Cited by 162Genetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and AnimalsGenetic Associations and Epidemiology

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Kernel-based whole-genome prediction of complex traits: a review

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Morota, Gota, Gianola, Daniel·2014·Frontiers in Genetics

DOI 10.3389/fgene.2014.00363PMC PMC4199321

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Abstract

Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.

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RETRACTED ARTICLE: Detection and replication of epistasis influencing transcription in humans
Hemani, G., Shakhbazov, K., Westra, H.-J., Esko, T., Henders, A. K., McRae, A. F., Yang, J., Gibson, G. C., Martin, N. G., Metspalu, A., et al. (2014) · Nature · doi:10.1038/nature13005 · Cited by 186
Retracted
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001) · Genetics · doi:10.1093/genetics/157.4.1819 · Cited by 7,917
Efficient Methods to Compute Genomic Predictions
VanRaden, P. M. (2008) · Journal of Dairy Science · doi:10.3168/jds.2007-0980 · Cited by 6,050
An Introduction to Applied Geostatistics
Bilonick, R. A. (1991) · Technometrics · doi:10.1080/00401706.1991.10484886 · Cited by 4,447
Common SNPs explain a large proportion of the heritability for human height
Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2010) · Nature Genetics · doi:10.1038/ng.608 · Cited by 4,446
XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance.
Fisher, R. A. (1919) · Transactions of the Royal Society of Edinburgh · doi:10.1017/s0080456800012163 · Cited by 4,387
ANew Look at the Statistical Model Identification
AKAlKE, H. (2011) · Cited by 4,177
Best Linear Unbiased Estimation and Prediction under a Selection Model
Henderson, C. (1975) · Biometrics · doi:10.2307/2529430 · Cited by 2,513
Five Years of GWAS Discovery
Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.11.029 · Cited by 2,425
Interpolation of Spatial Data
Stein, M. L. (1999) · Springer series in statistics · doi:10.1007/978-1-4612-1494-6 · Cited by 2,358
Semiparametric Regression
Ruppert, D., WAND, M. P., & Carroll, R. J. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511755453 · Cited by 2,243
Coefficients of Inbreeding and Relationship
Wright, S. (1922) · The American Naturalist · doi:10.1086/279872 · Cited by 2,226
Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP
Endelman, J. B. (2011) · The Plant Genome · doi:10.3835/plantgenome2011.08.0024 · Cited by 2,094
Invited review: Genomic selection in dairy cattle: Progress and challenges
Hayes, B. J., Bowman, P. J., Chamberlain, A. J., & Goddard, M. E. (2009) · Journal of Dairy Science · doi:10.3168/jds.2008-1646 · Cited by 1,807
That BLUP is a Good Thing: The Estimation of Random Effects
Robinson, G. K. (1991) · Statistical Science · doi:10.1214/ss/1177011926 · Cited by 1,694
The inheritance of liability to certain diseases, estimated from the incidence among relatives
Falconer, D. (1965) · Annals of Human Genetics · doi:10.1111/j.1469-1809.1965.tb00500.x · Cited by 1,662
Efficiency of marker-assisted selection in the improvement of quantitative traits.
