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.

Genome-Wide Association Study Reveals Genomic Regions Associated With Ten Agronomical Traits in Wheat Under Late-Sown Conditions

Abstract: Poor understanding of the genetic and molecular basis of heat tolerance component traits is a major bottleneck in designing heat tolerant wheat cultivars. The impact of terminal heat stress is generally reported in the case of late sown wheat. In this study, our aim was to identify genomic regions for various agronomic traits under late sown conditions by using genome-wide association approach. An association mapping panel of 205 wheat accessions was evaluated under late sown conditions at three different locations in India. Genotyping of the association panel revealed 15,886 SNPs, out of which 11,911 SNPs with exact physical locations on the wheat reference genome were used in association analysis. A total of 69 QTLs (10 significantly associated and 59 suggestive) were identified for ten different traits including productive tiller number (17), grain yield (14), plant height (12), grain filling rate (6), grain filling duration (5), days to physiological maturity (4), grain number (3), thousand grain weight (3), harvest index (3), and biomass (2). Out of these associated QTLs, 17 were novel for traits, namely PTL (3), GY (2), GFR (6), HI (3) and GNM (3). Moreover, five consistent QTLs across environments were identified for GY (4) and TGW (1). Also, 11 multi-trait SNPs and three hot spot regions on Chr1Ds, Chr2BS, Chr2DS harboring many QTLs for many traits were identified. In addition, identification of heat tolerant germplasm lines based on favorable alleles HD2888, IC611071, IC611273, IC75240, IC321906, IC416188, and J31-170 would facilitate their targeted introgression into popular wheat cultivars. The significantly associated QTLs identified in the present study can be further validated to identify robust markers for utilization in marker-assisted selection (MAS) for development of heat tolerant wheat cultivars.

Genetic Advancement in Dry Pea (Pisum sativum L.): Retrospect and Prospect

Abstract: Dry pea or field pea is one of the most important and highly productive cool season pulse crops grown worldwide. There are several biotic and abiotic stresses which are the key constraints in achieving potential production of dry pea. Among biotic stresses, fungal diseases such as powdery mildew, rust, root rots, wilt, common root rot and ascochyta blight are the most common and severely affecting the crop at different growth stages. In case of abiotic stresses, high temperature, drought and frost are frequently occurring and reduce quantity and quality of produces. Hence, genetic improvement for aforesaid traits is important globally and needs to be addressed using conventional and molecular breeding approaches together to accelerate the breeding programme. Genetic improvement of pea began with domestication and has been aid on by decades of research through incorporation of novel traits from wild germplasm and landraces as well as pyramiding multiple constructive alleles in well-adapted genetic backgrounds. Sincere efforts have been made during recent past in terms of improving plant type and tolerance/resistant to important biotic and abiotic stresses around the world. Several major and minor genes/QTLs have been dissected controlling the important biotic and abiotic stresses. The introgression of genes for these resistant sources is possible using marker-assisted selection to speed up dry pea breeding programme more efficiently and precisely due to the availability of comprehensive genetic maps and reliable DNA markers. This book chapter briefly elaborates about the research accomplishment made so far for improvement of major traits and future perspectives to enhance dry pea productivity through genetic improvement.

Design and analysis of mixture experiments with process variable

Abstract: A mixture experiment is an experiment in which the response is assumed to depend on the relative proportions of the ingredients present in the mixture and not on the total amount of the mixture. In such experiment process, variables do not form any portion of the mixture but the levels changed could affect the blending properties of the ingredients. Sometimes, the mixture experiments are costly and the experiments are to be conducted in less number of runs. Here, a general method for construction of efficient mixture experiments in a minimum number of runs by the method for projection of efficient response surface design onto the constrained region is obtained. The efficient designs with a less number of runs have been constructed for 3rd, 4th, and 5th component of mixture experiments with one process variable.