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...
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.
Mapping of genes that play major roles in cotton fiber development is an important step toward their cloning and manipulation, and provides a test of their relationships (if any) to agriculturally-important QTLs. Seven previously identified fiber mutants, four dominant (Li (1), Li (2), N (1) and Fbl) and three recessive (n (2), sma-4(h (a)), and sma-4(fz)), were genetically mapped in six F(2) populations comprising 124 or more plants each. For those mutants previously assigned to chromosomes by using aneuploids or by linkage to other morphological markers, all map locations were concordant except n (2), which mapped to the homoeolog of the chromosome previously reported. Three mutations with primary effects on fuzz fibers (N (1), Fbl, n (2)) mapped near the likelihood peaks for QTLs that affected lint fiber productivity in the same populations, perhaps suggesting pleiotropic effects on both fiber types. However, only Li (1) mapped within the likelihood interval for 191 previously detected lint fiber QTLs discovered in non-mutant crosses, suggesting that these mutations may occur in genes that played early roles in cotton fiber evolution, and for which new allelic variants are quickly eliminated from improved germplasm. A close positional association between sma-4(h ( a )), two leaf and stem-borne trichome mutants (t (1) , t (2)), and a gene previously implicated in fiber development, sucrose synthase, raises questions about the possibility that these genes may be functionally related. Increasing knowledge of the correspondence of the cotton and Arabidopsis genomes provides several avenues by which genetic dissection of cotton fiber development may be accelerated.
Root-knot nematodes Meloidogyne incognita (Kofoid and White) can cause severe yield loss in cotton (Gossypium hirsutum L.). The objectives of this study were to determine the inheritance and genomic location of genes conferring root-knot nematode resistance in M-120 RNR, a highly resistant G. hirsutum line with the Auburn 623 RNR source of resistance. Utilizing two interspecific F(2) populations developed from the same M-120 RNR by Gossypium barbadense (cv. Pima S-6) cross, genome-wide scanning with RFLP markers revealed a marker on Chromosome 7 and two on Chromosome 11 showing significant association with the resistant phenotype. The association was confirmed using SSR markers with the detection of a minor and a major dominant QTL on Chromosome 7 and 11, respectively. Combined across the two populations, the major QTL on Chromosome 11 Mi-C11 had a LOD score of 19.21 (9.69 and 9.61 for Pop1 and Pop2, respectively) and accounted for 63.7% (52.6 and 65.56% for Pop1 and Pop2, respectively) of the total phenotypic variation. The minor QTL locus on Chromosome 7 Mi ( 1 ) -C07 had a LOD score of 3.48 and accounted for 7.7% of the total phenotypic variation in the combined dataset but was detected in only one population. The allele from the M-120 RNR parent contributed to increased resistance in the Mi-C11 locus, but surprisingly, the Pima S-6 allele contributed to increased resistance in the Mi-C07 locus. The M-120 RNR allele in the Mi-C11 locus, derived from the Auburn 623 RNR, is likely to have originated from the Clevewilt 6 cultivar. Results from this study indicated that the SSR marker CIR316 may replace the laborious greenhouse screening in breeding programs to identify genotypes resistant to M. incognita.
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