Data on AFLP (eight primer pairs) and 14 phenotypic traits, collected on 55 elite and exotic bread wheat genotypes, were utilized for estimations of genetic diversity. We earlier used these 55 genotypes for a similar study using SSRs and SAMPL. As many as 615 scorable AFLP bands visualized included 287 (46.6%) polymorphic bands. The phenotypic traits included yield and its component traits, as well as physiomorphological traits like flag leaf area. Dendrograms were prepared using cluster analysis based on Jaccard's similarity coefficients in case of AFLP and on squared Euclidean distances in case of phenotypic traits. PCA was conducted using AFLP data and a PCA plot was prepared, which was compared with clustering patterns in two dendrograms, one each for AFLP and phenotypic traits. The results were also compared with published results that included studies conducted elsewhere using entirely different wheat germplasm and our own SSR and SAMPL studies based on the same 55 genotypes used in the present study. It was shown that molecular markers are superior to phenotypic traits and that AFLP and SAMPL are superior to other molecular markers for estimation of genetic diversity. On the basis of AFLP analysis and keeping in view the yield performance and stability, a pair of genotypes (E3876 and E677) was recommended for hybridization in order to develop superior cultivars.
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http://dx.doi.org/10.1023/b:bigi.0000012143.48298.71 | DOI Listing |
Theor Appl Genet
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College of Horticulture, Shenyang Agricultural University, Shenyang, 110866, People's Republic of China.
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Chongqing Engineering Research Center of Goose Genetic Improvement, Institute of Poultry Science, Chongqing Academy of Animal Science, Rongchang District, Chongqing 402460, PR China. Electronic address:
Multi-omics has helped elucidate the gene expression patterns and genomic variations closely associated with economically significant traits in geese. Despite the substantial genomic data generated through extensive goose studies, a unified platform for integrating these datasets is lacking. To address this gap, we introduced the Goose Multi-omics Database (GMD), which is accessible at http://goosedb.
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Livestock Farm Complex, Veterinary College and Research Institute, TANUVAS, Salem, 636 001, India.
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Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, 38541, Republic of Korea.
To improve the quality and yield of the Korean beef industry, selection criteria often focus on estimated breeding values for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS). This study estimated genetic parameters and assessed the accuracy of genomic estimated breeding values (GEBVs) using SNP weighting methods. We compared the accuracy of these methods with the genomic best linear unbiased prediction (GBLUP) and various Bayesian approaches (BayesA, BayesB, BayesC, and BayesCPi) for the specified traits.
View Article and Find Full Text PDFPlanta
January 2025
Department of Plant Sciences, University of the Free State, Bloemfontein, South Africa.
Sorghum kernel composition is a crucial characteristic that determines its functional qualities. The total protein content of sorghum grain increases under drought stress, but starch, protein digestibility, and micronutrient contents decrease. Sorghum (Sorghum bicolor L.
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