The published work this package implements, followed by material read for methods that are not implemented yet.
Abdollahi-Arpanahi, R., Lourenco, D. & Misztal, I. (2022). A comprehensive study on size and definition of the core group in the proven and young algorithm for single-step GBLUP. Genetics Selection Evolution 54:34.
Aguilar, I., Misztal, I., Johnson, D.L., Legarra, A., Tsuruta, S. & Lawlor, T.J. (2010). A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science 93:743-752.
Aguilar, I., Misztal, I., Legarra, A. & Tsuruta, S. (2011). Efficient computation of the genomic relationship matrix and other matrices used in single-step evaluation. Journal of Animal Breeding and Genetics 128:422-428.
Albert, J.H. & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association 88:669-679.
Alvarez, I., Niemi, J. & Simpson, M. (2014). Bayesian inference for a covariance matrix. arXiv:1408.4050.
Amestoy, P.R., Davis, T.A. & Duff, I.S. (1996). An approximate minimum degree ordering algorithm. SIAM Journal on Matrix Analysis and Applications 17:886-905.
Anderson, E., Bai, Z., Bischof, C., Blackford, S., Demmel, J., Dongarra, J., Du Croz, J., Greenbaum, A., Hammarling, S., McKenney, A. & Sorensen, D. (1999). LAPACK Users’ Guide, 3rd ed. SIAM, Philadelphia.
Bijma, P. (2010). Multilevel selection 4: modeling the relationship of indirect genetic effects and group size. Genetics 186:1029-1031.
Bijma, P., Muir, W.M. & Van Arendonk, J.A.M. (2007). Multilevel selection 1: quantitative genetics of inheritance and response to selection. Genetics 175:277-288.
Bolker, B.M., Brooks, M.E., Clark, C.J., Geange, S.W., Poulsen, J.R., Stevens, M.H.H. & White, J.-S.S. (2009). Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology & Evolution 24:127-135.
Brooks, S.P. & Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7:434-455.
Burkner, P.-C. (2017). brms: an R package for Bayesian multilevel models using Stan. Journal of Statistical Software 80(1):1-28.
Butler, D.G., Cullis, B.R., Gilmour, A.R., Gogel, B.J. & Thompson, R. (2017). ASReml-R Reference Manual, version 4. VSN International, Hemel Hempstead. (ar1() for equally spaced levels, exp() for continuous distance.)
Buttari, A., Langou, J., Kurzak, J. & Dongarra, J. (2009). A class of parallel tiled linear algebra algorithms for multicore architectures. Parallel Computing 35:38-53.
Chang, C.C., Chow, C.C., Tellier, L.C.A.M., Vattikuti, S., Purcell, S.M. & Lee, J.J. (2015). Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4:7.
Cheng, H., Fernando, R.L. & Garrick, D.J. (2018). JWAS: Julia implementation of whole-genome analysis software. Proceedings of the World Congress on Genetics Applied to Livestock Production 11:859, Auckland.
Christensen, O.F. & Lund, M.S. (2010). Genomic prediction when some animals are not genotyped. Genetics Selection Evolution 42:2.
Colleau, J.J. (2002). An indirect approach to the extensive calculation of relationship coefficients. Genetics Selection Evolution 34:409-421.
Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39:1-38.
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Fernando, R.L., Gianola, D. & Grossman, M. (1983). Identifying all connected subsets in a two-way classification without interaction. Journal of Dairy Science 66:1399-1402.
Fragomeni, B.O., Lourenco, D.A.L., Tsuruta, S., Masuda, Y., Aguilar, I., Legarra, A., Lawlor, T.J. & Misztal, I. (2015). Use of genomic recursions in single-step genomic best linear unbiased predictor with a large number of genotypes. Journal of Dairy Science 98:4090-4094.
Garcia-Baccino, C.A., Legarra, A., Christensen, O.F., Misztal, I., Pocrnic, I., Vitezica, Z.G. & Cantet, R.J.C. (2017). Metafounders are related to Fst fixation indices and reduce bias in single-step genomic evaluations. Genetics Selection Evolution 49:34.
Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1:515-534.
Gelman, A. & Rubin, D.B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science 7:457-472.
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Gupta, K. & Vats, D. (2020). Estimating Monte Carlo variance from multiple Markov chains. arXiv:2007.04229.
