Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals
Abstract
:1. Introduction
2. Methodology
3. Climate-Smart Crops: A Promising Option for Future Food Security
4. Integrated Genomic Selection for Making Climate-Smart Cereals
5. Genetic Gain: A Metric for Tracking Breeding Initiatives’ Forward Development
- (1)
- Genotyping and phenotyping of entities in a reference population and the building of a statistical format to study the effects of SNPs on morphological makeup, creating relational forecasting equations.
- (2)
- Newer candidates might not be phenotyped but are genotyped. Additionally, breeding values are calculated using phenotypic data and prediction models [54]. Owing to its increased genetic gain, reduced phenotyping, shorter cycle times, and improved selection accuracy, GS has been warmly accepted in breeding programs around the world over the past two decades. The feasibility of using GS in breeding crops is also being looked into, as it has given promising early evaluation results in the betterment of yield, biotic and abiotic stress resilience, and, of course, quality in cereal crops [55].
6. The Benefits of Genomic Selection over Conventional Approaches and Marker-Assisted Selection
- (a)
- To overcome the limitations of conventional phenotypic selection
- (b)
- Freedom of choice of selection at a specific stage
- (c)
- Helpful for backcross breeding
- (d)
- Pyramiding multiple monogenic traits
7. Genomic Selection Methodology
7.1. Design of Training Population
- (i)
- The size, genetic diversity, and relationship of the training population (TRN) to the test population (TST) are all critical factors determining genomic prediction precision. Specifically, the relationship between the cultivars in the TRN and those in the TST set, whether they are closely or distantly related, has been shown to impact the effectiveness of genomic prediction [108,109].
- (ii)
- The heritability of the traits under selection is another crucial factor affecting the accuracy of genomic forecasting. Characters with increased heritability, which are less complex and influenced by fewer genetic factors, can be effectively predicted using a smaller number of markers with comparatively greater effects [108,109].
- (iii)
- The accuracy or truth of genomic prediction is poorer for complicated traits that are influenced by an abundance of markers that do not exist in LD associated with QTL. Where there is a lack of correlation between markers and actual genetic factors influencing the trait, the accuracy of genomic prediction decreases [108,109].
7.2. Design of Statistical Models
- (a)
- Model Structure
- (b)
- Variable Selection
- (c)
- Parameter Estimation
- (d)
- Model Evaluation
7.3. Requirement for Advanced Breeding Populations for Genomics-Assisted Breeding (GAB); NAM, MAGIC, etc.
8. Integrated Genomic Selection: A Unique Approach to Boost the Capacity of Genomic Selection
8.1. Speed Breeding in Genomic Selection
8.2. Accelerating Rate of Breeding Cereals
8.3. High-Throughput Genotyping (HTG) and Genotype Imputing
8.4. High-throughput Phenotyping (HTP)
8.5. Genomic Crop Improvement by Next-Generation Sequencing (NGS)
8.6. Advances in Genotyping
8.6.1. The Illumina Golden Gate Assay
8.6.2. Genotyping by Sequencing (GBS)
8.6.3. Kompetitive Allele-Specific PCR (KASP)
8.6.4. TaqMan Assay
8.6.5. High-Resolution Melting (HRM) Analysis
8.6.6. MassARRAY
8.6.7. Restriction-Site-Associated DNA Sequencing (RAD-seq)
8.6.8. Amplicon Sequencing
8.7. Emerging Concept of Pangenomes and Super-Pangenomes
9. Statistical Tools for Integrated Genomic Selection
9.1. Breed Wheat Genomic Selection
- (1)
- Bwgs. cv, which performs replicated random model cross-validation on a training set of lines having genotypic and phenotypic data;
- (2)
- Bwgs.predict, which predicts the GEBV for those lines for which the genotype is known [272].
- (a)
- imputation of missing data;
- (b)
- dimension reduction;
- (c)
- GEBV estimation.
9.2. GMStool
- (1)
- Preparation:
- (2)
- Marker selection:
- (3)
- Final modeling.
