Study on combining ability, heterosis and genetic parameters of yield traits in rice

A study was conducted on heterosis, combining ability and genetic parameters of yield and yield components in rice. Five lines were crossed with two testers in line × tester manner to produce ten F1 hybrids. Results show that general combining ability (GCA) effect was only significant for total number of kernels per panicle, number of filled kernels and grain yield per plant, and specific combining ability (SCA) effect was significant for yield and all of its studied components (except for 100-kernel weight). Lines IR42 and Pouya showed a significant GCA for grain yield in opposite direction (20.9 and -13.7 g/plant, respectively). The two lines also showed highest significant GCA for number of filled kernels (22.7 and 23.3, respectively). In the total number of kernels, lines IR8 and IR42 and tester Usen showed the highest significant GCA (34.79, 27.97 and 12.56). In tiller number, only line IR36 and tester IR68897 had the highest significant GCA (3.51 and 0.84). Combination of IR68897×IR8 showed highest significant SCA for grain yield (9.7 g/plant), while in the case of number of filled kernels and tiller number, combinations IR68897×IR8 and Usen/IR36 showed a significant positive SCA (18.9 and 2.1, respectively), indicating that hybridization can be a choice for improving hybrids with better quantity of these traits. The highest general heritability (hbs) was obtained for tiller number (96.1%), indicating slight effects of the environment on the trait, while for other traits, a mild general heritability (~70%) was obtained, indicating considerable effect of environment on phenotypic expression of most yield traits. A low specific heritability (hn2) was obtained for all traits (18.2 to 26.3%), indicating that non-additive effects play an important role in genetic control of yield traits. Therefore, it seems that hybridization must be a choice for utilizing the putative heterosis in special crosses, and such a condition was observed for tiller number and grain yield in combinations of IR42×IR68897 and IR42×Usen.Key words: Rice, line × tester, combining ability, heritability, heterosis

The First Illumina-Based De Novo Transcriptome Sequencing and Analysis of Safflower Flowers

BACKGROUND: The safflower, Carthamus tinctorius L., is a worldwide oil crop, and its flowers, which have a high flavonoid content, are an important medicinal resource against cardiovascular disease in traditional medicine. Because the safflower has a large and complex genome, the development of its genomic resources has been delayed. Second-generation Illumina sequencing is now an efficient route for generating an enormous volume of sequences that can represent a large number of genes and their expression levels. METHODOLOGY/PRINCIPAL FINDINGS: To investigate the genes and pathways that might control flavonoids and other secondary metabolites in the safflower, we used Illumina sequencing to perform a de novo assembly of the safflower tubular flower tissue transcriptome. We obtained a total of 4.69 Gb in clean nucleotides comprising 52,119,104 clean sequencing reads, 195,320 contigs, and 120,778 unigenes. Based on similarity searches with known proteins, we annotated 70,342 of the unigenes (about 58% of the identified unigenes) with cut-off E-values of 10(-5). In total, 21,943 of the safflower unigenes were found to have COG classifications, and BLAST2GO assigned 26,332 of the unigenes to 1,754 GO term annotations. In addition, we assigned 30,203 of the unigenes to 121 KEGG pathways. When we focused on genes identified as contributing to flavonoid biosynthesis and the biosynthesis of unsaturated fatty acids, which are important pathways that control flower and seed quality, respectively, we found that these genes were fairly well conserved in the safflower genome compared to those of other plants. CONCLUSIONS/SIGNIFICANCE: Our study provides abundant genomic data for Carthamus tinctorius L. and offers comprehensive sequence resources for studying the safflower. We believe that these transcriptome datasets will serve as an important public information platform to accelerate studies of the safflower genome, and may help us define the mechanisms of flower tissue-specific and secondary metabolism in this non-model plant