Quantitative Trait Loci and Environmental Interactions Associated with Agronomic Performance of Wheat PDF Download
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Author: Benjamin Todd Campbell
Publisher:
ISBN:
Category :
Languages : en
Pages : 382
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Author: Benjamin Todd Campbell
Publisher:
ISBN:
Category :
Languages : en
Pages : 382
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Author: Junli Zhang (Doctoral student)
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 416
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Grain yield (GY) is always the first priority in wheat (Triticum aestivum L.) breeding; however, progress in improvement of this trait is hampered due to quantitative inheritance, low heritability, and confounding environmental effects. Thanks to the advancements of high throughput genotyping and phenotyping technologies, both molecular markers and physiological traits are now promising indirect selection tools in breeding for this trait and other traits. Besides grain yield, grain quality is another important respect in wheat breeding, and one of the quality traits is the Hagberg falling number (FN), which is commonly used in grain grading. The FN test has a genetic component but is also strongly influenced by environmental conditions during the reproductive growth stage, including excessive moisture, extreme temperature, and biotic and abiotic stresses. The objective of the current studies was to identify potential genomic regions and molecular markers that influence GY, three important physiological traits (canopy temperature, CT; chlorophyll content index, CCI; flag leaf senescence, FLS) that could impact grain yield during heat and moisture stress, and FN by QTL mapping approaches. A winter wheat population of 159 recombinant inbred lines (RILs) from the cross of ID0444 and Rio Blanco were used to map QTL for GY, CT, CCI and FLS, and a total of 110 hard white spring (HWS) wheat accessions from the National Small Grain Collection (NSGC) were used in genome-wide association mapping of FN. GY was evaluated under three field conditions, rainfed, terminal drought (water stress applied after anthesis), and fully irrigated, with a total of six location-year environments. QTL mapping was conducted for main effect (G) of GY, and the genotype x environment interaction (GEI) effect of GY. A total of 17 QTL were associated with G and 13 QTL associated with GEI, and nine of 13 QTL for GEI were mapped in the flanking chromosomal regions of QTL for GEI. One QTL, Q.Gy.ui-1B.2 found on chromosome 1B, was associated with GY in all six individual environments. Significant QTL x environment interaction (QEI), QTL x QTL interaction (QQI) and QTL x QTL x environment (QQEI) were also identified. The present study showed that the QEI and QQI were as important as the QTL main effect of GY, and they should be taken into consideration in future QTL studies and marker-assisted selection (MAS). The three physiological traits, CT, CCI and FLS, which have been reported to be closely related to grain yield of wheat in diverse environments, were evaluated in two terminal drought and one rainfed environments in southeastern Idaho. Correlation results showed that CT and FLS were highly correlated with GY but the relationship between CCI and GY varied among the three environments. FLS was closely related to heading date (HD) and its effect on grain yield might be determined by HD in the RIL population used in the study. Stepwise multiple regression showed that CT and FLS could predict grain yield effectively and could be used as indirect selection criteria in wheat breeding. A total of 27 main effect QTL (M-QTL) were identified on 12 chromosomes, explaining 5 to 14% of phenotypic variation. Seven epistatic QTL (E-QTL) were identified for FLS and CCI and these could explain 9-25% of the phenotypic variation, but most of them did not have a main effect. Most of the QTL were reported for the first time. FN tests were conducted using grain flour samples from the 110 HWS wheat accessions grown in five environments. A total of 1,740 SNP markers were used to detect SNP-FN associations using both general linear model (GLM) and mixed linear model (MLM). A total of 13 QTL located in nine chromosomal regions were identified in both GLM and MLM approaches. These new QTL have the potential to increase the selection efficiency for wheat breeding, and can be further explored to identify candidate genes.
Author: Reynolds, M.P.
Publisher: CIMMYT
ISBN: 9706481443
Category : Wheat
Languages : en
Pages : 207
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Author: Terry Glenn Berke
Publisher:
ISBN:
Category :
Languages : en
Pages : 266
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Author: Matthew P. Reynolds
Publisher: CABI
ISBN: 1845936337
Category : Science
Languages : en
Pages : 310
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Book Description
Agricultural, botanical, and social scientists from the four quarters of the world address the impact of climate change on crop productivity, some approaches to adapt plants to both biotic and abiotic stresses, and measures to reduce greenhouse gases. They cover predictions of climate change within the context of agriculture, adapting to biotic and abiotic stresses through crop breeding, sustainable and resource-conserving technologies for adapting to and mitigating climate change, and new tools for enhancing crop adaptation to climate change. Specific topics include economic impacts of climate change on agriculture to 2030, breeding for adaptation to heat and drought stress, managing resident soil microbial community structure and function to suppress the development of soil-borne diseases, and applying geographical information systems (GIS) and crop simulation modeling in climate change research.
