Phenotypic plasticity and breeding for future climates: Case studies in Zea mays

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Kusmec, Aaron Michael
Major Professor
Schnable, Patrick S
Dekkers, Jack C
Hufford, Matthew
Nettleton, Daniel S
Wang, Lizhi
Committee Member
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Plant breeding has been enormously successful at increasing crop yields over the course of the 20th century, but further progress that would meet increasing demands for food, fuel, and fiber is threatened by global climate change. Because yield is an environmentally-dependent phenotype, understanding the genetic and environmental interactions that underlie yield and other plastic phenotypes is important not only for breeding major crops, such as maize, wheat, and rice, but also for expanding breeding investments into so-called minor crops which contribute to food security through increased crop diversity. Breeding decisions often seek to balance competing objectives because many phenotypes are genetically correlated. Thus, understanding the genetic architecture of phenotypic plasticity is important for making informed decisions about selection targets. The genetic architecture of phenotypic plasticity for 23 diverse, adult phenotypes was investigated in the US maize nested association mapping population. Despite strong genetic correlations between mean phenotypes and plasticities, the genetic architectures for the mean and plasticity of each of the 23 phenotypes were largely structurally and functionally distinct. These results suggest that selection could operate at least semi-independently on mean phenotypes and plasticities for some maize phenotypes. In addition to the genes underlying plasticity, it is also necessary to know which environmental factors drive plasticity. This has been extensively researched in major crops, but such knowledge is often lacking for minor crops and natural populations. Exploratory methods for associating environmental factors and phenotypic plasticity have largely made use of exhaustive search and single-variable models which are limited by the size of the search space and the nature of the plastic cues, respectively. The use of a genetic algorithm is demonstrated to identify known environmental drivers of hybrid maize grain yield plasticity under minimal assumptions about the candidate environmental factors and using a multi-variable model. The identified environmental factors are supported by previous literature, responses to them are heritable, and candidate genes are identified. These results suggest that a genetic algorithm could be similarly employed in minor crops to identify candidate environmental drivers of plasticity for further physiological and agronomic research to improve rates of genetic gain. Breeders have been at least indirectly selecting on phenotypic plasticity throughout the 20th century to improve adaptation of crop cultivars to their target environments. Historical datasets can be used to assess the extent of this adaptation and project how crops may be affected by climate change. Exposure to temperatures >30 °C has strong, negative, non-linear effects on hybrid maize grain yield. Public yield trial data and historical weather observations are combined to estimate temperature response functions for 9,329 maize hybrids. These response functions indicate that maize has been at least indirectly adapted to temperature during the 20th century. However, projections from global climate models under various emissions scenarios indicate that further genetic and management adaptation will be crucial to maintaining maize yields as temperatures rise. These studies use maize as a model organism, but similar studies in diverse crop species, where the same level of investment is lacking, could provide outsized benefits and aid breeding efforts. The implications of these studies for the future of plant breeding and the role of phenotypic plasticity in a warming climate are discussed.
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