This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
Article Cite This: J. Agric. Food Chem. XXXX, XXX, XXX−XXX
pubs.acs.org/JAFC
EFSA Genetically Engineered Crop Composition Equivalence Approach: Performance and Consistency Rod A. Herman,*,†,‡ Emily Huang,‡ Brandon J. Fast,‡ and Carl Walker‡ †
Corteva Agriscience, Agriculture Division of DowDuPont, USA, 9330 Zionsville Road, Indianapolis, Indiana 46268, United States Corteva Agriscience, Agriculture Division of DowDuPont, USA, 8325 Northwest 62nd Avenue, Johnston, Iowa 50131, United States
Downloaded via 188.68.1.19 on March 30, 2019 at 12:51:49 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.
‡
ABSTRACT: The European Food Safety Authority (EFSA) oversees the safety assessment of genetically engineered (GE) crops in the European Union and has developed a study design and statistical approach for assessing the compositional equivalency between a GE crop and the corresponding non-GE crop on the basis of the results from a small number of concurrently grown reference lines. Confidence limits around the differences in mean analyte composition between the GE variety and the reference lines are compared with equivalence limits on the basis of the variability of the reference lines. Here, we evaluated the performance and consistency of the equivalence conclusions using a non-GE variety that is, by definition, equivalent to the non-GE crop. Using this approach across the same analytes with the same non-GE variety, it was found that equivalence could not be concluded for 19.7, 22.9, 25.4, and 53.5% of the analytes in four separate studies. In addition, equivalency conclusions for the same analyte often differed from study to study. These results call into question the consistency and value of this approach in the risk assessment of GE crops. KEYWORDS: genetically engineered, genetically modified, crop composition, equivalence, EFSA guidance, statistics
■
INTRODUCTION Crop-composition studies are almost universally required by regulatory agencies overseeing the safety assessment of genetically engineered (GE) crops. This requirement was instituted a quarter century ago as a result of the uncertainty as to whether transgenesis might cause unintended genetic effects beyond those caused by traditional breeding that could consequently alter crop composition in a way that might adversely affect safety.1 However, over 100 scientific studies on GE-crop composition have been conducted over the past 25 years and have clearly demonstrated that transgenesis is less likely to cause unexpected changes in crop composition compared with traditional breeding methods.2,3 Therefore, transgenesis has been shown to not create novel or increased risks compared with traditional breeding.2,4,5 Despite this wealth of evidence, the complexity and cost of crop-composition studies to support GE-crop approvals have increased tremendously over time, and some scientists are still suggesting even further complexity.6 The guidance on the design of crop-composition studies with GE crops authored by the European Food Safety Authority (EFSA) and adopted as regulation by the European Commission requires the largest and most complex experimental field design (a minimum of eight field sites with four replicate complete blocks per site, a non-GE isoline, three of at least six total non-GE commercial reference varieties per site, the GE line both sprayed and unsprayed for herbicide-tolerant traits, and no loss of any single field plot at a minimum of eight field sites)7 and is therefore the de facto design for these studies globally. Although classical difference tests using linear mixed models are used to compare test entries globally, EFSA has uniquely developed a novel approach for evaluating the equivalency of © XXXX American Chemical Society
the GE lines with the non-GE crop using the required concurrently grown non-GE reference varieties.8 Although difference tests between the GE line and isoline are a consideration under EFSA guidance,9 the results of the equivalence tests based on concurrently grown non-GE reference lines are the main determinant for whether a biological-relevance assessment must be conducted for each individual compositional analyte. EFSA guidance places equivalence limits into four categories as follows. (i) If the confidence limits around the difference between the GE-line mean and the reference-line mean (hereafter referred to simply as “difference”) fall within the equivalence interval based on the non-GE-reference-line variability (hereafter simply referred to as “equivalence interval” or “equivalence limits”), then equivalency can be concluded and no further assessment for that analyte is required. If (ii) one of the confidence limits for the difference falls outside the equivalence interval while the difference itself falls within the equivalence interval, (iii) the difference falls outside the equivalence interval while one confidence limit for the difference falls within the equivalence interval, or (iv) the entire confidence interval for the difference falls outside of the equivalence interval, then further assessment is required (see Figure 1 for examples). Thus, equivalence results are placed into four categories on the basis of a comparison between the GE line and non-GE reference lines, with only results falling into EFSA category i allowing a conclusion of equivalence. Received: Revised: Accepted: Published: A
