Intelligent reprogramming of wheat for enhancement of fungal and nematode disease resistance using advanced molecular techniques

Abstract

Wheat (Triticum aestivum L.) diseases are major factors responsible for substantial yield losses worldwide, which affect global food security. For a long time, plant breeders have been struggling to improve wheat resistance against major diseases by selection and conventional breeding techniques. Therefore, this review was conducted to shed light on various gaps in the available literature and to reveal the most promising criteria for disease resistance in wheat. However, novel techniques for molecular breeding in the past few decades have been very fruitful for developing broad-spectrum disease resistance and other important traits in wheat. Many types of molecular markers such as SCAR, RAPD, SSR, SSLP, RFLP, SNP, and DArT, etc., have been reported for resistance against wheat pathogens. This article summarizes various insightful molecular markers involved in wheat improvement for resistance to major diseases through diverse breeding programs. Moreover, this review highlights the applications of marker assisted selection (MAS), quantitative trait loci (QTL), genome wide association studies (GWAS) and the CRISPR/Cas-9 system for developing disease resistance against most important wheat diseases. We also reviewed all reported mapped QTLs for bunts, rusts, smuts, and nematode diseases of wheat. Furthermore, we have also proposed how the CRISPR/Cas-9 system and GWAS can assist breeders in the future for the genetic improvement of wheat. If these molecular approaches are used successfully in the future, they can be a significant step toward expanding food production in wheat crops.

Mapping of QTLs and meta-QTLs for Heterodera avenae Woll. resistance in common wheat (Triticum aestivum L.)

“…The use of molecular markers for a study of genetic basis of disease resistance in general, and for a study of CCN resistance in particular, has been an important area of research during the last three decades [ 35 ]. These studies allowed identification of individual genes using Mendelian methods and identification of QTLs using QTL analysis based on DNA-based molecular markers.…”

Detecting the Minimum Limit on Wheat Stripe Rust in the Latent Period Using Proximal Remote Sensing Coupled with Duplex Real-Time PCR and Machine Learning

“…At present, nucleic acid detection is the most commonly used method to estimate the content of pathogens in the laboratory, such as the polymerase chain reaction (PCR) [10,11], loop-mediated isothermal amplification method (LAMP) [12,13], and CRISPR/Cas-based detection method [14,15], which have risen rapidly in recent years. These technologies have been demonstrated to have reliable and high detection accuracy but generally depend on precision instruments and expensive reagents.…”

Editorial: Genetic and epigenetic regulation of disease resistance in horticultural plants

“…The discovery of novel candidate genes and research into the molecular processes behind disease resistance in horticultural plants are crucial steps in this regard. Many horticultural plant species now have high-quality, full reference genomes available thanks to advances in third generation sequencing techniques, enabling integrated analysis of multi-omics data using bioinformatics tools ( Debener, 2022 ; Jabran et al, 2023 ). These analyses help us better understand the evolution of horticultural plants and provide useful genomics resources for research on disease resistance features in plants.…”