Rhizosphere Microbiome: The Next‐Generation Crop Improvement Strategy

The recent advancements in forward genetics have expanded the applications of mutation techniques in advanced genetics and genomics, ahead of direct use in breeding programs. The advent of next-generation sequencing (NGS) has enabled easy identification and mapping of causal mutations within a short period and at relatively low cost. Identifying the genetic mutations and genes that underlie phenotypic changes is essential for understanding a wide variety of biological functions. To accelerate the mutation mapping for crop improvement, several high-throughput and novel NGS based forward genetic approaches have been developed and applied in various crops. These techniques are highly efficient in crop plants, as it is relatively easy to grow and screen thousands of individuals. These approaches have improved the resolution in quantitative trait loci (QTL) position/point mutations and assisted in determining the functional causative variations in genes. To be successful in the interpretation of NGS data, bioinformatics computational methods are critical elements in delivering accurate assembly, alignment, and variant detection. Numerous bioinformatics tools/pipelines have been developed for such analysis. This article intends to review the recent advances in NGS based forward genetic approaches to identify and map the causal mutations in the crop genomes. The article also highlights the available bioinformatics tools/pipelines for reducing the complexity of NGS data and delivering the concluding outcomes.

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Next-generation sequencing applications for wheat crop improvement

American Journal of Botany ◽

10.3732/ajb.1100309 ◽

2012 ◽

Vol 99 (2) ◽

pp. 365-371 ◽

Cited By ~ 69

Author(s):

Paul J. Berkman ◽

Kaitao Lai ◽

Michał T. Lorenc ◽

David Edwards

Keyword(s):

Next Generation Sequencing ◽

Crop Improvement ◽

Wheat Crop ◽

Next Generation ◽

Generation Sequencing

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Designing Chickpea For a Hotter Drier World

10.21203/rs.3.rs-875442/v1 ◽

2021 ◽

Author(s):

Oluwaseun Johnson Akinlade ◽

Kai Voss-Fels ◽

Roy Costilla ◽

Jana Kholova ◽

Sunita Choudhary ◽

...

Keyword(s):

Heat Stress ◽

Crop Improvement ◽

Grain Legumes ◽

Physiological Traits ◽

Improvement Strategy ◽

Heat Adaptation ◽

Future Production ◽

The World ◽

High Throughput Phenotyping ◽

Drought And Heat Stress

Abstract Chickpea (Cicer arietinum L.) is one of the most important grain legumes in the world, but its current and future production is threatened due to the increased incidence of drought and heat stress. To address this challenge, an integrated crop improvement strategy encompassing breeding, genomics, physiology and agronomy is required. Here, we review the physiological traits known to confer drought and heat adaptation in chickpea and identify areas of drought and heat adaptation research that should be prioritised in the future. Furthermore, we underscore approaches to efficiently phenotype chickpea adaptation traits and highlight the significant challenges and importance of understanding the nexus between canopy and root development. Finally, we present the opportunity to adopt multi-trait genomic prediction approaches to efficiently utilise key physiological traits, that can be assayed using high-throughput phenotyping platforms, to accelerate genetic gain in drought and heat prone environments.

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Underutilised and Neglected Crops: Next Generation Sequencing Approaches for Crop Improvement and Better Food Security

Global Perspectives on Underutilized Crops ◽

10.1007/978-3-319-77776-4_11 ◽

2018 ◽

pp. 287-380

Author(s):

L. F. De Filippis

Keyword(s):

Food Security ◽

Next Generation Sequencing ◽

Crop Improvement ◽

Next Generation ◽

Neglected Crops ◽

Generation Sequencing

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Efficient curation of genebanks using next generation sequencing reveals substantial duplication of germplasm accessions

10.1101/410779 ◽

2018 ◽

Cited By ~ 1

Author(s):

Narinder Singh ◽

Shuangye Wu ◽

W. John Raupp ◽

Sunish Sehgal ◽

Sanu Arora ◽

...

