Principal Variance Component Analysis of Crop Composition Data: A Case Study on Herbicide-Tolerant Cotton
Compositional studies on genetically modified (GM) and non-GM crops have consistently demonstrated that their respective levels of key nutrients and antinutrients are remarkably similar and that other factors such as germplasm and environment contribute more to compositional variability than transge...
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| Veröffentlicht in: | Journal of agricultural and food chemistry 2013-07, Vol.61 (26), p.6412-6422 |
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| container_issue | 26 |
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| container_title | Journal of agricultural and food chemistry |
| container_volume | 61 |
| creator | Harrison, Jay M Howard, Delia Malven, Marianne Halls, Steven C Culler, Angela H Harrigan, George G Wolfinger, Russell D |
| description | Compositional studies on genetically modified (GM) and non-GM crops have consistently demonstrated that their respective levels of key nutrients and antinutrients are remarkably similar and that other factors such as germplasm and environment contribute more to compositional variability than transgenic breeding. We propose that graphical and statistical approaches that can provide meaningful evaluations of the relative impact of different factors to compositional variability may offer advantages over traditional frequentist testing. A case study on the novel application of principal variance component analysis (PVCA) in a compositional assessment of herbicide-tolerant GM cotton is presented. Results of the traditional analysis of variance approach confirmed the compositional equivalence of the GM and non-GM cotton. The multivariate approach of PVCA provided further information on the impact of location and germplasm on compositional variability relative to GM. |
| doi_str_mv | 10.1021/jf400606t |
| format | Article |
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We propose that graphical and statistical approaches that can provide meaningful evaluations of the relative impact of different factors to compositional variability may offer advantages over traditional frequentist testing. A case study on the novel application of principal variance component analysis (PVCA) in a compositional assessment of herbicide-tolerant GM cotton is presented. Results of the traditional analysis of variance approach confirmed the compositional equivalence of the GM and non-GM cotton. The multivariate approach of PVCA provided further information on the impact of location and germplasm on compositional variability relative to GM.</description><identifier>ISSN: 0021-8561</identifier><identifier>EISSN: 1520-5118</identifier><identifier>DOI: 10.1021/jf400606t</identifier><identifier>PMID: 23647471</identifier><identifier>CODEN: JAFCAU</identifier><language>eng</language><publisher>Washington, DC: American Chemical Society</publisher><subject>analysis of variance ; Biological and medical sciences ; breeding ; case studies ; cotton ; crops ; Crops, Agricultural - chemistry ; Crops, Agricultural - genetics ; Crops, Agricultural - growth & development ; Crops, Agricultural - metabolism ; Drug Resistance ; Food industries ; Fundamental and applied biological sciences. Psychology ; germplasm ; Gossypium - chemistry ; Gossypium - genetics ; Gossypium - growth & development ; Gossypium - metabolism ; Herbicides ; nutrients ; Plants, Genetically Modified - chemistry ; Plants, Genetically Modified - growth & development ; Plants, Genetically Modified - metabolism ; Principal Component Analysis ; Seeds - chemistry ; Seeds - growth & development ; Seeds - metabolism ; United States ; variance</subject><ispartof>Journal of agricultural and food chemistry, 2013-07, Vol.61 (26), p.6412-6422</ispartof><rights>Copyright © 2013 American Chemical Society</rights><rights>2014 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a369t-b62faf936c703768f23d41c589b9346968c4d39c368c7de7dfdfefb36d646b5b3</citedby><cites>FETCH-LOGICAL-a369t-b62faf936c703768f23d41c589b9346968c4d39c368c7de7dfdfefb36d646b5b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/jf400606t$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/jf400606t$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>314,777,781,2752,27057,27905,27906,56719,56769</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27519478$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23647471$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Harrison, Jay