Literature DB >> 25196222

Compositional differences between near-isogenic GM and conventional maize hybrids are associated with backcrossing practices in conventional breeding.

Tyamagondlu V Venkatesh1, Kevin Cook, Bing Liu, Timothy Perez, Alan Willse, Ryan Tichich, Ping Feng, George G Harrigan.   

Abstract

Here, we show that differences between genetically modified (GM) and non-GM comparators cannot be attributed unequivocally to the GM trait, but arise because of minor genomic differences in near-isogenic lines. Specifically, this study contrasted the effect of three GM traits (drought tolerance, MON 87460; herbicide resistance, NK603; insect protection, MON 89034) on maize grain composition relative to the effects of residual genetic variation from backcrossing. Important features of the study included (i) marker-assisted backcrossing to generate genetically similar inbred variants for each GM line, (ii) high-resolution genotyping to evaluate the genetic similarity of GM lines to the corresponding recurrent parents and (iii) introgression of the different GM traits separately into a wide range of genetically distinct conventional inbred lines. The F1 hybrids of all lines were grown concurrently at three replicated field sites in the United States during the 2012 growing season, and harvested grain was subjected to compositional analysis. Proximates (protein, starch and oil), amino acids, fatty acids, tocopherols and minerals were measured. The number of statistically significant differences (α = 0.05), as well as magnitudes of difference, in mean levels of these components between corresponding GM variants was essentially identical to that between GM and non-GM controls. The largest sources of compositional variation were the genetic background of the different conventional inbred lines (males and females) used to generate the maize hybrids and location. The lack of any compositional effect attributable to GM suggests the development of modern agricultural biotechnology has been accompanied by a lack of any safety or nutritional concerns.
© 2014 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

Entities:  

Keywords:  Maize (Zea mays); compositional assessments; genetic modification; marker-assisted breeding; natural variability

Mesh:

Year:  2014        PMID: 25196222     DOI: 10.1111/pbi.12248

Source DB:  PubMed          Journal:  Plant Biotechnol J        ISSN: 1467-7644            Impact factor:   9.803


  9 in total

1.  Maize hybrids derived from GM positive and negative segregant inbreds are compositionally equivalent: any observed differences are associated with conventional backcrossing practices.

Authors:  Tyamagondlu V Venkatesh; Erin Bell; Anna Bickel; Kevin Cook; Benjamin Alsop; Martijn van de Mortel; Ping Feng; Alan Willse; Tim Perez; George G Harrigan
Journal:  Transgenic Res       Date:  2016-02       Impact factor: 2.788

2.  Evolution of risk assessment strategies for food and feed uses of stacked GM events.

Authors:  Catherine Kramer; Phil Brune; Justin McDonald; Monique Nesbitt; Alaina Sauve; Sabine Storck-Weyhermueller
Journal:  Plant Biotechnol J       Date:  2016-03-28       Impact factor: 9.803

Review 3.  Using Biotechnology-Led Approaches to Uplift Cereal and Food Legume Yields in Dryland Environments.

Authors:  Sangam L Dwivedi; Kadambot H M Siddique; Muhammad Farooq; Philip K Thornton; Rodomiro Ortiz
Journal:  Front Plant Sci       Date:  2018-08-27       Impact factor: 5.753

4.  Assessment of potential impacts associated with gene flow from transgenic hybrids to Mexican maize landraces.

Authors:  Bill Duncan; Elisa Leyva-Guerrero; Todd Werk; Duška Stojšin; Baltazar M Baltazar; Silverio García-Lara; Mariana Zavala-López; Juan Manuel de la Fuente-Martínez; Chen Meng
Journal:  Transgenic Res       Date:  2019-06-27       Impact factor: 2.788

Review 5.  Evaluation of the use of untargeted metabolomics in the safety assessment of genetically modified crops.

Authors:  Mohamed Bedair; Kevin C Glenn
Journal:  Metabolomics       Date:  2020-10-09       Impact factor: 4.290

6.  Transparency in risk-disproportionate regulation of modern crop-breeding techniques.

Authors:  Rod A Herman; Nicholas P Storer; Jennifer A Anderson; Firoz Amijee; Filip Cnudde; Alan Raybould
Journal:  GM Crops Food       Date:  2021-01-02       Impact factor: 3.074

7.  Introgressing cry1Ac for Pod Borer Resistance in Chickpea Through Marker-Assisted Backcross Breeding.

Authors:  Ajinder Kaur; Urvashi Sharma; Sarvjeet Singh; Ravinder Singh; Yogesh Vikal; Satnam Singh; Palvi Malik; Khushpreet Kaur; Inderjit Singh; Shayla Bindra; Bidyut Kumar Sarmah; Jagdeep Singh Sandhu
Journal:  Front Genet       Date:  2022-04-12       Impact factor: 4.772

Review 8.  Development of a construct-based risk assessment framework for genetic engineered crops.

Authors:  M P Beker; P Boari; M Burachik; V Cuadrado; M Junco; S Lede; M A Lema; D Lewi; A Maggi; I Meoniz; G Noé; C Roca; C Robredo; C Rubinstein; C Vicien; A Whelan
Journal:  Transgenic Res       Date:  2016-06-23       Impact factor: 2.788

9.  Evaluation of metabolomics profiles of grain from maize hybrids derived from near-isogenic GM positive and negative segregant inbreds demonstrates that observed differences cannot be attributed unequivocally to the GM trait.

Authors:  George G Harrigan; Tyamagondlu V Venkatesh; Mark Leibman; Jonathan Blankenship; Timothy Perez; Steven Halls; Alexander W Chassy; Oliver Fiehn; Yun Xu; Royston Goodacre
Journal:  Metabolomics       Date:  2016-03-15       Impact factor: 4.290

  9 in total

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