Evidence map›Paper›PMID 36131140›Full record

ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2022

Genome-wide association studies of grain yield and quality traits under optimum and low-nitrogen stress in tropical maize (Zea mays L.).

Noel Ndlovu, Charles Spillane, Peter C McKeown, Jill E Cairns, Biswanath Das, Manje Gowda

Open access · hybridAbstract read
In one paragraph

Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
8.2field-weighted citation impact, top 2% 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

33 citing papers in PubMed, 43 citations in OpenAlex.

  1. Genetic diversity, GWAS, and candidate genes identification for plant architecture traits in maize (Zea mays L.).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
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  4. Review
  5. Meta-QTLs and candidate genes for kernel protein content in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
  6. GWAS-Based Mining of Candidate Genes for Low-Nitrogen Tolerance in Maize.International journal of molecular sciences · 2026
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  7. Review
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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 at 2 institutions in 2 countries.

Noel NdlovuPlant & AgriBiosciences Research Centre, Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland.ORCID http://orcid.org/0000-0002-1832-0251
Charles SpillanePlant & AgriBiosciences Research Centre, Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland. charles.spillane@nuigalway.ie.ORCID http://orcid.org/0000-0003-3318-323X
Peter C McKeownPlant & AgriBiosciences Research Centre, Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland.ORCID http://orcid.org/0000-0002-7255-6062
Jill E CairnsInternational Maize and Wheat Improvement Center (CIMMYT), P.O. Box MP163, Harare, Zimbabwe.ORCID http://orcid.org/0000-0002-2735-3485
Biswanath DasInternational Maize and Wheat Improvement Center (CIMMYT), P.O. Box 1041-00621, Nairobi, Kenya.ORCID http://orcid.org/0000-0003-2928-5030
Manje GowdaInternational Maize and Wheat Improvement Center (CIMMYT), P.O. Box 1041-00621, Nairobi, Kenya. m.gowda@cgiar.org.ORCID http://orcid.org/0000-0003-4434-6364
Ollscoil na Gaillimhe – University of Galway · IEInternational Maize and Wheat Improvement Center · KE

Funding

Bill and Melinda Gates Foundation OPP1134248Gates Foundation INV-003439
6 · The paper itself

Abstract

key messageGenome-wide association study (GWAS) demonstrated that multiple genomic regions influence grain quality traits under nitrogen-starved soils. Using genomic prediction, genetic gains can be improved through selection for grain quality traits. Soils in sub-Saharan Africa are nitrogen deficient due to low fertilizer use and inadequate soil fertility management practices. This has resulted in a significant yield gap for the major staple crop maize, which is undermining nutritional security and livelihood sustainability across the region. Dissecting the genetic basis of grain protein, starch and oil content under nitrogen-starved soils can increase our understanding of the governing genetic systems and improve the efficacy of future breeding schemes. An association mapping panel of 410 inbred lines and four bi-parental populations were evaluated in field trials in Kenya and South Africa under optimum and low nitrogen conditions and genotyped with 259,798 SNP markers. Genetic correlations demonstrated that these populations may be utilized to select higher performing lines under low nitrogen stress. Furthermore, genotypic, environmental and GxE variations in nitrogen-starved soils were found to be significant for oil content. Broad sense heritabilities ranged from moderate (0.18) to high (0.86). Under low nitrogen stress, GWAS identified 42 SNPs linked to grain quality traits. These significant SNPs were associated with 51 putative candidate genes. Linkage mapping identified multiple QTLs for the grain quality traits. Under low nitrogen conditions, average prediction accuracies across the studied genotypes were higher for oil content (0.78) and lower for grain yield (0.08). Our findings indicate that grain quality traits are polygenic and that using genomic selection in maize breeding can improve genetic gain. Furthermore, the identified genomic regions and SNP markers can be utilized for selection to improve maize grain quality traits.

Indexed as

Genome-Wide Association StudyZea maysEdible GrainNitrogenPhenotypePlant BreedingPolymorphism, Single NucleotideNitrogen

Identifiers

PMID36131140
PMCPMC9734216
OpenAlexW4296564710

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.