Adjusting for Spatial Effects in Genomic Prediction
Author
Abstract
Suggested Citation
DOI: 10.1007/s13253-020-00396-1
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Gil McVean, 2009. "A Genealogical Interpretation of Principal Components Analysis," PLOS Genetics, Public Library of Science, vol. 5(10), pages 1-10, October.
- Arũnas P. Verbyla & Brian R. Cullis & Michael G. Kenward & Sue J. Welham, 1999. "The Analysis of Designed Experiments and Longitudinal Data by Using Smoothing Splines," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 269-311.
- Xiaolei Liu & Meng Huang & Bin Fan & Edward S Buckler & Zhiwu Zhang, 2016. "Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 12(2), pages 1-24, February.
- Somak Dutta & Debashis Mondal, 2015. "An h-likelihood method for spatial mixed linear models based on intrinsic auto-regressions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(3), pages 699-726, June.
- J. Besag & D. Higdon, 1999. "Bayesian analysis of agricultural field experiments," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 691-746.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Hans-Peter Piepho & Robert J. Tempelman & Emlyn R. Williams, 2020. "Guest Editors’ Introduction to the Special Issue on “Recent Advances in Design and Analysis of Experiments and Observational Studies in Agriculture”," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 453-456, December.
- Xia, Min & Shao, Haidong & Williams, Darren & Lu, Siliang & Shu, Lei & de Silva, Clarence W., 2021. "Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
- Salihoğlu, Tayfun & Albayrak, Ayşe Nur & Eryılmaz, Yaşasın, 2021. "A method for the determination of urban transformation areas in Kocaeli," Land Use Policy, Elsevier, vol. 109(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Woojoo Lee & Hans‐Peter Piepho & Youngjo Lee, 2021. "Resolving the ambiguity of random‐effects models with singular precision matrix," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(4), pages 482-499, November.
- Lee, Dae-Jin & Durbán, María, 2009. "P-spline anova-type interaction models for spatio-temporal smoothing," DES - Working Papers. Statistics and Econometrics. WS ws093312, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Justin N. Vaughn & Sandra E. Branham & Brian Abernathy & Amanda M. Hulse-Kemp & Adam R. Rivers & Amnon Levi & William P. Wechter, 2022. "Graph-based pangenomics maximizes genotyping density and reveals structural impacts on fungal resistance in melon," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
- Estavoyer, Maxime & François, Olivier, 2022. "Theoretical analysis of principal components in an umbrella model of intraspecific evolution," Theoretical Population Biology, Elsevier, vol. 148(C), pages 11-21.
- Welham, S.J. & Thompson, R., 2009. "A note on bimodality in the log-likelihood function for penalized spline mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 920-931, February.
- Hai Anh Tran & Hyun Jo & Thi Cuc Nguyen & Jeong-Dong Lee & Hak Soo Seo & Jong Tae Song, 2024. "Genome-Wide Association Analysis for Submergence Tolerance at the Early Vegetative and Germination Stages in Wild Soybean ( Glycine soja )," Agriculture, MDPI, vol. 14(9), pages 1-17, September.
- Zhanwei Zhuang & Shaoyun Li & Rongrong Ding & Ming Yang & Enqin Zheng & Huaqiang Yang & Ting Gu & Zheng Xu & Gengyuan Cai & Zhenfang Wu & Jie Yang, 2019. "Meta-analysis of genome-wide association studies for loin muscle area and loin muscle depth in two Duroc pig populations," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-21, June.
- Ruixue Du & Hiroshi Yamada, 2020. "Principle of Duality in Cubic Smoothing Spline," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
- Pei-Kuan Cong & Wei-Yang Bai & Jin-Chen Li & Meng-Yuan Yang & Saber Khederzadeh & Si-Rui Gai & Nan Li & Yu-Heng Liu & Shi-Hui Yu & Wei-Wei Zhao & Jun-Quan Liu & Yi Sun & Xiao-Wei Zhu & Pian-Pian Zhao , 2022. "Genomic analyses of 10,376 individuals in the Westlake BioBank for Chinese (WBBC) pilot project," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
- Beran, Jan & Liu, Haiyan, 2016. "Estimation of eigenvalues, eigenvectors and scores in FDA models with dependent errors," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 218-233.
- Philipp F. M. Baumann & Enzo Rossi & Alexander Volkmann, 2020.
"What Drives Inflation and How: Evidence from Additive Mixed Models Selected by cAIC,"
Papers
2006.06274, arXiv.org, revised Aug 2022.
- Philipp F. M. Baumann & Dr. Enzo Rossi & Alexander Volkmann, 2021. "What drives inflation and how? Evidence from additive mixed models selected by cAIC," Working Papers 2021-12, Swiss National Bank.
- Guangbao Guo & Guoqi Qian & Lu Lin & Wei Shao, 2021. "Parallel inference for big data with the group Bayesian method," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(2), pages 225-243, February.
- Gianola, Daniel & Fernando, Rohan L. & Schön, Chris-Carolin, 2020. "Inferring trait-specific similarity among individuals from molecular markers and phenotypes with Bayesian regression," Theoretical Population Biology, Elsevier, vol. 132(C), pages 47-59.
- Nicholas Longford, 2014. "On the inefficiency of the restricted maximum likelihood," Economics Working Papers 1415, Department of Economics and Business, Universitat Pompeu Fabra.
- Fernanda De Bastiani & Robert A. Rigby & Dimitrios M. Stasinopoulous & Audrey H.M.A. Cysneiros & Miguel A. Uribe-Opazo, 2018. "Gaussian Markov random field spatial models in GAMLSS," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 168-186, January.
- Ralph, Peter L., 2019. "An empirical approach to demographic inference with genomic data," Theoretical Population Biology, Elsevier, vol. 127(C), pages 91-101.
- Peña-Malavera Andrea & Bruno Cecilia & Fernandez Elmer & Balzarini Monica, 2014. "Comparison of algorithms to infer genetic population structure from unlinked molecular markers," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(4), pages 391-402, August.
- Håvard Rue & Ingelin Steinsland & Sveinung Erland, 2004. "Approximating hidden Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 877-892, November.
- Martin P. Boer & Hans-Peter Piepho & Emlyn R. Williams, 2020. "Linear Variance, P-splines and Neighbour Differences for Spatial Adjustment in Field Trials: How are they Related?," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 676-698, December.
- Murphy, Sean R. & Boschma, Suzanne P. & Harden, Steven, 2022. "A lucerne-digit grass pasture offers herbage production and rainwater productivity equal to a digit grass pasture fertilized with applied nitrogen," Agricultural Water Management, Elsevier, vol. 259(C).
More about this item
Keywords
Gaussian random field; Genomic prediction; Spatial effects; Subpopulation effects;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jagbes:v:25:y:2020:i:4:d:10.1007_s13253-020-00396-1. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.