Evaluating dimensionality reduction for genomic prediction

The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials. Improvements in genotyping technology have yielded high-dimensional genomic marker data which can be difficult to incorporate into statistical models. In this paper, we investigated the utility of applying dimensionality reduction (DR) methods as a pre-processing step for GS methods. We compared five DR methods and studied the trend in the prediction accuracies of each method as a function of the number of features retained. The effect of DR methods was studied using three models that involved the main effects of line, environment, marker, and the genotype by environment interactions. The methods were applied on a real data set containing 315 lines phenotyped in nine environments with 26,817 markers each. Regardless of the DR method and prediction model used, only a fraction of features was sufficient to achieve maximum correlation. Our results underline the usefulness of DR methods as a key pre-processing step in GS models to improve computational efficiency in the face of ever-increasing size of genomic data

Achievements and prospects of grass pea (Lathyrus sativus L.) improvement for sustainable food production

Grass pea offers an attractive choice for sustainable food production, owing to its intrinsic properties including limited water requirement and drought tolerance. However, low productivity and the presence of a neurotoxin (ODAP) have posed major obstacles to its genetic improvement. Also, biotechnological investments remain limited and the genome is complex and not well understood. Strategies that allow identification of genotypes with reduced ODAP content, coupling of low ODAP content with enhanced yield, and effective seed detoxification methods merit immediate attention. Breeder-friendly genomic tools are being increasingly made available to improve the efficiency of breeding protocols. To this end, the application of next-generation sequencing has provided a means of leveraging the repertoire of genomic resources for this somewhat neglected crop. In this review, we describe progress achieved in Lathyrus genetic improvement. We also explore potential opportunities in Lathyrus research and identify urgent research needs

Exploring DSSAT Model Genetic Coefficient Estimation Methodologies for Chickpea in Bundelkhand Region of Uttar Pradesh, India

In modern crop production, essential factors that contribute to narrowing yield gaps and minimizing production costs include making informed decisions about the selection of plant varieties, determining optimal sowing dates, determining appropriate plant populations, selecting suitable fertilizer rates, and implementing effective pest control methods. Two field experiments were conducted during the Rabi seasons of 2021 and 2022 at ICAR-Indian Institute of Pulses Research (IIPR), Kanpur using split-plot experimental design, where the main plots were three different sowing dates (20-25th October, November 10-15th, and 25th November-5th December), and the sub-plots were four chickpea cultivars (JG 16, RVG 202, IPC-07-66, and IPC-05-62), each with three replications. The genetic coefficients of the cultivars were estimated using both the iterative process (IP) and Generalized Likelihood Uncertainty Estimation (GLUE) methods in DSSAT v 4.7 to simulate the yields. Upon model validation, it was found that the average relative error (ARE) in predicting grain yield across the different sowing windows was between -25.7% to 29.1% when using the iterative process, while ARE was between -23.4% to 19% when using GLUE. The findings report more accurate simulations of chickpea growth and phenological development stages were recorded in normal sowings. And the model calibration suggest that GLUE provided superior estimates of genetic coefficients compared to the IP method. Therefore, it can be inferred that Glue is a more user-friendly and precise method

Major viral diseases in grain legumes: designing disease resistant legumes from plant breeding and OMICS integration

Grain legumes play a crucial role in human nutrition and as a staple crop for low-income farmers in developing and underdeveloped nations, contributing to overall food security and agroecosystem services. Viral diseases are major biotic stresses that severely challenge global grain legume production. In this review, we discuss how exploring naturally resistant grain legume genotypes within germplasm, landraces, and crop wild relatives could be used as promising, economically viable, and eco-environmentally friendly solution to reduce yield losses. Studies based on Mendelian and classical genetics have enhanced our understanding of key genetic determinants that govern resistance to various viral diseases in grain legumes. Recent advances in molecular marker technology and genomic resources have enabled us to identify genomic regions controlling viral disease resistance in various grain legumes using techniques such as QTL mapping, genome-wide association studies, whole-genome resequencing, pangenome and ‘omics’ approaches. These comprehensive genomic resources have expedited the adoption of genomics-assisted breeding for developing virus-resistant grain legumes. Concurrently, progress in functional genomics, especially transcriptomics, has helped unravel underlying candidate gene(s) and their roles in viral disease resistance in legumes. This review also examines the progress in genetic engineering-based strategies, including RNA interference, and the potential of synthetic biology techniques, such as synthetic promoters and synthetic transcription factors, for creating viral-resistant grain legumes. It also elaborates on the prospects and limitations of cutting-edge breeding technologies and emerging biotechnological tools (e.g., genomic selection, rapid generation advances, and CRISPR/Cas9-based genome editing tool) in developing virus-disease-resistant grain legumes to ensure global food security

