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GBS-based single dosage markers for linkage and QTL mapping allow gene mining for yield-related traits in sugarcane

Overview of attention for article published in BMC Genomics, January 2017
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Title
GBS-based single dosage markers for linkage and QTL mapping allow gene mining for yield-related traits in sugarcane
Published in
BMC Genomics, January 2017
DOI 10.1186/s12864-016-3383-x
Pubmed ID
Authors

Thiago Willian Almeida Balsalobre, Guilherme da Silva Pereira, Gabriel Rodrigues Alves Margarido, Rodrigo Gazaffi, Fernanda Zatti Barreto, Carina Oliveira Anoni, Cláudio Benício Cardoso-Silva, Estela Araújo Costa, Melina Cristina Mancini, Hermann Paulo Hoffmann, Anete Pereira de Souza, Antonio Augusto Franco Garcia, Monalisa Sampaio Carneiro

Abstract

Sugarcane (Saccharum spp.) is predominantly an autopolyploid plant with a variable ploidy level, frequent aneuploidy and a large genome that hampers investigation of its organization. Genetic architecture studies are important for identifying genomic regions associated with traits of interest. However, due to the genetic complexity of sugarcane, the practical applications of genomic tools have been notably delayed in this crop, in contrast to other crops that have already advanced to marker-assisted selection (MAS) and genomic selection. High-throughput next-generation sequencing (NGS) technologies have opened new opportunities for discovering molecular markers, especially single nucleotide polymorphisms (SNPs) and insertion-deletion (indels), at the genome-wide level. The objectives of this study were to (i) establish a pipeline for identifying variants from genotyping-by-sequencing (GBS) data in sugarcane, (ii) construct an integrated genetic map with GBS-based markers plus target region amplification polymorphisms and microsatellites, (iii) detect QTLs related to yield component traits, and (iv) perform annotation of the sequences that originated the associated markers with mapped QTLs to search putative candidate genes. We used four pseudo-references to align the GBS reads. Depending on the reference, from 3,433 to 15,906 high-quality markers were discovered, and half of them segregated as single-dose markers (SDMs) on average. In addition to 7,049 non-redundant SDMs from GBS, 629 gel-based markers were used in a subsequent linkage analysis. Of 7,678 SDMs, 993 were mapped. These markers were distributed throughout 223 linkage groups, which were clustered in 18 homo(eo)logous groups (HGs), with a cumulative map length of 3,682.04 cM and an average marker density of 3.70 cM. We performed QTL mapping of four traits and found seven QTLs. Our results suggest the presence of a stable QTL across locations. Furthermore, QTLs to soluble solid content (BRIX) and fiber content (FIB) traits had markers linked to putative candidate genes. This study is the first to report the use of GBS for large-scale variant discovery and genotyping of a mapping population in sugarcane, providing several insights regarding the use of NGS data in a polyploid, non-model species. The use of GBS generated a large number of markers and still enabled ploidy and allelic dosage estimation. Moreover, we were able to identify seven QTLs, two of which had great potential for validation and future use for molecular breeding in sugarcane.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 147 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 2%
France 1 <1%
Italy 1 <1%
Unknown 142 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 25%
Researcher 36 24%
Student > Master 14 10%
Student > Doctoral Student 8 5%
Student > Bachelor 6 4%
Other 23 16%
Unknown 23 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 90 61%
Biochemistry, Genetics and Molecular Biology 24 16%
Engineering 2 1%
Computer Science 2 1%
Environmental Science 1 <1%
Other 3 2%
Unknown 25 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 December 2017.
All research outputs
#20,412,387
of 22,962,258 outputs
Outputs from BMC Genomics
#9,311
of 10,686 outputs
Outputs of similar age
#357,317
of 422,224 outputs
Outputs of similar age from BMC Genomics
#166
of 216 outputs
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