9.3. SolGS
9.4. BGLR R-Package
9.5. GenSel
9.6. STGS
9.7. MTGS
9.8. Ime4GS
10. Issues and Challenges of Integrated Genomic Selection
11. Conclusions and Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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S. No. | Character | MAS | GS |
---|---|---|---|
1. | Marker number | Phenotype trait selected indirectly using genetic marker linked to the genomic region controlling trait of interest. | MAS variant based on GEBVs estimated using all the markers’ effects using a trained GS model. |
2. | Trait nature | Effective for oligogenic traits/major QTL traits with major effects. | Effective for traits with small effects along with major effects, i.e., polygenic traits/major and minor QTLs. |
3. | Prerequisite | Mapping and confirmation of markers connected with trait-associated QTL. | Training a good GS model in a TP utilizing genotype and phenotype data. |
4. | Approach | It is a targeted approach where only markers linked to a few validated major QTLs are used to implement MAS. | It is a holistic approach where all the markers used in training a GS model are used to implement GS in the BP. |
5. | Population nature | Applied on any population of a given crop if the QTL is validated, which is very rare. Relatively less effective for improving quantitative traits. | Workable on a BP that is related to or a derivative of the TP. Highly effective in improving quantitative traits. |
6. | Implementation | To complement any of the conventional breeding strategies like MA-Backcross, MA-Pedigree, MA-Recurrent selection. | More appropriately implemented in line with development breeding. |
7. | Genetic gain | Genetic gain per unit of time is less and much time is spent on QTL detection and validation. | Genetic gain per unit of time is relatively high as all the QTLs with major and minor effects are considered. |
8. | Limitations | Linkage drag, background noise, and environmental instability, especially for quantitative traits. | Factors influencing prediction accuracy. |
9. | Suitability | Complex genome, high polyploidy, heterozygosity, varied chromosome number, low/medium-density markers. | Improves the breeding efficiency and prediction, covers the entire genome, is preferred in purebred breeding across many animal species, forecasts the breeding potential of individual lines, and increases heritability estimation. |
S.No. | Crops | Model | Trait | References |
---|---|---|---|---|
1 | Maize | GBLUP | Grain yield | [131] |
RRBLUP | Grain yield | [132] | ||
100 kernel weight | [132] | |||
Bayes A, Bayes B, Bayes C, LASSO, and RKHS GBLUP and multigroup GBLUP | Grain yield | [133] | ||
RRBLUP and BSSV (Bayesian stochastic search variable) | Ear rot | [134] | ||
BLUP | Striga resistance Drought tolerance | [135] | ||
GBLUP | Drought tolerance | [136] | ||
RRBLUP and GBLUP | Water-logging tolerance | [137] | ||
2 | Barley | RRBLUP | Grain yield | [138] |
GBLUP and RKHS | Thousand kernel weight (TKW) | [139] | ||
GBLUP | DON resistance | [140] | ||
3 | Rice | Bayesian LASSO | Grain yield | [141] |
RRBLUP | Panicle weight | [142] | ||
GBLUP | Grain yield, Field grain, Field grain weight, The variance of field grain | [143] | ||
GBLUP, SVM, LASSO, and PLS | Field grain | [144] | ||
GBLUP | Field grain weight | [145] | ||
GBLUP, RKHS, and Bayes B | Panicle weight Nitrogen balance index | [146] | ||
GBLUP | Thousand-grain weight (TGW), Grain yield | [57] | ||
RRBLUP and LUP | Blast resistance | [147] | ||
GBLUP and RKHS | Drought tolerance | [148] |
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Sinha, D.; Maurya, A.K.; Abdi, G.; Majeed, M.; Agarwal, R.; Mukherjee, R.; Ganguly, S.; Aziz, R.; Bhatia, M.; Majgaonkar, A.; et al. Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals. Genes 2023, 14, 1484. https://doi.org/10.3390/genes14071484
Sinha D, Maurya AK, Abdi G, Majeed M, Agarwal R, Mukherjee R, Ganguly S, Aziz R, Bhatia M, Majgaonkar A, et al. Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals. Genes. 2023; 14(7):1484. https://doi.org/10.3390/genes14071484
Chicago/Turabian StyleSinha, Dwaipayan, Arun Kumar Maurya, Gholamreza Abdi, Muhammad Majeed, Rachna Agarwal, Rashmi Mukherjee, Sharmistha Ganguly, Robina Aziz, Manika Bhatia, Aqsa Majgaonkar, and et al. 2023. "Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals" Genes 14, no. 7: 1484. https://doi.org/10.3390/genes14071484
APA StyleSinha, D., Maurya, A. K., Abdi, G., Majeed, M., Agarwal, R., Mukherjee, R., Ganguly, S., Aziz, R., Bhatia, M., Majgaonkar, A., Seal, S., Das, M., Banerjee, S., Chowdhury, S., Adeyemi, S. B., & Chen, J. -T. (2023). Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals. Genes, 14(7), 1484. https://doi.org/10.3390/genes14071484