Literature DB >> 22361948

Fine mapping of QTLs for rice grain yield under drought reveals sub-QTLs conferring a response to variable drought severities.

Shalabh Dixit1, B P Mallikarjuna Swamy, Prashant Vikram, H U Ahmed, M T Sta Cruz, Modesto Amante, Dinesh Atri, Hei Leung, Arvind Kumar.   

Abstract

Fine-mapping studies on four QTLs, qDTY(2.1), qDTY(2.2), qDTY(9.1) and qDTY(12.1), for grain yield (GY) under drought were conducted using four different backcross-derived populations screened in 16 experiments from 2006 to 2010. Composite and bayesian interval mapping analyses resolved the originally identified qDTY(2.1) region of 42.3 cM into a segment of 1.6 cM, the qDTY(2.2) region of 31.0 cM into a segment of 6.7 cM, the qDTY(9.1) region of 32.1 cM into two segments of 9.4 and 2.4 cM and the qDTY(12.1) region of 10.6 cM into two segments of 3.1 and 0.4 cM. Two of the four QTLs (qDTY(9.1) and qDTY(12.1)) having effects under varying degrees of stress severity showed the presence of more than one region within the original QTL. The study found the presence of a donor allele at RM262 within qDTY(2.1) and RM24334 within qDTY(9.1) showing a negative effect on GY under drought, indicating the necessity of precise fine mapping of QTL regions before using them in marker-assisted selection (MAS). However, the presence of sub-QTLs together in close vicinity to each other provides a unique opportunity to breeders to introgress such regions together as a unit into high-yielding drought-susceptible varieties through MAS.

Entities:  

Mesh:

Substances:

Year:  2012        PMID: 22361948     DOI: 10.1007/s00122-012-1823-9

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


  23 in total

1.  Yield response to water deficit in an upland rice mapping population: associations among traits and genetic markers.

Authors:  H R Lafitte; A H Price; B Courtois
Journal:  Theor Appl Genet       Date:  2004-07-29       Impact factor: 5.699

2.  GGT 2.0: versatile software for visualization and analysis of genetic data.

Authors:  Ralph van Berloo
Journal:  J Hered       Date:  2008-01-24       Impact factor: 2.645

3.  Locating genes associated with root morphology and drought avoidance in rice via linkage to molecular markers.

Authors:  M C Champoux; G Wang; S Sarkarung; D J Mackill; J C O'Toole; N Huang; S R McCouch
Journal:  Theor Appl Genet       Date:  1995-06       Impact factor: 5.699

4.  The ethylene response factors SNORKEL1 and SNORKEL2 allow rice to adapt to deep water.

Authors:  Yoko Hattori; Keisuke Nagai; Shizuka Furukawa; Xian-Jun Song; Ritsuko Kawano; Hitoshi Sakakibara; Jianzhong Wu; Takashi Matsumoto; Atsushi Yoshimura; Hidemi Kitano; Makoto Matsuoka; Hitoshi Mori; Motoyuki Ashikari
Journal:  Nature       Date:  2009-08-20       Impact factor: 49.962

5.  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

6.  Bayesian mapping of multiple quantitative trait loci from incomplete inbred line cross data.

Authors:  M J Sillanpää; E Arjas
Journal:  Genetics       Date:  1998-03       Impact factor: 4.562

7.  Substitution mapping of dth1.1, a flowering-time quantitative trait locus (QTL) associated with transgressive variation in rice, reveals multiple sub-QTL.

Authors:  Michael J Thomson; Jeremy D Edwards; Endang M Septiningsih; Sandra E Harrington; Susan R McCouch
Journal:  Genetics       Date:  2006-02-01       Impact factor: 4.562

8.  Identification and characterization of large-effect quantitative trait loci for grain yield under lowland drought stress in rice using bulk-segregant analysis.

Authors:  Ramaiah Venuprasad; C O Dalid; M Del Valle; D Zhao; M Espiritu; M T Sta Cruz; M Amante; A Kumar; G N Atlin
Journal:  Theor Appl Genet       Date:  2009-10-17       Impact factor: 5.699

9.  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

10.  Improved resolution in the position of drought-related QTLs in a single mapping population of rice by meta-analysis.

Authors:  Farkhanda S Khowaja; Gareth J Norton; Brigitte Courtois; Adam H Price
Journal:  BMC Genomics       Date:  2009-06-22       Impact factor: 3.969

View more
  30 in total

1.  Physiological characterization and allelic diversity of selected drought tolerant traditional rice (Oryza sativa L.) landraces of Koraput, India.

Authors:  Swati S Mishra; Prafulla K Behera; Vajinder Kumar; Sangram K Lenka; Debabrata Panda
Journal:  Physiol Mol Biol Plants       Date:  2018-09-28

2.  Identification of Markers for Root Traits Related to Drought Tolerance Using Traditional Rice Germplasm.

Authors:  Harendra Verma; R N Sarma
Journal:  Mol Biotechnol       Date:  2021-08-16       Impact factor: 2.695

3.  Genome wide association study of MAGIC population reveals a novel QTL for salinity and sodicity tolerance in rice.

Authors:  S L Krishnamurthy; P C Sharma; D Dewan; B M Lokeshkumar; Suman Rathor; A S Warraich; N M Vinaykumar; Hei Leung; R K Singh
Journal:  Physiol Mol Biol Plants       Date:  2022-04-21

4.  Phenotyping for drought tolerance of crops in the genomics era.

Authors:  Roberto Tuberosa
Journal:  Front Physiol       Date:  2012-09-19       Impact factor: 4.566

5.  Systematic trait dissection in oilseed rape provides a comprehensive view, further insight, and exact roadmap for yield determination.

Authors:  Huabing Liang; Jiang Ye; Ying Wang; Xinfa Wang; Xue-Rong Zhou; Jacqueline Batley; Graham J King; Liang Guo; Jinxing Tu; Jiaqin Shi; Hanzhong Wang
Journal:  Biotechnol Biofuels Bioprod       Date:  2022-04-19

6.  Physiological mechanisms contributing to the QTL-combination effects on improved performance of IR64 rice NILs under drought.

Authors:  Amelia Henry; B P Mallikarjuna Swamy; Shalabh Dixit; Rolando D Torres; Tristram C Batoto; Mervin Manalili; M S Anantha; N P Mandal; Arvind Kumar
Journal:  J Exp Bot       Date:  2015-02-13       Impact factor: 6.992

7.  Composite Interval Mapping Based on Lattice Design for Error Control May Increase Power of Quantitative Trait Locus Detection.

Authors:  Jianbo He; Jijie Li; Zhongwen Huang; Tuanjie Zhao; Guangnan Xing; Junyi Gai; Rongzhan Guan
Journal:  PLoS One       Date:  2015-06-15       Impact factor: 3.240

Review 8.  Breeding rice for a changing climate by improving adaptations to water saving technologies.

Authors:  Maria Cristina Heredia; Josefine Kant; M Asaduzzaman Prodhan; Shalabh Dixit; Matthias Wissuwa
Journal:  Theor Appl Genet       Date:  2021-07-03       Impact factor: 5.699

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.  Enhancing phosphorus and zinc acquisition efficiency in rice: a critical review of root traits and their potential utility in rice breeding.

Authors:  T J Rose; S M Impa; M T Rose; J Pariasca-Tanaka; A Mori; S Heuer; S E Johnson-Beebout; M Wissuwa
Journal:  Ann Bot       Date:  2012-10-15       Impact factor: 4.357

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.