January 7, 2019 March 9, 2019 March 21, 2019 March 21, 2019 DOI: 10.1021/acs.jafc.9b00156 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry Table 1. Soybean-Field-Trial Locations and Reference Varieties field-site locations field-site number site site site site site site site site site site
1 2 3 4 5 6 7 8 9 10
reference number reference reference reference reference reference reference
1 2 3 4 5 6
study 1 Lonoke, AR Richland, IA Carlyle, IL Wyoming, IL Rockville, IN La Plata, MO Dudley, MO York, NE
study 2
Sycamore, GA Richland, IA Richland, IA Atlantic, IA Bagley, IA Carlyle, IL Carlyle, IL Wyoming, IL Wyoming, IL Frankfort, IN Sheridan, IN Fisk, MO Deerfield, MI La Plata, MO Fisk, MO York, NE Brunswick, NE Brunswick, NE York, NE Germansville, PA reference variety (field-site numbers)
study 4 Richland, IA Atlantic, IA Carlyle, IL Wyoming, IL Sheridan, IN Kirksville, MO Fisk, MO York, NE Germansville, PA
study 1
study 2
study 3
study 4
Arise 9E394 (1, 2, 4, 5) HiSOY 38C60 (3, 4, 5, 6, 7) Hoffman H387 (5, 6, 8) LG Seeds C3884N (1, 2, 3, 6) Phillips 363 (2, 4, 7, 8) Pioneer 93M62 (1, 3, 7, 8)
DSR-75213-72 (1, 3, 7, 8, 9) DSR-98860-7 (4, 5, 7, 9, 10) DSR-99914N (1, 3, 4, 5, 6) DSR-99915 (2, 3, 6, 7, 10) Porter 75148 (1, 2, 5, 8, 10) Williams 82 (2, 4, 6, 8, 9)
DSR-75213-72 (1, 3, 5, 8, 9) HiSOY 38C60 (2, 5, 6, 8, 10) IL3503 (1, 2, 4, 7, 8) Pioneer 93M62 (2, 3, 4, 6, 9) Porter 75148 (1, 3, 6, 7, 10) Williams 82 (4, 5, 7, 9, 10)
DSR-3510 (1, 2, 4, 6, 7) Dyna-Gro 3410SCN (2, 4, 8, 9) Dyna-Gro V388SCN (3, 6, 7, 9) L&M 34 (2, 3, 5, 7, 8) Pioneer 93Y41 (1, 3, 4, 5) Stine 3900-2 (1, 5, 6, 8, 9)
carried out by Covance Laboratories Inc. (Madison, WI) under U.S. EPA Good Laboratory Practices using validated methods, as described previously.3,11,12 Confidence limits around the difference in the mean composition for Maverick and the overall mean for the reference lines were generated for each compositional analyte in each study, and equivalence limits intended to represent the variability for the composition of non-GE crop were generated using the non-GE reference lines included in each study, as outlined by EFSA.7,8 Statistical analyses were conducted using SAS software, Version 9.4 (SAS Institute, Cary, NC). Statistical-Assumption Evaluation and Remedies. The graphical assessment of conditional studentized residuals, calculated using the GLIMMIX procedure in SAS, allowed assessment of model fit in terms of the assumption of normally and independently distributed errors with homogeneous variance. The normality assumption was evaluated by visual examination of residual normal probability plots. The homoscedasticity assumption was evaluated by visual examination of the side-by-side studentized residual box plots by sites and of scatter plots of studentized residuals by predicted values. The independence assumption was evaluated using the scatter plots of studentized residuals by predicted values. Methods for visual assessment of model assumptions are described by Dean and Voss.13 Each analyte was examined to determine if an appropriate transformation would remedy adherence to the normality and homoscedasticity assumptions. An alternative “site-heteroscedastic” variance structure was considered if visual assessments indicated evidence of heterogeneous error variance. The site-heteroscedastic variance structure differs only in that the residual variance assumption was changed to εijgk ∼ iid N(0, Vε,j), where the residual variance is a function of site j. Values of the corrected Akaike’s information criterion14 were used to flag end points where a site-heteroscedastic variance structure might be more appropriate, to ensure no end points were accidentally overlooked in visual assessments. On the basis of visual assessment, multiple analytes were concluded to show sufficiently heterogeneous error variances to warrant the site-heteroscedastic variance structure. A summary of the applied transformation and variance structure is shown in Table 2 for each analyte included in the mixed-model analysis. Data Interpretation. The frequency of concluding equivalence (and various degrees of nonequivalence) and the consistency among equivalence results for each analyte across studies were used to measure the performance and consistency of the EFSA statisticalequivalence methods and interpretation.
Here, we evaluate the performance and consistency of results using the EFSA compositional-equivalency approach by making use of a single non-GE isoline incorporated into four separate studies across different growing seasons. The isoline is a non-GE variety with genetics similar to the GE variety and, by definition, equivalent to the non-GE crop; thus an inability to conclude equivalence between this non-GE isoline variety and the non-GE crop would indicate a weakness for this method. In addition, the level of consistency of findings across studies provides a measure of the reliability of using a small number of non-GE reference lines to estimate the compositional variability of the non-GE crop. Although tangential to this investigation, it should be noted that the representativeness of the composition of non-GE varieties in predicting the composition of crops where the great majority (>90%) of the crop grown is GE (e.g., corn, cotton, and soybean in the United States)10 is questionable because non-GE varieties for these crops have become specialty or novelty items, and the background genetics of these non-GE varieties may be different from the majority of the crop that is currently being produced and consumed safely. Furthermore, the available number and genetic diversity of non-GE varieties may be limited because of their cultivation predominantly for niche markets.
