Abstract
Most of the traits in plants are not controlled by single gene, but rather a cluster of genes. The variation in activity of chromosome is controlled by the recombination established by the series of balanced system that is called linked balanced polygenic complex. Regardless of the complexity of genetic material, breeders have developed advanced and precise approaches to overcome the problems faced in the crop improvement. Thus, using genomic selection (GS), we can achieve a constellation of genes attributed by the region of chromosome. They integrated phenotype data with the right bioinformatics tools, statistical tests, and huge amount of genomic data to target the selection of best line having superior phenotypes along with the absolute knowledge of genotype. With GS we can identify the minor-effect genes, QTLs, and markers. Since decades of population explosion, it has been a challenge to deliver breeding targets for developing best-quality crops having required grain yield. Earlier, breeding of crops involved a series of cycle of phenotypic evaluation and crossing followed by the selection of superior phenotype which consumed more time and manpower. In GS there is no need for repeated phenotyping to select the elite lines. Along with the integration of genomic information, bioinformatics tools, and statistical models, we cannot overlook marker-trait associations, variant calling at genome level, and population structure that provide comprehensive information of all elements necessary in crop improvement. However, it is still challenging for the plant breeders to develop crops capable to thrive successfully in tough climate. In this chapter, we will explore the factors affecting the selection of superior genotype, how we can design a suitable breeding pipeline along with the concept of statistical model, advantages, and applications. For decades, translating the complex genomic data has been a major challenge for crop improvement. GS is a remarkable approach for genetic gain as it consumes only one-third time compared to the traditional selection process.
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Abbreviations
- AFLP:
-
Amplified fragment length polymorphism
- ARMS:
-
Amplification refractory mutation system
- BLUE:
-
Best linear unbiased estimation
- BLUP:
-
Best linear unbiased prediction
- CIMMIYT:
-
International Maize and Wheat Improvement Center
- CRISPR:
-
Clustered regularly interspaced short palindromic repeats
- DArT:
-
Diversity arrays technology
- DH:
-
Doubled haploids
- F1:
-
First filial generation
- GBLUP:
-
Genomic best linear unbiased prediction
- GEBVs:
-
Genomic estimated breeding values
- GS:
-
Genomic selection
- HTG:
-
High-throughput genotyping
- IRGAs:
-
Infrared gas analyzers
- IRT:
-
Infrared thermography
- ISSR:
-
Inter-simple sequence repeats
- LASSO:
-
Least absolute shrinkage and selection operator
- LD:
-
Linkage disequilibrium
- LS:
-
Least square
- MAS:
-
Marker-assisted selection
- MCMC:
-
Markov chain Monte Carlo
- MTA:
-
Marker-trait association
- NAM:
-
Nested association mapping
- NCII :
-
North Carolina II design
- Ne:
-
Effective population size
- NGS:
-
Next-generation sequencing
- PLS:
-
Partial least square
- PM-RKHS:
-
Pedigree plus molecular marker model using reproducing kernel Hilbert space regression
- QTL:
-
Quantitative trait loci
- RBFNN:
-
Radial basis function neural networks
- RF:
-
Random forest
- RFLP:
-
Restriction fragment length polymorphism
- RILs:
-
Recombinant inbred lines
- RTK-GPS:
-
Global position system-real-time kinematic
- RR:
-
Ridge regression
- SNPs:
-
Single nucleotide polymorphisms
- SSRs:
-
Simple sequence repeats
- STS:
-
Sequence-tagged site
- SVM:
-
Support vector machine
- TRAPs:
-
Target region amplification polymorphisms
- TRN:
-
Training population
- TST:
-
Testing population
- WBSR:
-
Weight Bayesian shrinkage regression
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Acknowledgment
National Agri-Biotechnology Institute (NABI), Department of Biotechnology, Govt. of India, Mohali is acknowledged for support. DeLCON (DBT-Electronic Library Consortium), Gurgaon, India, is acknowledged for the online journal access. T R Sharma is thankful to the Department of Science and Technology, Govt. of India, for J. C. Bose National Fellowship.
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Rahim, M.S. et al. (2020). Genomic Selection in Cereal Crops: Methods and Applications. In: Gosal, S., Wani, S. (eds) Accelerated Plant Breeding, Volume 1. Springer, Cham. https://doi.org/10.1007/978-3-030-41866-3_3
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