Lande, R., & Thompson, R. (1990) · Genetics · doi:10.1093/genetics/124.3.743 · Cited by 1,400
Invited Review: Reliability of genomic predictions for North American Holstein bulls
VanRaden, P. M., Tassell, C. P. V., Wiggans, G. R., Sonstegard, T. S., Schnabel, R. D., Taylor, J. F., & Schenkel, F. S. (2008) · Journal of Dairy Science · doi:10.3168/jds.2008-1514 · Cited by 1,336
An asymptotic equivalence of choice of model by cross validation and Akaike's criterion
Stone, M. (1977) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1977.tb01603.x · Cited by 1,286
Some results on Tchebycheffian spline functions
Kimeldorf, G., & Wahba, G. (1971) · Journal of Mathematical Analysis and Applications · doi:10.1016/0022-247x(71)90184-3 · Cited by 1,272
Genomic selection: prediction of accuracy and maximisation of long term response
Goddard, M. E. (2008) · Genetica · doi:10.1007/s10709-008-9308-0 · Cited by 1,094
Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction
Ozaki, K., Ohnishi, Y., Iida, A., Sekine, A., Yamada, R., Tsunoda, T., Sato, H., Sato, H., Hori, M., Nakamura, Y., et al. (2002) · Nature Genetics · doi:10.1038/ng1047 · Cited by 968
Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding
Campos, G. D. L., Hickey, J. M., Pong‐Wong, R., Daetwyler, H. D., & Calus, M. P. L. (2012) · Genetics · doi:10.1534/genetics.112.143313 · Cited by 928
The Impact of Genetic Architecture on Genome-Wide Evaluation Methods
Daetwyler, H. D., Pong‐Wong, R., Villanueva, B., & Woolliams, J. A. (2010) · Genetics · doi:10.1534/genetics.110.116855 · Cited by 838
Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers
Crossa, J. L., Campos, G. D. L., Pérez‐Rodríguez, P., Gianola, D., Burgueño, J. A., Araus, J. L., Makumbi, D., Singh, R. P., Dreisigacker, S., Yan, J., et al. (2010) · Genetics · doi:10.1534/genetics.110.118521 · Cited by 822
SIRE EVALUATION AND GENETIC TRENDS
Henderson, C. (1973) · Journal of Animal Science · doi:10.1093/ansci/1973.symposium.10 · Cited by 777
[Environment favorable to female physicians: introduction].
Kawashiro, N., Iino, Y., Kudo, F., & Suzuka, Y. (2010) · PubMed · doi:10.3950/jibiinkoka.113.719 · Cited by 776
Prediction of individual genetic risk to disease from genome-wide association studiesPreprint
Wray, N. R., Goddard, M. E., & Visscher, P. M. (2007) · Genome Research · doi:10.1101/gr.6665407 · Cited by 737
Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach
Daetwyler, H. D., Villanueva, B., & Woolliams, J. A. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003395 · Cited by 735
Genomic Selection in Plant Breeding: A Comparison of Models
Heslot, N., Yang, H., Sorrells, M. E., & Jannink, J. (2011) · Crop Science · doi:10.2135/cropsci2011.06.0297 · Cited by 732
SYSTEMS OF MATING. I. THE BIOMETRIC RELATIONS BETWEEN PARENT AND OFFSPRING
Wright, S. (1921) · Genetics · doi:10.1093/genetics/6.2.111 · Cited by 717
A reaction norm model for genomic selection using high-dimensional genomic and environmental data
Jarquín, D., Crossa, J., Lacaze, X., Du Cheyron, P., Daucourt, J., Lorgeou, J., Piraux, F., Guerreiro, L., Pérez, P., Calus, M., et al. (2014) · Theoretical and Applied Genetics · doi:10.1007/s00122-013-2243-1 · Cited by 687
Les mathématiques de l'hérédité
Malécot, G. (1948) · Masson eBooks · Cited by 669
Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels
Erbe, M., Hayes, B. J., Matukumalli, L. K., Goswami, S., Bowman, P. J., Reich, C. M., Mason, B., & Goddard, M. E. (2012) · Journal of Dairy Science · doi:10.3168/jds.2011-5019 · Cited by 661
Genomic and metabolic prediction of complex heterotic traits in hybrid maize
