Whole Genome Re-sequencing of Soybean Accession EC241780 Providing Genomic Landscape of Candidate Genes Involved in Rust Resistance.

BACKGROUND: In this study, whole genome re-sequencing of rust resistant soybean genotype EC241780 was performed to understand the genomic landscape involved in the resistance mechanism. METHODS: A total of 374 million raw reads were obtained with paired-end sequencing performed with Illumina HiSeq 2500 instrument, out of which 287.3 million high quality reads were mapped to Williams 82 reference genome. Comparative sequence analysis of EC241780 with rust susceptible cultivars Williams 82 and JS 335 was performed to identify sequence variation and to prioritise the candidate genes. RESULTS: Comparative analysis indicates that genotype EC241780 has high sequence similarity with rust resistant genotype PI 200492 and the resistance in EC241780 is conferred by the Rpp1 locus. Based on the sequence variations and functional annotations, three genes Glyma18G51715, Glyma18G51741 and Glyma18G51765 encoding for NBS-LRR family protein were identified as the most prominent candidate for Rpp1 locus. CONCLUSION: The study provides insights of genome-wide sequence variation more particularly at Rpp1 loci which will help to develop rust resistant soybean cultivars through efficient exploration of the genomic resource.

Envirotype-based delineation of environmental effects and genotype × environment interactions in Indian soybean (Glycine max, L.).

Soybean is a rainfed crop grown across a wide range of environments in India. Its grain yield is a complex trait governed by many minor genes and influenced by environmental effects and genotype × environment interactions. In the current investigation, grain yield data of different sets of 41, 30 and 48 soybean genotypes evaluated during 2019, 2020 and 2021, respectively across 19 locations and twenty years' data on 19 different climatic parameters at these locations was used to study the environmental effects on grain yield, to understand the genotype × environment interactions and to identify the mega-environments. Through analysis of variance (ANOVA), it was found that predominant portion of the variation was explained by environmental effects (E) (53.89, 54.86 and 60.56% during 2019, 2020 and 2021, respectively), followed by genotype × environment interactions (GEI) (31.29, 33.72 and 28.82% during 2019, 2020 and 2021, respectively). Principal Component Analysis (PCA) revealed that grain yield was positively associated with RH (Relative humidity at 2 m height), FRUE (Effect of temperature on radiation use efficiency), WSM (Wind speed at 2 m height) and RTA (Global solar radiation based on latitude and Julian day) and negatively associated with VPD (Deficit of vapour pressure), Trange (Daily temperature range), ETP (Evapotranspiration), SW (Insolation incident on a horizontal surface), n (Actual duration of sunshine) and N (Daylight hours). Identification of mega-environments is critical in enhancing the selection gain, productivity and varietal recommendation. Through envirotyping and genotype main effect plus genotype by environment interaction (GGE) biplot methods, nineteen locations across India were grouped into four mega-environments (MEs). ME1 included five locations viz., Bengaluru, Pune, Dharwad, Kasbe Digraj and Umiam. Eight locations-Anand, Amreli, Lokbharti, Bidar, Parbhani, Ranchi, Bhawanipatna and Raipur were included in ME2. Kota and Morena constitutes ME3, while Palampur, Imphal, Mojhera and Almora were included in ME4. Locations Imphal, Bidar and Raipur were found to be both discriminative and representative; these test locations can be utilized in developing wider adaptable soybean cultivars. Pune and Amreli were found to be high-yielding locations and can be used in large scale breeder seed production.

Identification and genetic diversity analysis of high-yielding charcoal rot resistant soybean genotypes.

