Global Regulatory Sciences, Monsanto Company, Chesterfield, Missouri, United States of America.
PLoS One. 2019 Jan 16;14(1):e0210747. doi: 10.1371/journal.pone.0210747. eCollection 2019.
The European Food Safety Authority (EFSA) mandates two sets of statistical tests in the comparative assessment of a genetically-modified (GM) crop: difference testing to demonstrate whether the GM crop is different from its appropriate non-traited control; and equivalence testing to demonstrate whether it is equivalent to conventional references with an history-of-safe-use. The equivalence testing method prescribed by EFSA confounds the so-called GM trait effect with genotypic differences between the reference varieties and non-traited control. Critically, these genotypic differences, which we define as a 'control background effect', are the result of conventional plant breeding. Thus, the result of EFSA equivalence testing often has little or nothing to do with the GM trait effect, which should be the sole focus of the comparative assessment. Here, an integrated method is introduced for both difference and equivalence testing that considers the differences of the three genotype groups (GM, control, and references) as a two-dimensional random variable. A novel statistical model is proposed, called the trait model, that treats the effects of the GM and control materials as fixed for their difference, and as random for their common background. For significance testing, the covariance structure of the three genotype groups is utilized to decompose the differences into the trait effect and the control background effect. The trait difference is then derived as a conditional mean, given the background effect. The comparative assessment can then focus on the conditional mean difference, which is independent of the control background effect. Furthermore, the trait model is flexible enough to include various types of genotype-by-environment (G×E) interactions inherent to the experimental design of the trial. Numerical evaluations and simulations show that this new method is substantially more efficient than the current EFSA method in reducing both Type I and Type II errors (protecting both the consumer and producer risk) after the background effect is removed from the test statistic, and successfully addresses two major criticisms (i.e. statistical model lack of G×E, and study-specific equivalence criterion) that have been raised.
欧洲食品安全局（EFSA）在转基因（GM）作物的比较评估中规定了两套统计测试：差异测试，以证明 GM 作物与其适当的非转基因对照品是否存在差异；等效性测试，以证明它是否与具有安全使用历史的常规对照品等效。EFSA 规定的等效性测试方法将所谓的 GM 性状效应与参考品种和非转基因对照品之间的基因型差异混淆在一起。关键是，这些基因型差异，我们称之为“对照背景效应”，是常规植物育种的结果。因此，EFSA 等效性测试的结果通常与 GM 性状效应几乎没有关系，而 GM 性状效应应该是比较评估的唯一重点。这里介绍了一种综合的方法，用于差异和等效性测试，将三个基因型组（GM、对照和参考）的差异视为二维随机变量。提出了一种新的统计模型，称为性状模型，该模型将 GM 和对照材料的效应视为固定的，用于它们的差异，而将它们的共同背景视为随机的。对于显著性检验，利用三个基因型组的协方差结构将差异分解为性状效应和对照背景效应。然后，将性状差异作为给定背景效应的条件均值推导出来。比较评估可以集中在条件均值差异上，而不依赖于对照背景效应。此外，性状模型足够灵活，可以包含试验设计中固有的各种类型的基因型与环境（G×E）相互作用。数值评估和模拟表明，在从测试统计中去除背景效应后，这种新方法在降低 I 型和 II 型错误（保护消费者和生产者风险）方面比当前的 EFSA 方法效率更高，并且成功解决了两个主要批评意见（即统计模型缺乏 G×E 和特定于研究的等效性标准）。
