Genetic dissection of canonical models of maize kernel growth and development

An understanding of the genetics of maize kernel growth and development will provide valuable insight into a complicated physiological process. The genetics of kernel growth and development is of interest not only to plant scientists who seek to understand the underlying molecular mechanism but also to plant breeders who seek to develop improved hybrids with faster growing, larger kernels. In order to better understand the genetics of kernel growth and development, biomass accumulation and moisture content were observed in a sample of hybrids and a set of doubled haploids derived from a cross of elite inbred lines and testcrossed to an elite inbred. Biomass accumulation and moisture content were assayed starting shortly after pollination and continued at regular time intervals until physiological maturity. Samples were taken in 2009 and 2010 which differed significantly for weather patterns, providing an opportunity to compare results from distinct environments.;Plots of biomass accumulation vs. growing degree days (GDD), indicated a sigmoid function would likely describe biomass accumulation. Nonlinear functions that produce a sigmoid curve were compared and contrasted to identify a model for description of biomass accumulation. Each of the functions selected for comparison had to meet three requirements. The function has to produce a sigmoid curve, the parameters should have biological meaning in the context of kernel biomass accumulation, and the model should easily converge over a range of growth patterns. Five functions were considered along with methods to account for the heteroscedastic errors. The Gompertz function was selected with the residuals modeled using the power function.;A function was sought that described moisture content in the same context as biomass accumulation. After observing moisture content regressed against GDD, the hypothesis was formed that moisture content could be modeled using the first derivative of the Gompertz function. The first derivative is also termed the rate of change function, and it was found that the rate of change function could be used to model the moisture content. We hypothesize that the moisture content can act as a latent variable for biochemical reaction rate when it is modeled using the rate of change function.;Finally, the two models for biomass accumulation and moisture content were applied to a mapping population consisted of testcrossed double haploids. Composite interval mapping and multiple-trait interval mapping were used to determine quantitative trait loci (QTL) associated with parameter values estimated from the nonlinear functions. Using these methods, ten QTL associated with biomass accumulation and twenty-eight QTL associated with moisture content were found. QTL associated with biomass or moisture often co-localized to the same region of the genome. A candidate gene search was conducted based on the QTL mapping results. Families of annotated genes responsible for synthesis of glucose and ethylene, defense, ribosomal proteins, and cell division and survival represent the most likely candidates.