Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice.

Rice is a major staple food across the world in which wide variations in nutrient composition are reported. Rice improvement programs need germplasm accessions with extreme values for any nutritional trait. Near infrared reflectance spectroscopy (NIRS) uses electromagnetic radiations in the NIR region to rapidly measure the biochemical composition of food and agricultural products. NIRS prediction models provide a rapid assessment tool but their applicability is limited by the sample diversity, used for developing them. NIRS spectral variability was used to select a diverse sample set of 180 accessions, and reference data were generated using association of analytical chemists and standard methods. Different spectral pre-processing (up to fourth-order derivatization), scatter corrections (SNV-DT, MSC), and regression methods (partial least square, modified partial least square, and principle component regression) were employed for each trait. Best-fit models for total protein, starch, amylose, dietary fiber, and oil content were selected based on high RSQ, RPD with low SEP(C) in external validation. All the prediction models had ratio of prediction to deviation (RPD) > 2 amongst which the best models were obtained for dietary fiber and protein with R 2 = 0.945 and 0.917, SEP(C) = 0.069 and 0.329, and RPD = 3.62 and 3.46. A paired sample t-test at a 95% confidence interval was performed to ensure that the difference in predicted and laboratory values was non-significant.

Genetic Diversity and Population Structure in Aromatic and Quality Rice (Oryza sativa L.) Landraces from North-Eastern India.

The North-eastern (NE) India, comprising of Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura, possess diverse array of locally adapted non-Basmati aromatic germplasm. The germplasm collections from this region could serve as valuable resources in breeding for abiotic stress tolerance, grain yield and cooking/eating quality. To utilize such collections, however, breeders need information about the extent and distribution of genetic diversity present within collections. In this study, we report the result of population genetic analysis of 107 aromatic and quality rice accessions collected from different parts of NE India, as well as classified these accessions in the context of a set of structured global rice cultivars. A total of 322 alleles were amplified by 40 simple sequence repeat (SSR) markers with an average of 8.03 alleles per locus. Average gene diversity was 0.67. Population structure analysis revealed that NE Indian aromatic rice can be subdivided into three genetically distinct population clusters: P1, joha rice accessions from Assam, tai rices from Mizoram and those from Sikkim; P2, aromatic rice accessions from Nagaland; and P3, chakhao rice germplasm from Manipur [corrected]. Pair-wise FST between three groups varied from 0.223 (P1 vs P2) to 0.453 (P2 vs P3). With reference to the global classification of rice cultivars, two major groups (Indica and Japonica) were identified in NE Indian germplasm. The aromatic accessions from Assam, Manipur and Sikkim were assigned to the Indica group, while the accessions from Nagaland exhibited close association with Japonica. The tai accessions of Mizoram along with few chakhao accessions collected from the hill districts of Manipur were identified as admixed. The results highlight the importance of regional genetic studies for understanding diversification of aromatic rice in India. The data also suggest that there is scope for exploiting the genetic diversity of aromatic and quality rice germplasm of NE India for rice improvement.

Mapping of yellow mosaic virus (YMV) resistance in soybean (Glycine max L. Merr.) through association mapping approach.

Yellow Mosaic Virus (YMV) is a serious disease of soybean. Resistance to YMV was mapped in 180 soybean genotypes through association mapping approach using 121 simple sequence repeats (SSR) and four resistance gene analogue (RGA)-based markers. The association mapping population (AMP) (96 genotypes) and confirmation population (CP) (84 genotypes) was tested for resistance to YMV at hot-spot consecutively for 3 years (2007-2009). The genotypes exhibited significant variability for YMV resistance (P < 0.01). Molecular genotyping and population structure analysis with 'admixture' co-ancestry model detected seven optimal sub-populations in the AMP. Linkage disequilibrium (LD) between the markers extended up to 35 and 10 cM with r2 > 0.15, and >0.25, respectively. The 4 RGA-based markers showed no association with YMV resistance. Two SSR markers, Satt301 and GMHSP179 on chromosome 17 were found to be in significant LD with YMV resistance. Contingency Chi-square test confirmed the association (P < 0.01) and the utility of the markers was validated in the CP. It would pave the way for marker assisted selection for YMV resistance in soybean. This is the first report of its kind in soybean.