Evidence map›Paper›PMID 28077090›Full record

ArticleBMC genomics2017

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

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 and 3 more

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
22.8field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 119 citations in OpenAlex.

  1. Review
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  8. Evaluation of a Sugarcane (Plants (Basel, Switzerland) · 2023
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  18. Construction of a High-Density Genetic Map ofFrontiers in plant science · 2021
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 3 institutions in 1 country.

Thiago Willian Almeida BalsalobreDepartamento de Biotecnologia e Produção Vegetal e Animal, Centro de Ciências Agrárias, Universidade Federal de São Carlos, Rodovia Anhanguera, Km 174, Araras, CEP 13600-970, São Paulo, Brazil.
Guilherme da Silva PereiraDepartamento de Genética, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, Avenida Pádua Dias 11, Piracicaba, CEP 13418-900, São Paulo, Brazil.
Gabriel Rodrigues Alves MargaridoDepartamento de Genética, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, Avenida Pádua Dias 11, Piracicaba, CEP 13418-900, São Paulo, Brazil.
Rodrigo GazaffiDepartamento de Biotecnologia e Produção Vegetal e Animal, Centro de Ciências Agrárias, Universidade Federal de São Carlos, Rodovia Anhanguera, Km 174, Araras, CEP 13600-970, São Paulo, Brazil.
Fernanda Zatti BarretoDepartamento de Biotecnologia e Produção Vegetal e Animal, Centro de Ciências Agrárias, Universidade Federal de São Carlos, Rodovia Anhanguera, Km 174, Araras, CEP 13600-970, São Paulo, Brazil.
Carina Oliveira AnoniDepartamento de Genética, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, Avenida Pádua Dias 11, Piracicaba, CEP 13418-900, São Paulo, Brazil.
Cláudio Benício Cardoso-SilvaDepartamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas, Avenida Monteiro Lobato 255, Campinas, CEP 13083-862, São Paulo, Brazil.
Estela Araújo CostaDepartamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas, Avenida Monteiro Lobato 255, Campinas, CEP 13083-862, São Paulo, Brazil.
Melina Cristina ManciniDepartamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas, Avenida Monteiro Lobato 255, Campinas, CEP 13083-862, São Paulo, Brazil.
Hermann Paulo HoffmannDepartamento de Biotecnologia e Produção Vegetal e Animal, Centro de Ciências Agrárias, Universidade Federal de São Carlos, Rodovia Anhanguera, Km 174, Araras, CEP 13600-970, São Paulo, Brazil.
Anete Pereira de SouzaDepartamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas, Avenida Monteiro Lobato 255, Campinas, CEP 13083-862, São Paulo, Brazil.
Antonio Augusto Franco GarciaDepartamento de Genética, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, Avenida Pádua Dias 11, Piracicaba, CEP 13418-900, São Paulo, Brazil.
Monalisa Sampaio CarneiroDepartamento de Biotecnologia e Produção Vegetal e Animal, Centro de Ciências Agrárias, Universidade Federal de São Carlos, Rodovia Anhanguera, Km 174, Araras, CEP 13600-970, São Paulo, Brazil. monalisa@cca.ufscar.br.
Universidade Federal de São Carlos · BRUniversidade de São Paulo · BRUniversidade Estadual de Campinas (UNICAMP) · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSugarcane (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.

resultsWe 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.

conclusionsThis 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.

Indexed as

Genetic LinkageGenotyping TechniquesSequence Analysis, DNAAllelesChromosome MappingData MiningGene DosageGenes, PlantGenetic MarkersMolecular Sequence AnnotationPolymorphism, GeneticQuantitative Trait LociSaccharumGenetic MarkersAllelic dosageMolecular markersPolyploidyQuantitative traitsSaccharum sppSNPs

Identifiers

PMID28077090
PMCPMC5225503
OpenAlexW2577612342

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.