Evidence map›Paper›PMID 32411159›Full record

ArticleFrontiers in plant science2020

Genetic Dissection of Nitrogen Use Efficiency in Tropical Maize Through Genome-Wide Association and Genomic Prediction.

Berhanu Tadesse Ertiro, Maryke Labuschagne, Michael Olsen, Biswanath Das, Boddupalli M Prasanna, Manje Gowda

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Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
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

23 citing papers in PubMed.

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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

6 authors.

Berhanu Tadesse ErtiroBako National Maize Research Center, Ethiopian Institute of Agricultural Research, Bako, Ethiopia.
Maryke LabuschagneDepartment of Plant Sciences, University of the Free State, Bloemfontein, South Africa.
Michael OlsenInternational Maize and Wheat Improvement Center, World Agroforestry Centre, Nairobi, Kenya.
Biswanath DasInternational Maize and Wheat Improvement Center, World Agroforestry Centre, Nairobi, Kenya.
Boddupalli M PrasannaInternational Maize and Wheat Improvement Center, World Agroforestry Centre, Nairobi, Kenya.
Manje GowdaInternational Maize and Wheat Improvement Center, World Agroforestry Centre, Nairobi, Kenya.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In sub-Saharan Africa, one of the major challenges to smallholder farmers is soil with low fertility and inability to apply nitrogen fertilizer externally due to the cost. Development of maize hybrids, which perform better in nitrogen depleted soils, is one of the promising solutions. However, breeding maize for nitrogen use efficiency (NUE) is hindered by expensive phenotypic evaluations and trait complexity under low N stress. Genome-wide association study (GWAS) and genomic prediction (GP) are promising tools to circumvent this interference. Here, we evaluated a mapping panel in diverse environments both under optimum and low N management. The objective of this study was to identify SNPs significantly associated with grain yield (GY) and other traits through GWAS and assess the potential of GP under low N and optimum conditions. Testcross progenies of 411 inbred lines were planted under optimum and low N conditions in several locations in Africa and Latin America. In all locations, low N fields were previously depleted over several seasons, and no N fertilizer was applied throughout the growing season. All inbred lines were genotyped with genotyping by sequencing. Genotypic and GxE interaction variances were significant, and heritability estimates were moderate to high for all traits under both optimum and low N conditions. Genome-wide LD decay at

Indexed as

association mappinggenomic predictionLDmarker-assisted selectionNUESNP

Identifiers

PMID32411159
PMCPMC7198882

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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.