We report genetic maps for diploid (D) and tetraploid (AtDt) Gossypium genomes composed of sequence-tagged sites (STS) that foster structural, functional, and evolutionary genomic studies. The maps include, respectively, 2584 loci at 1.72-cM ‫006ف(‬ kb) intervals based on 2007 probes (AtDt) and 763 loci at 1.96-cM ‫005ف(‬ kb) intervals detected by 662 probes (D). Both diploid and tetraploid cottons exhibit negative crossover interference; i.e., double recombinants are unexpectedly abundant. We found no major structural changes between Dt and D chromosomes, but confirmed two reciprocal translocations between At chromosomes and several inversions. Concentrations of probes in corresponding regions of the various genomes may represent centromeres, while genome-specific concentrations may represent heterochromatin. Locus duplication patterns reveal all 13 expected homeologous chromosome sets and lend new support to the possibility that a more ancient polyploidization event may have predated the A-D divergence of 6-11 million years ago. Identification of SSRs within 312 RFLP sequences plus direct mapping of 124 SSRs and exploration for CAPS and SNPs illustrate the "portability" of these STS loci across populations and detection systems useful for marker-assisted improvement of the world's leading fiber crop. These data provide new insights into polyploid evolution and represent a foundation for assembly of a finished sequence of the cotton genome.

Soybean is the world’s leading source of vegetable protein and demand for its seed continues to grow. Breeders have successfully increased soybean yield, but the genetic architecture of yield and key agronomic traits is poorly understood. We developed a 40-mating soybean nested association mapping (NAM) population of 5,600 inbred lines that were characterized by single nucleotide polymorphism (SNP) markers and six agronomic traits in field trials in 22 environments. Analysis of the yield, agronomic, and SNP data revealed 23 significant marker-trait associations for yield, 19 for maturity, 15 for plant height, 17 for plant lodging, and 29 for seed mass. A higher frequency of estimated positive yield alleles was evident from elite founder parents than from exotic founders, although unique desirable alleles from the exotic group were identified, demonstrating the value of expanding the genetic base of US soybean breeding.

A set of nested association mapping (NAM) families was developed by crossing 40 diverse soybean [ (L.) Merr.] genotypes to the common cultivar. The 41 parents were deeply sequenced for SNP discovery. Based on the polymorphism of the single-nucleotide polymorphisms (SNPs) and other selection criteria, a set of SNPs was selected to be included in the SoyNAM6K BeadChip for genotyping the parents and 5600 RILs from the 40 families. Analysis of the SNP profiles of the RILs showed a low average recombination rate. We constructed genetic linkage maps for each family and a composite linkage map based on recombinant inbred lines (RILs) across the families and identified and annotated 525,772 high confidence SNPs that were used to impute the SNP alleles in the RILs. The segregation distortion in most families significantly favored the alleles from the female parent, and there was no significant difference of residual heterozygosity in the euchromatic vs. heterochromatic regions. The genotypic datasets for the RILs and parents are publicly available and are anticipated to be useful to map quantitative trait loci (QTL) controlling important traits in soybean.

Digital imagery can help to quantify seasonal changes in desirable crop phenotypes that can be treated as quantitative traits. Because limitations in precise and functional phenotyping restrain genetic improvement in the postgenomic era, imagery-based phenomics could become the next breakthrough to accelerate genetic gains in field crops. Whereas many phenomic studies focus on exploratory analysis of spectral data without obvious interpretative value, we used field images to directly measure soybean canopy development from phenological stage V2 to R5. Over 3 years, we collected imagery using ground and aerial platforms of a large and diverse nested association panel comprising 5555 lines. Genome-wide association analysis of canopy coverage across sampling dates detected a large quantitative trait locus (QTL) on soybean (Glycine max, L. Merr.) chromosome 19. This QTL provided an increase in yield of 47.3 kg ha−1. Variance component analysis indicated that a parameter, described as average canopy coverage, is a highly heritable trait (h2 = 0.77) with a promising genetic correlation with grain yield (0.87), enabling indirect selection of yield via canopy development parameters. Our findings indicate that fast canopy coverage is an early season trait that is inexpensive to measure and has great potential for application in breeding programs focused on yield improvement. We recommend using the average canopy coverage in multiple trait schemes, especially for the early stages of the breeding pipeline (including progeny rows and preliminary yield trials), in which the large number of field plots makes collection of grain yield data challenging.

Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set.