Meta-analysis of Polyploid Cotton QTL Shows Unequal Contributions of Subgenomes to a Complex Network of Genes and Gene Clusters Implicated in Lint Fiber Development

Abstract

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...

Gene copy number evolution during tetraploid cotton radiation

“…Further, for both DR genes and randomly selected genes, most homoeologous restriction site mutations occurred in the D genome (Figures 1 and 3), in accordance with the discovery of more DNA-level variations in the D than the A genome (Reinisch et al, 1994;Jiang et al, 1998;Small and Wendel, 2002;Grover et al, 2007). More DNA-level variation found in the D genome than the A genome may be symptomatic of mechanisms that also contribute to the greater abundance of QTLs responsible for phenotypic variation, including fiber-related QTLs detected on this genome from a nonfiber-producing ancestor (Jiang et al, 1998;Rong et al, 2007). Collectively, these results point to a pivotal function of the D genome in tetraploid cotton evolution.…”

Repeated polyploidization of Gossypium genomes and the evolution of spinnable cotton fibres

“…The 105 (0.2%) genes upregulated in 10 DPA fibre of wild (versus elite) tetraploid G. barbadense (Supplementary Table 5.3) include 30 (37%; P , 0.001) of the 81 NUMT genes, including 8 NADH dehydrogenase and 4 cytochrome-c-related genes. All are within the QTL hotspot D t 01 that affects fibre fineness, length, and uniformity 22 , suggesting a fibre-specific change in electron transfer in G. barbadense domestication.…”

“…Fig. 2.2) but are over-represented in genes coding for cell-wall-associated, kinase or nucleotide-binding proteins (Supplementary QTL hotspots affecting multiple fibre traits 22 may reflect coordinated changes in expression of functionally diverse cotton genes. A total of 671 (1.79%) genes with .100 reads per million reads were differentially expressed in fibres from wild versus domesticated G. hirsutum (mostly at 10 DPA) and/or G. barbadense (mostly at 20 DPA) (Supplementary Table 5.3).…”

“…Among 48 genes upregulated in domesticated G. hirsutum at 10 DPA, 20 (42%) are among 1,582 (4.2%) genes within QTL hotspot D t 09.2 (ref. 22) affecting length, uniformity, and short-fibre content, with 13 (27%) out of 677 (1.8%) genes in homoeologous hotspot A t 09 affecting fibre elongation and fineness. Out of 45 genes downregulated in domesticated G. barbadense at 20 DPA, 16 (35.6%) map to D t 09.2, and 8 (17.7%) to A t 09.…”

Genetic Mapping in Cotton

“…F 2 population has more cons for detecting QTLs with additive effects and can also be utilized for assessing the dominance pattern. A number of QTLs for cotton has been found using this population [56,[71][72][73]. Backcross population produce false results if dominant factors are allowed as additive and dominance are overlapped.…”