Genetic Dissection of Nitrogen Use Efficiency in Tropical Maize Through Genome-Wide Association and Genomic Prediction.
LD
NUE
SNP
association mapping
genomic prediction
marker-assisted selection
Journal
Frontiers in plant science
ISSN: 1664-462X
Titre abrégé: Front Plant Sci
Pays: Switzerland
ID NLM: 101568200
Informations de publication
Date de publication:
2020
2020
Historique:
received:
05
12
2019
accepted:
30
03
2020
entrez:
16
5
2020
pubmed:
16
5
2020
medline:
16
5
2020
Statut:
epublish
Résumé
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
Identifiants
pubmed: 32411159
doi: 10.3389/fpls.2020.00474
pmc: PMC7198882
doi:
Types de publication
Journal Article
Langues
eng
Pagination
474Informations de copyright
Copyright © 2020 Ertiro, Labuschagne, Olsen, Das, Prasanna and Gowda.
Références
Curr Opin Biotechnol. 2006 Apr;17(2):155-60
pubmed: 16504497
Development. 2010 Apr;137(8):1243-50
pubmed: 20223762
Bioinformatics. 2012 Sep 15;28(18):2397-9
pubmed: 22796960
Genetics. 2015 Jul;200(3):965-74
pubmed: 25943393
Nature. 2005 Aug 4;436(7051):714-9
pubmed: 16079849
J Anim Breed Genet. 2007 Dec;124(6):331-41
pubmed: 18076470
Annu Rev Plant Biol. 2003;54:357-74
pubmed: 14502995
Proc Natl Acad Sci U S A. 2014 Dec 30;111(52):18775-80
pubmed: 25512525
Theor Appl Genet. 2012 Mar;124(4):769-76
pubmed: 22075809
Sci Prog. 2016;99(Pt 1):59-67
pubmed: 27120814
Theor Appl Genet. 2004 Aug;109(4):681-9
pubmed: 15300382
Am Nat. 2007 Sep;170(3):343-57
pubmed: 17879186
PLoS One. 2009 Dec 24;4(12):e8451
pubmed: 20041112
Genetics. 2006 Feb;172(2):1165-77
pubmed: 16079235
Plant Mol Biol. 2007 Jul;64(4):387-95
pubmed: 17437065
PLoS One. 2011 May 04;6(5):e19379
pubmed: 21573248
Bioinformatics. 2007 Oct 1;23(19):2633-5
pubmed: 17586829
Science. 2008 Jan 18;319(5861):330-3
pubmed: 18202289
Front Plant Sci. 2019 Nov 22;10:1502
pubmed: 31824533
BMC Plant Biol. 2012 Jan 27;12:16
pubmed: 22284310
Front Plant Sci. 2017 Nov 08;8:1916
pubmed: 29167677
Euphytica. 2019;215(4):80
pubmed: 31057179
BMC Plant Biol. 2013 Dec 28;13:227
pubmed: 24373137
BMC Plant Biol. 2010 Jul 14;10:143
pubmed: 20626916
Trends Genet. 2002 Feb;18(2):83-90
pubmed: 11818140
Trends Plant Sci. 2017 Nov;22(11):961-975
pubmed: 28965742
Heredity (Edinb). 2015 Mar;114(3):291-9
pubmed: 25407079
Front Plant Sci. 2019 Jan 30;9:1919
pubmed: 30761177
Plant Physiol. 2008 Jul;147(3):969-77
pubmed: 18612074
PLoS Genet. 2016 Feb 01;12(2):e1005767
pubmed: 26828793
Theor Appl Genet. 2015 Oct;128(10):1957-68
pubmed: 26152570
Methods Mol Biol. 2014;1115:53-67
pubmed: 24415469