National Academy of Agricultural Sciences (NAAS)
|
PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
Rice (Oryza sativa L.) is one of the most important cereal crops worldwide and plays a crucial role in global food security. Despite remarkable achievements in enhancing rice productivity, limited emphasis has been placed on improving grain quality traits that contribute significantly to consumer preference and export value. The present study was undertaken to investigate the genetic basis of yield and grain quality traits through quantitative trait loci (QTL) mapping in rice using segregating populations. A total of 304 F?:? lines derived from contrasting parental genotypes were evaluated for important quality and agronomic traits, while 96 polymorphic molecular markers were employed for genotypic analysis. Linkage map construction was performed using QTL IciMapping version 4.2, and QTL detection was carried out using composite interval mapping through Windows QTL Cartographer. Chi-square analysis was used to assess marker segregation, while linkage groups were established at a minimum LOD threshold of 3.0. Significant QTLs were identified using a LOD threshold of 2.5 and validated through permutation testing at a 5% significance level. The results revealed substantial variability among the parental lines and segregating generations for elongation ratio, elongation index, days to 50% flowering, days to maturity, and plant height. The F? hybrids generally exhibited superior performance for several traits, indicating the presence of heterosis, whereas the F? populations showed considerable segregation and transgressive variation. Several genomic regions associated with grain quality and agronomic traits were identified, explaining significant proportions of phenotypic variation. The findings provide valuable insights into the genetic architecture of economically important traits and offer useful molecular markers for marker-assisted selection and the development of high-yielding rice cultivars with superior grain quality characteristics.
Churchill, G. A., & Doerge, R. W. (1994). Empirical threshold values for quantitative trait mapping. Genetics, 138(3): 963-971. https://doi.org/10.1093/genetics/138.3.963
Meng, L., Li, H., Zhang, L., & Wang, J. (2015). QTL IciMapping: integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. The Crop Journal, 3(3): 269-283. https://doi.org/10.1016/j.cj.2015.01.001
Sabouri, H., & Biabani, A. (2009). Toward the mapping of agronomic characters on a rice genetic map: quantitative trait loci analysis under saline condition. Biotechnology, 8(1): 144-149. https://doi.org/10.3923/biotech.2009.144.149
Stam, P. (1993). Construction of integrated genetic linkage maps by means of a new computer package: Join Map. The plant journal, 3(5): 739-744. https://doi.org/10.1111/j.1365-313X.1993.00739.x
Swamy, B. P. M., Kaladhar, K., Anuradha, K., Batchu, A. K., Longvah, T., & Sarla, N. (2018). QTL analysis for grain iron and zinc concentrations in two O. nivara derived backcross populations. Rice Science, 25(4): 197-207. https://doi.org/10.1016/j.rsci.2018.06.001
Wang, S., Basten, C. J., and Zeng, Z.B. (2012). Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC.
Citations
|
|
|
