Multi-omics revolution to promote plant breeding efficiency

Abstract

Crop production is the primary goal of agricultural activities, which is always taken into consideration. However, global agricultural systems are coming under increasing pressure from the rising food demand of the rapidly growing world population and changing climate. To address these issues, improving high-yield and climate-resilient related-traits in crop breeding is an effective strategy. In recent years, advances in omics techniques, including genomics, transcriptomics, proteomics, and metabolomics, paved the way for accelerating plant/crop breeding to cope with the changing climate and enhance food production. Optimized omics and phenotypic plasticity platform integration, exploited by evolving machine learning algorithms will aid in the development of biological interpretations for complex crop traits. The precise and progressive assembly of desire alleles using precise genome editing approaches and enhanced breeding strategies would enable future crops to excel in combating the changing climates. Furthermore, plant breeding and genetic engineering ensures an exclusive approach to developing nutrient sufficient and climate-resilient crops, the productivity of which can sustainably and adequately meet the world’s food, nutrition, and energy needs. This review provides an overview of how the integration of omics approaches could be exploited to select crop varieties with desired traits.

Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant’s Abiotic Stress Tolerance Responses

“…It is crucial in data integration to combine data from several sources in order to build a model that can be used to predict complicated features and increase prediction accuracy. In order to predict phenotypes, an increasing variety of statistical models, including both linear and nonlinear models, have been created and are currently in use [ 336 ]. Several linear models, such as Genomic Best Linear Unbiased Prediction (GBLUP), Linear mixed models (LMMs), Bayesian sparse linear mixed model (BSLMM) and Penalized linear mixed model with generalized method of moments estimator (MpLMMGMM) model, are widely used to model multi-omics data with higher phenotypic prediction [ 336 ].…”

Polyploidization and genomic selection integration for grapevine breeding: a perspective

“…This approach can be an excellent option to establish the relationship between transcript abundance and phenotypic variance while simultaneously gaining insights into the regulatory functions of genetic variations responsible for phenotypic changes. Earlier, the GWAS-TWAS integrative approach was used in rice ( Anacleto et al., 2019 ; Mahmood et al., 2022 ) and cotton ( Li et al., 2020 ; Mahmood et al., 2022 ). Combined GWAS and metabolome-wide association studies (MWAS) can simultaneously screen a vast number of grapevine accessions for possible associations between their genomes and diverse metabolites.…”

Editorial: Sustainable horticulture: from omic sciences to new breeding techniques

“…The improvement of important agronomic and quality traits for sustainable horticulture can be obtained by using several approaches, ranging from the high-throughput sequencing technologies in the field of the omics sciences (e.g. genomics, transcriptomics, proteomics, metabolomics, and phenomics) and the new breeding techniques (NBT), to the conservation, enhancement, and the use of plant genetic resources (PGR) with unique organoleptic and functional characteristics ( Enfissi et al., 2021 ; Mahmood et al., 2022 ; Shen et al., 2022 ).…”