■
study 3
MATERIALS AND METHODS
General Approach. Four field studies were conducted over four years (2009−2012) using the non-GE soybean variety Maverick as the isoline for various GE events and breeding stacks being evaluated for compositional equivalence under the EFSA protocol.3,11,12 Studies conducted in 2009, 2010, 2011, and 2012 are referred to herein as study 1, study 2, study 3, and study 4, respectively. Each study included a minimum of eight field sites with four complete blocks per site. Six total non-GE reference lines were included per study, with three planted at each field site (Table 1). The non-GE reference lines were obtained from numerous commercial seed sources to help ensure that diverse genetic backgrounds would be represented, and reference lines were similar in relative maturity to Maverick. The same 71 compositional analytes (Table 2) were measured in each study (with the exception of study 1 where selenium was not measured) using methods previously described.12 Analyte quantitation was B
DOI: 10.1021/acs.jafc.9b00156 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry Table 2. Model-Selection Information transformation applied, variance structure category
analyte
proximate proximate proximate proximate proximate fiber fiber mineral mineral
ash carbohydrates crude fat moisture protein ADF NDF calcium phosphorus
proximate proximate proximate proximate proximate fiber fiber fiber amino acid amino acid amino acid amino acid amino acid amino acid amino acid amino acid amino acid amino acid amino acid amino acid amino acid amino acid amino acid amino acid amino acid amino acid fatty acid fatty acid fatty acid fatty acid fatty acid fatty acid fatty acid fatty acid mineral mineral mineral mineral mineral mineral mineral mineral mineral vitamin vitamin vitamin vitamin vitamin
ash carbohydrates crude fat moisture protein ADF NDF total dietary fiber alanine arginine aspartic acid cystine glutamic acid glycine histidine isoleucine leucine lysine methionine phenylalanine proline serine threonine tryptophan tyrosine valine 16:0 palmitic 18:0 stearic 18:1 oleic 18:2 linoleic 18:3 linolenic 20:0 arachidic 20:1 eicosenoic 22:0 behenic calcium copper iron magnesium manganese phosphorus potassium selenium zinc α-tocopherol (vitamin E) δ-tocopherol γ-tocopherol total tocopherol vitamin B1 (thiamine HCl)
study 1 Forage none, s-ha none, s-h none, s-h none, s-h none, h none, h none, s-h ln(Y), s-h none, s-h Seed none, s-h none, s-h none, h none, s-h ln(Y), s-h none, s-h none, s-h none, h none, h none, h none, h none, s-h none, h none, h none, s-h none, h none, h none, s-h none, h none, h none, h none, h none, h none, h none, h none, h none, s-ha none, s-h none, s-h none, s-h none, s-h none, s-h Y3, s-h none, s-h sqrt(y), s-h none, s-h ln(Y), s-h none, h ln(Y), s-h none, s-h none, h none, s-h none, s-h none, s-h none, s-h none, s-h none, h
C
study 2 none, none, none, none, none, none, none, none, none,
s-h s-h s-h s-h s-h h h h h
none, s-h none, h none, h none, s-h Y, s-h none, s-h none, s-h none, h none, s-h none, h none, h none, h none, h none, h none, h none, h none, h none, s-h none, h none, h none, s-h none, h none, h none, h none, h none, h none, hb none, s-h none, s-h ln(Y), s-h none, s-h none, s-h none, s-h none, s-h none, s-h none, s-h ln(Y), s-h none, h ln(Y), s-h none, h none, s-h ln(Y), s-h none, s-h ln(Y), s-h none, s-h none, s-h none, s-h none, s-h
study 3
study 4
none, s-h none, hb none, h none, s-h none, s-h none, s-h none, s-h ln(Y), s-h none, h
none, none, none, none, none, none, none, none, none,
s-h s-h s-h s-h h h h s-h h
none, s-h none, s-h none, s-h ln(Y), s-h none, s-h none, h none, s-h none, s-h none, h none, h none, s-h none, s-h none, h none, h none, s-h none, h none, h none, s-h none, s-h none, h none, s-h none, s-h none, s-h none, h none, h none, s-h none, s-h none, s-h none, s-h none, s-h none, s-h none, s-h Y3, s-h none, h none, s-h none, s-h ln(Y), s-h none, h none, s-h none, s-h none, h sqrt(y), s-h none, s-h none, s-h none, s-h none, s-h none, s-h none, s-h
none, s-h none, h none, s-h none, s-h none, s-h none, h none, h none, h none, s-h none, s-h none, s-h none, s-h none, h none, h none, h none, h none, s-h none, s-h none, s-h none, h none, h none, s-h none, s-h none, h none, h none, s-h none, s-h none, s-h none, s-h none, s-h none, s-h none, s-h none, h none, h none, s-h none, s-h none, s-h none, s-h none, s-h none, s-h none, h sqrt(y), h none, s-h none, s-h none, h none, s-h none, s-h none, s-h
DOI: 10.1021/acs.jafc.9b00156 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry Table 2. continued transformation applied, variance structure category vitamin vitamin vitamin vitamin vitamin vitamin bioactive bioactive bioactive bioactive bioactive bioactive bioactive bioactive
analyte vitamin B2 (riboflavin) vitamin B3 (niacin) vitamin B5 (pantothenic acid) vitamin B6 (pyridoxine HCl) vitamin B9 (folic acid) vitamin C (ascorbic acid) lectin phytic acid raffinose stachyose total daidzein equivalent total genistein equivalent total glycitein equivalent trypsin inhibitor
study 1 Seed none, h none, s-h none, s-h none, s-h none, s-h none, s-h none, s-h none, h none, s-h none, h none, h none, h none, s-h none, s-h
study 2
study 3
study 4
none, h none, s-h none, s-h none, s-h none, h none, s-h ln(Y), h none, h none, s-h none, s-h ln(Y), s-h none, h ln(Y), s-h ln(Y), h
none, s-h none, s-h none, s-h none, s-h none, s-h none, s-h none, s-h none, s-h none, s-h none, s-h ln(Y), s-h ln(Y), s-h none, s-h ln(Y), h
none, s-h none, s-h none, s-h none, h none, s-h none, s-h none, s-h none, h none, s-h none, s-h ln(Y), h none, h none, h none, h
a
Site heteroscedastic. bHomoscedastic.