Charcoal rot disease caused by Macrophomina phaseolina (Tassi) Goid is one of the most devastating diseases in soybean in India. During 2018, 226 diverse soybean genotypes were evaluated for genetic resistance under hot-spot conditions. Out of them, a subset of 151 genotypes were selected based on Percent Disease Incidence (PDI) and better agronomic performance. Out of these 151 genotypes evaluated during 2019, 43 genotypes were selected based on PDI and superior agronomic performance for further field evaluation and molecular characterization. During 2020 and 2021, these forty-three genotypes, were evaluated for PDI, Area Under Disease Progress Curve (AUDPC), and grain yield. In 2020, genotype JS 20-20 showed least PDI (0.42) and AUDPC (9.37).Highest grain yield was recorded by the genotype JS 21-05 (515.00 g). In 2021, genotype JS 20-20 exhibited least PDI (0.00) and AUDPC (0.00).Highest grain yield was recorded in JS 20-98 (631.66 g). Across both years, JS 20-20 had the least PDI (0.21) and AUDPC (4.68), while grain yield was highest in JS 20-98 (571.67 g). Through MGIDI (multi-trait genotype-ideotype distance) analysis, JS 21-05 (G19), JS 22-01 (G43), JS 20-98 (G28) and JS 20-20 (G21) were identified as the ideotypes with respect to the traits that were evaluated. Two unique alleles, Satt588 (100 bp) on linkage group K (Chromosome no 9) and Sat_218 (200 bp) on linkage group H (Chromosome no 12), were specific for thetwo resistant genotypes JS 21-71and DS 1318, respectively. Through cluster analysis, it was observed that the genotypes bred at Jabalpur were more genetically related.

Progress and prospectus in genetics and genomics of Phytophthora root and stem rot resistance in soybean (Glycine max L.).

Soybean is one of the largest sources of protein and oil in the world and is also considered a "super crop" due to several industrial advantages. However, enhanced acreage and adoption of monoculture practices rendered the crop vulnerable to several diseases. Phytophthora root and stem rot (PRSR) caused by Phytophthora sojae is one of the most prevalent diseases adversely affecting soybean production globally. Deployment of genetic resistance is the most sustainable approach for avoiding yield losses due to this disease. PRSR resistance is complex in nature and difficult to address by conventional breeding alone. Genetic mapping through a cost-effective sequencing platform facilitates identification of candidate genes and associated molecular markers for genetic improvement against PRSR. Furthermore, with the help of novel genomic approaches, identification and functional characterization of Rps (resistance to Phytophthora sojae) have also progressed in the recent past, and more than 30 Rps genes imparting complete resistance to different PRSR pathotypes have been reported. In addition, many genomic regions imparting partial resistance have also been identified. Furthermore, the adoption of emerging approaches like genome editing, genomic-assisted breeding, and genomic selection can assist in the functional characterization of novel genes and their rapid introgression for PRSR resistance. Hence, in the near future, soybean growers will likely witness an increase in production by adopting PRSR-resistant cultivars. This review highlights the progress made in deciphering the genetic architecture of PRSR resistance, genomic advances, and future perspectives for the deployment of PRSR resistance in soybean for the sustainable management of PRSR disease.

Breeding for higher yield, early maturity, wider adaptability and waterlogging tolerance in soybean (Glycine max L.): A case study.

Breeding for higher yield and wider adaptability are major objectives of soybean crop improvement. In the present study, 68 advanced breeding lines along with seven best checks were evaluated for yield and attributing traits by following group balanced block design. Three blocks were constituted based on the maturity duration of the breeding lines. High genetic variability for the twelve quantitative traits was found within and across the three blocks. Several genotypes were found to outperform check varieties for yield and attributing traits. During the same crop season, one of the promising entries, NRC 128,was evaluated across seven locations for its wider adaptability and it has shown stable performance in Northern plain Zone with > 20% higher yield superiority over best check PS 1347. However, it produced 9.8% yield superiority over best check in Eastern Zone. Screening for waterlogging tolerance under artificial conditions revealed that NRC 128 was on par with the tolerant variety JS 97-52. Based on the yield superiority, wider adaptability and waterlogging tolerance, NRC 128 was released and notified by Central Varietal Release Committee (CVRC) of India, for its cultivation across Eastern and Northern Plain Zones of India.