Genome-based trait prediction in multi- environment breeding trials in groundnut.

KEY MESSAGE: Comparative assessment identified naïve interaction model, and naïve and informed interaction GS models suitable for achieving higher prediction accuracy in groundnut keeping in mind the high genotype × environment interaction for complex traits. Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K 'Axiom_Arachis' SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400-0.600) were obtained for pods/plant, shelling  %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut.

Genetics, genomics, and breeding of black gram [Vigna mungo (L.) Hepper].

Black gram [Vigna mungo (L.) Hepper] is a highly nutritious grain legume crop, mainly grown in South and Southeast Asia, with the largest area in India, where the crop is challenged by several biotic and abiotic stresses leading to significant yield losses. Improving genetic gains to increase on-farm yields is the primary goal of black gram breeding programs. This could be achieved by developing varieties resistant to major diseases like mungbean yellow mosaic disease, urdbean leaf crinkle virus, Cercospora leaf spot, anthracnose, powdery mildew, and insect pests such as whitefly, cowpea aphids, thrips, stem flies, and bruchids. Along with increasing on-farm yields, incorporating market-preferred traits ensures the adoption of improved varieties. Black gram breeding programs rely upon a limited number of parental lines, leading to a narrow genetic base of the developed varieties. For accelerating genetic gain, there is an urgent need to include more diverse genetic material for improving traits for better adaptability and stress resistance in breeding populations. The present review summarizes the importance of black gram, the major biotic and abiotic stresses, available genetic and genomic resources, major traits for potential crop improvement, their inheritance, and the breeding approaches being used in black gram for the development of new varieties.

Genetic approaches for assessment of phosphorus use efficiency in groundnut (Arachis hypogaea L.).

Production of phosphorus efficient genotypes in groundnut can improve and also reduces environmental pollution. Identification of P-efficient groundnut genotypes is a need of the hour to sustain in P-deficient soils. The pot experiment showed significant differences between genotypes (G) and treatments (T) for all the traits and G × T interaction for majority of traits. The G × T × Y interaction effects were also significant for all the traits except leaf P% (LP%), leaf acid phosphatase (LAP) and root dry weight (RDW). In lysimeter experiment, the effect of G, T and G × T were significant for leaf dry weight (LDW), stem dry weight (SDW), total transpiration (TT) and transpiration efficiency (TE). For traits, LDW, SDW, TT, TE, ICGV 00351 and ICGS 76; for SDW, TT, ICGV 02266 are best performers under both P-sufficient and deficient conditions. Based on P-efficiency indices and surrogate traits of P-uptake, ICGV's 02266, 05155, 00308, 06040 and 06146 were considered as efficient P-responding genotypes. From GGE biplot, ICGV 06146 under P-deficient and TAG 24 under both P-sufficient and deficient conditions are portrayed as best performer. ICGV 06146 was identified as stable pod yielder and a promising genotype for P-deficient soils. The genotypes identified in this study can be used as a parent in developing mapping population to decipher the genetics and to devleop groundnut breeding lines suitable to P-deficient soils.

Assessment of the genetic diversity and population structure of groundnut germplasm collections using phenotypic traits and SNP markers: Implications for drought tolerance breeding.

Profiling the genetic composition and relationships among groundnut germplasm collections is essential for the breeding of new cultivars. The objectives of this study were to assess the genetic diversity and population structure among 100 improved groundnut genotypes using agronomic traits and high-density single nucleotide polymorphism (SNP) markers. The genotypes were evaluated for agronomic traits and drought tolerance at the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT)/India across two seasons. Ninety-nine of the test genotypes were profiled with 16363 SNP markers. Pod yield per plant (PY), seed yield per plant (SY), and harvest index (HI) were significantly (p < 0.05) affected by genotype × environment interaction effects. Genotypes ICGV 07222, ICGV 06040, ICGV 01260, ICGV 15083, ICGV 10143, ICGV 03042, ICGV 06039, ICGV 14001, ICGV 11380, and ICGV 13200 ranked top in terms of pod yield under both drought-stressed and optimum conditions. PY exhibited a significant (p ≤ 0.05) correlation with SY, HI, and total biomass (TBM) under both test conditions. Based on the principal component (PC) analysis, PY, SY, HSW, shelling percentage (SHP), and HI were allocated in PC 1 and contributed to the maximum variability for yield under the two water regimes. Hence, selecting these traits could be successful for screening groundnut genotypes under drought-stressed and optimum conditions. The model-based population structure analysis grouped the studied genotypes into three sub-populations. Dendrogram for phenotypic and genotypic also grouped the studied 99 genotypes into three heterogeneous clusters. Analysis of molecular variance revealed that 98% of the total genetic variation was attributed to individuals, while only 2% of the total variance was due to variation among the subspecies. The genetic distance between the Spanish bunch and Virginia bunch types ranged from 0.11 to 0.52. The genotypes ICGV 13189, ICGV 95111, ICGV 14421, and ICGV 171007 were selected for further breeding based on their wide genetic divergence. Data presented in this study will guide groundnut cultivar development emphasizing economic traits and adaptation to water-limited agro-ecologies, including in Ethiopia.

Combining High Oleic Acid Trait and Resistance to Late Leaf Spot and Rust Diseases in Groundnut (Arachis hypogaea L.).