Genomic-Mediated Breeding Strategies for Global Warming in Chickpeas (<i>Cicer arietinum</i> L.)

Although chickpea (Cicer arietinum L.) has high yield potential, its seed yield is often low and unstable due to the impact of abiotic stresses, such as drought and heat. As a result of global warming, both drought and heat are estimated to be major yield constraints between one-quarter and one-third per annum. In the present review, genomic-mediated breeding strategies to increase resilience against global warming. Exacerbated drought and heat stresses have been examined to understand the latest advancement happening for better management of these challenges. Resistance mechanisms for drought and heat stresses consist of (i) escape via earliness, (ii) avoidance via morphological traits such as better root traits, compound leaves, or multipinnate leaves and double-/multiple-podded traits, and (iii) tolerance via molecular and physiological traits, such as special tissue and cellular abilities. Both stresses in chickpeas are quantitatively governed by minor genes and are profoundly influenced by edaphic and other environmental conditions. High-yield genotypes have traditionally been screened for resistance to drought and heat stresses in the target selection environment under stress conditions or in the simulacrum mediums under controlled conditions. There are many drought- and heat-tolerant genotypes among domestic and wild Cicer chickpeas, especially in accessions of C. reticulatum Ladiz., C. echinospermum P.H. Davis, and C. turcicum Toker, J. Berger, and Gokturk. The delineation of quantitative trait loci (QTLs) and genes allied to drought- and heat-related attributes have paved the way for designing stress-tolerant cultivars in chickpeas. Transgenic and “omics” technologies hold newer avenues for the basic understanding of background metabolic exchanges of QTLs/candidate genes for their further utilization. The overview of the effect of drought and heat stresses, its mechanisms/adaptive strategies, and markers linked to stress-related traits with their genetics and sources are pre-requisites for framing breeding programs of chickpeas with the intent of imparting drought tolerance. Ideotype chickpeas for resistance to drought and heat stresses were, therefore, developed directly using marker-aided selection over multiple locations. The current understanding of molecular breeding supported by functional genomics and omics technologies in developing drought- and heat-tolerant chickpea is discussed in this review

Translational Chickpea Genomics Consortium to Accelerate Genetic Gains in Chickpea (Cicer arietinum L.)

The Translational Chickpea Genomics Consortium (TCGC) was set up to increase the production and productivity of chickpea (Cicer arietinum L.). It represents research institutes from six major chickpea growing states (Madhya Pradesh, Maharashtra, Andhra Pradesh, Telangana, Karnataka and Uttar Pradesh) of India. The TCGC team has been engaged in deploying modern genomics approaches in breeding and popularizing improved varieties in farmers&rsquo; fields across the states. Using marker-assisted backcrossing, introgression lines with enhanced drought tolerance and fusarium wilt resistance have been developed in the genetic background of 10 elite varieties of chickpea. Multi-location evaluation of 100 improved lines (70 desi and 30 kabuli) during 2016&ndash;2017 and 2018&ndash;2019 enabled the identification of top performing desi and kabuli lines. In total, 909 Farmer Participatory Varietal Selection trials were conducted in 158 villages in 16 districts of the five states, during 2017&ndash;2018, 2018&ndash;2019, and 2019&ndash;2020, involving 16 improved varieties. New molecular breeding lines developed in different genetic backgrounds are potential candidates for national trials under the ICAR-All India Coordinated Research Project on Chickpea. The comprehensive efforts of TCGC resulted in the development and adoption of high-yielding varieties that will increase chickpea productivity and the profitability of chickpea growing farmers