Author : Junli Zhang (Doctoral student)
Publisher :
ISBN 13 :
Total Pages : 416 pages
Book Rating : 4.:/5 (865 download)
Book Synopsis Quantitative Trait Locus Mapping of Agronomic, Physiological, and End-use Quality Traits of Common Wheat (T. Aestivum) by : Junli Zhang (Doctoral student)
Download or read book Quantitative Trait Locus Mapping of Agronomic, Physiological, and End-use Quality Traits of Common Wheat (T. Aestivum) written by Junli Zhang (Doctoral student) and published by . This book was released on 2013 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
