Genetic dissection of rice (Oryza sativa L.) tiller, plant height, and grain yield based on QTL mapping and metaanalysis
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Tiller number per plant (TN) and plant height (PH) are important agronomic traits related to grain yield (GY) in rice (Oryza sativa L.). A total of 30 additive quantitative trait loci (A-QTL) and 9 significant additive × environment interaction QTLs (AE-QTL) were detected, while the phenotypic and QTL correlations confirmed the intrinsic relationship of the three traits. These QTLs were integrated with 986 QTLs from previous studies by metaanalysis. Consensus maps contained 7156 markers for a total map length of 1112.71 cM, onto which 863 QTLs were projected; 78 meta-QTLs (MQTLs) covering 11 of the 30 QTLs were detected from the cross between Dongnong422 and Kongyu131 in this study. A total of 705 predicted genes were distributed over the 21 MQTL intervals with physical length <0.3 Mb; 13 of the 21 MQTLs, and 34 candidate genes related to grain yield and plant development, were screened. Five major QTLs, viz. qGY6-2, qPH7-2, qPH6-3, qTN6-1, and qTN7-1, were not detected in the MQTL intervals and could be used as newly discovered QTLs. Candidate genes within these QTL intervals will play a meaningful role in molecular marker-assisted selection and map-based cloning of rice TN, PH, and GY.
KeywordsQTL analysis Metaanalysis Rice (Oryza sativa L.) Yield Plant height Tiller number per plant
This work was supported by the National Natural Science Foundation (31601377).
Compliance with ethical standards
Conflict of interest
The authors declare no conflicts of interest regarding the publication of this paper.
- Ballini E, Morel JB, Droc G, Price A, Courtois B, Notteghem JL, Tharreau D (2008) A genome-wide meta-analysis of rice blast resistance genes and quantitative trait loci provides new insights into partial and complete resistance. Mol Plant Microbe Interact MPMI 21(7):859–868CrossRefPubMedGoogle Scholar
- Hiroshi S, Cogan Noel OI, Spangenberg GC, Forster JW (2012) Quantitative trait locus (QTL) meta-analysis and comparative genomics for candidate gene prediction in perennial ryegrass (Lolium perenne L.). BMC Genet 13(1):1–12Google Scholar
- Hong Z, Ueguchitanaka M, Umemura K, Uozu S, Fujioka S, Takatsuto S, Yoshida S, Ashikari M, Kitano H, Matsuoka M (2003) A rice brassinosteroid-deficient mutant, ebisu dwarf (d2), is caused by a loss of function of a new member of cytochrome P450. Plant Cell 15(12):2900–2910CrossRefPubMedPubMedCentralGoogle Scholar
- Immanuel SC, Nagarajan P, Thiyagarajan K, Bharathi M, Rabindran R (2011) Genetic parameters of variability, correlation and path coefficient studies for grain yield and other yield attributes among rice blast disease resistant genotypes of rice (Oryza sativa L.). Afr J Biotech 10(17):3322–3334CrossRefGoogle Scholar
- Larsen RJ, Marx ML (1985) An introduction to probability and its applications, vol 85, (2). Prentice Hall, Englewood Cliffs, pp 2061–2071Google Scholar
- Peng S, Khush GS, Cassman KG (1994) Evolution of the new plant ideo-type for increased yield potential. In: Cassman KG (ed) Breaking the yield barrier. IRRI, Los Banos, pp 5–20Google Scholar
- Ranawake A, Amarasinghe U (2015) Changes in yield potential of traditional rice cultivars with variability in plant height, tillers per plant, fertility and days to maturity. J Sci Res Rep 4:114–122Google Scholar
- Rong J, Feltus FA, Waghmare VN, Pierce GJ, Peng WC, Draye X, Saranga Y, Wright RJ, Wilkins TA, May OL (2007) Meta-analysis of polyploid cotton QTL shows unequal contributions of subgenomes to a complex network of genes and gene clusters implicated in lint fiber development. Genetics 176(4):2577–2588CrossRefPubMedPubMedCentralGoogle Scholar
- Sarla N, Pradeep M, Reddy LV, Siddiq EA (2005) Identification and mapping of yield and yield related QTLs from an Indian accession of Oryza rufipogon. BMC Genet 6(1):1–12Google Scholar
- Septiningsih E, Prasetiyono J, Lubis E, Tai T, Tjubaryat T, Moeljopawiro S, McCouch S (2003) Identification of quantitative trait loci for yield and yield components in an advanced backcross population derived from the Oryza sativa variety IR64 and the wild relative O. rufipogon. Theor Appl Genet 107(8):1419–1432CrossRefPubMedGoogle Scholar
- Temnykh S, DeClerck G, Lukashova A, Lipovich L, Cartinhour S, McCouch S (2001) Computational and experimental analysis of microsatellites in rice (Oryza sativa L.): frequency, length variation, transposon associations, and genetic marker potential. Genome Res 11(8):1441–1452CrossRefPubMedPubMedCentralGoogle Scholar
- Venuprasad R, Dalid CO, Del VM, Zhao D, Espiritu M, Sta Cruz MT, Amante M, Kumar A, Atlin GN (2009) Identification and characterization of large-effect quantitative trait loci for grain yield under lowland drought stress in rice using bulk-segregant analysis. Theor Appl Genet 120(1):177–190CrossRefPubMedGoogle Scholar
- Xing Y, Tan Y, Xu C, Hua J, Sun X (2001) Mapping quantitative trait loci for grain appearance traits of rice using a recombinant inbred line population. Acta Bot Sin 43(8):840–845Google Scholar
- Xiong ZM, Hangzhou H (1994) Research outline on rice genetics in China. Chin Rice Res Newsl 2:10Google Scholar
- Zhang H, Uddin MS, Zou C, Xie C, Xu Y, Li WX (2014) Meta-analysis and candidate gene mining of low-phosphorus tolerance in maize. J Plant Ecol 56(3):262–270Google Scholar