M</creatorcontrib><creatorcontrib>Howard, Delia</creatorcontrib><creatorcontrib>Malven, Marianne</creatorcontrib><creatorcontrib>Halls, Steven C</creatorcontrib><creatorcontrib>Culler, Angela H</creatorcontrib><creatorcontrib>Harrigan, George G</creatorcontrib><creatorcontrib>Wolfinger, Russell D</creatorcontrib><title>Principal Variance Component Analysis of Crop Composition Data: A Case Study on Herbicide-Tolerant Cotton</title><title>Journal of agricultural and food chemistry</title><addtitle>J. Agric. Food Chem</addtitle><description>Compositional studies on genetically modified (GM) and non-GM crops have consistently demonstrated that their respective levels of key nutrients and antinutrients are remarkably similar and that other factors such as germplasm and environment contribute more to compositional variability than transgenic breeding. We propose that graphical and statistical approaches that can provide meaningful evaluations of the relative impact of different factors to compositional variability may offer advantages over traditional frequentist testing. A case study on the novel application of principal variance component analysis (PVCA) in a compositional assessment of herbicide-tolerant GM cotton is presented. Results of the traditional analysis of variance approach confirmed the compositional equivalence of the GM and non-GM cotton. The multivariate approach of PVCA provided further information on the impact of location and germplasm on compositional variability relative to GM.</description><subject>analysis of variance</subject><subject>Biological and medical sciences</subject><subject>breeding</subject><subject>case studies</subject><subject>cotton</subject><subject>crops</subject><subject>Crops, Agricultural - chemistry</subject><subject>Crops, Agricultural - genetics</subject><subject>Crops, Agricultural - growth & development</subject><subject>Crops, Agricultural - metabolism</subject><subject>Drug Resistance</subject><subject>Food industries</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>germplasm</subject><subject>Gossypium - chemistry</subject><subject>Gossypium - genetics</subject><subject>Gossypium - growth & development</subject><subject>Gossypium - metabolism</subject><subject>Herbicides</subject><subject>nutrients</subject><subject>Plants, Genetically Modified - chemistry</subject><subject>Plants, Genetically Modified - growth & development</subject><subject>Plants, Genetically Modified - metabolism</subject><subject>Principal Component Analysis</subject><subject>Seeds - chemistry</subject><subject>Seeds - growth & development</subject><subject>Seeds - metabolism</subject><subject>United States</subject><subject>variance</subject><issn>0021-8561</issn><issn>1520-5118</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpt0ctO3DAUBmALFcGUsuAFWm8qtYu0vsR20t0otFAJCSSg2-jEF-RRJk5tZzFvj6sZYNOVLZ9Pv4-PEbqg5BsljH7fuJoQSWQ-QisqGKkEpc07tCKlWDVC0lP0PqUNIaQRipygU8ZlrWpFV8jfRT9pP8OI_0D0MGmLu7Cdw2SnjNcTjLvkEw4OdzHM-1Ly2YcJX0KGH3iNO0gW3-fF7HA5vbZx8NobWz2E0UYoKV3IOUwf0LGDMdnzw3qGHn_9fOiuq5vbq9_d-qYCLttcDZI5cC2XWhGuZOMYNzXVommHlteylY2uDW81LxtlrDLOOOsGLo2s5SAGfoa-7HPnGP4uNuV-65O24wiTDUvqKW95w6RgrNCve6pjSCla18_RbyHuekr6f5PtXydb7MdD7DJsrXmVL6Ms4PMBQNIwuvJ07dObU4K2tWqK-7R3DkIPT7GYx3tGaLmIcsqleEsCnfpNWGL5hfSflp4BLo2VVw</recordid><startdate>20130703</startdate><enddate>20130703</enddate><creator>Harrison, Jay M</creator><creator>Howard, Delia</creator><creator>Malven, Marianne</creator><creator>Halls, Steven C</creator><creator>Culler, Angela H</creator><creator>Harrigan, George G</creator><creator>Wolfinger, Russell D</creator><general>American Chemical Society</general><scope>FBQ</scope><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>7X8</scope></search><sort><creationdate>20130703</creationdate><title>Principal Variance Component Analysis of Crop Composition Data: A Case Study on Herbicide-Tolerant Cotton</title><author>Harrison, Jay M ; Howard, Delia ; Malven, Marianne ; Halls, Steven C ; Culler, Angela H ; Harrigan, George G ; Wolfinger, Russell D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a369t-b62faf936c703768f23d41c589b9346968c4d39c368c7de7dfdfefb36d646b5b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>analysis of