Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

6 Issues per year


IMPACT FACTOR 2016: 0.646
5-year IMPACT FACTOR: 1.191

CiteScore 2016: 0.94

SCImago Journal Rank (SJR) 2016: 0.625
Source Normalized Impact per Paper (SNIP) 2016: 0.596

Mathematical Citation Quotient (MCQ) 2016: 0.06

Online
ISSN
1544-6115
See all formats and pricing
More options …
Volume 12, Issue 3 (Jun 2013)

Issues

Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

Sensitivity to prior specification in Bayesian genome-based prediction models

Christina Lehermeier
  • Plant Breeding, Technische Universität München, Emil-Ramann-Straße 4, 85354 Freising, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Valentin Wimmer
  • Plant Breeding, Technische Universität München, Emil-Ramann-Straße 4, 85354 Freising, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Theresa Albrecht
  • Plant Breeding, Technische Universität München, Emil-Ramann-Straße 4, 85354 Freising, Germany
  • Current address: Institute for Crop Production and Plant Breeding, Bavarian State Research Center for Agriculture, Am Gereuth 6, 85354 Freising, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Hans-Jürgen Auinger
  • Plant Breeding, Technische Universität München, Emil-Ramann-Straße 4, 85354 Freising, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Daniel Gianola
  • Department of Animal Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA; and Institute for Advanced Study, Technische Universität München, Lichtenbergstraße 2a, 85748 Garching, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Volker J. Schmid
  • Department of Statistics, Ludwig-Maximilians-Universität München, Ludwigstraße 33, 80539 München, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Chris-Carolin Schön
  • Corresponding author
  • Plant Breeding, Technische Universität München, Emil-Ramann-Straße 4, 85354 Freising, Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2013-04-24 | DOI: https://doi.org/10.1515/sagmb-2012-0042

Abstract

Different statistical models have been proposed for maximizing prediction accuracy in genome-based prediction of breeding values in plant and animal breeding. However, little is known about the sensitivity of these models with respect to prior and hyperparameter specification, because comparisons of prediction performance are mainly based on a single set of hyperparameters. In this study, we focused on Bayesian prediction methods using a standard linear regression model with marker covariates coding additive effects at a large number of marker loci. By comparing different hyperparameter settings, we investigated the sensitivity of four methods frequently used in genome-based prediction (Bayesian Ridge, Bayesian Lasso, BayesA and BayesB) to specification of the prior distribution of marker effects. We used datasets simulated according to a typical maize breeding program differing in the number of markers and the number of simulated quantitative trait loci affecting the trait. Furthermore, we used an experimental maize dataset, comprising 698 doubled haploid lines, each genotyped with 56110 single nucleotide polymorphism markers and phenotyped as testcrosses for the two quantitative traits grain dry matter yield and grain dry matter content. The predictive ability of the different models was assessed by five-fold cross-validation. The extent of Bayesian learning was quantified by calculation of the Hellinger distance between the prior and posterior densities of marker effects. Our results indicate that similar predictive abilities can be achieved with all methods, but with BayesA and BayesB hyperparameter settings had a stronger effect on prediction performance than with the other two methods. Prediction performance of BayesA and BayesB suffered substantially from a non-optimal choice of hyperparameters.

Keywords: genome-based prediction; genomic selection; Bayesian learning; shrinkage prior; plant breeding

References

  • Albrecht, T., V. Wimmer, H.-J. Auinger, M. Erbe, C. Knaak, M. Ouzunova, H. Simianer and C.-C. Schön (2011): “Genome-based prediction of testcross values in maize,” Theor. Appl. Genet., 123, 339–350.Web of ScienceGoogle Scholar

  • Atkinson, K. E. (1989): An introduction to numerical analysis, New York: Wiley.Google Scholar

  • Bernardo, J. M. and A. F. M. Smith (2002): Bayesian theory, Chichester: Wiley.Google Scholar

  • Browning, B. L. and S. R. Browning (2009): “A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals,” Am. J. Hum. Genet., 84, 210–223.Web of ScienceGoogle Scholar

  • Celeux, G., F. Forbes, C. P. Robert and D. M. Titterington (2006): “Deviance information criteria for missing data models,” Bayesian Anal., 1, 651–673.Google Scholar

  • Ching, A., K. S. Caldwell, M. Jung, M. Dolan, O. S. Smith, S. Tingey, M. Morgante and A. J. Rafalski (2002): “SNP frequency, haplotype structure and linkage disequilibrium in elite maize inbred lines,” BMC Genet., 3. DOI:10.1186/1471-2156-3-19.CrossrefGoogle Scholar

