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Enhancing Genomic Prediction With Genome-wide Association Studies In Multiparental Maize Populations

Y. Bian, J. Holland
Published 2017 · Biology, Medicine

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Genome-wide association mapping using dense marker sets has identified some nucleotide variants affecting complex traits that have been validated with fine-mapping and functional analysis. However, many sequence variants associated with complex traits in maize have small effects and low repeatability. In contrast to genome-wide association study (GWAS), genomic prediction (GP) is typically based on models incorporating information from all available markers, rather than modeling effects of individual loci. We considered methods to integrate results of GWASs into GP models in the context of multiple interconnected families. We compared association tests based on a biallelic additive model constraining the effect of a single-nucleotide polymorphism (SNP) to be equal across all families in which it segregates to a model in which the effect of a SNP can vary across families. Association SNPs were then included as fixed effects into a GP model that also included the random effects of the whole genome background. Simulation studies revealed that the effectiveness of this joint approach depends on the extent of polygenicity of the traits. Congruent with this finding, cross-validation studies indicated that GP including the fixed effects of the most significantly associated SNPs along with the polygenic background was more accurate than the polygenic background model alone for moderately complex but not highly polygenic traits measured in the maize nested association mapping population. Individual SNPs with strong and robust association signals can effectively improve GP. Our approach provides a new integrative modeling approach for both reliable gene discovery and robust GP.
This paper references
10.1038/ng.2310
Genome-wide Efficient Mixed Model Analysis for Association Studies
Xiaoping Zhou (2012)
10.1126/science.1174320
Genetic Properties of the Maize Nested Association Mapping Population
M. McMullen (2009)
10.1186/s13059-014-0572-2
De novo assembly of bacterial transcriptomes from RNA-seq data
B. Tjaden (2014)
10.1534/genetics.107.084293
Nonparametric Methods for Incorporating Genomic Information Into Genetic Evaluations: An Application to Mortality in Broilers
O. González-Recio (2008)
10.1534/g3.112.003699
Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments
Vanessa S. Windhausen (2012)
10.1038/ng.546
Mixed linear model approach adapted for genome-wide association studies
Zhiwu Zhang (2010)
10.1534/genetics.112.143313
Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding
G. de los Campos (2013)
10.1038/ng.746
Genome-wide association study of leaf architecture in the maize nested association mapping population
Feng Tian (2011)
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)
10.2135/CROPSCI2011.06.0297
Genomic Selection in Plant Breeding: A Comparison of Models
N. Heslot (2012)
Prediction of total genetic value using genome-wide dense marker maps.
T. Meuwissen (2001)
10.1534/genetics.113.152207
Genomic BLUP Decoded: A Look into the Black Box of Genomic Prediction
D. Habier (2013)
10.1371/journal.pone.0126880
Accounting for Genetic Architecture Improves Sequence Based Genomic Prediction for a Drosophila Fitness Trait
Ulrike Ober (2015)
10.1186/s13059-015-0716-z
Genetic properties of the MAGIC maize population: a new platform for high definition QTL mapping in Zea mays
Matteo Dell'Acqua (2015)
10.1534/genetics.114.161943
Usefulness of Multiparental Populations of Maize (Zea mays L.) for Genome-Based Prediction
C. Lehermeier (2014)
10.2135/CROPSCI2013.05.0315
Genomewide selection when major genes are known
R. Bernardo (2014)
10.1534/genetics.113.159152
The Genetic Architecture Of Maize Height
Jason Peiffer (2014)
10.1186/1471-2105-12-186
Extension of the bayesian alphabet for genomic selection
D. Habier (2010)
10.2135/CROPSCI2001.4111
What If We Knew All the Genes for a Quantitative Trait in Hybrid Crops
R. Bernardo (2001)
Marker-assisted selection to increase effective population size by reducing Mendelian segregation variance.
