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Paul, Ranjit Kumar
- Identification of cis- and trans-expression quantitative trait loci using Bayesian framework
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Authors
Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
Source
Current Science, Vol 122, No 10 (2022), Pagination: 1214-1219Abstract
The detection and identification of expression quantitative trait loci (eQTLs) for biological characteristics like gene expression is an important focus of genomics. The existence of cis- and trans-eQTLs is crucial for establishing their cumulative significance to the desired traits. A crucial aspect of genomics is identifying the cis- and trans-eQTLs that capture substantial changes in the expression of distant genes. The goal of the present study was to use an integrated hierarchical Bayesian model to identify the cis- and trans-eQTLS. Molecular approaches are utilized to categorize just the candidate genes when quantitative trait loci or eQTLs are identified. Variations inside or near the gene are hypothesized to determine the genetic variances that reflect transcript levels. The identification of eQTLs has helped us better understand gene regulation and complex trait analysis. The present study focused on barley crops, and only cis-eQTLs were identified; no additional eQTL hotspots were determined. Mouse gene expressions were used to study trans-eQTLs and substantial cis- and trans-eQTLs, as well as four eQTL hotspots were identifiedKeywords
Barley, gene expression, hotspots, integrated hierarchical model, quantitative trait loci.Full Text
References
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- The volatility spillover of potato prices in different markets of India
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PDF Views:76
Authors
Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
Source
Current Science, Vol 123, No 3 (2022), Pagination: 482-487Abstract
Agricultural commodity prices, particularly the prices of perishable commodities, are volatile. The interdependency of market prices of agricultural commodities makes it difficult for accurate modelling. In the present study, two variants of multivariate generalized autoregressive conditional heteroscedastic models, namely DCC and BEKK, have been applied for modelling the price volatility of potato in five major markets in India, i.e. Agra, Delhi, Bengaluru, Mumbai and Ahmedabad. It is observed that the Agra market has the highest price variability, whereas Mumbai has the least. All the studied market prices showed a significant presence of conditional heteroscedasticity. To this end, Volatility Impulse Response Function has been used to assess the impacts of a specific shock on the price volatility spillovers of potatoes among the studied markets. The volatility spillover has been computed for all the markets.Full Text
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- Paul, R. K., Rana, S. and Saxena, R., Effectiveness of price forecasting techniques for capturing asymmetric volatility for onion in selected markets of Delhi. Indian J. Agric. Sci., 2016, 86, 303–309.
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- Deep Learning Technique for Forecasting the Price of Cauliflower
Abstract Views :92 |
PDF Views:46
Authors
Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India., IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India., IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
Source
Current Science, Vol 124, No 9 (2023), Pagination: 1065-1073Abstract
Vegetables are the staple food in our diets. Vegetable prices are difficult to forecast because they are influenced by a variety of factors, including weather, demand and supply chain, Government policies, etc. and exhibit volatile fluctuations. Marketing of vegetables is complex, especially because of their perishability, seasonality and bulkiness. An accurate and timely forecast of vegetables is essential to help its stakeholders. Previous studies observed that traditional statistical models are unable to capture the complex behaviour of vegetable markets. In this study, a comparative assessment has been carried out among the traditional time-series model, machine learning and deep learning techniques in order to find the best-suited model. For empirical illustration, cauliflower markets have been chosen as it is one of India’s most important and popular winter. In order to identify the complexity in the price of cauliflower, the machine learning technique, i.e. artificial neural network and deep learning technique, i.e. long short-term memory model have been implemented. In addition, the traditional stochastic time-series model, i.e. autoregressive integrated moving average model, was used to compare the prediction accuracy of the above models. To this end, the moving window forecast approach was also implemented to evaluate the sensitivity of these models with respect to forecast length. It can be concluded that the deep learning model outperforms the traditional time-series model and the machine learning technique for both short- and long-term forecasting.Keywords
Cauliflower, Deep Learning Technique, Machine Learning, Statistical Models, Vegetable Prices.Full Text
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