■
RESULTS Approach. A non-GE variety of soybean (Maverick) was included in multiple field trials and was used here to assess the performance of the EFSA approach for evaluating the compositional equivalence of GE lines to the non-GE crop on the basis of a small number of concurrently grown non-GE reference lines. Because Maverick is a non-GE variety, the frequency of instances where equivalence cannot be concluded for this variety when compared with the non-GE reference lines provides a real-world evaluation of the background “noise” present using this approach. The evaluation of multiple studies using this same non-GE variety also allows the consistency of equivalence results to be assessed for the same analyte across different experiments. Firm Conclusion of Equivalence. Under EFSA guidance, equivalence can be concluded only in cases where the confidence intervals for the difference between the means for the GE line and reference lines fall within the equivalence limits based on the variability of these reference lines (category i). Category i results were observed in all four studies with the non-GE variety Maverick for 27 of the 71 analytes (38.0%) evaluated across the four studies (Table 3). For some analytes in some studies, equivalence intervals could not be constructed because of estimates of zero variance being attributable to reference varieties. This situation essentially equates to an equivalence interval with a breadth of zero around the point of no difference, resulting in the requirement of providing a biological-relevance argument for those analytes where this occurs. Estimates of zero variance occurred for some analytes accompanied by a conclusion of category i in other studies (6, 4, 2, and 11 times in studies 1, 2, 3, and 4, respectively), resulting in 42 analytes with category i or zero variance estimated for the reference lines across studies (Table 3). Note that a single analyte (selenium) in this latter group was not evaluated in study 1 but was found to be category i in study 2 with zero variability for the reference lines estimated in studies 3 and 4. Likely Conclusion of Equivalence. Under EFSA guidance, when the difference falls within the equivalence limits, but the confidence interval around this difference does not fall completely within the equivalence limits (category ii), the GE variety is described as likely equivalent to the reference
lines; however, a biological-relevance argument is required for such analytes because equivalence cannot be concluded. Sixteen of the 71 analytes (22.5%) evaluated across the four studies had this conclusion for the non-GE Maverick variety in at least one study, and no study in this group had the mean for these analytes fall outside of the equivalence limits (all categories i and ii). In studies 1, 2, 3, and 4, category ii outcomes were observed in this group of analytes 3, 7, 3, and 10 times, respectively (Table 4). However, within this group of 16 analytes, equivalence intervals could not be constructed as a result of zero variance being attributable to reference varieties five times in study 4, again requiring a biological-relevance argument to be provided. Conclusion of Nonequivalence. Under EFSA guidance, when the difference between the mean for the GE variety and the mean for the reference lines falls outside of the equivalence limits representing the reference-line variability, and the confidence interval overlaps the equivalence interval (category iii) or lies completely outside of the equivalence limits (category iv), the GE variety is described as likely not equivalent to the reference lines or not equivalent to the reference lines, respectively. Thirteen of the 71 analytes (18.3%) evaluated across the four studies had category iii or iv outcomes for the non-GE Maverick variety in at least one study. However, within this group of 13 analytes, equivalence intervals could not be constructed because zero variance was attributable to reference varieties three times in study 4. Consistency of Equivalence Conclusions. Of the 71 analytes evaluated across the four studies, 27 (38.0% of the 71) had category i outcomes in all four studies, indicating strong evidence of equivalence and that the analytes in question did not require a discussion of biological relevance. Eleven (15.5% of the 71) analytes gave category ii outcomes in at least one study along with category i outcomes in the other studies; although equivalence between the non-GE variety Maverick and the reference lines was at least likely for these analytes in the studies with category ii outcomes, these cases required a biological-relevance argument to be presented to meet EFSA requirements. Of the 71 analytes evaluated across the four studies, 13 (18.3%) had an outcome of likely nonequivalence or not equivalent (category iii or iv) in at least one study. For eight of D
DOI: 10.1021/acs.jafc.9b00156 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry
