In this study, adoption rates of modern wheat varieties in India have been estimated using expert elicitation methodology. The study has found that the wheat varietal output has increased during the period 2010-2015. The most widely cultivated wheat varieties in the study states are HD 2967, PBW 343, PBW 550, Lok 1 and PBW 502. The temporal and spatial diversity indices have been calculated based on the perceived adoption rates. Wheat varietal turnover has been highest in Punjab (7.50 years) and lowest in Rajasthan (19.25 years). The Berger Parker index has shown that relative abundance of varieties was lowest in Punjab (1.76) and highest in Madhya Pradesh (7.10). The concentration of wheat area under dominant varieties was highest in Punjab and lowest in Rajasthan as indicated by the Marglef index. The cultivation of older varieties and dominance of a few varieties deprive the farmers of the advantages of productivity gains, genetic improvement, in addition to increasing crop vulnerability to pests and diseases. The study has concluded that besides varietal development, it is also important to focus on reducing the socioeconomic and institutional barriers to adoption of improved crop varieties. In this direction, it is important to create an enabling institutional environment for increasing the rate of varietal replacement, promote spatial heterogeneity in crop varieties cultivated, identify and effectively bring the potential varieties under the seed chain system and enhance the outreach of improved wheat varieties with an inclusive approach to reach even the resource-poor farmers.
Purpose Polycystic ovarian syndrome (PCOS) is a multi-faceted endocrinopathy frequently observed in reproductive-aged females, causing infertility. Cumulative evidence revealed that genetic and epigenetic variations, along with environmental factors, were linked with PCOS. Deciphering the molecular pathways of PCOS is quite complicated due to the availability of limited molecular information. Hence, to explore the influence of genetic variations in PCOS, we mapped the GWAS genes and performed a computational analysis to identify the SNPs and their impact on the coding and non-coding sequences. Methods The causative genes of PCOS were searched using the GWAS catalog, and pathway analysis was performed using ClueGO. SNPs were extracted using an Ensembl genome browser, and missense variants were shortlisted. Further, the native and mutant forms of the deleterious SNPs were modeled using I-TASSER, Swiss-PdbViewer, and PyMOL. MirSNP, PolymiRTS, miRNASNP3, and SNP2TFBS, SNPInspector databases were used to find SNPs in the miRNA binding site and transcription factor binding site (TFBS), respectively. EnhancerDB and HaploReg were used to characterize enhancer SNPs. Linkage Disequilibrium (LD) analysis was performed using LDlink. Results 25 PCOS genes showed interaction with 18 pathways. 7 SNPs were predicted to be deleterious using different pathogenicity predictions. 4 SNPs were found in the miRNA target site, TFBS, and enhancer sites and were in LD with reported PCOS GWAS SNPs. Conclusion Computational analysis of SNPs residing in PCOS genes may provide insight into complex molecular interactions among genes involved in PCOS pathophysiology. It may also aid in determining the causal variants and consequently contributing to predicting disease strategies.
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