J. Wang (2000)
10.1198/016214508000000337
The Bayesian Lasso
Trevor H Park (2008)
10.1534/genetics.112.147850
Applications of Population Genetics to Animal Breeding, from Wright, Fisher and Lush to Genomic Prediction
W. Hill (2014)
10.1186/1471-2105-12-S10-S15
Validation of an arterial tortuosity measure with application to hypertension collection of clinical hypertensive patients
K. Diedrich (2011)
10.2135/CROPSCI2009.11.0662
Plant Breeding with Genomic Selection: Gain per Unit Time and Cost
E. Heffner (2010)
10.1534/g3.115.021121
Ensemble Learning of QTL Models Improves Prediction of Complex Traits
Y. Bian (2015)
10.1534/genetics.167.1.485
Quantitative Trait Locus Mapping Based on Resampling in a Vast Maize Testcross Experiment and Its Relevance to Quantitative Genetics for Complex Traits
C. Schoen (2004)
10.1038/hdy.2013.16
Genomic prediction in CIMMYT maize and wheat breeding programs
J. Crossa (2014)
10.3835/PLANTGENOME2014.05.0023
Novel Methods to Optimize Genotypic Imputation for Low-Coverage, Next-Generation Sequence Data in Crop Plants
Kelly Swarts (2014)
10.1038/ng.2314
An efficient multi-locus mixed model approach for genome-wide association studies in structured populations
Vincent Ségura (2012)
10.1534/genetics.109.101501
Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree
G. de los Campos (2009)
10.1038/hdy.2014.123
Joint-multiple family linkage analysis predicts within-family variation better than single-family analysis of the maize nested association mapping population
F. Ogut (2015)
10.1038/mp.2013.184
P-values in genomics: Apparent precision masks high uncertainty
L. Lazzeroni (2014)
10.1038/ng.2313
Maize HapMap2 identifies extant variation from a genome in flux
Jer-Ming Chia (2012)
10.1371/journal.pone.0114919
A Genome-Wide Association Study for Clinical Mastitis in First Parity US Holstein Cows Using Single-Step Approach and Genomic Matrix Re-Weighting Procedure
Francesco Tiezzi (2015)
10.1371/journal.pone.0093017
Improving the Accuracy of Whole Genome Prediction for Complex Traits Using the Results of Genome Wide Association Studies
Zhe Zhang (2014)
10.1007/s00122-011-1702-9
Evaluation of genome-wide selection efficiency in maize nested association mapping populations
Zhigang Guo (2011)
10.1186/1297-9686-41-56
A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers
G. Moser (2009)
10.3835/PLANTGENOME2010.12.0029
Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program
E. Heffner (2011)
10.2135/CROPSCI2008.10.0595
Ridge Regression and Extensions for Genomewide Selection in Maize
H. Piepho (2009)
10.2135/CROPSCI2006.11.0690
Prospects for genomewide selection for quantitative traits in maize
R. Bernardo (2007)
Maize HapMap 2 identi fi es extant variation from a genome in fl ux
Chia J-M (2012)
10.1038/hdy.2015.113
Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement
J. E. Spindel (2016)
10.1534/genetics.107.084285
Reproducing Kernel Hilbert Spaces Regression Methods for Genomic Assisted Prediction of Quantitative Traits
D. Gianola (2008)
10.1534/genetics.109.100727
Mapping in Structured Populations by Resample Model Averaging
W. Valdar (2009)
10.1371/journal.pgen.1005045
Resistance to Gray Leaf Spot of Maize: Genetic Architecture and Mechanisms Elucidated through Nested Association Mapping and Near-Isogenic Line Analysis
J. Benson (2015)
10.1002/9781119107743.CH05
Maize Breeding in the United States: Views from Within Monsanto
D. Butruille (2015)
10.1534/genetics.109.103952
Additive Genetic Variability and the Bayesian Alphabet
D. Gianola (2009)
Theoretical basis of the Beavis effect.
S. Xu (2003)
10.1007/s00122-009-1166-3
Accuracy of genotypic value predictions for marker-based selection in biparental plant populations
Robenzon E. Lorenzana (2009)
10.1017/S0016672308009981
Increased accuracy of artificial selection by using the realized relationship matrix.
B. Hayes (2009)
10.2135/CROPSCI2008.08.0512
Genomic Selection for Crop Improvement
E. Heffner (2009)
10.1101/010207
Association Mapping across Numerous Traits Reveals Patterns of Functional Variation in Maize
J. Wallace (2014)
10.1186/1471-2164-15-1068
Limits on the reproducibility of marker associations with southern leaf blight resistance in the maize nested association mapping population
Y. Bian (2014)
10.1201/9781420049381.CH10
QTL Analyses: Power, Precision, and Accuracy
W. Beavis (1997)
10.1038/ng.747
Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population
Kristen L. Kump (2011)
10.3168/jds.2007-0980
Efficient methods to compute genomic predictions.
