Genetic diversity and population structure in the US Upland cotton was established and core sets of allelic richness were identified for developing association mapping populations in cotton. Elite plant breeding programs could likely benefit from the unexploited standing genetic variation of obsolete cultivars without the yield drag typically associated with wild accessions. A set of 381 accessions comprising 378 Upland (Gossypium hirsutum L.) and 3 G. barbadense L. accessions of the United States cotton belt were genotyped using 120 genome-wide SSR markers to establish the genetic diversity and population structure in tetraploid cotton. These accessions represent more than 100 years of Upland cotton breeding in the United States. Genetic diversity analysis identified a total of 546 alleles across 141 marker loci. Twenty-two percent of the alleles in Upland accessions were unique, specific to a single accession. Population structure analysis revealed extensive admixture and identified five subgroups corresponding to Southeastern, Midsouth, Southwest, and Western zones of cotton growing areas in the United States, with the three accessions of G. barbadense forming a separate cluster. Phylogenetic analysis supported the subgroups identified by STRUCTURE. Average genetic distance between G. hirsutum accessions was 0.195 indicating low levels of genetic diversity in Upland cotton germplasm pool. The results from both population structure and phylogenetic analysis were in agreement with pedigree information, although there were a few exceptions. Further, core sets of different sizes representing different levels of allelic richness in Upland cotton were identified. Establishment of genetic diversity, population structure, and identification of core sets from this study could be useful for genetic and genomic analysis and systematic utilization of the standing genetic variation in Upland cotton.

In large yield trials, variation in soil fertility (or, more generally, yield potential) can result in substantial heterogeneity within blocks and, thus, poor precision in treatment estimates. Precision may be improved using statistical analyses in which this spatial variation is accounted for in estimation of treatment or entry means. Three such types of spatial analysis are trend analysis, the Papadakis method, and analyses based on correlated errors models (which account for spatial variation through correlations between yields of neighboring plots). We reviewed the theory and empirical performance of these spatial analyses and compared them with the classical analyses. The classical analyses can be justified solely on the basis of randomization; spatial analyses depend on the model specified for the variation in yield potential. Performance depends on the polynomial used to describe yield potential in trend analysis, on the neighboring plots used to estimate fertility in the Papadakis analysis, and on the correlation structure in the correlated errors models. Empirical comparisons were based on data from 11 corn (Zea mays L.) yield trials and 1 soybean (Glycine max L.) trial, each showing evidence of heterogeneity within blocks. In comparison with the classical randomized blocks analysis, precision tended to be best for the trend and the trend plus correlated errors analyses, with the Papadakis method intermediate. Ranking of entries differed across analyses, because each analysis adjusts for spatial variation in a different way. Using a spatial analysis technique can improve precision, but selecting the most appropriate analysis for a given data set can be hard.

Most cotton (Gossypium spp.) breeders today, without recourse to critical data, assume that the genetic base in modern New World cotton cultivars is narrow. The objectives of this study were to: (i) determine the average coefficient of parentage for 260 upland cotton (G. hirsutum L.) cultivars released between 1970 and 1990; and (ii) determine the contributions of a number of public and private breeding programs and of various ancestral lines to the genetic diversity of those cultivars. Coefficients of parentage among 260 cultivars showed an average value of 0.07. This estimate suggests substantial remaining diversity. This conclusion must take into account possible bias from widespread reselection of cotton cultivars and the accompanying assumption of a genetic correlation of 0.75 between generations. The most influential breeding programs, in terms of genetic contributions to cultivar development, were Stoneville Pedigreed Seed Company, Coker's Pedigreed Seed Company, and New Mexico Agricultural Experiment Station. Historically, the most influential cultivar is Stoneville 2. The genetic contribution of 54 ancestral lines, including nine introductions, accounted for less than 25% of the total genetic variation among the 260 cultivars. This low value is thought to result from the loss of genetic information through the process of reselection. The genetic base in modern cotton cultivars is not particularly narrow and continue to offer opportunities for cultivar improvement.

One of the most significant, long-term public U.S. Upland cotton {Gossypium hirsutum L.) germplasm enhancement programs is known as the Pee Dee germplasm program. The unique, genetic foundation of the Pee Dee germplasm was created using germplasm from Upland, Sea Island {Gossypium barbadense L.), and primitive diploid cottons. Since the program's inception in 1935, the Pee Dee germplasm program has released >80 improved germplasm lines and cultivars. In this study, the agronomic and fiber quality performance of Pee Dee germplasm was evaluated across southeastern U.S. environments to estimate genetic improvement within the Pee Dee germplasm program. Results suggest that the Pee Dee germplasm enhancement program has (i) maintained usable genetic variation and (ii) maintained high fiber quality potential while concomitantly improving agronomic performance. Although the results highlight the need to continue improving lint percent, lint yield, and bolls m'^, there is also evidence to suggest that Pee Dee germplasm can continue being utilized to develop the next generation of high-fiberquality and high-yielding cotton cultivars.

Leaf shape varies spectacularly among plants. Leaves are the primary source of photoassimilate in crop plants, and understanding the genetic basis of variation in leaf morphology is critical to improving agricultural productivity. Leaf shape played a unique role in cotton improvement, as breeders have selected for entire and lobed leaf morphs resulting from a single locus, okra (L-D 1 ), which is responsible for the major leaf shapes in cotton. The L-D 1 locus is not only of agricultural importance in cotton, but through pioneering chimeric and morphometric studies, it has contributed to fundamental knowledge about leaf development. Here we show that an HD-Zip transcription factor homologous to the LATE MERISTEM IDENTITY1 (LMI1) gene of Arabidopsis is the causal gene underlying the L-D 1 locus. The classical okra leaf shape allele has a 133-bp tandem duplication in the promoter, correlated with elevated expression, whereas an 8-bp deletion in the third exon of the presumed wild-type normal allele causes a frame-shifted and truncated coding sequence. Our results indicate that subokra is the ancestral leaf shape of tetraploid cotton that gave rise to the okra allele and that normal is a derived mutant allele that came to predominate and define the leaf shape of cultivated cotton. Virusinduced gene silencing (VIGS) of the LMI1-like gene in an okra variety was sufficient to induce normal leaf formation. The developmental changes in leaves conferred by this gene are associated with a photosynthetic transcriptomic signature, substantiating its use by breeders to produce a superior cotton ideotype. cotton | leaf shape | okra | gene cloning