An Integrated Approach to Empirical Bayesian Whole Genome Prediction Modeling
Tóm tắt
Từ khóa
Tài liệu tham khảo
Cai, X., Huang, A., and Xu, S. (2011). Fast empirical Bayesian LASSO for multiple quantitative trait locus mapping. BMC Bioinformatics 12, 211.
Casella, G. (1985). An Introduction to Empirical Bayes Analysis. The American Statistician 39, 83-87.
Daetwyler, H. D., Capitan, A., Pausch, H., et al. (2014). Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nature Genetics 46, 858-865.
de Los Campos, G., Hickey, J. M., Pong-Wong, R., Daetwyler, H. D., and Calus, M. P. (2013). Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193, 327-345.
Fragomeni, B. d. O., Misztal, I., Lourenco, D. L., Aguilar, I., Okimoto, R., and Muir, W. M. (2014). Changes in variance explained by top SNP windows over generations for three traits in broiler chicken. Frontiers in Genetics 5, 332.
Garrick, D. J., Taylor, J. F., and Fernando, R. L. (2009). Deregressing estimated breeding values and weighting information for genomic regression analyses. Genetics Selection Evolution 41, 55.
Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1, 515-533.
George, E. I., and McCulloch, R. E. (1993). Variable Selection via Gibbs Sampling. Journal of the American Statistical Association 88, 881-889.
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., and Fernando, R. (2009). Additive Genetic Variability and the Bayesian Alphabet. Genetics 183, 347-363.
Gianola, D., Foulley, J. L., and Fernando, R. (1986). Prediction of breeding values when variances are not known. Genetics, Selection, Evolution 18, 485-498.
Gilmour, A. R., Thompson, R., and Cullis, B. R. (1995). Average information REML: An efficient algorithm for variance parameter estimation in linear mixed models. Biometrics 51, 1440-1450.
Harville, D. A. (1974). Bayesian inference for variance components using only error contrasts. Biometrika 61, 383-385.
——– (1977). Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems. Journal of the American Statistical Association 72, 320-338.
Hayashi, T., and Iwata, H. (2010). EM algorithm for Bayesian estimation of genomic breeding values. BMC Genetics 11, 3.
Hayes, B., and Goddard, M. E. (2001). The distribution of the effects of genes affecting quantitative traits in livestock. Genetics Selection Evolution 33, 209-229.
Hayes, B. J., Bowman, P. J., Chamberlain, A. J., and Goddard, M. E. (2009). Invited review: Genomic selection in dairy cattle: progress and challenges (vol 92, pg 433, 2009). Journal of Dairy Science 92, 1313-1313.
Huang, A., Xu, S., and Cai, X. (2015). Empirical Bayesian elastic net for multiple quantitative trait locus mapping. Heredity 114, 107-115.
Johnson, D. L., and Thompson, R. (1995). Restricted Maximum-Likelihood-Estimation of Variance-Components for Univariate Animal-Models Using Sparse-Matrix Techniques and Average Information. Journal of Dairy Science 78, 449-456.
Karkkainen, H. P., and Sillanpaa, M. J. (2012). Back to basics for Bayesian model building in genomic selection. Genetics 191, 969-987.
Lehermeier, C., Wimmer, V., Albrecht, T., et al. (2013). Sensitivity to prior specification in Bayesian genome-based prediction models. Statistical Applications in Genetics and Molecular Biology 12, 375-391.
Meuwissen, T. H., Solberg, T. R., Shepherd, R., and Woolliams, J. A. (2009). A fast algorithm for BayesB type of prediction of genome-wide estimates of genetic value. Genetics, Selection, Evolution 41, 2.
Meuwissen, T. H. E., Hayes, B. J., and Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819-1829.
R Core Team (2013). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
Resende, M. F. R., Munoz, P., Resende, M. D. V., et al. (2012). Accuracy of Genomic Selection Methods in a Standard Data Set of Loblolly Pine (Pinus taeda L.). Genetics 190, 1503-1510.
Robinson, G. K. (1991). that blup is a good thing: the estimation of random effects. Statistical Science, 6 15-32.
Rockova, V., and George, E. I. (2014). EMVS: The EM Approach to Bayesian Variable Selection. Journal of the American Statistical Association 109, 828-846.
Shepherd, R. K., Meuwissen, T. H., and Woolliams, J. A. (2010). Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers. BMC Bioinformatics 11, 529.
Stranden, I., and Christensen, O. F. (2011). Allele coding in genomic evaluation. Genetics, Selection, Evolution 43, 25.
Stranden, I., and Garrick, D. J. (2009a). Derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. Journal of Dairy Science 92, 2971-2975.
——– (2009b). Technical note: Derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. Journal of Dairy Science 92, 2971-2975.
Sun, X., Qu, L., Garrick, D. J., Dekkers, J. C., and Fernando, R. L. (2012). A fast EM algorithm for BayesA-like prediction of genomic breeding values. PLoS One 7, e49157.
Technow, F. (2013). Simulation of genomic data in applied genetics. R package version 0.4.
Wiggans, G. R., VanRaden, P. M., and Cooper, T. A. (2011). The genomic evaluation system in the United States: Past, present, future. Journal of Dairy Science 94, 3202-3211.
Wimmer, V., Lehermeier, C., Albrecht, T., Auinger, H.-J., Wang, Y., and Schön, C.-C. (2013). Genome-Wide Prediction of Traits with Different Genetic Architecture Through Efficient Variable Selection. Genetics 195, 573-587.
Xu, S. (2007). An Empirical Bayes Method for Estimating Epistatic Effects of Quantitative Trait Loci. Biometrics 63, 513-521.
Yang, W., Chen, C., and Tempelman, R. J. (2015). Improving the computational efficiency of fully Bayes inference and assessing the effect of misspecification of hyperparameters in whole-genome prediction models. Genetics Selection Evolution 47, 13.
Yang, W., and Tempelman, R. J. (2012). A Bayesian antedependence model for whole genome prediction. Genetics 190, 1491-1501.