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Rice ( Oryza sativa L.) Grain Size, Shape, and Weight-Related QTLs Identified Using GWAS with Multiple GAPIT Models and High-Density SNP Chip DNA Markers.

التفاصيل البيبلوغرافية
العنوان: Rice ( Oryza sativa L.) Grain Size, Shape, and Weight-Related QTLs Identified Using GWAS with Multiple GAPIT Models and High-Density SNP Chip DNA Markers.
المؤلفون: Kabange NR; Department of Southern Area Crop Science, National Institute of Crop Science, Rural Development Administration (RDA), Miryang 50424, Republic of Korea., Dzorkpe GD; Council for Scientific and Industrial Research (CSIR), Crops Research Institute, Kumasi 3785, Ghana., Park DS; Department of Southern Area Crop Science, National Institute of Crop Science, Rural Development Administration (RDA), Miryang 50424, Republic of Korea., Kwon Y; Department of Southern Area Crop Science, National Institute of Crop Science, Rural Development Administration (RDA), Miryang 50424, Republic of Korea., Lee SB; Department of Southern Area Crop Science, National Institute of Crop Science, Rural Development Administration (RDA), Miryang 50424, Republic of Korea., Lee SM; Department of Southern Area Crop Science, National Institute of Crop Science, Rural Development Administration (RDA), Miryang 50424, Republic of Korea., Kang JW; Department of Southern Area Crop Science, National Institute of Crop Science, Rural Development Administration (RDA), Miryang 50424, Republic of Korea., Jang SG; Department of Southern Area Crop Science, National Institute of Crop Science, Rural Development Administration (RDA), Miryang 50424, Republic of Korea., Oh KW; Department of Southern Area Crop Science, National Institute of Crop Science, Rural Development Administration (RDA), Miryang 50424, Republic of Korea., Lee JH; Department of Southern Area Crop Science, National Institute of Crop Science, Rural Development Administration (RDA), Miryang 50424, Republic of Korea.
المصدر: Plants (Basel, Switzerland) [Plants (Basel)] 2023 Nov 30; Vol. 12 (23). Date of Electronic Publication: 2023 Nov 30.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101596181 Publication Model: Electronic Cited Medium: Print ISSN: 2223-7747 (Print) Linking ISSN: 22237747 NLM ISO Abbreviation: Plants (Basel) Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Basel, Switzerland : MDPI AG, [2012]-
مستخلص: This study investigated novel quantitative traits loci (QTLs) associated with the control of grain shape and size as well as grain weight in rice. We employed a joint-strategy multiple GAPIT (Genome Association and Prediction Integrated Tool) models [(Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK)), Fixed and random model Circulating Probability Uniform (FarmCPU), Settlement of MLM Under Progressive Exclusive Relationship (SUPER), and General Linear Model (GLM)]-High-Density SNP Chip DNA Markers (60,461) to conduct a Genome-Wide Association Study (GWAS). GWAS was performed using genotype and grain-related phenotypes of 143 recombinant inbred lines (RILs). Data show that parental lines (Ilpum and Tung Tin Wan Hein 1, TTWH1, Oryza sativa L., ssp. japonica and indica , respectively) exhibited divergent phenotypes for all analyzed grain traits), which was reflected in their derived population. GWAS results revealed the association between seven SNP Chip makers and QTLs for grain length, co-detected by all GAPIT models on chromosomes (Chr) 1-3, 5, 7, and 11, were qGL1-1 BFSG (AX-95918134, Chr1: 3,820,526 bp) explains 65.2-72.5% of the phenotypic variance explained (PVE). In addition, qGW1-1 BFSG (AX-273945773, Chr1: 5,623,288 bp) for grain width explains 15.5-18.9% of PVE. Furthermore, BLINK or FarmCPU identified three QTLs for grain thickness independently, and explain 74.9% ( qGT1 Blink , AX-279261704, Chr1: 18,023,142 bp) and 54.9% ( qGT2-1 Farm , AX-154787777, Chr2: 2,118,477 bp) of the observed PVE. For the grain length-to-width ratio (LWR), the qLWR2 BFSG (AX-274833045, Chr2: 10,000,097 bp) explains nearly 15.2-32% of the observed PVE. Likewise, the major QTL for thousand-grain weight (TGW) was detected on Chr6 ( qTGW6 BFSG , AX-115737727, 28,484,619 bp) and explains 32.8-54% of PVE. The qTGW6 BFSG QTL coincides with qGW6-1 Blink for grain width and explained 32.8-54% of PVE. Putative candidate genes pooled from major QTLs for each grain trait have interesting annotated functions that require functional studies to elucidate their function in the control of grain size, shape, or weight in rice. Genome selection analysis proposed makers useful for downstream marker-assisted selection based on genetic merit of RILs.