Table 3. Equivalence Results and Direction of Difference of Maverick from the Reference Lines For All Studies Giving Category i Equivalence Results with or without Some Studies Estimating Zero Variance for the Reference Lines category (direction of difference) tissue
analyte
study 1
forage forage forage forage forage forage forage forage seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed
ADF ash carbohydrates crude fat moisture NDF protein calcium ADF ash carbohydrates moisture NDF protein total dietary fiber aspartic acid cystine isoleucine lysine methionine proline serine tryptophan tyrosine 16:0 palmitic 22:0 behenic copper iron magnesium manganese selenium α-tocopherol (vitamin E) γ-tocopherol total tocopherols vitamin B1 (thiamine HCl) vitamin B2 (riboflavin) vitamin B5 (pantothenic acid) vitamin B6 (pyridoxine HCl) vitamin B9 (folic acid) stachyose total daidzein equivalent trypsin inhibitor
i (↓) i (↓) i (↑) i (↑) i (↓) 0 variance (↑) i (↑) i (↑) 0 variance (↓) i (↑) i (↑) i (↑) 0 variance (↑) i (↑) i (↑) 0 variance (↑) i (↑) i (↑) 0 variance (↓) i (↑) i (↓) 0 variance (↑) i (↑) i (↑) i (↑) i (↑) i (↑) i (↑) i (↑) i (↓) missing (−) i (↑) i (↓) i (↓) i (↑) i (↓) i (↑) i (↑) i (↓) i (↓) i (↓) i (↑)
study 2 i (↑) i (↑) i (↑) 0 variance i (↑) 0 variance i (↓) i (↑) i (↑) i (↑) i (↑) i (↓) i (↑) i (↓) i (↑) i (↓) i (↑) 0 variance 0 variance i (↑) i (↓) i (↑) i (↑) i (↑) i (↑) i (↓) i (↑) i (↑) i (↓) i (↓) i (↑) i (↑) i (↑) i (↓) i (↑) i (↑) i (↑) i (↑) i (↑) i (↓) i (↓) i (↑)
study 3
(↑) (↓)
(↑) (↓)
i (↑) i (↓) i (↑) i (↓) i (↓) i (↑) i (↓) i (↑) i (↑) i (↓) i (↑) i (↑) i (↑) i (↓) i (↑) i (↑) i (↑) i (↑) 0 variance (↓) i (↑) i (↓) i (↑) i (↑) i (↑) i (↑) i (↓) i (↑) i (↑) i (↓) i (↓) 0 variance (↑) i (↑) i (↑) i (↓) i (↑) i (↓) i (↑) i (↑) i (↓) i (↓) i (↓) i (↓)
study 4 0 variance i (↓) i (↑) 0 variance 0 variance i (↑) 0 variance i (↑) i (↓) i (↓) i (↑) i (↑) 0 variance i (↓) i (↑) i (↓) i (↑) 0 variance i (↓) i (↑) 0 variance 0 variance 0 variance i (↑) i (↑) i (↓) i (↑) i (↑) i (↑) i (↓) 0 variance i (↑) i (↑) i (↓) i (↓) 0 variance i (↑) i (↓) i (↓) i (↓) i (↓) i (↑)
(↑)
(↓) (↓) (↓)
(↓)
(↑)
(↓) (↑) (↑)
(↓)
(↓)
GE crop by definition) and suggests that observations of nonequivalence are artifactual. This indicates that the approach of estimating equivalence based on a small set of reference lines is responsible for these inconsistent outcomes. Directionality of Differences. The mean of the non-GE variety Maverick differed from the mean of the non-GE reference lines in a consistent direction (higher or lower) across studies for 46 (64.8%) of the 71 analytes (Tables 3 and 4). In all but four cases, where inconsistent directionality occurred, category i or an estimated zero variance for reference lines was concluded in all studies. This suggests that the mean for the non-GE Maverick variety was similar to that of the included reference lines for these analytes, and this similarity favored confidence intervals for the mean falling within the equivalence limits for the reference lines (conclusion of
these analytes, only one study out of the four fell in these categories. For glutamic acid in seed, two studies (2 and 4) resulted in category iv outcomes, whereas the remaining two studies resulted in category ii outcomes. Glycine gave a category i outcome in study 1, a category iii outcome in studies 2 and 3, and an estimated zero variance for the reference lines in study 4. Three analytes (threonine, 18:0 stearic, and 18:2 linoleic) had category iii and iv outcomes in three of the four studies, and each had a category ii outcome in study 2. Therefore, a consistent conclusion of nonequivalence between the non-GE Maverick variety and the non-GE crop was not obtained for any analyte across the four studies. This lack of consistency in observing nonequivalence for the non-GE variety Maverick compared with the non-GE crop is expected for a non-GE variety (which is equivalent to the nonE
DOI: 10.1021/acs.jafc.9b00156 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry
Table 4. Equivalence Results and Direction of Difference of Maverick from the Reference Lines Where at Least One Study Gave Category ii, iii, or iv Equivalence Results category (direction of difference) category grouping
tissue
analyte
study 1
study 2
study 3
study 4
three studies ≥ category iii
seed seed seed seed seed seed seed seed seed seed seed seed seed forage seed seed seed seed seed seed seed seed seed seed seed seed seed seed seed
threonine 18:0 stearic 18:2 linoleic glutamic acid glycine histidine valine 18:1 oleic 20:0 arachidic 20:1 eicosenoic lectin total genistein equivalent total glycitein equivalent phosphorus crude fat alanine arginine leucine phenylalanine 18:3 linolenic calcium phosphorus potassium zinc δ-tocopherol vitamin B3 (niacin) vitamin C (ascorbic acid) phytic acid raffinose
iv (↑) iv (↑) iv (↓) ii (↓) i (↑) ii (↑) i (↑) ii (↑) iii (↑) i (↓) i (↑) i (↑) i (↑) ii (↓) i (↓) i (↑) ii (↓) i (↑) i (↓) i (↓) i (↑) i (↑) i (↓) i (↑) i (↓) i (↑) i (↓) i (↑) ii (↓)
