Evidence map›Paper›PMID 37600177›Full record

ArticleFrontiers in plant science2023

A first look at the ability to use genomic prediction for improving the ratooning ability of sugarcane.

Md Sariful Islam, Keo Corak, Per McCord, Amanda M Hulse-Kemp, Alexander E Lipka

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Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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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

5 authors.

Md Sariful IslamSugarcane Field Station, USDA-ARS, Canal Point, FL, United States.
Keo CorakGenomics and Bioinformatics Research Unit, USDA-ARS, Raleigh, NC, United States.
Per McCordSugarcane Field Station, USDA-ARS, Canal Point, FL, United States.
Amanda M Hulse-KempGenomics and Bioinformatics Research Unit, USDA-ARS, Raleigh, NC, United States.
Alexander E LipkaDepartment of Crop Sciences, University of Illinois, Urbana-Champaign, IL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The sugarcane ratooning ability (RA) is the most important target trait for breeders seeking to enhance the profitability of sugarcane production by reducing the planting cost. Understanding the genetics governing the RA could help breeders by identifying molecular markers that could be used for genomics-assisted breeding (GAB). A replicated field trial was conducted for three crop cycles (plant cane, first ratoon, and second ratoon) using 432 sugarcane clones and used for conducting genome-wide association and genomic prediction of five sugar and yield component traits of the RA. The RA traits for economic index (EI), stalk population (SP), stalk weight (SW), tonns of cane per hectare (TCH), and tonns of sucrose per hectare (TSH) were estimated from the yield and sugar data. A total of six putative quantitative trait loci and eight nonredundant single-nucleotide polymorphism (SNP) markers were associated with all five tested RA traits and appear to be unique. Seven putative candidate genes were colocated with significant SNPs associated with the five RA traits. The genomic prediction accuracies for those tested traits were moderate and ranged from 0.21 to 0.36. However, the models fitting fixed effects for the most significant associated markers for each respective trait did not give any advantages over the standard models without fixed effects. As a result of this study, more robust markers could be used in the future for clone selection in sugarcane, potentially helping resolve the genetic control of the RA in sugarcane.

Indexed as

candidate genegenomic selectionGWASmarker trait associationsugarcane production

Identifiers

PMID37600177
PMCPMC10433174

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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.