Expanding Gene-Editing Potential in Crop Improvement with Pangenomes

Abstract

Pangenomes aim to represent the complete repertoire of the genome diversity present within a species or cohort of species, capturing the genomic structural variance between individuals. This genomic information coupled with phenotypic data can be applied to identify genes and alleles involved with abiotic stress tolerance, disease resistance, and other desirable traits. The characterisation of novel structural variants from pangenomes can support genome editing approaches such as Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR associated protein Cas (CRISPR-Cas), providing functional information on gene sequences and new target sites in variant-specific genes with increased efficiency. This review discusses the application of pangenomes in genome editing and crop improvement, focusing on the potential of pangenomes to accurately identify target genes for CRISPR-Cas editing of plant genomes while avoiding adverse off-target effects. We consider the limitations of applying CRISPR-Cas editing with pangenome references and potential solutions to overcome these limitations.

Breeding crops for drought-affected environments and improved climate resilience

“…While progress toward this end has been made and continues, there are still many issues to be resolved to predict the complex response surfaces of multi-trait phenomes and yield reaction-norms for the TPE of agricultural ecosystems based on the characterization of plant genomes, and to enable the ambition of prediction-based design of drought tolerant crops for current and future climates ( Messina et al, 2011 , 2018 , 2022a ; Cooper et al, 2014a , 2014b ; Technow et al, 2015 ; Bustos-Korts et al, 2019a , 2019b , 2021 ; Millet et al, 2019 ; Ramstein et al, 2019 ; Voss-Fels et al, 2019a ; Langridge et al, 2021 ; Varshney et al, 2021a , 2021b ; Diepenbrock et al, 2022 ; Powell et al, 2022 ; Welcker et al, 2022 ; Zhao et al, 2022 ). Through the ongoing advances in genome sequencing capabilities to enable the investigation of trait genetic diversity within breeding populations, together with combinations of novel trait mapping studies and targeted genetic manipulation strategies, key regions of plant genomes have been identified that contain genes and natural sequence variation that underpins the expression of trait phenotypic variation at different scales ( Yu and Buckler, 2006 ; Yu et al, 2006 , 2008 ; Salvi et al, 2007 ; Buckler et al, 2009 ; Myles et al, 2009 ; Dong et al, 2012 ; Mace et al, 2013 ; Guo et al, 2014 ; Thoen et al, 2016 ; Wisser et al, 2019 ; Voss-Fels et al, 2019b ; Bayer et al, 2020 ; Simmons et al, 2021 ; Massel et al, 2021 ; Tao et al, 2021 ; Liu and Qin, 2021 ; Diepenbrock et al, 2022 ; Tay Fernandez et al, 2022a , 2022b ; Welcker et al, 2022 ). These genomic regions represent target entry points to further investigate and model at different scales the properties and contributions of the gene networks that are responsible for trait genetic variation and expression of phenot...…”

“…These genomic regions represent target entry points to further investigate and model at different scales the properties and contributions of the gene networks that are responsible for trait genetic variation and expression of phenotypic variation for trait networks in breeding populations. They also provide targets for directed manipulation to create novel genetic and phenotypic variation and potential components of the G2P models that will be required to predict the contributions of traits and trait networks to crop performance at the agricultural ecosystem level ( Figure 5 ; Cooper et al, 2005 , 2009 ; Hammer et al, 2006 ; Messina et al, 2011 , 2022a , 2022c ; Dong et al, 2012 ; Kleessen et al, 2013 ; Guo et al, 2014 ; Marjoram et al, 2014 ; Shi et al, 2015 , 2017 ; Millet et al, 2019 ; Bustos-Korts et al, 2019a , 2019b , 2021 ; Ersoz et al, 2020 ; Massel et al, 2021 ; Rice and Lipka, 2021 ; Diepenbrock et al, 2022 ; Gleason et al, 2022 ; Powell et al, 2022 ; Schussler et al, 2022 ; Tay Fernandez et al, 2022b ; Welcker et al, 2022 ; Zhao et al, 2022 ).…”

Temperature‐smart plants: A new horizon with omics‐driven plant breeding

“…Machine learning is also instrumental in constructing pan-genomes (Golicz et al, 2016;Song et al, 2020;Bayer et al, 2021;Danilevicz et al, 2020;Torkamaneh et al, 2021;Jha et al, 2022;Ebler et al, 2022), enabling the identification of core, dispensable, and specific genes that expedite functional validation and reveal regulatory roles in genomics (Khan et al, 2020;Tay Fernandez et al, 2022;Zanini et al, 2022;Shi et al, 2023). Furthermore, ML algorithms have found applications in crop yield and complex traits prediction, crop growth monitoring, precision agriculture, and automated irrigation (Yoosefzadeh- Najafabadi et al, 2021;Yoosefzadeh-Najafabadi et al, 2022b;Jeyaraj et al, 2022;Li et al, 2022c;Croci et al, 2023).…”

Pangenomics and Crop Genome Adaptation in a Changing Climate

“…While CRISPR is a highly effective tool, the challenge of identifying target genes remains. This can be supported using pangenomes [ 84 ]. Pangenomes can be used as a reference for specific, multiplexed editing of SVs using CRISPR-Cas [ 84 ], allowing genes and traits of interest to be reintroduced without introducing deleterious traits [ 85 ].…”