  • Crossa, J., G. de los Campos, P. Pérez, D. Gianola, J. Burgueño, J. L. Araus, D. Makumbi, R. P. Singh, S. Dreisigacker, J. Yan, V. Arief, M. Banziger and H.-J. Braun (2010): “Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers,” Genetics, 186, 713–724.Web of ScienceGoogle Scholar

  • Cullis, B. R., A. B. Smith and N. E. Coombes (2006): “On the design of early generation variety trials with correlated data,” J. Agric. Biol. Environ. Stat., 11, 381–393.CrossrefGoogle Scholar

  • Dekkers, J. C. M. (2007): “Marker-assisted selection for commercial crossbred performance,” J. Anim. Sci., 85, 2104–2114.Google Scholar

  • de los Campos, G. and P. Pérez (2012): BLR: Bayesian Linear Regression, URL http://CRAN.R-project.org/package=BLR, R package version 1.3. Accessed on November 30, 2012.

  • de los Campos, G., H. Naya, D. Gianola, J. Crossa, A. Legarra, E. Manfredi, K. Weigel and J. M. Cotes (2009): “Predicting quantitative traits with regression models for dense molecular markers and pedigree,” Genetics, 182, 375–385.Web of ScienceGoogle Scholar

  • de los Campos, G., J. M. Hickey, R. Pong-Wong, H. D. Daetwyler and M. P. L. Calus (2013): “Whole genome regression and prediction methods applied to plant and animal breeding,” Genetics, 193, 327–345.Web of ScienceGoogle Scholar

  • Falconer, D. S. and T. F. C. Mackay (1996): Introduction to Quantitative Genetics, Essex: Longman.Google Scholar

  • Ganal, M. W., G. Durstewitz, A. Polley, A. Bérard, E. S. Buckler, A. Charcosset, J. D. Clarke, E.-M. Graner, M. Hansen, J. Joets, M.-C. L. Paslier, M. D. McMullen, P. Montalent, M. Rose, C.-C. Schön, Q. Sun, H. Walter, O. C. Martin and M. Falque (2011): “A large maize (Zea mays L.) SNP genotyping array: Development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome,” PLoS ONE, 6, 1–15.Google Scholar

  • Gelman, A., J. B. Carlin, H. S. Stern and D. B. Rubin (2004): Bayesian data analysis, London: Chapman and Hall.PubMedGoogle Scholar

  • Gianola, D., G. de los Campos, W. G. Hill, E. Manfredi and R. Fernando (2009): “Additive genetic variability and the Bayesian alphabet,” Genetics, 138, 347–363.Web of ScienceGoogle Scholar

  • Grubbs, F. E. (1950): “Sample criteria for testing outlying observations,” Ann. Math. Stat., 21, 27–58.Google Scholar

  • Habier, D., R. L. Fernando and J. C. M. Dekkers (2007): “The impact of genetic relationship information on genome-assisted breeding values,” Genetics, 177, 2389–2397.Web of ScienceGoogle Scholar

  • Habier, D., J. Tetens, F.-R. Seefried, P. Lichtner and G. Thaller (2010): “The impact of genetic relationship information on genomic breeding values in German Holstein cattle,” Genet. Sel. Evol., 42, 1–12.Web of ScienceGoogle Scholar

  • Habier, D., R. L. Fernando, K. Kizilkaya and D. J. Garrick (2011): “Extension of the Bayesian alphabet for genomic selection,” BMC Bioinformatics, 12, 1–24.Web of ScienceGoogle Scholar

  • Heffner, E. L., M. E. Sorrells and J.-L. Jannink (2009): “Genomic selection for crop improvement,” Crop Sci., 49, 1–12.Web of ScienceCrossrefGoogle Scholar

  • Heslot, N., H.-P. Yang, M. E. Sorrells and J.-L. Jannink (2012): “Genomic selection in plant breeding: A comparison of models,” Crop. Sci., 52, 146–160.Google Scholar

  • Hill, W. G. and A. Robertson (1968): “Linkage disequilibrium in finite populations,” Theor. Appl. Genet., 38, 226–231.Google Scholar

  • Jannink, J.-L., A. J. Lorenz and H. Iwata (2010): “Genomic selection in plant breeding: from theory to practice,” Brief. Funct. Genomics, 9, 166–177.CrossrefWeb of ScienceGoogle Scholar

  • Kneib, T., S. Konrath and L. Fahrmeir (2011): “High dimensional structured additive regression models: Bayesian regularization, smoothing and predictive performance,” J. Roy. Stat. Soc. C-App., 60, 51–70.Web of ScienceCrossrefGoogle Scholar

  • Le Cam, L. (1986): Asymptotic methods in statistical decision theory, New York: Springer-Verlag.Google Scholar

