Xuechen Zhang1, Sergey Shabala1, Anthony Koutoulis2, Lana Shabala1, Meixue Zhou3. 1. School of Land and Food, University of Tasmania, P.O. Box 46, Kings Meadows, Tasmania, TAS 7249, Australia. 2. School of Biological Sciences, University of Tasmania, Private Bag 55, Hobart, TAS 7001, Australia. 3. School of Land and Food, University of Tasmania, P.O. Box 46, Kings Meadows, Tasmania, TAS 7249, Australia. Meixue.Zhou@utas.edu.au.
Abstract
MAIN CONCLUSION: We projected meta-QTL (MQTL) for drought, salinity, and waterlogging tolerance to the physical map of barley through meta-analysis. The positions of these MQTL were refined and candidate genes were identified. Drought, salinity and waterlogging are three major abiotic stresses limiting barley yield worldwide. Breeding for abiotic stress-tolerant crops has drawn increased attention, and a large number of quantitative trait loci (QTL) for drought, salinity, and waterlogging tolerance in barley have been detected. However, very few QTL have been successfully used in marker-assisted selection (MAS) in breeding. In this study, we summarized 632 QTL for drought, salinity and waterlogging tolerance in barley. Among all these QTL, only 195 major QTL were used to conduct meta-analysis to refine QTL positions for MAS. Meta-analysis was used to map the summarized major QTL for drought, salinity, and waterlogging tolerance from different mapping populations on the barley physical map. The positions of identified meta-QTL (MQTL) were used to search for candidate genes for drought, salinity, and waterlogging tolerance in barley. Both MQTL3H.4 and MQTL6H.2 control drought tolerance in barley. Fine-mapped QTL for salinity tolerance, HvNax4 and HvNax3, were validated on MQTL1H.4 and MQTL7H.2, respectively. MQTL2H.1 and MQTL5H.3 were also the target regions for improving salinity tolerance in barley. MQTL4H.4 is the main region controlling waterlogging tolerance in barley with fine-mapped QTL for aerenchyma formation under waterlogging conditions. Detected and refined MQTL and candidate genes are crucial for future successful MAS in barley breeding.
MAIN CONCLUSION: We projected meta-QTL (MQTL) for drought, salinity, and waterlogging tolerance to the physical map of barley through meta-analysis. The positions of these MQTL were refined and candidate genes were identified. Drought, salinity and waterlogging are three major abiotic stresses limiting barley yield worldwide. Breeding for abiotic stress-tolerant crops has drawn increased attention, and a large number of quantitative trait loci (QTL) for drought, salinity, and waterlogging tolerance in barley have been detected. However, very few QTL have been successfully used in marker-assisted selection (MAS) in breeding. In this study, we summarized 632 QTL for drought, salinity and waterlogging tolerance in barley. Among all these QTL, only 195 major QTL were used to conduct meta-analysis to refine QTL positions for MAS. Meta-analysis was used to map the summarized major QTL for drought, salinity, and waterlogging tolerance from different mapping populations on the barley physical map. The positions of identified meta-QTL (MQTL) were used to search for candidate genes for drought, salinity, and waterlogging tolerance in barley. Both MQTL3H.4 and MQTL6H.2 control drought tolerance in barley. Fine-mapped QTL for salinity tolerance, HvNax4 and HvNax3, were validated on MQTL1H.4 and MQTL7H.2, respectively. MQTL2H.1 and MQTL5H.3 were also the target regions for improving salinity tolerance in barley. MQTL4H.4 is the main region controlling waterlogging tolerance in barley with fine-mapped QTL for aerenchyma formation under waterlogging conditions. Detected and refined MQTL and candidate genes are crucial for future successful MAS in barley breeding.
Authors: Ting Wang; Takayuki Tohge; Alexander Ivakov; Bernd Mueller-Roeber; Alisdair R Fernie; Marek Mutwil; Jos H M Schippers; Staffan Persson Journal: Plant Physiol Date: 2015-08-04 Impact factor: 8.340
Authors: Kassa Semagn; Yoseph Beyene; Marilyn L Warburton; Amsal Tarekegne; Stephen Mugo; Barbara Meisel; Pierre Sehabiague; Boddupalli M Prasanna Journal: BMC Genomics Date: 2013-05-10 Impact factor: 3.969
Authors: Kornelia Gudys; Justyna Guzy-Wrobelska; Agnieszka Janiak; Michał A Dziurka; Agnieszka Ostrowska; Katarzyna Hura; Barbara Jurczyk; Katarzyna Żmuda; Daria Grzybkowska; Joanna Śróbka; Wojciech Urban; Jolanta Biesaga-Koscielniak; Maria Filek; Janusz Koscielniak; Krzysztof Mikołajczak; Piotr Ogrodowicz; Karolina Krystkowiak; Anetta Kuczyńska; Paweł Krajewski; Iwona Szarejko Journal: Front Plant Sci Date: 2018-06-12 Impact factor: 5.753