High oleic trait, resistance to rust and late leaf spot (LLS) are important breeding objectives in groundnut. Rust and LLS cause significant economic loss, and high oleic trait is an industry preferred trait that enhances economic returns. This study reports marker-assisted selection to introgress high oleic content, resistance to LLS and rust into Kadiri 6 (K 6), a popular cultivar. The alleles for target traits were selected using linked allele-specific, simple sequence repeats and single nucleotide polymorphic markers. The F1s (384), intercrossed F1s (441), BC1F1s (380), BC1F2s (195), and BC1F3s (343) were genotyped to obtain desired allelic combination. Sixteen plants were identified with homozygous high oleic, LLS and rust resistance alleles in BC1F2, which were advanced to BC1F3 and evaluated for disease resistance, yield governing and nutritional quality traits. Phenotyping with Near-Infrared Reflectance Spectroscopy identified three lines (BC1F3-76, BC1F3-278, and BC1F3-296) with >80% oleic acid. The identified lines exhibit high levels of resistance to LLS and rust diseases (score of 3.0-4.0) with preferred pod and kernel features. The selected lines are under yield testing trials in multi-locations for release and commercialization. The lines reported here demonstrated combining high oleic trait with resistance to LLS and rust diseases.

Interaction of flavonols with human serum albumin: a biophysical study showing structure-activity relationship and enhancement when coated on silver nanoparticles.

Binding affinities of flavonols namely quercetin, myricetin, and kaempferol to human serum albumin (HSA) were determined fluorimetrically and the order was observed to be myricetin > quercetin > kaempferol demonstrating structure-activity relationship. Quercetin-coated silver nanoparticles (AgNPs) show higher binding affinity to HSA compared to free quercetin with binding constants 6.04 × 107 M-1 and 4.2 × 106 M-1, respectively. Using site-specific markers it is concluded that free quercetin and that coated on AgNPs bind at different sites. Significant structural changes in circular dichroism (CD) spectra of HSA were recorded with quercetin-coated AgNPs compared to free quercetin. These results were further substantiated by time-resolved fluorescence spectroscopy where fluorescence life time of the tryptophan residue in HSA-quercetin-coated AgNPs complex decreased to 3.63 ns from 4.22 ns in HSA-quercetin complex. Isothermal calorimetric studies reveal two binding modes for quercetin-coated AgNPs and also higher binding constants compared to free quercetin. These higher binding affinities are attributed to altered properties of quercetin when coated on AgNPs enabling it to reach the binding sites other than site II where free quercetin mainly binds.

Genotype × Environment Studies on Resistance to Late Leaf Spot and Rust in Genomic Selection Training Population of Peanut (Arachis hypogaea L.).

Foliar fungal diseases especially late leaf spot (LLS) and rust are the important production constraints across the peanut growing regions of the world. A set of 340 diverse peanut genotypes that includes accessions from gene bank of International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), elite breeding lines from the breeding program, and popular cultivars were screened for LLS and rust resistance and yield traits across three locations in India under natural and artificial disease epiphytotic conditions. The study revealed significant variation among the genotypes for LLS and rust resistance at different environments. Combined analysis of variance revealed significant environment (E) and genotype × environment (G×E) interactions for both the diseases indicating differential response of genotypes in different environments. The present study reported 31 genotypes as resistant to LLS and 66 to rust across the locations at 90 DAS with maturity duration 103 to 128 days. Twenty-eight genotypes showed resistance to both the diseases across the locations, of which 19 derived from A. cardenasii, five from A. hypogaea, and four from A. villosa. Site regression and Genotype by Genotype x Environment (GGE) biplot analysis identified eight genotypes as stable for LLS, 24 for rust and 14 for pod yield under disease pressure across the environments. Best performing environment specific genotypes were also identified. Nine genotypes resistant to LLS and rust showed 77% to 120% increase in pod yield over control under disease pressure with acceptable pod and kernel features that can be used as potential parents in LLS and rust resistance breeding. Pod yield increase as a consequence of resistance offered to foliar fungal diseases suggests the possibility of considering 'foliar fungal disease resistance' as a must-have trait in all the peanut cultivars that will be released for cultivation in rainfed ecologies in Asia and Africa. The phenotypic data of the present study will be used for designing genomic selection prediction models in peanut.

Correlation of Vitamin D3 Levels and SCORAD Index in Atopic Dermatits: A Case Control Study.

INTRODUCTION: Atopic Dermatitis (AD) is a common chronic inflammatory condition characterized clinically by pruritus and eczematous lesions. An inverse relationship has been suggested between serum 25-hydroxyvitamin D concentration and severity of atopic dermatitis. AIM: We carried out this controlled cross-sectional study to evaluate the association between the serum vitamin D3 levels and SCORAD index. MATERIALS AND METHODS: For this study, 40 patients with clinical diagnosis of AD based on UK diagnostic criteria were enrolled and 40 patients with minor ailments like superficial bacterial, fungal or viral infections and not suffering from atopic dermatitis were taken as controls. Salient presentations were recorded in a pre-set proforma. Serum 25-hydroxyvitamin D levels were determined through Sandwich-ELISA technique. SCORAD (Scoring AD) index was used to evaluate the severity of the disease. RESULTS: Mean value of serum 25-hydroxyvitamin D levels in cases was 30.38 nmol/l whereas in controls, it was 53.46 nmol/l. The decrease in serum levels in cases was statistically highly significant (p-value <0.001). Mean±S.D of serum vitamin D levels in mild disease was 33.29±5.89 nmol/l, in moderate disease was 31.52±6.04 nmol/l and in severe form of disease was 21.24±3.17nmol/l. The correlation between SCORAD and serum 25-hydroxyvitamin D levels was also statistically significant. CONCLUSION: The data suggests an inverse relationship between serum levels of vitamin D3 and the SCORAD Index.