P. VanRaden (2008)
10.1038/ng.548
Variance component model to account for sample structure in genome-wide association studies
H. Kang (2010)
10.1038/nmeth.1681
FaST linear mixed models for genome-wide association studies
C. Lippert (2011)
10.1534/genetics.107.080101
Efficient Control of Population Structure in Model Organism Association Mapping
H. Kang (2008)



This paper is referenced by
10.1007/s00122-018-3103-9
Multi-year linkage and association mapping confirm the high number of genomic regions involved in oilseed rape quantitative resistance to blackleg
V. Kumar (2018)
10.1093/jxb/erz545
Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform
D. H. Lyra (2020)
10.1007/s11032-019-1013-4
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Nan Wang (2019)
10.3389/fpls.2018.00343
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M. S. McElroy (2018)
10.1002/csc2.20104
A connected half-sib family training population for genomic prediction in barley
D. Sweeney (2020)
10.3389/fgene.2020.00316
Combining QTL Analysis and Genomic Predictions for Four Durum Wheat Populations Under Drought Conditions
Meryem Zaïm (2020)
10.3389/fpls.2019.01129
Improving Genomic Selection With Quantitative Trait Loci and Nonadditive Effects Revealed by Empirical Evidence in Maize
Xiaogang Liu (2019)
10.1016/j.xplc.2019.100005
Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants
Y. Xu (2020)
10.3389/fpls.2020.00197
Incorporating Genome-Wide Association Mapping Results Into Genomic Prediction Models for Grain Yield and Yield Stability in CIMMYT Spring Bread Wheat
Deepmala Sehgal (2020)
10.1101/704668
Genomewide analysis of biomass responses to water withholding in young plants of maize inbred lines with expired plant variety protection certificate
Maja Mazur (2019)
10.3389/fpls.2017.01916
Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations
A. Zhang (2017)
10.1007/s00122-019-03412-2
Genomics-assisted breeding for ear rot resistances and reduced mycotoxin contamination in maize: methods, advances and prospects
David Sewordor Gaikpa (2019)
10.3835/PLANTGENOME2018.06.0045
Genome-Wide Analysis and Prediction of Resistance to Goss's Wilt in Maize.
J. Cooper (2019)
10.3390/AGRONOMY9090479
Genomic Selection—Considerations for Successful Implementation in Wheat Breeding Programs
D. Larkin (2019)
10.1101/626234
Genomic basis of European ash tree resistance to ash dieback fungus
Jonathan James Stocks (2019)
10.1101/208181
Leveraging Transcriptomics Data for Genomic Prediction Models in Cassava
R. Lozano (2017)
10.1038/s41559-019-1036-6
Genomic basis of European ash tree resistance to ash dieback fungus
J. J. Stocks (2019)
10.1007/s10681-019-2339-z
Increasing accuracy and reducing costs of genomic prediction by marker selection
Massaine Bandeira e Sousa (2019)
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)
10.3389/fgene.2019.01224
GWAS-Assisted Genomic Prediction to Predict Resistance to Septoria Tritici Blotch in Nordic Winter Wheat at Seedling Stage
F. Odilbekov (2019)
10.1534/g3.117.300199
Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower
P. Thorwarth (2017)
10.1007/s00122-019-03276-6
Training population selection and use of fixed effects to optimize genomic predictions in a historical USA winter wheat panel
J. M. Sarinelli (2019)
10.1111/gcbb.12620
Genome‐wide association and genomic prediction for biomass yield in a genetically diverse Miscanthus sinensis germplasm panel phenotyped at five locations in Asia and North America
L. Clark (2019)
10.3390/plants9020275
Seed Weight as a Covariate in Association and Prediction Studies for Biomass Traits in Maize Seedlings
V. Galic (2020)
10.3389/fpls.2020.01001
Genomic Prediction of Agronomic Traits in Common Bean (Phaseolus vulgaris L.) Under Environmental Stress
B. Keller (2020)
10.1007/978-981-10-7461-5_24
Genomic Selection in Rice Breeding
Jennifer Spindel (2018)
10.3389/fpls.2020.00534
Genomic Prediction of Kernel Zinc Concentration in Multiple Maize Populations Using Genotyping-by-Sequencing and Repeat Amplification Sequencing Markers
Rui Guo (2020)
10.1007/13836_2020_78
Enhancing Crop Breeding Using Population Genomics Approaches
Ryan J. Andres (2020)
10.1038/s41437-020-0336-6
Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding
M. A. Scott (2020)
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