  • Meuwissen, T. H. E., B. J. Hayes and M. E. Goddard (2001): “Prediction of total genetic value using genome-wide dense marker maps,” Genetics, 157, 1819–1829.Google Scholar

  • Park, T. and G. Casella (2008): “The Bayesian Lasso,” J. Am. Stat. Assoc., 103, 681–686.Google Scholar

  • Pérez, P., G. de los Campos, J. Crossa and D. Gianola (2010): “Genomic-enabled prediction based on molecular markers and pedigree using the BLR package in R,” Plant Genome, 3, 106–116.Google Scholar

  • Piepho, H.-P. (2009): “Ridge regression and extensions for genomewide selection in maize,” Crop. Sci., 49, 1165–1176.Web of ScienceGoogle Scholar

  • Plummer, M., N. Best, K. Cowles and K. Vines (2006): “CODA: convergence diagnosis and output analysis for MCMC,” R News, 6, 7–11, URL http://CRAN.R-project.org/doc/Rnews/. Accessed on December 10, 2012.

  • Riedelsheimer, C., A. Czedik-Eysenberg, C. Grieder, J. Lisec, F. Technow, R. Sulpice, T. Altmann, M. Stitt, L. Willmitzer and A. E. Melchinger (2012): “Genomic and metabolic prediction of complex heterotic traits in hybrid maize,” Nat. Genet., 44, 217–220.Web of ScienceGoogle Scholar

  • Roos, M. and L. Held (2011): “Sensitivity analysis in Bayesian generalized linear mixed models for binary data,” Bayesian Anal., 6, 259–278.Google Scholar

  • R Development Core Team (2012): R: A language and environment for statistical computing, R foundation for statistical computing, Vienna, Austria, URL http://www.R-project.org/, ISBN 3-900051-07-0. Accessed on November 30, 2012.

  • Schaeffer, L. R. (2006): “Strategy for applying genome-wide selection in dairy cattle,” J. Anim. Breed. Genet., 123, 218–223.Google Scholar

  • Schön, C.-C., H. F. Utz, S. Groh, B. Truberg, S. Openshaw and A. E. Melchinger (2004): “Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits,” Genetics, 167, 485–498.Google Scholar

  • Silverman, B. W. (1986): Density estimation, London: Chapman and Hall.Google Scholar

  • Sorensen, D. and D. Gianola (2002): Likelihood, Bayesian, and MCMC methods in quantitative genetics, New York: Springer.Google Scholar

  • Spiegelhalter, D. J., N. G. Best, B. P. Carlin and A. van der Linde (2002): “Bayesian measures of model complexity and fit,” J. R. Stat. Soc. B, 64, 583–639.Google Scholar

  • Wimmer, V., T. Albrecht, H.-J. Auinger and C.-C. Schön (2012): “synbreed: a framework for the analysis of genomic prediction data using R,” Bioinformatics, 28, 2086–2087.CrossrefWeb of SciencePubMedGoogle Scholar

About the article

Corresponding author: Chris-Carolin Schön, Plant Breeding, Technische Universität München, Emil-Ramann-Straße 4, 85354 Freising, Germany


Published Online: 2013-04-24

Published in Print: 2013-06-01


Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2012-0042.

Export Citation

©2013 by Walter de Gruyter Berlin Boston. Copyright Clearance Center

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[2]
Theresa Albrecht, Hans-Jürgen Auinger, Valentin Wimmer, Joseph O. Ogutu, Carsten Knaak, Milena Ouzunova, Hans-Peter Piepho, and Chris-Carolin Schön
Theoretical and Applied Genetics, 2014, Volume 127, Number 6, Page 1375
[3]
Daniel Gianola and Guilherme J.M. Rosa
Annual Review of Animal Biosciences, 2015, Volume 3, Number 1, Page 19
[4]
Alencar Xavier, William M. Muir, Bruce Craig, and Katy Martin Rainey
Theoretical and Applied Genetics, 2016, Volume 129, Number 10, Page 1933
[5]
C. Lehermeier, G. de los Campos, V. Wimmer, and C.-C. Schön
Journal of Animal Breeding and Genetics, 2017, Volume 134, Number 3, Page 232
[6]
Ning Gao, Jiaqi Li, Jinlong He, Guang Xiao, Yuanyu Luo, Hao Zhang, Zanmou Chen, and Zhe Zhang
BMC Genetics, 2015, Volume 16, Number 1
[7]
C. Chen and R. J. Tempelman
Journal of Agricultural, Biological, and Environmental Statistics, 2015, Volume 20, Number 4, Page 491
[8]
Robert J. Tempelman
Journal of Agricultural, Biological, and Environmental Statistics, 2015, Volume 20, Number 4, Page 442

Comments (0)

Please log in or register to comment.
Log in