatorcontrib>Nakaya, Akihiro</creatorcontrib><creatorcontrib>Tabata, Satoshi</creatorcontrib><title>Genotype Matrix Mapping: Searching for Quantitative Trait Loci Interactions in Genetic Variation in Complex Traits</title><title>DNA research</title><addtitle>DNA Res</addtitle><description>Abstract
In order to reveal quantitative trait loci (QTL) interactions and the relationship between various interactions in complex traits, we have developed a new QTL mapping approach, named genotype matrix mapping (GMM), which searches for QTL interactions in genetic variation. The central approach in GMM is the following. (1) Each tested marker is given a virtual matrix, named a genotype matrix (GM), containing intersecting lines and rows equal to the total allele number for that marker in the population analyzed. (2) QTL interactions are then estimated and compared through virtual networks among the GMs. To evaluate the contribution of marker combinations to a quantitative phenotype, the GMM method divides the samples into two non-overlapping subclasses, S
0 and S
1; the former contains the samples that have a specific genotype pattern to be evaluated, and the latter contains samples that do not. Based on this division, the F-measure is calculated as an index of significance. With the GMM method, we extracted significant marker combinations consisting of one to three interacting markers. The results indicated there were multiple QTL interactions affecting the phenotype (flowering date). GMM will be a valuable approach to identify QTL interactions in genetic variation of a complex trait within a variety of organisms.</description><subject>Algorithms</subject><subject>Alleles</subject><subject>Biological and medical sciences</subject><subject>Chromosome Mapping - methods</subject><subject>Chromosome Mapping - statistics & numerical data</subject><subject>Databases, Genetic</subject><subject>Flowers - growth & development</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Genes. Genome</subject><subject>Genetic Markers</subject><subject>Genetic Variation</subject><subject>Genetics of eukaryotes. Biological and molecular evolution</subject><subject>Genotype</subject><subject>Models, Genetic</subject><subject>Molecular and cellular biology</subject><subject>Molecular genetics</subject><subject>Phenotype</subject><subject>Quantitative Trait Loci</subject><subject>Trifolium - genetics</subject><subject>Trifolium - growth & development</subject><issn>1340-2838</issn><issn>1756-1663</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkk1v1DAQhi0Eou2WI1fkC6iXUH_FSTggoRWUSlsh1MLVmnXs1iixU9up2n-PV1lReqovM5p5_I6tdxB6S8lHSjp-2nuIJp32aSSMvECHtKllRaXkL0vOBalYy9sDdJTSH0IErXnzGh3QlpRDxSGKZ8aH_DAZfAE5uvsSpsn560_40kDUNyXFNkT8cwafXYbs7gy-iuAy3gTt8LnPJoLOLviEncdFzmSn8W-IDnbVXXEdxmkw98u9dIxeWRiSebOPK_Tr29er9fdq8-PsfP1lU2nRdLlihtu-saxnuqddY62lQlBN-bZmtmnrVpi25tCD4LYBxqjc1hIssW1trGDAV-jzojvN29H02vgcYVBTdCPEBxXAqacd727UdbhTrGm6jrAi8GEvEMPtbFJWo0vaDAN4E-akZEcEl1Q-CzIiJeXFrRWqFlDHkFI09t9rKFE7O9Vip1rsLPy7_7_wSO_9K8D7PQBJw2AjeO3SI9d1lJclKNzJwoV5embmXwDpuvU</recordid><startdate>20071031</startdate><enddate>20071031</enddate><creator>Isobe, Sachiko</creator><creator>Nakaya, Akihiro</creator><creator>Tabata, Satoshi</creator><general>Oxford University Press</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TM</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20071031</creationdate><title>Genotype Matrix Mapping: Searching for Quantitative Trait Loci Interactions in Genetic Variation in Complex Traits</title><author>Isobe, Sachiko ; Nakaya, Akihiro ; Tabata, Satoshi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c479t-2e3fd7f2d2cd197fff1441c13b52f78584e853ada43f7a2216b56af0f85ef42a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Algorithms</topic><topic>Alleles</topic><topic>Biological and medical sciences</topic><topic>Chromosome Mapping - methods</topic><topic>Chromosome Mapping - statistics & numerical data</topic><topic>Databases, Genetic</topic><topic>Flowers - growth & development</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Genes. Genome</topic><topic>Genetic Markers</topic><topic>Genetic Variation</topic><topic>Genetics of eukaryotes. Biological and molecular evolution</topic><topic>Genotype</topic><topic>Models, Genetic</topic><topic>Molecular and cellular biology</topic><topic>Molecular genetics</topic><topic>Phenotype</topic><topic>Quantitative Trait Loci</topic><topic>Trifolium - genetics</topic><topic>Trifolium - growth & development</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Isobe, Sachiko</creatorcontrib><creatorcontrib>Nakaya, Akihiro</creatorcontrib><creatorcontrib>Tabata, Satoshi</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Nucleic Acids Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>DNA research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Isobe, Sachiko</au><au>Nakaya, Akihiro</au><au>Tabata, Satoshi</au><au>Yano, M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Genotype Matrix Mapping: Searching for Quantitative Trait Loci Interactions in Genetic Variation in Complex Traits</atitle><jtitle>DNA research</jtitle><addtitle>DNA Res</addtitle><date>2007-10-31</date><risdate>2007</risdate><volume>14</volume><issue>5</issue><spage>217</spage><epage>225</epage><pages>217-225</pages><issn>1340-2838</issn><eissn>1756-1663</eissn><abstract>Abstract
In order to reveal quantitative trait loci (QTL) interactions and the relationship between various interactions in complex traits, we have developed a new QTL mapping approach, named genotype matrix mapping (GMM), which searches for QTL interactions in genetic variation. The central approach in GMM is the following. (1) Each tested marker is given a virtual matrix, named a genotype matrix (GM), containing intersecting lines and rows equal to the total allele number for that marker in the population analyzed. (2) QTL interactions are then estimated and compared through virtual networks among the GMs. To evaluate the contribution of marker combinations to a quantitative phenotype, the GMM method divides the samples into two non-overlapping subclasses, S
0 and S
1; the former contains the samples that have a specific genotype pattern to be evaluated, and the latter contains samples that do not. Based on this division, the F-measure is calculated as an index of significance. With the GMM method, we extracted significant marker combinations consisting of one to three interacting markers. The results indicated there were multiple QTL interactions affecting the phenotype (flowering date). GMM will be a valuable approach to identify QTL interactions in genetic variation of a complex trait within a variety of organisms.</abstract><cop>Oxford</cop><pub>Oxford University Press</pub><pmid>18000014</pmid><doi>10.1093/dnares/dsm020</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Alleles Biological and medical sciences Chromosome Mapping - methods Chromosome Mapping - statistics & numerical data Databases, Genetic Flowers - growth & development Fundamental and applied biological sciences. Psychology Genes. Genome Genetic Markers Genetic Variation Genetics of eukaryotes. Biological and molecular evolution Genotype Models, Genetic Molecular and cellular biology Molecular genetics Phenotype Quantitative Trait Loci Trifolium - genetics Trifolium - growth & development |
| title | Genotype Matrix Mapping: Searching for Quantitative Trait Loci Interactions in Genetic Variation in Complex Traits |
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