The QTL mapping results were compared with the genotypically selected and random samples of the same size on the base of a RIL population. The results demonstrated that there were no obvious differences in the trait distribution and marker segregation distortion between the genotypically selected and random samples with the same population size. However, a significant increase in QTL detection power, sensitivity, specificity, and QTL resolution in the genotypically selected samples were observed. Moreover, the highly significant effect was detected in small size of genotypically selected samples. In QTL mapping, phenotyping is a more sensitive limiting factor than genotyping so that the selection of samples could be an attractive strategy for increasing genome-wide QTL mapping resolution. The efficient selection of samples should be more helpful for QTL maker assistant selection, fine mapping, and QTL cloning.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/S0379-4172(06)60091-7 | DOI Listing |
Theor Appl Genet
January 2025
Institute of Wheat Research, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province) Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Linfen, China.
Total 60-QRC for FLM traits were detected by meta-genomics analysis, nine major and stable QTL identified by DH population and validated, and a novel QTL Qflw.sxau-6BL was fine mapped. The flag leaf is an "ideotypic" morphological trait providing photosynthetic assimilates in wheat.
View Article and Find Full Text PDFTheor Appl Genet
January 2025
Department of Plant Sciences, University of Idaho Aberdeen, R and E Center, Aberdeen, ID, 83210, USA.
Two dwarf bunt resistance QTLs were mapped to chromosome 6D, and KASP markers associated with the loci were developed and validated in a panel of regionally adapted winter wheats. UI Silver is an invaluable adapted resistant cultivar possessing the two identified QTL potentially associated with genes Bt9 and Bt10 and will be useful in future cultivar development to improve dwarf bunt resistance. Dwarf bunt, caused by Tilletia controversa, is a fungal disease of wheat that can cause complete loss of grain yield and quality during epidemics.
View Article and Find Full Text PDFPlants (Basel)
December 2024
Key Laboratory of Plant Functional Genomics of the Ministry of Education/Zhongshan Biological Breeding Laboratory, Yangzhou University, Yangzhou 225009, China.
The whiteness of rice grains (WRG) is a key indicator of appearance quality, directly impacting its commercial value. The trait is quantitative, influenced by multiple factors, and no specific genes have been cloned to date. In this study, we first examined the correlation between the whiteness of polished rice, cooked rice, and rice flour, finding that the whiteness of rice flour significantly correlated with both polished and cooked rice.
View Article and Find Full Text PDFGenes (Basel)
November 2024
State Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an 271018, China.
Background: Sulfur (S) is a vital element for the normal growth and development of plants, performing crucial biological functions in various life processes.
Methods: This study investigated thirteen S utilization efficiency (SUE)-related traits at the seedling stage of wheat using a recombinant inbred line (RIL) population. The quantitative trait loci (QTLs) were mapped by genetic mapping.
Genes (Basel)
November 2024
Dipartimento di Agraria, Università degli Studi di Sassari, 07100 Sassari, Italy.
Objectives: The aim of this study was to investigate the genomic structure of the cattle breeds selected for meat and milk production and to identify selection signatures between them.
Methods: A total of 391 animals genotyped at 41,258 SNPs and belonging to nine breeds were considered: Angus (N = 62), Charolais (46), Hereford (31), Limousin (44), and Piedmontese (24), clustered in the Meat group, and Brown Swiss (42), Holstein (63), Jersey (49), and Montbéliarde (30), clustered in the Milk group. The population stratification was analyzed by principal component analysis (PCA), whereas selection signatures were identified by univariate (Wright fixation index, F) and multivariate (canonical discriminant analysis, CDA) approaches.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!