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G×E Interaction and QTL Analysis for Agronomic Traits under Different Environments in Rice

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dc.contributor.advisor고희종-
dc.contributor.authorHUANG XING-
dc.date.accessioned2017-07-13T17:38:46Z-
dc.date.available2018-01-23-
dc.date.issued2015-02-
dc.identifier.other000000026051-
dc.identifier.urihttps://hdl.handle.net/10371/120999-
dc.description학위논문 (박사)-- 서울대학교 대학원 : 식물생산과학부, 2015. 2. 고희종.-
dc.description.abstractHeat stress is one of the major environmental stresses in rice cultivation that can occur both in vegetative and reproductive phases. Temperature beyond critical thresholds can reduce the growth duration of the rice, and more severely it can cause an increase at spikelet sterility, reduce grain-filling duration, and enhance respiratory loss, resulting in yield loss. Understanding how crops interact with their environments is increasingly important in breeding programs, especially in light of highly anticipated climate changes. Development of heat tolerant cultivars is one of the most effective methods to maintain rice production under climate changes in temperate regions. This study was conducted to investigate the genotype x environment interactions of rice cultivars and RILs under different environments, three different latitude regions accounting for seven environments. In addition, QTL mapping analysis for high temperature using single environment analysis and QTL x environment interaction using multi-environment analysis were also undertaken in present study to detect genomic regions linked to important phenotypic traits for adaptation. A total of 105 rice cultivars and a set of recombinant inbred lines (RILS) derived from Dasanbyeo (indica) / TR22183 (japonica) crosses were used as plant materials to study the genotype X environment interactions and QTL analysis for high temperature in rice across several environments. Those plant materials were grown in the three locations which showed seven diverse environments including Suwon (Korea) during 2010 and 2011 rice growing seasons, Shanghai (China) during 2010 and 2011 growing seasons, and IRRI (Philippines) during 2010 growing season, 2011 wet season and 2011 dry season. A randomized block design with two replications was used. The materials were cultivated with conventional methods in each location. Eight important agronomical traits including days to heading (DTH), culm length (CL), panicle length (PL), panicle number per plant (PN), spikelet number per panicle (SN), spikelet fertility (SF), 100-grain weight (GW), and grain yield (GY) were measured across seven environments for 105 rice cultivars, 150 RILs population, and their parental lines. The collected agronomic traits were subjected to AMMI (additive main effects and multiplicative interactions) model analysis using CropStat 2.3 software. In regards to QTL mapping analysis, leaves of rice seedlings at the three-leaf stage were harvested and subjected to genomic DNA extraction according to the CTAB method. A 384-plex GoldenGate oligo pool assay (OPA) set and distributed evenly through the 12 rice chromosomes was employed for genotyping 150 RILs population and the parents using VeraCode technology on an Illumina BeadXpress Reader. The results showed that most of rice cultivars exhibited yield stability across all environments and only a few genotypes were unstable. For grain yield, the environment effect of environment at IRRIWS2010 was the highest one, followed by Shanghai2010, IRRIWS2011, IRRIDS2010, Shanghai2011, Suwon2011, and Suwon2010. It is suggested that the yield of tested cultivars was affected by genotypes, environments and G × E interaction, simultaneously. In the meantime, the largest environment effect on spikelet fertility was detected in Shanghai2010 compared to that of in IRRIDS2011 and the other environments which showed less environment effects. Among the evaluated traits, SF and GY showed a high G × E interaction with the mean value of 57% and 39%, respectively suggesting the genotype stability of these two traits were unstable in different trails. The data also revealed that the lowest environment effect was detected in Suwon, followed by in Shanghai,and IRRI. The effect in IRRI were stable compared to those in Shanghai indicating that Suwon is suitable to obtain a stable cultivars with low G × E interaction environment and can be suitable for control of trials. IRRI was also suitable for investigating stable cultivars with high G × E interaction for high temperature screening. In addition, based on AMMI results, environment B (Shanghai 2010), F (IRRI dry season), C (IRRI wet season) were highly related to the GY revealing these environment suitable for heat tolerant screening. In year replications, environment E (Shanghai2010) did not show highly related to the GY, indicating that heat stresses in Shanghai were not stable. Therefore, it can be concluded that IRRI is more suitable for screening heat stress compared to the other two locations used in present study.
The results come from regional trial data analysis have reference values for crop breeders, and multi-locational screening is a good strategy for developing heat tolerant varieties in rice. For the traits measured in multiple environments, 37 QTLs were detected in which some of the QTLs were detected in at least two environments. Of these, six QTLs were detected for days to heading, six for culm length, four for panicle length, three for panicle number, four for spikelet number, seven for grain weight, three for grain yield and four for spikelet fertility.
The results obtained in the present study provide a scientific basis of stability and adaptability of rice genotypes against high temperature stress and would be helpful in developing rice varieties for high temperature tolerance.
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dc.description.tableofcontentsContents
General abstract I
Contents IV
General Introduction VI
Chapter I . Genotype x environment interaction for agronomic traits in rice cultivars under different environments based on AMMI model 1
Abstract 1
Introduction 2
Materials and methods 5
Plant materials and experimental design 5
Phenotypic evaluation 5
Statistical analysis 5
Results 7
Meteorological environments 7
Phenotypic analysis 7
Analysis of variance and G×E interactions on GY and SF 13
Correlation and path coefficient analysis for agronomic traits 27
Discussion 28
Chapter II. Genotype × environment interaction for agronomic traits in rice RILs under different environments based on AMMI model 30
Abstract 30
Introduction 31
Materials and methods 34
Development of plant materials 34
Experimental locations and growth conditions 34
Phenotypic data collection 34
Statistical analysis 35
Results 36
Observation of experimental sites condition 36
Agronomic traits in RILs 41
Correlation and path coefficient analysis for agronomic traits in RILs 47
Analysis of variance and G×E interactions 48
Discussion 52
Chapter III. QTL analysis for agronomic traits under different environments in an RIL population 60
Abstract 60
Introduction 62
Materials and methods 65
Plant materials and traits evaluation 65
DNA Extraction and Genotyping Analysis 66
Statistical analysis 66
Results 68
Linkage map construction 68
Identification of main-effect QTLs for heat tolerance using single environment analysis 69
Identification of QTLs using multi-environment analysis 79
Discussion 85
Refferences 99
Abstract in Korean 110
Acknowledgement 112
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dc.formatapplication/pdf-
dc.format.extent2017248 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectG×E Interaction and QTL Analysis for Agronomic Traits under Different Environments in Rice-
dc.subject.ddc633-
dc.titleG×E Interaction and QTL Analysis for Agronomic Traits under Different Environments in Rice-
dc.typeThesis-
dc.description.degreeDoctor-
dc.citation.pages120-
dc.contributor.affiliation농업생명과학대학 식물생산과학부-
dc.date.awarded2015-02-
Appears in Collections:
College of Agriculture and Life Sciences (농업생명과학대학)Dept. of Plant Science (식물생산과학부)Theses (Ph.D. / Sc.D._식물생산과학부)
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