Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a facility organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Small area estimation. The organization has 454 authors who have published 870 publications receiving 7987 citations.

Non-linear statistical models on estimation of maximum size of Tor putitora (Hamilton) in different aquatic environments

Abstract: Tor putitora is one of the most important coldwater fish species. The population of this fish species has declined sharply in the recent past and is threatened with multifaceted dangers. As the size of fish plays an important role in fish stock assessment, in the present investigation, an attempt has been made to estimate the maximum size of Tor putitora in different aquatic environments by using nonlinear statistical models. We can expect a maximum length of approximately 3097 mm and 2994 mm for Tor putitora in the aquatic environments of Kumaun lakes and Gobindsagar reservoir respectively. It is seen that the estimated maximum size of Tor putitora in both environments are well acceptable in view of the reported maximum sizes in India and abroad.

Single Marker Analysis and Mapping of QTLs Governing Fruit Weight in Eggplant (Solanum melongena L.)

Abstract: Eggplant is one of the most popular solanaceous vegetable crops in India and across the world. Development of high-yielding varieties and hybrids is the major focus of breeders. Yield is a very complex trait affected by various genetic and external factors. The lack of knowledge of the inheritance of fruit traits in eggplant has limited the use of molecular markers to fasten breeding programme. The authors conducted the present investigation at the research field of Vegetable Science Division, ICAR-IARI, New Delhi, India. A total of 168 F2 plants derived from Pusa Safed Baingan 1 × Pusa Uttam were phenotyped for fruit weight. Wide variations were observed for this trait ranging from 15.2 to 248.3 g, and the skewness was less than 1, suggesting that the trait followed a normal distribution in F2 and suitable for QTL analysis. Polymorphism survey using 241 SSR markers showed 18 polymorphic markers, which were subjected to bulked segregant analysis. Single-marker analysis using genotypic data revealed the positions of 3 QTLs influencing fruit weight. One QTL (qsfwpu1) was detected in linkage group 1, and two QTLs (qsfwpu3 and qsfwpu2) were identified in linkage group 4. These QTLs explained 14.06–19.44% to total phenotypic variation and were classified as major QTLs, suggesting that these QTLs play an important role in controlling yield. These QTLs may be very useful in the future molecular marker-assisted breeding of eggplant to develop a variety and cloning of target genes of interest.

Centenary of Soil and Air Borne Wheat Karnal Bunt Disease Research: A Review.

Abstract: Karnal bunt (KB) of wheat (Triticum aestivum L.), known as partial bunt has its origin in Karnal, India and is caused by Tilletia indica (Ti). Its incidence had grown drastically since late 1960s from northwestern India to northern India in early 1970s. It is a seed, air and soil borne pathogen mainly affecting common wheat, durum wheat, triticale and other related species. The seeds become inedible, inviable and infertile with the precedence of trimethylamine secreted by teliospores in the infected seeds. Initially the causal pathogen was named Tilletia indica but was later renamed Neovossia indica. The black powdered smelly spores remain viable for years in soil, wheat straw and farmyard manure as primary sources of inoculum. The losses reported were as high as 40% in India and also the cumulative reduction of national farm income in USA was USD 5.3 billion due to KB. The present review utilizes information from literature of the past 100 years, since 1909, to provide a comprehensive and updated understanding of KB, its causal pathogen, biology, epidemiology, pathogenesis, etc. Next generation sequencing (NGS) is gaining popularity in revolutionizing KB genomics for understanding and improving agronomic traits like yield, disease tolerance and disease resistance. Genetic resistance is the best way to manage KB, which may be achieved through detection of genes/quantitative trait loci (QTLs). The genome-wide association studies can be applied to reveal the association mapping panel for understanding and obtaining the KB resistance locus on the wheat genome, which can be crossed with elite wheat cultivars globally for a diverse wheat breeding program. The review discusses the current NGS-based genomic studies, assembly, annotations, resistant QTLs, GWAS, technology landscape of diagnostics and management of KB. The compiled exhaustive information can be beneficial to the wheat breeders for better understanding of incidence of disease in endeavor of quality production of the crop.

Interactive Web Tool for Standardizing Proteomics Workflow for Liquid Chromatography-Mass Spectrometry Data.

Abstract: Introduction: The proteomics experiments involve several steps and there are many choices available for each step in the workflow. Therefore, standardization of proteomics workflow is an essential task for design of proteomics experiments. However, there are challenges associated with the quantitative measurements based on liquid chromatography-mass spectrometry such as heterogeneity due to technical variability and missing values. Methods: We introduce a web application, Proteomics Workflow Standardization Tool (PWST) to standardize the proteomics workflow. The tool will be helpful in deciding the most suitable choice for each step of the experimentation. This is based on identifying steps/choices with least variability such as comparing Coefficient of Variation (CV). We demonstrate the tool on data with categorical and continuous variables. We have used the special cases of general linear model, analysis of covariance and analysis of variance with fixed effects to study the effects due to various sources of variability. We have provided various options that will aid in finding the contribution of sum of squares for each variable and the CV. The user can analyze the data variability at protein and peptide level even in the presence of missing values. Availability and implementation: The source code for “PWST” is written in R and implemented as shiny web application that can be accessed freely from https://ulbbf.shinyapps.io/pwst/.

On variance estimation in unequal probability sampling1

Abstract: Summary The purpose of this article is to propose a model-based estimator of the variance of the Horvitz-Thompson estimator. Empirical investigations reveal that the estimator is seldom greatly biased and is quite satisfactory from the stability point of view.