Author: Jayfred Gaham Villegas Godoy
Publisher:
ISBN:
Category :
Languages : en
Pages : 229
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Wheat (Triticum aestivum L.) is the main source of food for roughly one-third of the world's population. In order to satisfy demand, wheat is planted over millions of acres and exposed to various abiotic and biotic stresses such as heat stress and stripe rust (Puccinia striiformis). Development of cultivars with improved agronomic performance and stable yields is necessary to prevent yield losses and possibly food shortage. A quantitative trait loci (QTL) mapping study was performed using a recombinant inbred population derived from a cross between elite spring wheat varieties 'Kelse' and 'Scarlet' to identify QTL associated with heat tolerance under natural and controlled conditions. Our analysis yielded 19 QTL linked to 14 traits related to heat tolerance. A pleiotropic region for yield components was detected on chromosome 4AL which can be a valuable resource of favorable alleles for heat tolerance. Genome-wide association analysis was conducted on a population of elite North American germplasm to detect significant marker-traits associations (MTAs) for resistance to stripe rust infection and improved grain yield and yield component traits. Eleven highly significant (FDR
Author: Guomei Wang
Publisher:
ISBN:
Category : Soft wheat
Languages : en
Pages : 160
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Kernel hardness (KHA) is a major factor determining break flour yield (BFY) and end-use quality of common wheat (Triticum aestivum L.). Within the soft wheat class, genotypes with consistently softer grains than common soft wheat are considered to be 'extra-soft'. In addition, 'extra-soft' wheats have greater BFY than common soft wheat lines. In order to better understand this interrelationship, a set of 164 F6-recombinant inbred lines (RILs) developed from a soft x 'extra-soft' wheat cross was evaluated for KHA, BFY, and other related traits in six field environments. The estimates of broad-sense heritability for KHA and BFY ranged from 0.84 to 0.96 and 0.56 to 0.76, respectively. Significant environmental effects and genotype by environment interactions were detected for all traits evaluated. A comprehensive genetic linkage map was created with 650 molecular markers based on this mapping population. Three chromosome translocations, 1BL. 1RS, 2N^S-2AS. 2AL and 5B:7B, were identified during linkage analysis. A total of 47 quantitative trait loci (QTL) were identified for nine traits including KHA, BFY, bran yield (BRN), unground middling yield (MID), plant height (PHT), days to heading (HDD), thousand-kernel weight (TKW), grain protein content (GPC), and test weight (TWT). The number of QTL per trait ranged from three for MID to nine for GPC. The phenotypic variance explained by individual QTL ranged from 5.8 to 47.6%. Among five QTL identified for KHA, the two most important QTL were located on chromosomes 4DS (Xbarc1118-Rht-D1 interval) and 4BS (Xwmc617-Rht-B1 interval), indicating that the 'extra-soft' characteristic was not controlled by the 5DS Hardness (Ha) locus which encodes the two puroindoline genes pinA and pinB. The co-location of QTL for KHA, BFY, BRN, and MID on 4DS suggested that genetic factors affecting KHA may have a pleiotropic effect on BFY. Two co-located QTL for TWT, TKW and PHT were detected on 4DS and 4BS, and a QTL for HDD was detected on 4DS, indicating that these QTL may represent the consequence of the semi-dwarfing green-revolution genes Rht-D1 and Rht-B1 located on 4DS and 4BS, respectively. Additional analysis suggested that the QTL for KHA on 4DS and 4BS are the effects of genes linked to Rht-D1 and Rht-B1, rather than pleiotropic effects of these genes. Some coincident QTL for the traits that were evaluated represent the interrelationships of phenotypic traits, where both KHA and BFY were associated with HDD and TWT based on path coefficient analysis. Association mapping can be an effective means for identifying, validating, and fine mapping genes and QTL in crop plants. To test this approach, a set of 94 diverse elite wheat lines was phenotyped for five important traits and genotyped with 487 molecular markers. In this study, the marker-trait association analysis showed that the gene pinB (Ha locus) was significantly associated with KHA as it is known to define the difference between soft and hard wheat classes. Additionally, the significant associations of marker XwPt-7187 with KHA, XwPt-1250 and XwPt-4628 with TWT, and Xgwm512 with PHT mark the first report of such associations in these genomic regions. This study, aiming at the genetic dissection of wheat kernel extra-softness and related traits, enhanced our understanding of both genetic control of and environmental effects on these important traits. Path coefficient analysis showed the promise of an alternative phenotypic selection approach that is more cost effective than direct measurement of kernel quality. Three chromosome translocations were discovered and their approximate chromosome break points were located. Numerous QTL were identified for these important traits. The major QTL can serve as a start point for fine mapping that eventually lead to the cloning of the QTL through map-based or candidate gene approach. Association mapping, as an alternate approach and complementary tool to QTL mapping, was demonstrated feasible in wheat for identification of marker-trait associations and cross validation of QTL or genes identified from bi-parent mapping populations.
Author: Arron Hyrum Carter
Publisher:
ISBN:
Category : Wheat
Languages : en
Pages : 157
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Author: Nicholas Adam Crowley
Publisher:
ISBN: 9781124124001
Category : Mosaic viruses
Languages : en
Pages :
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Author: Izabela L. Ciechanowska
Publisher:
ISBN:
Category : Wheat
Languages : en
Pages : 0
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Book Description
Marker-assisted selection requires the identification of molecular markers associated with major genes and quantitative trait loci (QTL) using linkage analysis. In this study, we used 167 doubled haploid (DH) lines derived from two unregistered spring wheat (Triticum aestivum L.) parental lines that belong to the Canada Western Special Purpose (CWSP) class to map QTLs associated with five traits using inclusive composite interval mapping (ICIM). Using ICIM, least square means phenotype data across 3-4 environments, and a genetic map of 2,676 SNPs out of the wheat 90K SNP array, we identified ten QTLs associated with maturity (4A and 5B), plant lodging (4B, 5A, 5D, and 7D), grain yield (2D), leaf rust (4A) and stem rust (1A and 2B). Each QTL individually accounted for 6.0-22.3% of the phenotypic variance and together accounted for 8.6-38.2% of each trait. QTLs identified for rusts using ICIM had a minor effect (6.0-9.0%) or a major effect (22.3%). Our major effect QTL at 22.3% was discovered on chromosome 2B and contributed to stem rust response. Its physical location has been associated with disease response in previous studies. Results from this study provide additional valuable information to wheat researchers, in particular that the area on chromosome 2B should be considered for future analyses.