variance</topic><topic>Biological and medical sciences</topic><topic>breeding</topic><topic>case studies</topic><topic>cotton</topic><topic>crops</topic><topic>Crops, Agricultural - chemistry</topic><topic>Crops, Agricultural - genetics</topic><topic>Crops, Agricultural - growth & development</topic><topic>Crops, Agricultural - metabolism</topic><topic>Drug Resistance</topic><topic>Food industries</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>germplasm</topic><topic>Gossypium - chemistry</topic><topic>Gossypium - genetics</topic><topic>Gossypium - growth & development</topic><topic>Gossypium - metabolism</topic><topic>Herbicides</topic><topic>nutrients</topic><topic>Plants, Genetically Modified - chemistry</topic><topic>Plants, Genetically Modified - growth & development</topic><topic>Plants, Genetically Modified - metabolism</topic><topic>Principal Component Analysis</topic><topic>Seeds - chemistry</topic><topic>Seeds - growth & development</topic><topic>Seeds - metabolism</topic><topic>United States</topic><topic>variance</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Harrison, Jay M</creatorcontrib><creatorcontrib>Howard, Delia</creatorcontrib><creatorcontrib>Malven, Marianne</creatorcontrib><creatorcontrib>Halls, Steven C</creatorcontrib><creatorcontrib>Culler, Angela H</creatorcontrib><creatorcontrib>Harrigan, George G</creatorcontrib><creatorcontrib>Wolfinger, Russell D</creatorcontrib><collection>AGRIS</collection><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>MEDLINE - Academic</collection><jtitle>Journal of agricultural and food chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Harrison, Jay M</au><au>Howard, Delia</au><au>Malven, Marianne</au><au>Halls, Steven C</au><au>Culler, Angela H</au><au>Harrigan, George G</au><au>Wolfinger, Russell D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Principal Variance Component Analysis of Crop Composition Data: A Case Study on Herbicide-Tolerant Cotton</atitle><jtitle>Journal of agricultural and food chemistry</jtitle><addtitle>J. Agric. Food Chem</addtitle><date>2013-07-03</date><risdate>2013</risdate><volume>61</volume><issue>26</issue><spage>6412</spage><epage>6422</epage><pages>6412-6422</pages><issn>0021-8561</issn><eissn>1520-5118</eissn><coden>JAFCAU</coden><abstract>Compositional studies on genetically modified (GM) and non-GM crops have consistently demonstrated that their respective levels of key nutrients and antinutrients are remarkably similar and that other factors such as germplasm and environment contribute more to compositional variability than transgenic breeding. We propose that graphical and statistical approaches that can provide meaningful evaluations of the relative impact of different factors to compositional variability may offer advantages over traditional frequentist testing. A case study on the novel application of principal variance component analysis (PVCA) in a compositional assessment of herbicide-tolerant GM cotton is presented. Results of the traditional analysis of variance approach confirmed the compositional equivalence of the GM and non-GM cotton. The multivariate approach of PVCA provided further information on the impact of location and germplasm on compositional variability relative to GM.</abstract><cop>Washington, DC</cop><pub>American Chemical Society</pub><pmid>23647471</pmid><doi>10.1021/jf400606t</doi><tpages>11</tpages></addata></record> |
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| source | MEDLINE; ACS Publications |
| subjects | analysis of variance Biological and medical sciences breeding case studies cotton crops Crops, Agricultural - chemistry Crops, Agricultural - genetics Crops, Agricultural - growth & development Crops, Agricultural - metabolism Drug Resistance Food industries Fundamental and applied biological sciences. Psychology germplasm Gossypium - chemistry Gossypium - genetics Gossypium - growth & development Gossypium - metabolism Herbicides nutrients Plants, Genetically Modified - chemistry Plants, Genetically Modified - growth & development Plants, Genetically Modified - metabolism Principal Component Analysis Seeds - chemistry Seeds - growth & development Seeds - metabolism United States variance |
| title | Principal Variance Component Analysis of Crop Composition Data: A Case Study on Herbicide-Tolerant Cotton |
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