Genome-wide association study of four yield-related traits at the R6 stage in soybean.
GWAS
Quantitative trait locus
R6 stage
Single nucleotide polymorphism (SNP)
Soybean [Glycine max (L.) Merr.]
Yield-related traits
Journal
BMC genetics
ISSN: 1471-2156
Titre abrégé: BMC Genet
Pays: England
ID NLM: 100966978
Informations de publication
Date de publication:
29 03 2019
29 03 2019
Historique:
received:
06
06
2018
accepted:
06
03
2019
entrez:
30
3
2019
pubmed:
30
3
2019
medline:
6
2
2020
Statut:
epublish
Résumé
The 100-pod fresh weight (PFW), 100-seed fresh weight (SFW), 100-seed dry weight (SDW) and moisture content of fresh seeds (MCFS) at the R6 stage are crucial factors for vegetable soybean yield. However, the genetic basis of yield at the R6 stage remains largely ambiguous in soybean. To better understand the molecular mechanism underlying yield, we investigated four yield-related traits of 133 soybean landraces in two consecutive years and conducted a genome-wide association study (GWAS) using 82,187 single nucleotide polymorphisms (SNPs). The GWAS results revealed a total of 14, 15, 63 and 48 SNPs for PFW, SFW, SDW and MCFS, respectively. Among these markers, 35 SNPs were repeatedly identified in all evaluated environments (2015, 2016, and the average across the two years), and most co-localized with yield-related QTLs identified in previous studies. AX-90496773 and AX-90460290 were large-effect markers for PFW and MCFS, respectively. The two markers were stably identified in all environments and tagged to linkage disequilibrium (LD) blocks. Six potential candidate genes were predicted in LD blocks; five of them showed significantly different expression levels between the extreme materials with large PFW or MCFS variation at the seed development stage. Therefore, the five genes Glyma.16g018200, Glyma.16g018300, Glyma.05g243400, Glyma.05g244100 and Glyma.05g245300 were regarded as candidate genes associated with PFW and MCFS. These results provide useful information for the development of functional markers and exploration of candidate genes in vegetable soybean high-yield breeding programs.
Sections du résumé
BACKGROUND
The 100-pod fresh weight (PFW), 100-seed fresh weight (SFW), 100-seed dry weight (SDW) and moisture content of fresh seeds (MCFS) at the R6 stage are crucial factors for vegetable soybean yield. However, the genetic basis of yield at the R6 stage remains largely ambiguous in soybean.
RESULTS
To better understand the molecular mechanism underlying yield, we investigated four yield-related traits of 133 soybean landraces in two consecutive years and conducted a genome-wide association study (GWAS) using 82,187 single nucleotide polymorphisms (SNPs). The GWAS results revealed a total of 14, 15, 63 and 48 SNPs for PFW, SFW, SDW and MCFS, respectively. Among these markers, 35 SNPs were repeatedly identified in all evaluated environments (2015, 2016, and the average across the two years), and most co-localized with yield-related QTLs identified in previous studies. AX-90496773 and AX-90460290 were large-effect markers for PFW and MCFS, respectively. The two markers were stably identified in all environments and tagged to linkage disequilibrium (LD) blocks. Six potential candidate genes were predicted in LD blocks; five of them showed significantly different expression levels between the extreme materials with large PFW or MCFS variation at the seed development stage. Therefore, the five genes Glyma.16g018200, Glyma.16g018300, Glyma.05g243400, Glyma.05g244100 and Glyma.05g245300 were regarded as candidate genes associated with PFW and MCFS.
CONCLUSION
These results provide useful information for the development of functional markers and exploration of candidate genes in vegetable soybean high-yield breeding programs.
Identifiants
pubmed: 30922237
doi: 10.1186/s12863-019-0737-9
pii: 10.1186/s12863-019-0737-9
pmc: PMC6440021
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
39Références
BMC Genomics. 2014 Jan 02;15:1
pubmed: 24382143
Mol Biol Rep. 2012 Oct;39(10):9435-43
pubmed: 22740134
Plant Physiol. 2003 Mar;131(3):872-7
pubmed: 12644639
Theor Appl Genet. 2016 Jan;129(1):117-30
pubmed: 26518570
Annu Rev Plant Biol. 2003;54:357-74
pubmed: 14502995
Plant Cell. 2010 Jun;22(6):1733-48
pubmed: 20551347
Plant Mol Biol. 2001 Sep;47(1-2):9-27
pubmed: 11554482
Plant J. 2007 Jun;50(5):825-38
pubmed: 17419836
Proc Natl Acad Sci U S A. 2001 Sep 25;98(20):11479-84
pubmed: 11562485
Brief Funct Genomics. 2010 Mar;9(2):166-77
pubmed: 20156985
J Exp Bot. 2017 May 17;68(11):2717-2729
pubmed: 28472462
Plant Mol Biol. 2008 May;67(1-2):125-34
pubmed: 18299802
Plant Sci. 2018 Jan;266:95-101
pubmed: 29241572
Nat Genet. 2006 Feb;38(2):203-8
pubmed: 16380716
Nat Genet. 2010 Nov;42(11):961-7
pubmed: 20972439
Plant Mol Biol. 2005 Mar;57(4):461-85
pubmed: 15821975
Theor Appl Genet. 2005 Sep;111(5):851-61
pubmed: 16059730
Sci Rep. 2016 Feb 09;6:20728
pubmed: 26856884
PLoS Genet. 2014 Sep 11;10(9):e1004573
pubmed: 25211220
Trends Plant Sci. 2007 Feb;12(2):57-63
pubmed: 17224302
Genetics. 2007 May;176(1):421-40
pubmed: 17339212
Theor Appl Genet. 2012 Feb;124(3):447-58
pubmed: 21997761
BMC Genet. 2016 Jun 18;17(1):85
pubmed: 27316671
Plant J. 2015 Feb;81(4):625-36
pubmed: 25641104
BMC Genomics. 2015 Mar 20;16:217
pubmed: 25887991
New Phytol. 2010 Oct;188(1):242-53
pubmed: 20618914
Methods. 2001 Dec;25(4):402-8
pubmed: 11846609
Theor Appl Genet. 2009 Aug;119(3):429-36
pubmed: 19462149
Theor Appl Genet. 2012 Aug;125(4):671-83
pubmed: 22481120
Plant Foods Hum Nutr. 2000;55(4):323-33
pubmed: 11086875
Plant Cell. 2004 Dec;16(12):3285-303
pubmed: 15539469
J Genet. 2014 Aug;93(2):355-63
pubmed: 25189230
Genetics. 2003 Sep;165(1):353-65
pubmed: 14504242