Literature DB >> 16231161

Grain yield responses to moisture regimes in a rice population: association among traits and genetic markers.

G H Zou1, H W Mei, H Y Liu, G L Liu, S P Hu, X Q Yu, M S Li, J H Wu, L J Luo.   

Abstract

Drought is a major constraint to rice (Oryza sativa L.) production in rainfed and poorly irrigated environments. Identifying genomic regions influencing the response of yield and its components to water deficits will aid our understanding of the genetic mechanism of drought tolerance (DT) of rice and the development of DT varieties. Grain yield (GY) and its components of a recombinant inbred population developed from a lowland rice and an upland rice were investigated under different water levels in 2003 and 2004 in a rainout DT screening facility. Correlation and path analysis indicated that spikelet fertility (SF) was particularly important for grain yield with direct effect (P=0.60) under drought stress, while spikelet number per panicle (SN) contributed the most to grain yield (P=0.41) under well-watered condition. A total of 32 quantitative trait loci (QTLs) for grain yield and its components were identified. The phenotypic variation explained by individual QTLs varied from 1.29% to 14.76%. Several main effect QTLs affecting SF, 1,000-grain weight (TGW), panicle number (PN), and SN were mapped to the same regions on chromosome 4 and 8. These QTLs were detected consistently across 2 years and under both water levels in this study. Several digenic interactions among yield components were also detected. The identification of genomic regions associated with GY and its components under stress will be useful to improve drought tolerance of rice by marker-aided approaches.

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Year:  2005        PMID: 16231161     DOI: 10.1007/s00122-005-0111-3

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  4 in total

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Authors:  A. Blum; R. Munns; J. B. Passioura; N. C. Turner; R. E. Sharp; J. S. Boyer; H. T. Nguyen; T. C. Hsiao; DPS. Verma; Z. Hong
Journal:  Plant Physiol       Date:  1996-04       Impact factor: 8.340

2.  Effects of Phenotyping Environment on Identification of Quantitative Trait Loci for Rice Root Morphology under Anaerobic Conditions.

Authors:  A. Kamoshita; Jingxian Zhang; J. Siopongco; S. Sarkarung; H. T. Nguyen; L. J. Wade
Journal:  Crop Sci       Date:  2002-01       Impact factor: 2.319

3.  Mapping QTLs for root morphology of a rice population adapted to rainfed lowland conditions.

Authors:  A. Kamoshita; J. Wade; L. Ali; S. Pathan; J. Zhang; S. Sarkarung; T. Nguyen
Journal:  Theor Appl Genet       Date:  2002-02-22       Impact factor: 5.699

4.  Quantitative trait loci associated with drought tolerance at reproductive stage in rice.

Authors:  Jonaliza C Lanceras; Grienggrai Pantuwan; Boonrat Jongdee; Theerayut Toojinda
Journal:  Plant Physiol       Date:  2004-04-30       Impact factor: 8.340

  4 in total
  20 in total

1.  NAL1 allele from a rice landrace greatly increases yield in modern indica cultivars.

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Journal:  Proc Natl Acad Sci U S A       Date:  2013-12-02       Impact factor: 11.205

2.  Evaluation of near-isogenic lines for drought resistance QTL and fine mapping of a locus affecting flag leaf width, spikelet number, and root volume in rice.

Authors:  Xipeng Ding; Xiaokai Li; Lizhong Xiong
Journal:  Theor Appl Genet       Date:  2011-06-17       Impact factor: 5.699

3.  Mapping QTLs for plant phenology and production traits using indica rice (Oryza sativa L.) lines adapted to rainfed environment.

Authors:  K K Suji; K R Biji; R Poornima; K Silvas Jebakumar Prince; K Amudha; S Kavitha; Sumeet Mankar; R Chandra Babu
Journal:  Mol Biotechnol       Date:  2012-10       Impact factor: 2.695

4.  Meta-analysis of grain yield QTL identified during agricultural drought in grasses showed consensus.

Authors:  B P Mallikarjuna Swamy; Prashant Vikram; Shalabh Dixit; H U Ahmed; Arvind Kumar
Journal:  BMC Genomics       Date:  2011-06-16       Impact factor: 3.969

5.  OsGRAS23, a rice GRAS transcription factor gene, is involved in drought stress response through regulating expression of stress-responsive genes.

Authors:  Kai Xu; Shoujun Chen; Tianfei Li; Xiaosong Ma; Xiaohua Liang; Xuefeng Ding; Hongyan Liu; Lijun Luo
Journal:  BMC Plant Biol       Date:  2015-06-13       Impact factor: 4.215

6.  Quantitative trait locus mapping of deep rooting by linkage and association analysis in rice.

Authors:  Qiaojun Lou; Liang Chen; Hanwei Mei; Haibin Wei; Fangjun Feng; Pei Wang; Hui Xia; Tiemei Li; Lijun Luo
Journal:  J Exp Bot       Date:  2015-05-28       Impact factor: 6.992

7.  Characterization of near-isogenic lines carrying QTL for high spikelet number with the genetic background of an indica rice variety IR64 (Oryza sativa L.).

Authors:  Daisuke Fujita; Analiza G Tagle; Leodegario A Ebron; Yoshimichi Fukuta; Nobuya Kobayashi
Journal:  Breed Sci       Date:  2012-03-20       Impact factor: 2.086

8.  Insight into differential responses of upland and paddy rice to drought stress by comparative expression profiling analysis.

Authors:  Xipeng Ding; Xiaokai Li; Lizhong Xiong
Journal:  Int J Mol Sci       Date:  2013-03-04       Impact factor: 5.923

9.  Genetic, physiological, and gene expression analyses reveal that multiple QTL enhance yield of rice mega-variety IR64 under drought.

Authors:  B P Mallikarjuna Swamy; Helal Uddin Ahmed; Amelia Henry; Ramil Mauleon; Shalabh Dixit; Prashant Vikram; Ram Tilatto; Satish B Verulkar; Puvvada Perraju; Nimai P Mandal; Mukund Variar; S Robin; Ranganath Chandrababu; Onkar N Singh; Jawaharlal L Dwivedi; Sankar Prasad Das; Krishna K Mishra; Ram B Yadaw; Tamal Lata Aditya; Biswajit Karmakar; Kouji Satoh; Ali Moumeni; Shoshi Kikuchi; Hei Leung; Arvind Kumar
Journal:  PLoS One       Date:  2013-05-08       Impact factor: 3.240

Review 10.  Towards the understanding of complex traits in rice: substantially or superficially?

Authors:  Toshio Yamamoto; Junichi Yonemaru; Masahiro Yano
Journal:  DNA Res       Date:  2009-04-09       Impact factor: 4.458

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