Abstract
The whole genome regression approach to genomic selection is based on using large numbers of DNA markers to predict breeding values of individuals in animal and plant breeding programs. It is related to GBLUP introduced in Chapter 11, but it is distinguished from GBLUP by simultaneously modeling the effects of many DNA markers, and using the sum of marker allele effect estimates to predict breeding values of individuals. Since the introduction of the concept in 2001 (Meuwissen et al. 2001), it has revolutionized crop and livestock breeding. Genomic selection is a very active area of research so that new algorithms, software and methods are constantly being developed. Within this context we will briefly introduce a few key ideas and provide some examples for demonstration.
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Literature Cited
Andrews, D. F., & Mallows, C. L. (1974). Scale mixtures of normal distributions. Journal of the Royal Statistical Society: Series B: Methodological, 36, 99–102.
Brooks, S. P., & Roberts, G. O. (1998). Convergence assessment techniques for Markov chain Monte Carlo. Statistics and Computing, 8, 319–335. doi:10.1023/A:1008820505350.
Buckler, E. S., Holland, J. B., Bradbury, P. J., Acharya, C. B., Brown, P. J., Browne, C., Ersoz, E., Flint-Garcia, S., Garcia, A., Glaubitz, J. C., Goodman, M. M., Harjes, C., Guill, K., Kroon, D. E., Larsson, S., Lepak, N. K., Li, H., Mitchell, S. E., Pressoir, G., Peiffer, J. A., Rosas, M. O., Rocheford, T. R., Romay, M. C., Romero, S., Salvo, S., Villeda, H. S., da Silva, H. S., Sun, Q., Tian, F., Upadyayula, N., Ware, D., Yates, H., Yu, J., Zhang, Z., Kresovich, S., & McMullen, M. D. (2009). The genetic architecture of maize flowering time. Science, 325, 714–718. doi:10.1126/science.1174276.
Cleveland, M., Hickey, J., & Forni, S. (2012). A common dataset for genomic analysis of livestock populations. G3 (Bethesda), 2, 429–435. doi:10.1534/g3.111.001453.
de los Campos, G., Perez Rodriguez, P. (2014). BGLR: Bayesian generalized linear regression.
de los Campos, G., Naya, H., Gianola, D., Crossa, J., Legarra, A., Manfredi, E., Weigel, K., & Cotes, J. M. (2009). Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics, 182, 375–385. doi:10.1534/genetics.109.101501.
de los Campos, G., Gianola, D., Rosa, G. J., Weigel, K. A., & Crossa, J. (2010). Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genetics Research, 92, 295–308.
de los Campos, G., Hickey, J. M., Pong-Wong, R., Daetwyler, H. D., & Calus, M. P. L. (2013). Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics, 193, 327–345. doi:10.1534/genetics.112.143313.
Garrick, D. J., Taylor, J. F., Fernando, R. L., et al. (2009). Deregressing estimated breeding values and weighting information for genomic regression analyses. Genetics, Selection, Evolution, 41, 10–1186.
Gelman, A., & Rubi, D. B. (1992) Inference from iterative simulation using multiplesequences, Stat. Sci. 7, 457–472.
Gianola, D. (2013). Priors in whole-genome regression: The Bayesian alphabet returns. Genetics, 194, 573–596.
Gianola, D., de los Campos, G., Hill, W. G., Manfredi, E., & Fernando, R. (2009). Additive genetic variability and the Bayesian alphabet. Genetics, 183, 347–363. doi:10.1534/genetics.109.103952.
Habier, D., Fernando, R. L., & Dekkers, J. C. M. (2007). The impact of genetic relationship information on genome-assisted breeding values. Genetics, 177, 2389–2397. doi:10.1534/genetics.107.081190.
Habier, D., Fernando, R. L., Kizilkaya, K., & Garrick, D. J. (2011). Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics, 12, 186. doi:10.1186/1471-2105-12-186.
