Sugarcane improvement: how far can we go?

Dal-Bianco, Maximiller; Carneiro, Monalisa Sampaio; Hotta, Carlos Takeshi; Chapola, Roberto Giacomini; Hoffmann, Hermann Paulo; Garcia, Antônio Augusto Franco; Souza, Gláucia Mendes

doi:10.1016/j.copbio.2011.09.002

Cited by 103 publications

(74 citation statements)

References 39 publications

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“…Recently, small sugar production increases of approximately 1 to 1.5% per year have been obtained. The average yield of sugarcane in Brazil, the world's largest sugarcane producer, is approximately 74 t ha -1 ; however, the theoretical production potential is approximately 400 t ha -1 (Dal-Bianco et al, 2012;Matsuoka et al, 2014;Waclawovsky et al, 2010). Waclawovsky et al (2010) showed that, in Brazil, the commercial maximum yield (large land areas) was 260 t ha -1 and an experimental maximum (individual trials on smaller land areas) was 299 t ha -1 .…”

Section: Mixed Modeling Of Yield Components and Brown Rust Resistancementioning

confidence: 99%

“…Using these main traits (SW, BRIX, POL%C, POL%J, TCH, and TPH) throughout the selection period of a breeding program, the cultivars may meet the expectations of highly accumulated sucrose and high production in terms of weight. However, the genetic gain for these traits with the conventional breeding process is nearly stagnant (Dal-Bianco et al, 2012). Several limitations inherent in a breeding program may be noted: (i) a lack of knowledge of the genetic material that is present in germplasm banks, which could be used to perform cross-breeding with the greatest potential to generate superior cultivars; (ii) sparse and mismanaged experimental trials; (iii) failures and a lack of standardization in the collection of phenotypic data; (iv) lack of environmental correlation analysis of the phenotypic data; (v) lack of knowledge of the genetic basis of the traits of interest; (vi) neglect of disease and pest occurrence; and (g) low investment in research and biotechnology (Breseghello and Coelho, 2013;Mahon, 1983;Prohens, 2011).…”

Section: Probabilities Segregations and Heritability Of Resistance mentioning

confidence: 99%

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Mixed Modeling of Yield Components and Brown Rust Resistance in Sugarcane Families

et al. 2016

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Sugarcane (Saccharum spp.) is a complex autopolyploid with high potential for biomass production that can be converted into sugar and ethanol. Genetic improvement is extremely important to generate more productive and resistant cultivars. Populations of improved sugarcane are generally evaluated for several traits simultaneously and in multi-environment trials. In this study, we evaluated two full-sib families of sugarcane (SR1 and SR2) at two locations and 3 yr for stalk diameter, stalk height, stalk number, stalk weight, soluble solid content (Brix), sucrose content of cane, sucrose content of juice, fi ber, cane yield, sucrose yield, and resistance to brown rust (Puccinia melanocephala). Using a mixed model approach, we included appropriate variance-covariance (VCOV) structures for modeling heterogeneity and correlation of genetic eff ects and nongenetic residual eff ects. Th e genotypic correlations between traits were calculated across the adjusted means as the standard Pearson product-moment coeffi cient. Th rough the VCOV structures estimated for each trait, in general, the heritabilities ranged from 0.78 to 0.94. Additionally, we detected 17 and 12 signifi cant genotypic correlations between the evaluated traits for SR1 and SR2, respectively. Th e analysis of the severity data for brown rust revealed that 66 and 32% of the full-sib genotypes in SR1 and SR2, respectively, had at least 90% probability of being resistant. Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; BLUP, best linear unbiased prediction; FIB, fi ber; GEI, genotype ´ environment interaction; GLMM, generalized linear mixed model; LMM, linear mixed model; METs, multi-environment trials; POL%C, sucrose content of cane in percentage; POL%J, sucrose content of juice in percentage; REML, restricted maximum likelihood; SD, stalk diameter; SH, stalk height; SN, stalk number; SR1, SP80-3280 ´ RB835486 full-sib family of sugarcane 1; SR2, SP81-3250 ´ RB925345 full-sib family of sugarcane 2; SW, stalk weight; TCH, tonnes of cane per hectare; TPH, tonnes of sucrose per hectare; VCOV, variance-covariance. core ideas• A linear mixed model is efficient in production data analysis of sugarcane.• In general, the broad-sense heritability of the traits were high, ranging from 0.78 to 0.94.• A generalized linear mixed model can be applied in brown rust analysis of sugarcane.• Multi-environment trials were applied to the genetic improvement of sugarcane.