ii (↑) ii (↑) ii (↓) iv (↓) iii (↑) ii (↑) iii (↑) i (↑) i (↑) i (↓) i (↑) i (↓) i (↑) i (↑) ii (↑) ii (↑) ii (↓) i (↑) ii (↓) i (↓) ii (↑) i (↑) i (↓) i (↑) i (↓) i (↑) ii (↑) ii (↑) i (↓)
iii (↑) iii (↑) iii (↓) ii (↓) iii (↑) iii (↑) ii (↑) i (↑) i (↑) iii (↓) ii (↑) i (↓) i (↑) i (↓) i (↓) i (↑) ii (↓) i (↑) i (↓) i (↓) i (↑) i (↑) ii (↓) i (↑) i (↓) i (↑) ii (↑) i (↑) i (↓)
iv (↑) iv (↑) iv (↓) iv (↓) 0 variance 0 variance 0 variance iv (↑) ii (↑) i (↓) iii (↑) iii (↓) iii (↑) 0 variance i (↓) ii (↑) ii (↓) ii (↑) 0 variance ii (↓) ii (↑) ii (↑) ii (↓) ii (↑) ii (↓) ii (↑) 0 variance 0 variance 0 variance
two studies ≥ category iii one study ≥ category iii
all studies category i or ii
(↑) (↑) (↑)
(↓)
(↓)
(↑) (↑) (↓)
differences be further evaluated for safety on the basis of the variability of the crop as a whole. Of the 71 compositional analytes measured across these four studies for the non-GE Maverick line (used as an isoline for GE varieties), 44 (62.0%) resulted in the inability to conclude equivalence in at least one of the four studies (categories ii, iii, iv, or zero estimate for reference-line variability). If the composition of the GE variety was identical to the non-GE Maverick isoline, then this same inability to conclude equivalence would occur for the GE variety. In studies 1, 2, 3, and 4, equivalence could be concluded for 54 (77.1%), 53 (74.6%), 57 (80.3%), and 33 (46.5%) of the 71 analytes (70 for study 1), respectively. Approximately the same result is expected for the GE varieties in these studies. Figure 1 graphically provides examples of differing conclusions on compositional equivalence for the same analyte across different studies.
equivalence). It has been previously noted for the EFSA equivalence test for GE crops that “the conformation of the composition of the isoline (background genetics of GE line) to the average composition (or the compositional distribution) of the chosen reference lines may largely determine the results of such statistical tests”.5 The results presented here support that premise. Recently, methods to control for this confounding of GE-trait effects with the variability introduced through traditional breeding have been proposed.15 Implications for Compositional Equivalence of GE Varieties. The non-GE Maverick soybean variety was included as an isoline in four studies designed to evaluate the compositional normalcy of GE-varieties that were transformed into this variety. In these cases, Maverick was used to evaluate the composition of the background genetics of the variety into which the GE traits were transformed. However, it is noteworthy that the Maverick variety (or any isoline) is not perfectly matched with the GE line because the GE line is generated from a single somatic cell in tissue culture and therefore is subject to somaclonal variation. In addition, intravarietal variation is known to exist, even in commercial varieties, such that a single seed selection from that isoline is expected to differ somewhat in composition from the variety from which it originated.16,17 Thus, with enough statistical power, these expected differences between the composition of the isoline and matched non-GE isoline will frequently be statistically detected.9 Thus, it is critical that any significant
■
DISCUSSION Globally, compositional equivalence is evaluated using difference testing between the GE variety and its non-GE isoline. When a difference is significant, the normal compositional variability of the crop is considered on the basis of the greater knowledge of composition for that crop. For this reason, a curated public database of non-GE-crop-composition results was created by the International Life Sciences Institute (ILSI).18−20 However, EFSA uniquely requires use of a newly developed method to estimate the variability of the F
DOI: 10.1021/acs.jafc.9b00156 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry
Figure 1. Across-study comparison of non-GE Maverick soybean equivalence to non-GE soybean reference lines for two amino acids and one fatty acid illustrating disparate equivalency conclusions. Plots are styled after EFSA guidance. The solid horizontal line at zero marks the point of no difference from the reference-line mean. Diamonds connected by a vertical line mark reference-line equivalence intervals (a single diamond indicates zero variance for the reference lines), solid circles mark Maverick mean differences from reference lines, and dashes mark Maverick confidence limits. EFSA equivalence categories are shown in Roman numerals (“nc” indicates that the variability for the reference lines was estimated to be zero). Note that in study 3, a natural-logarithm transformation was applied to oleic acid data to ensure that model assumptions were met, which reduced the magnitude of relative differences and associated confidence and equivalence intervals.