Keyword(s):

Genetic Diversity ◽

Next Generation Sequencing ◽

Crop Improvement ◽

Aegilops Tauschii ◽

Cost Effective ◽

Ex Situ ◽

Next Generation ◽

Sequencing Platform ◽

Efficient Management ◽

Generation Sequencing

ABSTRACTGenebanks are valuable resources for crop improvement through the acquisition, ex-situ conservation and sharing of unique germplasm among plant breeders and geneticists. With over seven million existing accessions and increasing storage demands and costs, genebanks need efficient characterization and curation to make them more accessible and usable and to reduce operating costs, so that the crop improvement community can most effectively leverage this vast resource of untapped novel genetic diversity. However, the sharing and inconsistent documentation of germplasm often results in unintentionally duplicated collections with poor characterization and many identical accessions that can be hard or impossible to identify without passport information and unmatched accession identifiers. Here we demonstrate the use of genotypic information from these accessions using a cost-effective next generation sequencing platform to find and remove duplications. We identify and characterize over 50% duplicated accessions both within and across genebank collections of Aegilops tauschii, an important wild relative of wheat and source of genetic diversity for wheat improvement. We present a pipeline to identify and remove identical accessions within and among genebanks and curate globally unique accessions. We also show how this approach can also be applied to future collection efforts to avoid the accumulation of identical material. When coordinated across global genebanks, this approach will ultimately allow for cost effective and efficient management of germplasm and better stewarding of these valuable resources.

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Benchmarking Variant Identification Tools for Plant Diversity Discovery

10.21203/rs.2.9666/v2 ◽

2019 ◽

Author(s):

Xing Wu ◽

Christopher Heffelfinger ◽

Hongyu Zhao ◽

Stephen L. Dellaporta

Keyword(s):

Next Generation Sequencing ◽

High Throughput Sequencing ◽

Crop Improvement ◽

Variant Calling ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Variant Discovery ◽

Variant Filtering ◽

Generation Sequencing

Abstract Background The ability to accurately and comprehensively identify genomic variations is critical for plant studies utilizing high-throughput sequencing. Most bioinformatics tools for processing next-generation sequencing data were originally developed and tested in human studies, raising questions as to their efficacy for plant research. A detailed evaluation of the entire variant calling pipeline, including alignment, variant calling, variant filtering, and imputation was performed on different programs using both simulated and real plant genomic datasets. Results A comparison of SOAP2, Bowtie2, and BWA-MEM found that BWA-MEM was consistently able to align the most reads with high accuracy, whereas Bowtie2 had the highest overall accuracy. Comparative results of GATK HaplotypCaller versus SAMtools mpileup indicated that the choice of variant caller affected precision and recall differentially depending on the levels of diversity, sequence coverage and genome complexity. A cross-reference experiment of S. lycopersicum and S. pennellii reference genomes revealed the inadequacy of single reference genome for variant discovery that includes distantly-related plant individuals. Machine-learning-based variant filtering strategy outperformed the traditional hard-cutoff strategy resulting in higher number of true positive variants and fewer false positive variants. A 2-step imputation method, which utilized a set of high-confidence SNPs as the reference panel, showed up to 60% higher accuracy than direct LD-based imputation. Conclusions Programs in the variant discovery pipeline have different performance on plant genomic dataset. Choice of the programs is subjected to the goal of the study and available resources. This study serves as an important guiding information for plant biologists utilizing next-generation sequencing data for diversity characterization and crop improvement.

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Abiotic stresses in rice production: Impacts and management

ORYZA- An International Journal on Rice ◽

10.35709/ory.2021.58.spl.4 ◽

2021 ◽

Vol 58 (Special) ◽

pp. 103-125

Author(s):

Himanshu Pathak ◽

Mahesh Kumar ◽

Kutubuddin A Molla ◽

Koushik Chakraborty

Keyword(s):

Abiotic Stresses ◽

Crop Improvement ◽

Rice Production ◽

Mitigation Strategies ◽

Improvement Strategy ◽

Nutrition Security ◽

Mitigation And Adaptation ◽

Yield And Quality ◽

Food And Nutrition ◽

Production Technologies

Rice, a key staple food crop in the world and India, offers food and nutrition security to millions of the global population. Abiotic (water, soil, atmospheric) stresses affect yield and quality of rice. This necessitates stress-resilient rice production technologies sufficiently fortified by novel stress mitigation and adaptation strategies. Recent crop improvement strategy has partially managed to resolve the challenges presented by abiotic stresses such as high temperature, drought, salinity, alkalinity, waterlogging and mineral deficiency. The complication and multiplicity of abiotic stresses necessitate the use of extensive, integrative and multi-disciplinary techniques to achieve resilience. Crop improvement, along with the agronomic interventions, is essential to stabilise the productivity and profitability of rice production. This article gives an overview of the potential impacts of abiotic stress on rice and suggests the adaptation and mitigation strategies.

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