Hadfield, J.D. (n.d.). MCMCglmm course notes, chapter 2, Bayesian analysis and MCMC. https://jarrodhadfield.github.io/MCMCglmm/course-notes/bayesian.html (read 2026-10-18).
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Hoeschele, I. & Tier, B. (1995). Estimation of variance components of threshold characters by marginal posterior modes and means via Gibbs sampling. Genetics Selection Evolution 27:519-540.
Huang, A. & Wand, M.P. (2013). Simple marginally noninformative prior distributions for covariance matrices. Bayesian Analysis 8:439-452.
Johnson, V.E. (2007). Bayesian model assessment using pivotal quantities. Bayesian Analysis 2:719-734.
Kastner, G. & Fruhwirth-Schnatter, S. (2014). Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models. Computational Statistics & Data Analysis 76:408-423.
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Legarra, A., Bermann, M., Mei, Q. & Christensen, O.F. (2024a). Redefining and interpreting genomic relationships of metafounders. Genetics Selection Evolution 56:34.
Legarra, A., Bermann, M., Mei, Q. & Christensen, O.F. (2024b). Estimating genomic relationships of metafounders across and within breeds using maximum likelihood, pseudo-expectation-maximization maximum likelihood and increase of relationships. Genetics Selection Evolution 56:35.
Legarra, A., Christensen, O.F., Vitezica, Z.G., Aguilar, I. & Misztal, I. (2015). Ancestral relationships using metafounders: finite ancestral populations and across population relationships. Genetics 200:455-468.
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Liu, Z., Goddard, M.E., Reinhardt, F. & Reents, R. (2014). A single-step genomic model with direct estimation of marker effects. Journal of Dairy Science 97:5833-5850.
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Marees, A.T., de Kluiver, H., Stringer, S., Vorspan, F., Curis, E., Marie-Claire, C. & Derks, E.M. (2018). A tutorial on conducting genome-wide association studies: quality control and statistical analysis. International Journal of Methods in Psychiatric Research 27:e1608.
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Masuda, Y. (2026). Introduction to BLUPF90 suite programs, Variance component estimation by Gibbs sampling. https://masuday.github.io/blupf90_tutorial/vc_gs.html (read 2026-10-18).
Masuda, Y., Misztal, I., Legarra, A., Tsuruta, S., Lourenco, D.A.L., Fragomeni, B.O. & Aguilar, I. (2017). Technical note: avoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic best linear unbiased prediction solved with the preconditioned conjugate gradient. Journal of Animal Science 95:49-52.
Meuwissen, T.H.E. & Luo, Z. (1992). Computing inbreeding coefficients in large populations. Genetics Selection Evolution 24:305-313.
Misztal, I. (2017). Message 832 to the BLUPF90 discussion list, 7 June 2017. https://github.com/ABGG-UGA/blupf90-public/blob/master/archive/md/0832.md
Misztal, I. et al. (2018). Manual for BLUPF90 family of programs (renumf90, preGSf90). University of Georgia, Athens.
Misztal, I., Legarra, A. & Aguilar, I. (2014). Using recursion to compute the inverse of the genomic relationship matrix. Journal of Dairy Science 97:3943-3952.
Mrode, R.A. & Pocrnic, I. (2023). Linear Models for the Prediction of the Genetic Merit of Animals, 4th ed. CABI, Wallingford. doi:10.1079/9781800620506.0000.
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Pocrnic, I., Lourenco, D.A.L., Masuda, Y., Legarra, A. & Misztal, I. (2016). The dimensionality of genomic information and its effect on genomic prediction. Genetics 203:573-581.
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Vandenplas, J., Eding, H., Calus, M.P.L. & Vuik, C. (2018). Deflated preconditioned conjugate gradient method for solving single-step BLUP models efficiently. Genetics Selection Evolution 50:51.
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Vats, D., Flegal, J.M. & Jones, G.L. (2019). Multivariate output analysis for Markov chain Monte Carlo. Biometrika 106:321-337.
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Surveyed, not implemented
Read for methods the package does not implement.
Arakawa, A. et al. (2022). Performance of the No-U-Turn sampler in multi-trait variance component estimation using genomic data. Genetics Selection Evolution 54:29.
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