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Section: Mixed Modeling Of Yield Components and Brown Rust Resistancementioning

confidence: 99%

Section: Probabilities Segregations and Heritability Of Resistance mentioning

confidence: 99%

Mixed Modeling of Yield Components and Brown Rust Resistance in Sugarcane Families

et al. 2016

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“…[2] While moving towards the sugarcane sequencing, new challenges are confronted like polyploid and aneuploid structure and it is also presumed that there are 10À12 copies (alleles) of complete homologous genes. [3] Association mapping has made remarkable attention to recognize genes responsible for different traits with agricultural and evolutionary significance. Recent advancement in genomic technology allows taking the advantages of natural diversity and when coupled with the new decisive statistical analysis methods, association mapping has become a tempting and an affordable research programme.…”

Section: Article; Agriculture and Environmental Biotechnologymentioning

confidence: 99%

Association mapping of cane weight and tillers per plant in sugarcane

Bilal

Saeed

Nasir

et al. 2015

Biotechnology & Biotechnological Equipment

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Identification of agronomic traits tightly linked to molecular markers is a major footstep to implement the marker assisted selection in breeding programmes. Sugarcane is the major cash crop and the source of more than 2/3 of world sugar production. In this study, 20 simple sequence repeat (SSR) markers were used to find the linkage for sugarcane yield components; cane weight and tillers per plant using 28 sugarcane genotypes. In total, 114 alleles were found, ranging from 2-13 alleles per locus with a mean value of 5.7. High levels of polymorphism were detected through SSR amplifications among all genotypes studied. SSR markers, CEMB-14A and CEMB-14B, were found linked (P < 0.01) to tillers per plant while CEMB-5A, CEMB-5C and CEMB-7B were found associated (P < 0.05) with cane weight. Identification of new and novel SSR markers in sugarcane have potential use in genetic diversity studies, DNA fingerprinting, DNA markers-based screening of germplasm for breeding programme and also in determination of true hybrids in the offspring.

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“…Erianthus, is being explored in sugarcane improvement (reviewed in Dal-Bianco et al, 2012, de Siqueira Ferreira et al, 2013. This is normally done by crossing S. officinarum…”

Section: Breeding Approachesmentioning

confidence: 99%

“…The current status of improving sugarcane biomass by using the genetic tools is hindered by its genome complexity, low transformation efficiency, transgene inactivation (gene silencing and regulation), somaclonal variation and difficulty in backcrossing (Ingelbrecht et al, 1999, Hotta et al, 2010, Arruda, 2012, Dal-Bianco et al, 2012. Targets tackled so far on sugarcane include sucrose and biomass yield increase (i.e.…”

Section: Molecular Genetic Approachesmentioning

confidence: 99%

Analysis of genes controlling biomass traits in the genome of sugarcane (Saccharum spp. hybrids)

Hoang¹

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Sugarcane (Saccharum spp. hybrids) is a leading industrial crop in tropical and subtropical regions worldwide. More recently, sugarcane has been selected as a key feedstock for biofuels due to its rapid growth, high fiber content and favorable energy input/output ratio.Breeding sugarcane varieties with biomass for efficient conversion to biofuels can be optimized by understanding the genetic control of biomass composition. However, the genetic analysis of these traits is hindered by the genomic complexity, and the limited availability of a reference genome. The aims of this project were: the development of a high-throughput profiling method for rapid screening of the key biomass traits in a sugarcane population; the construction of a new full-length transcriptome reference database; and the identification of transcripts associated with sugar and fiber accumulation in sugarcane.For the screening of genotypes, newly developed predictive models employing nearinfrared (NIR) spectral analysis, coupled with the high performance liquid chromatography (HPLC), were shown to allow high-throughput profiling of major components in the fiber and sugar fractions in sugarcane biomass. Contrasting genotypes of low fiber and high fiber (minimum of ~29% and maximum of 61% total dry biomass) were identified amongst 331 samples from 186 sugarcane genotypes. The population studied exhibited a wide range of fiber/sugar ratio, from 0.4 (as low as that of the typical commercial sugarcane variety) to 2.2 (similar to that of energy-cane). In addition, the lignin content (the central factor in the biomass recalcitrance) ranged from 6 to 14% of the total dry biomass. To aid genotyping, a new sugarcane transcriptome (termed as SUGIT database) was constructed using PacBio full-length isoform sequencing (Iso-Seq), and a cDNA library derived from 22 diverse sugarcane genotypes, of the key tissues (leaf, internode and root), at different developmental stages (from immature to mature). Comparative analysis showed that this new SUGIT database included more full-length transcripts, longer predicted transcripts, and higher average length of the largest 1,000 proteins, compared to a de novo assembly from Illumina RNA-Seq short-read data from the same sample set. The annotation suggested that the majority (~94%) of the SUGIT database was from coding RNAs, while a very small proportion (~2%) could be long non-coding RNAs. About 70-82% of the RNASeq reads from different tissues mapped back to the SUGIT database, suggesting that it represented well the targeted tissues, while about 69% of this database was aligned with the sorghum genome, confirming the high conservation of orthologs in the genic regions of ii the two genomes. Applying the SUGIT database to differential expression analysis (FDR, false discovery rate corrected p-value <0.05), 1,649 transcript isoforms were identified as being differentially expressed between the young and mature tissues in the sugarcane plant, while 555 transcript isoforms were differentially expressed between th...

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Sugarcane improvement: how far can we go?

Cited by 103 publications

References 39 publications

Mixed Modeling of Yield Components and Brown Rust Resistance in Sugarcane Families

Mixed Modeling of Yield Components and Brown Rust Resistance in Sugarcane Families

Association mapping of cane weight and tillers per plant in sugarcane

Analysis of genes controlling biomass traits in the genome of sugarcane (Saccharum spp. hybrids)

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