No abstract

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.

Despite rapidly decreasing costs and innovative technologies, sequencing of angiosperm genomes is not yet undertaken lightly. Generating larger amounts of sequence data more quickly does not address the difficulties of sequencing and assembling complex genomes de novo. The cotton (Gossypium spp.) genomes represent a challenging case. To this end, a coalition of cotton genome scientists has developed a strategy for sequencing the cotton genomes, which will vastly expand opportunities for cotton research and improvement worldwide.

QTL mapping experiments yield heterogeneous results due to the use of different genotypes, environments, and sampling variation. Compilation of QTL mapping results yields a more complete picture of the genetic control of a trait and reveals patterns in organization of trait variation. A total of 432 QTL mapped in one diploid and 10 tetraploid interspecific cotton populations were aligned using a reference map and depicted in a CMap resource. Early demonstrations that genes from the non-fiberproducing diploid ancestor contribute to tetraploid lint fiber genetics gain further support from multiple populations and environments and advanced-generation studies detecting QTL of small phenotypic effect. Both tetraploid subgenomes contribute QTL at largely non-homeologous locations, suggesting divergent selection acting on many corresponding genes before and/or after polyploid formation. QTL correspondence across studies was only modest, suggesting that additional QTL for the target traits remain to be discovered. Crosses between closely-related genotypes differing by single-gene mutants yield profoundly different QTL landscapes, suggesting that fiber variation involves a complex network of interacting genes. Members of the lint fiber development network appear clustered, with cluster members showing heterogeneous phenotypic effects. Meta-analysis linked to synteny-based and expression-based information provides clues about specific genes and families involved in QTL networks. MOST naturally occurring genetic variation in populations reflects polymorphic alleles that individually have relatively small effects but collectively result in continuous variation among members of the population. Through genetic mapping, the number and location of loci associated with complex trait variation, i.e., quantitative trait loci or QTL, can be estimated and used to infer the genetic basis of traits that differ between varieties and/or species (Paterson et al. 1988). DNA markers linked to QTL can also be used as diagnostic tools in the selection of desirable genotypes (markerassisted selection) and as a starting point for cloning of QTL. For these reasons, vast numbers of QTL representing a myriad of traits have been mapped in agronomically important crops, and also in botanical models and animals. A handful of genes underlying QTL have been cloned (e.g., Frary et al. 2000) based largely on fine mapping (Paterson et al. 1990).A recurring complication in the use of QTL data is that different parental combinations and/or experiments conducted in different environments often result in identification of partly or wholly nonoverlapping sets of QTL. The majority of such differences in the QTL landscape are presumed to be due to environment sensitivity of genes. The use of stringent statistical thresholds to infer QTL while controlling experiment-wise error rates (Lander and Botstein 1989;Churchill and Doerge 1994) implies that only a small fraction of these nonoverlapping QTL can be attributed to falsepositive results. Small QTL wit...