References: Int J Genomics. 2019 Feb 25;2019:3160275. (PMID: 30931320)
Int J Mol Sci. 2021 Aug 03;22(15):. (PMID: 34361093)
Theor Appl Genet. 2021 Sep;134(9):2767-2776. (PMID: 34021769)
Plant Biotechnol J. 2021 Feb;19(2):300-310. (PMID: 32757315)
Trends Plant Sci. 2013 Apr;18(4):218-26. (PMID: 23218902)
Plant Sci. 2015 Sep;238:330-9. (PMID: 26259199)
Plant Cell. 2012 Jun;24(6):2562-77. (PMID: 22685166)
Nat Genet. 2011 Oct 23;43(12):1266-9. (PMID: 22019783)
Nat Plants. 2017 Apr 10;3:17043. (PMID: 28394310)
Genomics Proteomics Bioinformatics. 2021 Aug;19(4):629-640. (PMID: 34492338)
Plant Cell. 2013 Apr;25(4):1243-57. (PMID: 23624715)
PLoS Genet. 2017 Jun 5;13(6):e1006823. (PMID: 28582424)
Front Plant Sci. 2021 Jun 17;12:665883. (PMID: 34220889)
Plant J. 2017 Sep;91(5):849-860. (PMID: 28621888)
Plant Mol Biol. 2007 Nov;65(4):467-85. (PMID: 17610133)
Proc Natl Acad Sci U S A. 2022 Aug 23;119(34):e2208759119. (PMID: 35969741)
Front Plant Sci. 2020 Nov 09;11:579255. (PMID: 33240298)
Plant Reprod. 2018 Sep;31(3):237-251. (PMID: 29523952)
Rice (N Y). 2022 Mar 18;15(1):18. (PMID: 35303197)
Front Genet. 2016 Dec 27;7:221. (PMID: 28083016)
Biomed Res Int. 2018 Mar 15;2018:7658032. (PMID: 29736396)
Nat Genet. 2007 May;39(5):623-30. (PMID: 17417637)
Nat Genet. 2013 Jun;45(6):707-11. (PMID: 23583977)
Breed Sci. 2015 Sep;65(4):308-18. (PMID: 26366113)
J Plant Physiol. 2009 Dec 15;166(18):2071-6. (PMID: 19540016)
Gigascience. 2019 Feb 1;8(2):. (PMID: 30535326)
Proc Natl Acad Sci U S A. 2010 Nov 9;107(45):19579-84. (PMID: 20974950)
Plant Physiol. 2017 Aug;174(4):2487-2500. (PMID: 28687557)
Plant Physiol. 2015 Aug;168(4):1762-76. (PMID: 26082401)
J Exp Bot. 2022 Aug 11;73(14):5033-5051. (PMID: 35526193)
Curr Biol. 2016 Mar 7;26(5):640-6. (PMID: 26877080)
J Anim Sci. 2007 Apr;85(4):881-5. (PMID: 17085728)
Plant J. 2020 Aug;103(3):1174-1188. (PMID: 32365409)
Plant Cell. 2019 Jun;31(6):1257-1275. (PMID: 30940685)
Plant Biotechnol J. 2022 Jul;20(7):1346-1362. (PMID: 35315188)
Breed Sci. 2017 Dec;67(5):427-434. (PMID: 29398936)
Cell Res. 2008 Dec;18(12):1199-209. (PMID: 19015668)
BMC Genomics. 2008 Jan 27;9:44. (PMID: 18221561)
Rice (N Y). 2022 Sep 6;15(1):47. (PMID: 36068333)
Annu Rev Genet. 2014;48:99-118. (PMID: 25149369)
Mol Biol Rep. 2010 Feb;37(2):653-60. (PMID: 19288292)
Plant Cell. 2017 Mar;29(3):560-574. (PMID: 28298519)
Genetics. 2001 Apr;157(4):1819-29. (PMID: 11290733)
Nat Commun. 2018 Mar 27;9(1):1240. (PMID: 29588443)
Mol Plant. 2018 May 7;11(5):736-749. (PMID: 29567449)
Springerplus. 2016 Dec 9;5(1):2086. (PMID: 28018794)
