Online citations, reference lists, and bibliographies.

Ridge Regression And Other Kernels For Genomic Selection With R Package RrBLUP

Jeffrey B. Endelman
Published 2011 · Biology

Cite This
Download PDF
Analyze on Scholarcy
Share
Many important traits in plant breeding are polygenic and therefore recalcitrant to traditional marker-assisted selection. Genomic selection addresses this complexity by including all markers in the prediction model. A key method for the genomic prediction of breeding values is ridge regression (RR), which is equivalent to best linear unbiased prediction (BLUP) when the genetic covariance between lines is proportional to their similarity in genotype space. This additive model can be broadened to include epistatic effects by using other kernels, such as the Gaussian, which represent inner products in a complex feature space. To facilitate the use of RR and nonadditive kernels in plant breeding, a new software package for R called rrBLUP has been developed. At its core is a fast maximum-likelihood algorithm for mixed models with a single variance component besides the residual error, which allows for effi cient prediction with unreplicated training data. Use of the rrBLUP software is demonstrated through several examples, including the identifi cation of optimal crosses based on superior progeny value. In cross-validation tests, the prediction accuracy with nonadditive kernels was signifi cantly higher than RR for wheat (Triticum aestivum L.) grain yield but equivalent for several maize (Zea mays L.) traits. THE ABILITY TO PREDICT COMPLEX TRAITS from marker data is becoming increasingly important in plant breeding (Bernardo, 2008). Th e earliest attempts, now over 20 years old, involved fi rst identifying signifi cant markers and then combining them in a multiple regression model (Lande and Th ompson, 1990). Th e focus over the last decade has been on genomic selection methods, in which all markers are included in the prediction model (Bernardo and Yu, 2007; Heff ner et al., 2009; Jannink et al., 2010). One of the fi rst methods proposed for genomic selection was ridge regression (RR), which is equivalent to best linear unbiased prediction (BLUP) in the context of mixed models (Whittaker et al., 2000; Meuwissen et al., 2001). Th e basic RR-BLUP model is
This paper references
10.2135/CROPSCI2008.03.0131
Molecular Markers and Selection for Complex Traits in Plants: Learning from the Last 20 Years
R. Bernardo (2008)
10.2135/CROPSCI2008.08.0512
Genomic Selection for Crop Improvement
E. Heffner (2009)
10.1007/s10681-007-9449-8
BLUP for phenotypic selection in plant breeding and variety testing
H. P. Piepho (2007)
10.1534/genetics.107.080101
Efficient Control of Population Structure in Model Organism Association Mapping
H. Kang (2008)
10.1534/genetics.107.075358
Using Quantitative Trait Loci Results to Discriminate Among Crosses on the Basis of Their Progeny Mean and Variance
Shengqiang Zhong (2007)
SAS 9.2 for Windows. SAS Institute
Sas Institute (1994)
10.1017/S0016672310000285
Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods.
G. de los Campos (2010)
10.2135/CROPSCI2006.01-0057
Number and fitness of selected individuals in marker-assisted and phenotypic recurrent selection
R. Bernardo (2006)
10.2135/CROPSCI1994.0011183X003400010003X
Prediction of maize single-cross performance using RFLPs and information from related hybrids
Rex N Bernardo (1994)
10.1534/genetics.107.081190
The Impact of Genetic Relationship Information on Genome-Assisted Breeding Values
D. Habier (2007)
Efficiency of marker-assisted selection in the improvement of quantitative traits.
R. Lande (1990)
Quantitative traits in plant breeding
R. Bernardo (2010)
Prediction of total genetic value using genome-wide dense marker maps.
T. Meuwissen (2001)
Th e impact of genetic relationship information on genome - assisted breeding val
R. L. Fernando
10.1086/521987
Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering.
S. Browning (2007)
10.1534/genetics.110.118521
Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers
J. Crossa (2010)
10.1111/J.1469-1809.1999.AHG634_0351_17.X
Marker-assisted selection using ridge regression.
J. Whittaker (2000)
10.1080/00401706.2000.10485983
Ridge Regression: Biased Estimation for Nonorthogonal Problems
A. Hoerl (2000)
10.2135/CROPSCI2008.10.0595
Ridge Regression and Extensions for Genomewide Selection in Maize
H. Piepho (2009)
TASSEL : Soft ware for association mapping of complex traits in diverse samples . Available at http : / / www . maizegenetics . net / tassel ( verifi ed 21 Nov . 2011 )
Z. Zhang (2007)
10.1017/S0016672308009981
Increased accuracy of artificial selection by using the realized relationship matrix.
B. Hayes (2009)
10.2135/CROPSCI2006.11.0690
Prospects for genomewide selection for quantitative traits in maize
R. Bernardo (2007)
10.1002/9780470316856
Variance Components
D. Glaser (2003)
10.1093/bfgp/elq001
Genomic selection in plant breeding: from theory to practice.
J. Jannink (2010)
10.1145/212094.212114
Overfitting and undercomputing in machine learning
Thomas G. Dietterich (1995)
10.1534/genetics.107.084285
Reproducing Kernel Hilbert Spaces Regression Methods for Genomic Assisted Prediction of Quantitative Traits
D. Gianola (2008)
10.3835/PLANTGENOME2010.04.0005
Genomic-Enabled Prediction Based on Molecular Markers and Pedigree Using the Bayesian Linear Regression Package in R.
P. Pérez (2010)
10.1145/882116.882120
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A. Widjaja (2003)
10.1093/bioinformatics/btm308
TASSEL: software for association mapping of complex traits in diverse samples
P. Bradbury (2007)
Genomicenabled prediction based on molecular markers and pedigree using the Bayesian Linear Regression package in R. Plant Gen
P Pérez (2010)
10.2135/CROPSCI2011.06.0297
Genomic Selection in Plant Breeding: A Comparison of Models
N. Heslot (2012)
10.1038/ng1702
A unified mixed-model method for association mapping that accounts for multiple levels of relatedness
J. Yu (2006)



