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Phenotypic Evaluation

Phenotypic evaluations.

For the total of ~1000 NILs across the four populations, phenotypic evaluations will determine the subset in which key aspects of the fiber development network are altered by introgression. Phenotyping will be done in south Georgia and west Texas at locations representative of field conditions in the cotton-growing regions of the two largest US cotton-producing states, in collaboration with leading breeders. We intend to repeat the field studies in two years, but it may be necessary to select genotypes for expression profiling based on only one year's data; hence the use of two diverse locations will more meaningfully sample a range of conditions. A graduate student will have primary responsibility for data collection at each location. Further, we will invite the cotton genetics community generally to visit these sites and collect additional data, beginning to build a data resource that takes advantage of these populations to identify both striking and subtle phenotypes associated with naturally-occurring genetic variation in traits that are beyond the scope of the present project. The informatics component will provide a web-based interface to a transaction database that can receive and update data on NILs from a variety of sources, and also provide access to the accumulated marker and phenotypic data. We will focus on traits essential to cotton domestication, including plant height, flower number, branching habit, and flowering time, and selected measures of fiber yield and quality, utilizing the University of Georgia Research MicroGin to the degree possible. Fiber quality components will be measured using the HVI (High Volume Instrumentation) to determine fiber fineness, diameter, length, and to detect immature, "undyeable" fibers called neps. Field-collected phenotypic traits, phenotypic and genotypic correlations, trait heritabilities, and comparisons to cultivar checks will be calculated for each population using SAS. For each measured trait, analysis of variance-based comparison of each NIL in a set to its recurrent parent will permit us to determine the number, magnitude of effect, and genomic distribution pattern of donor segments that account for variation in each trait and population, following approaches modified from Paterson et al. This analysis will add unprecedented resolution to prior studies of the genetic control of these key traits.


We welcome your comments and suggestions.