Riedelsheimer, C., Czedik‐Eysenberg, A., Grieder, C., Lisec, J., Technow, F., Sulpice, R., Altmann, T., Stitt, M., Willmitzer, L., & Melchinger, A. E. (2012) · Nature Genetics · doi:10.1038/ng.1033 · Cited by 629
Systems genetics approaches to understand complex traits
Civelek, M., & Lusis, A. J. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3575 · Cited by 621
Powerful SNP-Set Analysis for Case-Control Genome-wide Association Studies
Wu, M. C., Kraft, P. L., Epstein, M. P., Taylor, D. M., Chanock, S. J., Hunter, D. J., & Lin, X. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.05.002 · Cited by 597
Marker assisted selection using best linear unbiased prediction
Fernando, R. L., & Grossman, M. B. (1989) · Genetics Selection Evolution · doi:10.1186/1297-9686-21-4-467 · Cited by 522
Theory and Analysis of Threshold Characters
Gianola, D. (1982) · Journal of Animal Science · doi:10.2527/jas1982.5451079x · Cited by 507
Self-citation
Additive Genetic Variability and the Bayesian Alphabet
Daniel Gianola, B., Campos, G. D. L., Hill, W. G., Manfredi, E., & Fernando, R. L. (2009) · Genetics · doi:10.1534/genetics.109.103952 · Cited by 492
AN ANALYSIS OF VARIABILITY IN NUMBER OF DIGITS IN AN INBRED STRAIN OF GUINEA PIGS
Wright, S. (1934) · Genetics · doi:10.1093/genetics/19.6.506 · Cited by 476
Priors in whole-genome regression: the Bayesian alphabet returns
Gianola, D. (2013) · Genetics · doi:10.1534/genetics.113.151753 · Cited by 457
Genomic prediction in CIMMYT maize and wheat breeding programs
Crossa, J. L., Pérez‐Rodríguez, P., Hickey, J. M., Burgueño, J. A., Ornella, L., Cerón‐Rojas, J. J., Zhang, X., Dreisigacker, S., Babu, R., Li, Y., et al. (2013) · Heredity · doi:10.1038/hdy.2013.16 · Cited by 457
Reproducing Kernel Hilbert Spaces Regression Methods for Genomic Assisted Prediction of Quantitative Traits
Gianola, D., & van Kaam, J. B. C. H. M. (2008) · Genetics · doi:10.1534/genetics.107.084285 · Cited by 457
Genomic-Assisted Prediction of Genetic Value With Semiparametric Procedures
Gianola, D., Fernando, R. L., & Stella, A. (2006) · Genetics · doi:10.1534/genetics.105.049510 · Cited by 450
Factors Affecting Accuracy From Genomic Selection in Populations Derived From Multiple Inbred Lines: A Barley Case Study
Zhong, S., Dekkers, J. C. M., Fernando, R. L., & Jannink, J. (2009) · Genetics · doi:10.1534/genetics.108.098277 · Cited by 421
Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions
Heslot, N., Akdemir, D., Sorrells, M. E., & Jannink, J.-L. (2014) · Theoretical and Applied Genetics · doi:10.1007/s00122-013-2231-5 · Cited by 409
On the Additive and Dominant Variance and Covariance of Individuals Within the Genomic Selection Scope
Vitezica, Z. G., Varona, L., & Legarra, A. (2013) · Genetics · doi:10.1534/genetics.113.155176 · Cited by 400
Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods
DE LOS CAMPOS, G., GIANOLA, D., ROSA, G. J. M., WEIGEL, K. A., & CROSSA, J. (2010) · Genetics Research · doi:10.1017/s0016672310000285 · Cited by 361
Self-citation
Using the genomic relationship matrix to predict the accuracy of genomic selection
Goddard, M. E., Hayes, B. J., & Meuwissen, T. H. E. (2011) · Journal of Animal Breeding and Genetics · doi:10.1111/j.1439-0388.2011.00964.x · Cited by 359
Bias and Sampling Error of the Estimated Proportion of Genotypic Variance Explained by Quantitative Trait Loci Determined From Experimental Data in Maize Using Cross Validation and Validation With Independent Samples
Utz, H. F., Melchinger, A. E., & Schön, C. (2000) · Genetics · doi:10.1093/genetics/154.4.1839 · Cited by 359
Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers
Su, G., Christensen, O. F., Ostersen, T., Henryon, M. A., & Lund, M. S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045293 · Cited by 358
