Establishing diagnostic platform for environmental biosafety assessment of genetically modified plants based on the decision-tree method

doi:10.3724/SP.J.1003.2010.215

Abstract

Abstract:

Transgenic biotechnology and its products provide important solutions for the great challenge of global food security. Biosafety assessment of genetically modified organisms (GMOs) including their food and environmental safety is a prerequisite for the commercialization and safe application of transgenic biotechnology products. However, existing methodologies cannot meet the urgent requirements for rapid biosafety assessment of the increasing number of new and sophisticated GMOs. Therefore, a new, more efficient and objective biosafety assessment methodology is needed. The decision tree, a widely used methodology for data mining and analysis, has a particular function in solving complicated problems. This article introduces the concepts, characteristics, and categorization of a decision tree, as well as methods for decision tree construction. Our objective is to explore the potential of establishing a diagnostic platform for biosafety assessment of GMOs in an efficient and accurate way. This biosafety assessment platform should also be useful for predicting the safety of the new generation of GMOs, and for educating the public on environmental biosafety, thereby providing a solid base for further development of transgenic technology and safe application of transgenic products.

Key words: decision making, transgenic crop, risk assessment, ecological risk, human-computer dialogue

Lei Wang, Chao Yang, Bao-Rong Lu. Establishing diagnostic platform for environmental biosafety assessment of genetically modified plants based on the decision-tree method[J]. Biodiv Sci, 2010, 18(3): 215-226.

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URL: https://www.biodiversity-science.net/EN/10.3724/SP.J.1003.2010.215

https://www.biodiversity-science.net/EN/Y2010/V18/I3/215

Figures/Tables 4

Fig. 1 A sketch map of decision tree assessing the risk of target gene escaping from transgenic crops to wild relatives. Root and internal nodes represent classification attributes such as wild relatives, outcrossing rate and trait of target gene, denoted by ellipse. Edges represent classification strategies such as the existence of wild relatives, denoted by arrows and the classification strategy options above. Terminal nodes represent classes namely classification results, denoted by triangle. The height of the entire decision tree is three, and the indication of the height for all nodes is provided at the right of the figure. Y, Present; N, Absent; A, Benefit; Nu, Neutral; D, Disbenefit.

Fig. 2 Another sketch map of decision tree assessing the risk of target gene escaping from transgenic crops to its wild relatives. Compared with , three classification attributes in this decision tree have different locations. Thus although established by the same training set, decision trees in and have distinct frameworks and complexity.

Table 1 An example of training set used for establishing a decision tree to assess environmental risks caused by transgene flow from genetically modified rice (Oryza sativa), soybean (Glycine max), and wheat (Triticum aestivum) to their wild relatives (O. rufipogon, G. soja,Aegilops tauschii)

转基因受体作物 Transgene recipient crop	释放环境(省份) Environment (province)	目标基因 Target gene	野生近缘种* Wild relatives*	野生种异交率 Outcrossing rate of wild relatives	转基因特性 Transgene characteristics	风险等级 Rank of risk
栽培稻 Rice	广西 Guangxi	cry1Ab	Y	<10%	A	IV
栽培稻 Rice	广西 Guangxi	psy	Y	<10%	Nu	I
栽培稻 Rice	广西 Guangxi	dam	Y	<10%	D	IV
栽培稻 Rice	山西 Shanxi	cry1Ab	N	<10%	A	I
栽培稻 Rice	山西 Shanxi	psy	N	<10%	Nu	I
栽培稻 Rice	山西 Shanxi	dam	N	<10%	D	I
大豆 Soybean	吉林 Jilin	cp4 epsps	Y	<2%	A	III
大豆 Soybean	吉林 Jilin	fad2	Y	<2%	Nu	I
大豆 Soybean	吉林 Jilin	barnase	Y	<2%	D	II
大豆 Soybean	青海 Qinghai	cp4 epsps	N	<2%	A	I
大豆 Soybean	青海 Qinghai	fad2	N	<2%	Nu	I
大豆 Soybean	青海 Qinghai	barnase	N	<2%	D	I
小麦 Wheat	新疆 Xinjiang	als	Y	<1%	A	II
小麦 Wheat	新疆 Xinjiang	bla	Y	<1%	Nu	I
小麦 Wheat	新疆 Xinjiang	TA29-barnase	Y	<1%	D	II
小麦 Wheat	辽宁 Liaoning	als	N	<1%	A	I
小麦 Wheat	辽宁 Liaoning	bla	N	<1%	Nu	I
小麦 Wheat	辽宁 Liaoning	TA29-barnase	N	<1%	D	I

Fig. 3 A road map of the establishment of the decision tree assessing the risk of target gene in transgenic crops escaping to wild relatives.

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