non-GE crop using a small number of reference lines included in the regulatory study conducted with the specific GE variety. Here, we used the results of four independent composition studies to evaluate the performance and consistency of EFSA equivalence outcomes for a single non-GE line (Maverick), which was included in the studies as an isoline (for the GE lines being evaluated in the regulatory studies). Maverick is a non-GE variety, so failure to conclude equivalence to the nonGE crop is a weakness in the method. Approximately 60% of analytes consistently appeared equivalent between the non-GE variety (Maverick) and non-GE reference varieties on the basis of the EFSA guidance; however, zero variance attributable to variety prevented calculation of equivalence intervals in some studies, which further reduced the ability to conclude equivalence. The remaining 40% of the analytes would require a biological-relevance discussion in at least one of the four studies on the basis of the equivalence test, with additional discussion for those analytes where equivalence limits could not be calculated. Overall, in studies 1, 2, 3, and 4, 16 (22.9%), 18 (25.4%), 14 (19.7%), and 38 (53.5%) analytes in the non-GE Maverick variety would require a biological-relevance argument because of an inconclusive finding of equivalence (category ii), a false conclusion of likely nonequivalence or nonequivalence (category iii or iv, respectively), or the inability to construct equivalence intervals because zero variance was attributable to reference varieties (Figure 2). Such high noise levels appear to be inherent in the EFSA equivalence approach and are most likely attributable to (1) the limited genetic diversity for the
reference lines, (2) differences in the variability across and within growing environments from study to study, (3) how the composition of the reference lines compares with the compositional diversity of the crop as a whole, and (4) how the genetics of the reference lines compares with the background genetics of the GE variety (non-GE genetics of the isoline).9 Perhaps for this reason, some EFSA scientists are beginning to advocate for the use of a non-GE-cropcomposition database similar to the one hosted by ILSI, which is used by regulators in other parts of the world to establish reference intervals.21 It is noteworthy that the majority of the data contained in the ILSI crop-composition database originated from regulatory studies that included nonGE reference lines. The results of our investigation call into question the ability of the current EFSA equivalency approach to selectively or consistently identify true nonequivalence between the composition of a GE variety and the corresponding non-GE crop. The requirements to include concurrently grown non-GE varieties in composition studies for GE crops are unduly burdensome as they do not provide increased confidence in safety, especially in light of a quarter century of research indicating that crop composition is less affected by transgenic breeding compared with traditional breeding.22 It has been suggested that the range of analyte values from a sample of varieties and growing environments, such as those included in the ILSI crop-composition database, is too wide to protect against the risk associated with adversely altering crop composition.21 However, extreme composition values within a G
DOI: 10.1021/acs.jafc.9b00156 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry
Figure 2. Distribution of equivalence conclusions (Roman numerals) across studies for the non-GE Maverick variety. The letters “nc” indicate that the variability for the reference lines was estimated to be zero.
■
sample that accurately represent rare compositions are necessarily less extreme than those safely found in the much larger population from which they were sampled.23 As such, any method used to narrow the reference interval in support of compositional safety should aim to exclude only erroneous data rather than excluding an arbitrary percentage in the extremes of the compositional distribution. Such methods might be based on outlier tests (perhaps nonparametric), but validation of such methods as being fit for purpose should be completed before their mandatory implementation in regulation to avoid poor performance under real-world use.
