“Targeted Sequencing by Gene Synteny,” a New Strategy for Polyploid Species: Sequencing and Physical Structure of a Complex Sugarcane Region

Costa

Rody

et al. 2020

Sci Rep

Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% when using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. As a result of this approach, we achieved an accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane.

Section: Introductionmentioning

confidence: 99%

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Costa

Rody

et al. 2020

Sci Rep

“…Sugarcane possesses one of the most complex genomes known among crops (O'Hara and Mundree, 2016;Mancini et al, 2018;De Souza Barbosa et al, 2020) which could, until recently, only be assembled at the scaffold level (Grativol et al, 2014;Okura et al, 2016;Riaño-Pachón and Mattiello, 2017;Souza et al, 2019). Only in 2018 did an approach coupling next-generation and long-read sequencing with high-throughput chromatin conformation capture enable a chromosome-and allele-level genome of a Ssp clone to be assembled (Zhang et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…After diverging from Miscanthus 3.1-4.6 MYA (Kim et al, 2014), the Saccharum lineage experienced at least two rounds of whole-genome duplication (Zhang et al, 2018), while Sbi remained diploid. Therefore, sorghum genomic resources are a valuable resource in genetic studies in sugarcane (Paterson et al, 2009), in which they have been extensively employed (Okura et al, 2016;Mancini et al, 2018;Bedre et al, 2019). As the genomes of both species are now available, comparisons of the diversity, organization and expression of PKs between these two species enable us to perform in-depth explorations of the evolutionary history of these proteins, which are relevant to numerous biological processes.…”

Section: Discussionmentioning

confidence: 99%

“…These hybridizations produced large (∼10 Gb) (D'Hont et al, 1998), highly polyploid (D'Hont and Glaszmann, 2001) and aneuploid (Sforça et al, 2019) genomes. These genomes also contain numerous repetitive elements, mainly retrotransposons, which can account for more than 50% of the total number of sequences (Figueira et al, 2012;Kim et al, 2013;Mancini et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The sugarcane and sorghum kinomes: insights into evolutionary expansion and diversification

Pimenta

Garcia

et al. 2020

Preprint

The protein kinase (PK) superfamily is one of the largest superfamilies in plants and is the core regulator of cellular signaling. Even considering this substantial importance, the kinomes of sugarcane and sorghum have not been profiled. Here we identified and profiled the complete kinomes of the polyploid Saccharum spontaneum (Ssp) and Sorghum bicolor (Sbi), a close diploid relative. The Sbi kinome was composed of 1,210 PKs; for Ssp, we identified 2,919 PKs when disregarding duplications and allelic copies, which were related to 1,345 representative gene models. The Ssp and Sbi PKs were grouped into 20 groups and 120 subfamilies and exhibited high compositional similarities and evolutionary divergences. By utilizing the collinearity between these species, this study offers insights about Sbi and Ssp speciation, PK differentiation and selection. We assessed the PK subfamily expression profiles via RNA-Seq, identifying significant similarities between Sbi and Ssp. Moreover, through coexpression networks, we inferred a core structure of kinase interactions with specific key elements. This study is the first to categorize the allele specificity of a kinome and provides a wide reservoir of molecular and genetic information, enhancing the understanding of the evolutionary history of Sbi and Ssp PKs.HighlightThis study describes the catalog of kinase gene family in Saccharum spontaneum and Sorghum bicolor, providing a reservoir of molecular features and expression patterns based on RNA-Seq and co-expression networks.

“…S. officinarum has a more efficient process of sugar production but is susceptible to several biotic and abiotic stresses, in contrast to S. spontaneum, which has a low sucrose content but is resistant to different types of stress 1,3,5 . Sugarcane cultivars have unique chromosome sets (with numbers ranging from 80 to 130) 6 with highly complex genomic organization 1 , a polyploid genome (with overall ploidy estimated to be between 6 and 14) 7 , a frequent occurrence of aneuploidy at the locus level depending on the number of homologous chromosomes in hybrid cultivars 8 , an estimated whole-genome size of 10 Gb 9 , and a high content of repetitive regions (50% of genome size) 10 . This complexity has challenged the efforts of the scientific community to unravel the genetic architecture of sugarcane in terms of the molecular mechanisms underlying different phenotypes, particularly efforts to detect regions of phenotype-genotype associations.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Costa

Rody

et al. 2020

Preprint

Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% by using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. Using different feature selection techniques, we achieved accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane. 3/154/15 10/15