Hayes, B., & Goddard, M. (2010). Genome-wide association and genomic selection in animal breeding This article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”. Genome, 53(11), 876–883.
Heslot, N., Yang, H.-P., Sorrells, M. E., & Jannink, J.-L. (2012). Genomic selection in plant breeding: A comparison of models. Crop Science, 52, 146. doi:10.2135/cropsci2011.06.0297.
Hill, W. G., Goddard, M. E., & Visscher, P. M. (2008). Data and theory point to mainly additive genetic variance for complex traits. PLoS Genetics, 4, e1000008. doi:10.1371/journal.pgen.1000008.
Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12, 55–67. doi:10.1080/00401706.1970.10488634.
Maltecca, C., Parker, K. L., & Cassady, J. P. (2012). Application of multiple shrinkage methods to genomic predictions. Journal of Animal Science, 90, 1777–1787. doi:10.2527/jas.2011-4350.
Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157, 1819–1829.
Park, T., & Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association, 103, 681–686. doi:10.1198/016214508000000337.
Pérez, P., de los Campos, G. (2014). Genome-wide regression & prediction with the BGLR statistical package [WWW Document]. URL about:reader?url=http%3A%2F%2F www.genetics.org%2Fcontent%2Fearly%2F2014%2F07%2F06%2Fgenetics.114.164442.short. Accessed 24 Oct 2015.
Pérez, P., de los Campos, G., Crossa, J., & Gianola, D. (2010). Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R. The Plant Genome Journal, 3, 106. doi:10.3835/plantgenome2010.04.0005.
Plummer, M., Best, N., Cowles, K., & Vines, K. (2006). CODA: Convergence diagnosis and output analysis for MCMC. R News, 6, 7–11.
Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric regression. Cambridge, UK: Cambridge University Press.
Saatchi, M., McClure, M. C., McKay, S. D., Rolf, M. M., Kim, J., Decker, J. E., Taxis, T. M., Chapple, R. H., Ramey, H. R., Northcutt, S. L., Bauck, S., Woodward, B., Dekkers, J. C., Fernando, R. L., Schnabel, R. D., Garrick, D. J., & Taylor, J. F. (2011). Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genetics Selection Evolution, 43, 40. doi:10.1186/1297-9686-43-40.
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B: Statistical Methodology, 64, 583–639. doi:10.1111/1467-9868.00353.
Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society: Series B: Methodological, 58, 267–288.
Toft, N., Innocent, G. T., Gettinby, G., & Reid, S. W. J. (2007). Assessing the convergence of Markov Chain Monte Carlo methods: An example from evaluation of diagnostic tests in absence of a gold standard. Preventive Veterinary Medicine, 79, 244–256. doi:10.1016/j.prevetmed.2007.01.003.
VanRaden, P. M., Van Tassell, C. P., Wiggans, G. R., Sonstegard, T. S., Schnabel, R. D., Taylor, J. F., & Schenkel, F. S. (2009). Invited review: Reliability of genomic predictions for North American Holstein bulls. Journal of dairy science, 92(1), 16–24.
Wimmer, V., Albrecht, T., Auinger, H.-J., & Schön, C.-C. (2012). Synbreed: A framework for the analysis of genomic prediction data using R. Bioinformatics, 28, 2086–2087. doi:10.1093/bioinformatics/bts335.
Wolc, A., Stricker, C., Arango, J., Settar, P., Fulton, J. E., O'Sullivan, N. P., ... & Lamont, S. J. (2011). Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model. Genetics Selection Evolution, 43(1), 5.
Yi, N., & Xu, S. (2008). Bayesian LASSO for quantitative trait loci mapping. Genetics, 179, 1045–1055.
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Isik, F., Holland, J., Maltecca, C. (2017). Genomic Selection. In: Genetic Data Analysis for Plant and Animal Breeding. Springer, Cham. https://doi.org/10.1007/978-3-319-55177-7_12
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DOI: https://doi.org/10.1007/978-3-319-55177-7_12
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