Abstract
In crop improvement programs, genomic selection (GS) deals with the selection of superior genotypes to enhance the genetic gain for the trait of economic importance with reduced breeding cycle. Even for the complex quantitative traits that are governed by several genes with each exhibiting small effects, GS has been shown to be a promising tool in contrary to the marker assisted selection (MAS) useful for the traits controlled by few major genes. With the advent of high-throughput genotyping platforms such as SNP (single-nucleotide polymorphism), GS offers ample opportunities to develop marker-based model for the genetic evaluation. There are several factors, that is, heritability of the trait, effective population size, linkage disequilibrium (LD) of markers with quantitative trait loci (QTL) play crucial role in determining the genomic selection accuracy. Among different factors affecting the GS accuracy, choosing an appropriate GS model is an important one. In this chapter, we focus on different variants of Bayesian regression model used for genomic selection. The models and software for the genomic selection using different Bayesian methods are discussed. Besides, genomic selection accuracy for the yield trait of wheat is also demonstrated.
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References
Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829
Heffner EL, Sorrells ME, Jannink J-L (2009) Genomic selection for crop improvement. Crop Sci 49(1):1–12
Bhat JA, Ali S, Salgotra RK, Mir ZA, Dutta S, Jadon V, Tyagi A, Mushtaq M, Jain N, Singh PK, Singh GP (2016) Genomic selection in the era of next generation sequencing for complex traits in plant breeding. Front Genet 7:221
Poland JA, Endelman J, Dawson J, Rutkoski J, Wu S, Manes Y, Dreisigacker S, Crossa J, Sánchez-Villeda H, Sorrells M, Jannink JL (2012) Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome 5(3):103–113
Goddard M (2009) Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136(2):245–257
Shi S, Li X, Fang L, Liu A, Su G, Zhang Y, Luobu B, Ding X, Zhang S (2021) Genomic prediction using bayesian regression models with global–local prior. Front Genet 12:426
Gianola D, de Los CG, Hill WG, Manfredi E, Fernando R (2009) Additive genetic variability and the Bayesian alphabet. Genetics 183(1):347–363
Habier D, Fernando RL, Dekkers JC (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177(4):2389–2397
VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91(11):4414–4423
Piepho HP (2009) Ridge regression and extensions for genome wide selection in maize. Crop Sci 49(4):1165–1176
Wang J, Zhou Z, Zhang Z, Li H, Liu D, Zhang Q, Bradbury PJ, Buckler ES, Zhang Z (2018) Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits. Heredity 121(6):648–662
Jiang Y, Reif JC (2015) Modeling epistasis in genomic selection. Genetics 201(2):759–768
Habier D, Fernando RL, Kizilkaya K, Garrick DJ (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12(1):1–2
de Los CG, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MP (2013) Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193(2):327–345
Park T, Casella G (2008) The bayesian lasso. J Am Stat Assoc 103(482):681–686
González-Camacho JM, de Los CG, Pérez P, Gianola D, Cairns JE, Mahuku G, Babu R, Crossa J (2012) Genome-enabled prediction of genetic values using radial basis function neural networks. Theor Appl Genet 125(4):759–771
Yi N, Xu S (2008) Bayesian LASSO for quantitative trait loci mapping. Genetics 179(2):1045–1055
Pong-Wong R, Woolliams J (2014) Bayes U: a genomic prediction method based on the horseshoe prior. In: Proceedings, 10th world congress of genetics applied to livestock production, Vancouver
Carvalho CM, Polson NG, Scott JG (2010) The horseshoe estimator for sparse signals. Biometrika 97(2):465–480
Piironen J, Vehtari A (2017) Sparsity information and regularization in the horseshoe and other shrinkage priors. Electron J Stat 11(2):5018–5051
R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Pérez P, de los Campos G (2014) BGLR: a statistical package for whole genome regression and prediction. Genetics 198(2):483–495
Crossa J, Perez P, Hickey J, Burgueno J, Ornella L, Cerón-Rojas J, Zhang X, Dreisigacker S, Babu R, Li Y, Bonnett D (2014) Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity 112(1):48–60
Sorensen D, Gianola D, Gianola D (2002) Likelihood, Bayesian and MCMC methods in quantitative genetics. Springer, New York
Kizilkaya K, Fernando RL, Garrick DJ (2010) Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes. J Anim Sci 88(2):544–551
Sun X, Habier D, Fernando RL, Garrick DJ, Dekkers JC (2011) Genomic breeding value prediction and QTL mapping of QTLMAS2010 data using Bayesian methods. BMC Proc 5(3):1–8
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Meher, P.K., Kumar, A., Pradhan, S.K. (2022). Genomic Selection Using Bayesian Methods: Models, Software, and Application. In: Wani, S.H., Kumar, A. (eds) Genomics of Cereal Crops. Springer Protocols Handbooks. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2533-0_13
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DOI: https://doi.org/10.1007/978-1-0716-2533-0_13
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