Successes and insights of an industry biotech program to enhance maize agronomic traits

Simmons, Carl R.; Lafïtte, H.R.; Reimann, Kellie S.; Brugière, Norbert; Roesler, Keith R.; Albertsen, Marc C.; Greene, Thomas W.; Habben, Jeffrey E.

doi:10.1016/j.plantsci.2021.110899

Cited by 54 publications

(72 citation statements)

References 36 publications

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“…0 , 3 , 1000 = = = P K N random network was between 3 and 100,000 (Table 3). To obtain such a large range in the measured attractor period phenotypes when N and K are constant across the simulations implies that knowledge of the specific rules at play in a gene network is critical to making useful predictions, a point argued at length by WOLFRAM (2002) and also supported by extensive empirical results obtained from studies conducted to manipulate traits of maize (DONG et al 2012;GUO et al 2014;SIMMONS et al 2021). Moreover, these results call into question the utility of ensemble average properties of random Boolean networks, such as the average attractor period that we and many others have used as the trait phenotype of random Boolean networks (BAGLEY and GLASS 1996;BASTOLLA and PARISI 1996;BASTOLLA and PARISI 1997;FOX and HILL 2001;KAUFFMAN 1993), for predicting the phenotype of any particular real-world gene network.…”

Section: Discussionmentioning

confidence: 96%

“…In the many applied fields of genetics there is interest in the practicality of manipulating genes and thus gene networks to realize improved trait phenotypes (e.g., medicine, drug discovery, plant breeding, synthetic biology). Such manipulation can involve creation of de novo genetic variation or selection on the alleles contributing to standing genetic variation (MESSINA et al 2011;GUO et al 2014;VOSS-FELS et al 2019;WURTZEL et al 2019;SIMMONS et al 2021;POWELL et al 2021). To achieve the expectations of this ambition requires that we can predict the behavior of networks, and their consequences for trait phenotypes, if we change some of their components, e.g., nodes or edge properties (COOPER et al 2005).…”

Section: Discussionmentioning

confidence: 99%

“…While quantitative genetics theory has been used to great effect without the need for a detailed understanding of how specific genes work to determine trait phenotypes, the past few decades have brought a wealth of knowledge about the molecular structure and function of genes and proteins influencing quantitative traits, e.g., in the crop plant maize (Zea mays) (SIMMONS et al 2021). Increasingly this research has shown that molecular components at work in the living cell interact with each other, often in highly specific and context-dependent networks (HOLLAND 2001;HOLLAND 2006;BENFEY and MITCHELL-OLDS 2008;MARJORAM et al 2014).…”

Section: Introductionmentioning

confidence: 99%

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Predicting trait phenotypes from knowledge of the topology of gene networks

Beatty¹,

Hagen²,

Cooper

2021

Preprint

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In many fields there is interest in manipulating genes and gene networks to realize improved trait phenotypes. The practicality of doing so, however, requires accepted theory on the properties of gene networks that is well-tested by empirical results. The extension of quantitative genetics to include models that incorporate properties of gene networks expands the long tradition of studying epistasis resulting from gene-gene interactions. Here we consider NK models of gene networks by applying concepts from graph theory and Boolean logic theory, motivated by a desire to model the parameters that influence predictive skill for trait phenotypes under the control of gene networks; N defines the number of graph nodes, the number of genes in the network, and K defines the number of edges per node in the graph, representing the gene-gene interactions. We define and consider the attractor period of an NK network as an emergent trait phenotype for our purposes. A long-standing theoretical treatment of the dynamical properties of random Boolean networks suggests a transition from long to short attractor periods as a function of the average node degree K and the bias probability P in the applied Boolean rules. In this paper we investigate the appropriateness of this theory for predicting trait phenotypes on random and real microorganism networks through numerical simulation. We show that: (i) the transition zone between long and short attractor periods depends on the number of network nodes for random networks; (ii) networks derived from metabolic reaction data on microorganisms also show a transition from long to short attractor periods, but at higher values of the bias probability than in random networks with similar numbers of network nodes and average node degree; (iii) the distribution of phenotypes measured on microorganism networks shows more variation than random networks when the bias probability in the Boolean rules is above 0.75; and (iv) the topological structure of networks built from metabolic reaction data is not random, being best approximated, in a statistical sense, by a lognormal distribution. The implications of these results for predicting trait phenotypes where the genetic architecture of a trait is a gene network are discussed.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting trait phenotypes from knowledge of the topology of gene networks

Beatty¹,

Hagen²,

Cooper

2021

Preprint

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“…Numerous candidate genes associated with many diverse QTL have been listed, however most citations were at an elementary stage with a focus on yield drag [85]. Recent reviews [86,87] indicate that alterations in the expression of specific genes may contribute to the agronomic improvement of quantitative traits. Additional capabilities to simultaneously insert 10 or more additional and/or edited genes have been developed [8889] and open-source methodologies to insert genes are available [90].…”

Section: Concluding Comments and Proposed Path Forwardmentioning

confidence: 99%

“…However, as technological progress continues, variety development through the simultaneous addition and/or changes in expression of more than "a few" genes will occur [87][88][89][90][91][92]. Also, it may be that judicial precedent on the definitions of: "a few", "predominant", and "essential" becomes problematic or contradictory in terms of supporting the advancement of genetic gain through plant breeding.…”

Section: A Proposalmentioning

confidence: 99%

The Future of Essentially Derived Variety (EDV) Status: Predominantly More Explanations or Essential Change

Jsc¹

2021

Preprint

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This review examines the categorization of Essentially Derived Varieties (EDV) introduced in the 1991 revision of the Convention of the Union internationale pour la protection des obtentions végétales (UPOV). Challenges in the implementation of the concept and progress made on a crop-by-crop basis to provide greater clarity and more efficient implementation are reviewed. The current approach to EDV remains valid provided i) clarity on thresholds can be achieved including through resource intensive research on an individual crop species basis and ii) that threshold clarity does not lead to perverse incentives to avoid detection of essential derivation. However, technological advances leading to new varieties resulting from the simultaneous introduction or change in expression of more than “a few” genes will so challenge the concept to require a new Convention. Revision could include deletion of the concept of essential derivation and revision on a crop-by-crop basis of the breeder exception. Countries that allow utility patents for individual plant varieties per se should consider removing that possibility unless plant breeders utilize those encouragements for risk taking and investment to broaden the germplasm base upon which the long-term sustainability of plant breeding resides.

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