Front Plant Sci. 2021 Jul 12;12:605799. (PMID: 34322138)
Nat Genet. 2012 Jun 24;44(8):950-4. (PMID: 22729225)
Nat Genet. 2015 Aug;47(8):949-54. (PMID: 26147620)
Front Plant Sci. 2019 May 07;10:543. (PMID: 31134107)
Cell Res. 2012 Dec;22(12):1666-80. (PMID: 23147796)
New Phytol. 2020 Oct;228(1):163-178. (PMID: 32464682)
Front Plant Sci. 2023 Mar 08;14:1139961. (PMID: 36968393)
PLoS Genet. 2016 Feb 01;12(2):e1005767. (PMID: 26828793)
J Exp Bot. 2002 Dec;53(379):2325-31. (PMID: 12432025)
J Exp Bot. 2015 Nov;66(21):6715-30. (PMID: 26246614)
Planta. 2019 Jun;249(6):1851-1861. (PMID: 30848355)
Front Genet. 2022 Aug 19;13:955347. (PMID: 36061203)
Front Plant Sci. 2022 Jan 17;12:800989. (PMID: 35111179)
Plant J. 2011 Feb;65(3):359-67. (PMID: 21265890)
PLoS One. 2021 Nov 22;16(11):e0260133. (PMID: 34807926)
Nat Genet. 2006 Aug;38(8):904-9. (PMID: 16862161)
PLoS One. 2012;7(10):e48242. (PMID: 23118960)
Int J Mol Sci. 2022 Sep 05;23(17):. (PMID: 36077547)
Plant Sci. 2016 Jun;247:104-14. (PMID: 27095404)
Nat Plants. 2018 Feb;4(2):98-107. (PMID: 29379156)
Semin Cell Dev Biol. 2018 Nov;83:123-132. (PMID: 29288799)
Front Plant Sci. 2017 Oct 31;8:1863. (PMID: 29163589)
Annu Rev Plant Biol. 2019 Apr 29;70:435-463. (PMID: 30795704)
Theor Appl Genet. 2006 Apr;112(6):1164-71. (PMID: 16453132)
Biochemistry. 2006 Dec 26;45(51):15168-78. (PMID: 17176038)
Plant Cell. 2022 Jan 20;34(1):633-654. (PMID: 34741605)
Planta. 2010 Aug;232(3):691-9. (PMID: 20549230)
Int J Mol Sci. 2022 Apr 11;23(8):. (PMID: 35457060)
Nat Plants. 2015 Dec 21;2:15203. (PMID: 27250749)
Nat Plants. 2020 Aug;6(8):921-928. (PMID: 32661279)
Proc Natl Acad Sci U S A. 2012 Dec 26;109(52):21534-9. (PMID: 23236132)
Front Plant Sci. 2022 Sep 12;13:970270. (PMID: 36172551)
Nat Genet. 2006 Feb;38(2):203-8. (PMID: 16380716)
Plant Sci. 2019 Jul;284:192-202. (PMID: 31084872)
Plant Physiol. 2019 May;180(1):497-508. (PMID: 30765480)
Nat Genet. 2016 Apr;48(4):447-56. (PMID: 26950093)
معلومات مُعتمدة: Project No. RS-2022-RD010353 This work was supported by the Young Scientist Research Program of the Korea-Africa Food and Agriculture Cooperation Initiative and the Rural Development Administration (KAFACI/RDA), Republic of Korea.
فهرسة مساهمة: Keywords: GWAS; SNP chip DNA marker; genomic selection; grain traits; multiple GAPIT models; rice
تواريخ الأحداث: Date Created: 20231209 Latest Revision: 20231209
رمز التحديث: 20240829
مُعرف محوري في PubMed: PMC10708019
DOI: 10.3390/plants12234044
PMID: 38068684
قاعدة البيانات: MEDLINE
الوصف
تدمد:2223-7747
DOI:10.3390/plants12234044