This paper is referenced by
10.1093/bioinformatics/btaa199
Estimation of dynamic SNP-heritability with Bayesian Gaussian process models.
Arttu Arjas (2020)
10.3390/ijms21041342
Genomic Prediction for Grain Yield and Yield-Related Traits in Chinese Winter Wheat
Mohsin Ali (2020)
10.1534/g3.112.003699
Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments
Vanessa S. Windhausen (2012)
10.1534/g3.115.017533
Genome-Wide Association Study Based on Multiple Imputation with Low-Depth Sequencing Data: Application to Biofuel Traits in Reed Canarygrass
Guillaume P Ramstein (2015)
10.3389/fpls.2020.01001
Genomic Prediction of Agronomic Traits in Common Bean (Phaseolus vulgaris L.) Under Environmental Stress
B. Keller (2020)
10.2135/CROPSCI2016.08.0639
Population Structure and Genetic Diversity Analysis of Germplasm from the Winter Wheat Eastern European Regional Yield Trial (WWEERYT)
Craig T. Beil (2017)
10.1101/183467
Integrating genomic resources for a threatened Caribbean coral (Orbicella faveolata) using a genetic linkage map developed from individual larval genotypes
Jacob Snelling (2017)
10.1007/s11032-019-0983-6
Exploring the performance of genomic prediction models for soybean yield using different validation approaches
Vuk Đorđević (2019)
10.1007/s11032-019-1013-4
Genome-wide association study and genomic prediction analyses of drought stress tolerance in China in a collection of off-PVP maize inbred lines
Nan Wang (2019)
10.3389/fpls.2019.01195
Improving and Maintaining Winter Hardiness and Frost Tolerance in Bread Wheat by Genomic Selection
Sebastian Michel (2019)
10.1186/s12284-020-00374-8
Natural Sequence Variations and Combinations of GNP1 and NAL1 Determine the Grain Number per Panicle in Rice
Yun Wang (2020)
10.1002/tpg2.20012
Dominance and G×E interaction effects improve genomic prediction and genetic gain in intermediate wheatgrass (Thinopyrum intermedium)
Prabin Bajgain (2020)
10.3390/plants9010099
Genomic Predictions Using Low-Density SNP Markers, Pedigree and GWAS Information: A Case Study with the Non-Model Species Eucalyptus cladocalyx
P. Ballesta (2020)
10.1111/pce.12898
Allelic variations and differential expressions detected at quantitative trait loci for salt stress tolerance in wheat.
B. C. Oyiga (2018)
10.1186/s12864-020-6737-3
Effect of number of annual rings and tree ages on genomic predictive ability for solid wood properties of Norway spruce
Linghua Zhou (2020)
10.1007/s00122-018-3186-3
Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality
Bettina Lado (2018)
10.1007/s12155-017-9867-y
Breeding for Biomass Yield in Switchgrass Using Surrogate Measures of Yield
M. Casler (2017)
10.1371/journal.pone.0138903
Variable-Selection Emerges on Top in Empirical Comparison of Whole-Genome Complex-Trait Prediction Methods
D. Haws (2015)
10.1007/s00425-018-2976-9
A deep convolutional neural network approach for predicting phenotypes from genotypes
W. Ma (2018)
10.1534/g3.116.035410
Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space
Daniela Bustos-Korts (2016)
10.1101/389825
Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat
Margaret R. Krause (2018)
10.1101/124081
Extensions of BLUP models for genomic prediction in heterogeneous populations: Application in a diverse switchgrass sample
Guillaume P Ramstein (2017)
10.1371/journal.pone.0179191
Leveraging genomic prediction to scan germplasm collection for crop improvement
Leonardo de Azevedo Peixoto (2017)
10.1101/031179
Genome-wide association and prediction reveals the genetic architecture of cassava mosaic disease resistance and prospects for rapid genetic improvement
Marnin D. Wolfe (2015)
10.1007/978-3-319-42291-6_41
Application of Machine Learning-Based Classification to Genomic Selection and Performance Improvement
Zhixu Qiu (2016)
10.2135/CROPSCI2016.06.0538
A Simple Package to Script and Simulate Breeding Schemes: The Breeding Scheme Language
Shiori Yabe (2017)
10.1101/158543
Genome-wide association mapping and genomic prediction unravels CBSD resistance in a Manihot esculenta breeding population
Siraj Ismail Kayondo (2017)
10.1007/s00122-019-03434-w
Genome-wide association study of the seed transmission rate of soybean mosaic virus and associated traits using two diverse population panels
Qiong Liu (2019)
10.1007/978-3-319-32274-2_19
Genome-Based Breeding
Can-Hong Cheng (2016)
10.1038/s41598-018-38348-y
Crossword: A data-driven simulation language for the design of genetic-mapping experiments and breeding strategies
Walid Korani (2018)
10.1101/565218
A nematode-specific gene underlies bleomycin-response variation in Caenorhabditis elegans
Shannon C. Brady (2019)
10.1371/journal.pone.0224695
Comparison of shape quantification methods for genomic prediction, and genome-wide association study of sorghum seed morphology
Lisa Sakamoto (2019)
See more
Semantic Scholar Logo Some data provided by SemanticScholar