■
REFERENCES
(1) Safety evaluation of foods derived by modern biotechnology: concepts and principles; Organisation for Economic Co-operation and Development, 1993. (2) Herman, R. A.; Price, W. D. Unintended compositional changes in genetically modified (GM) crops: 20 years of research. J. Agric. Food Chem. 2013, 61, 11695−11701. (3) Fast, B. J.; Schafer, A. C.; Johnson, T. Y.; Potts, B. L.; Herman, R. A. Insect-Protected Event DAS-81419−2 Soybean (Glycine max L.) Grown in the United States and Brazil Is Compositionally Equivalent to Nontransgenic Soybean. J. Agric. Food Chem. 2015, 63, 2063−2073. (4) Schnell, J.; Steele, M.; Bean, J.; Neuspiel, M.; Girard, C.; Dormann, N.; Pearson, C.; Savoie, A.; Bourbonniere, L.; Macdonald, P. A comparative analysis of insertional effects in genetically engineered plants: considerations for pre-market assessments. Transgenic Res. 2015, 24, 1−17. (5) Herman, R. A.; Fast, B. J.; Scherer, P. N.; Brune, A. M.; de Cerqueira, D. T.; Schafer, B. W.; Ekmay, R. D.; Harrigan, G. G.; Bradfisch, G. A. Stacking transgenic event DAS-Ø15Ø7−1 alters maize composition less than traditional breeding. Plant Biotechnology Journal 2017, 15, 1264−1272. (6) Christ, B.; Pluskal, T.; Aubry, S.; Weng, J.-K. Contribution of untargeted metabolomics for future assessment of biotech crops. Trends Plant Sci. 2018, 23, 1047−1056. (7) Commission Implementing Regulation (EU) No 503/2013 of 3 April 2013 on applications for authorisation of genetically modified food and feed in accordance with Regulation (EC) No 1829/2003 of
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Tel.: 317-337-3551. ORCID
Rod A. Herman: 0000-0002-2201-1048 Notes
The authors declare the following competing financial interest(s): The authors work for a company that develops and markets GE seed. H
DOI: 10.1021/acs.jafc.9b00156 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry the European Parliament and of the Council and amending Commission Regulations (EC) No 641/2004 and (EC) No 1981/ 2006. Off. J. Eur. Union 2013, L157, 1. (8) van der Voet, H.; Perry, J. N.; Amzal, B.; Paoletti, C. A statistical assessment of differences and equivalences between genetically modified and reference plant varieties. BMC Biotechnol. 2011, 11, 15. (9) Herman, R. A.; Fast, B. J.; Mathesius, C.; Delaney, B. Isoline use in crop composition studies with genetically modified crops under EFSA guidance − Short communication. Regul. Toxicol. Pharmacol. 2018, 95, 204−206. (10) James, C. Global status of commercialized biotech/GM crops: 2016; ISAAA Brief 52; International Service for the Acquisition of Agri-biotech Applications, 2016. (11) Lepping, M. D.; Herman, R. A.; Potts, B. L. Compositional Equivalence of DAS-444Ø6−6 (AAD-12 + 2mEPSPS + PAT) Herbicide-Tolerant Soybean and Nontransgenic Soybean. J. Agric. Food Chem. 2013, 61, 11180−11190. (12) Herman, R. A.; Phillips, A. M.; Lepping, M. D.; Sabbatini, J. Compositional safety of DAS-68416-4 (AAD-12) herbicide-tolerant soybean. J. Nutr. Food Sci. 2011, 1, 103. (13) Dean, A.; Voss, D.; Draguljić, D. Checking Model Assumptions. In Design and Analysis of Experiments; Springer: New York, NY, 2017; pp 103−137. (14) Burnham, K. P.; Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach; Springer Science & Business Media, 2003. (15) Jiang, C.; Meng, C.; Schapaugh, A. Comparative analysis of genetically-modified crops: Part 1. Conditional difference testing with a given genetic background. PLoS One 2019, 14, e0210747. (16) Fasoula, V. A.; Boerma, H. R. Intra-cultivar variation for seed weight and other agronomic traits within three elite soybean cultivars. Crop Sci. 2007, 47, 367−373. (17) Tokatlidis, I. S.; Tsikrikoni, C.; Tsialtas, J. T.; Lithourgidis, A. S.; Bebeli, P. J. Variability within cotton cultivars for yield, fibre quality and physiological traits. J. Agric. Sci. 2008, 146, 483−490. (18) Ridley, W. P.; Shillito, R. D.; Coats, I.; Steiner, H. Y.; Shawgo, M.; Phillips, A.; Dussold, P.; Kurtyka, L. Development of the International Life Sciences Institute Crop Composition Database. J. Food Compos. Anal. 2004, 17, 423−438. (19) Alba, R.; Phillips, A.; Mackie, S.; Gillikin, N.; Maxwell, C.; Brune, P.; Ridley, W.; Fitzpatrick, J.; Levine, M.; Harris, S. Improvements to the International Life Sciences Institute Crop Composition Database. J. Food Compos. Anal. 2010, 23, 741−748. (20) Sult, T.; Barthet, V. J.; Bennett, L.; Edwards, A.; Fast, B.; Gillikin, N.; Launis, K.; New, S.; Rogers-Szuma, K.; Sabbatini, J.; et al. Report: release of the International Life Sciences Institute Crop Composition Database Version 5. J. Food Compos. Anal. 2016, 51, 106−111. (21) Paoletti, C.; Favilla, S.; Leo, A.; Neri, F.; Broll, H.; Fernandez, A. Variability of crops’ compositional characteristics: What do experimental data show? J. Agric. Food Chem. 2018, 66, 9507. (22) Herman, R. A.; Chassy, B. M.; Parrott, W. Compositional assessment of transgenic crops: an idea whose time has passed. Trends Biotechnol. 2009, 27, 555−557. (23) Herman, R. A.; Scherer, P. N.; Phillips, A. M.; Storer, N. P.; Krieger, M. Safe composition levels of transgenic crops assessed via a clinical medicine model. Biotechnol. J. 2010, 5, 172−182.
I
DOI: 10.1021/acs.jafc.9b00156 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