Accelerating Improvement of Livestock with Genomic Selection
Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2013) · Annual Review of Animal Biosciences · doi:10.1146/annurev-animal-031412-103705 · Cited by 357
Statistical Learning from a Regression Perspective
Berk, R. A. (2016) · Springer texts in statistics · doi:10.1007/978-3-319-44048-4 · Cited by 356
Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor
Campos, G. D. L., Vazquez, A. I., Fernando, R. L., Klimentidis, Y. C., & Sørensen, D. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003608 · Cited by 352
Genomic BLUP Decoded: A Look into the Black Box of Genomic Prediction
Habier, D., Fernando, R. L., & Garrick, D. J. (2013) · Genetics · doi:10.1534/genetics.113.152207 · Cited by 343
Proceedings of the 5th World Congress on Genetics Applied to Livestock Production
Keightley, P. D., Heath, S., Hardge, T., May, L. D. S., Archibald, A. L., & Bulfield, G. (1994) · Cited by 297
Genomic Prediction in Maize Breeding Populations with Genotyping-by-Sequencing
Crossa, J. L., Beyene, Y., Semagn, K., Pérez‐Rodríguez, P., Hickey, J. M., Chen, C., Campos, G. D. L., Burgueño, J. A., Windhausen, V. S., Buckler, E. S., et al. (2013) · G3 Genes Genomes Genetics · doi:10.1534/g3.113.008227 · Cited by 274
Ridge Regression and Extensions for Genomewide Selection in Maize
Piepho, H. (2009) · Crop Science · doi:10.2135/cropsci2008.10.0595 · Cited by 273
Beyond Missing Heritability: Prediction of Complex Traits
Makowsky, R., Pajewski, N. M., Klimentidis, Y. C., Vazquez, A. I., Duarte, C. W., Allison, D. B., & Campos, G. D. L. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002051 · Cited by 260
Systems of Mating
Templeton, A. R. (2006) · Wiley eBooks · doi:10.1002/0470047356.ch3 · Cited by 258
Effect of total allelic relationship on accuracy of evaluation and response to selection.
Nejati‐Javaremi, A., Smith, C. D., & Gibson, J. P. (1997) · Journal of Animal Science · doi:10.2527/1997.7571738x · Cited by 251
Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat
Pérez‐Rodríguez, P., Gianola, D., González‐Camacho, J. M., Crossa, J. L., Manès, Y., & Dreisigacker, S. (2012) · G3 Genes Genomes Genetics · doi:10.1534/g3.112.003665 · Cited by 233
A Powerful and Flexible Multilocus Association Test for Quantitative Traits
Kwee, L. C., Liu, D., Lin, X., Ghosh, D., & Epstein, M. P. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2007.10.010 · Cited by 227
Parametric and Nonparametric Statistical Methods for Genomic Selection of Traits with Additive and Epistatic Genetic Architectures
Howard, R., Carriquiry, A. L., & Beavis, W. D. (2014) · G3 Genes Genomes Genetics · doi:10.1534/g3.114.010298 · Cited by 226
Genome-enabled prediction of genetic values using radial basis function neural networks
González‐Camacho, J. M., Campos, G. D. L., Pérez‐Rodríguez, P., Gianola, D., Cairns, J. E., Mahuku, G., Babu, R., & Crossa, J. L. (2012) · Theoretical and Applied Genetics · doi:10.1007/s00122-012-1868-9 · Cited by 216
Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation
Saatchi, M., McClure, M. C., McKay, S., Rolf, M. M., Kim, J. W., Decker, J. E., Taxis, T. M., Chapple, R. H., Ramey, H. R., Northcutt, S. L., et al. (2011) · Genetics Selection Evolution · doi:10.1186/1297-9686-43-40 · Cited by 213
Best Linear Unbiased Prediction of Nonadditive Genetic Merits in Noninbred Populations
Henderson, C. (1985) · Journal of Animal Science · doi:10.2527/jas1985.601111x · Cited by 210
Genomic breeding value prediction: methods and procedures
Calus, M. P. L. (2009) · animal · doi:10.1017/s1751731109991352 · Cited by 208
A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers
Moser, G., Tier, B., Crump, R. E., Khatkar, M. S., & Raadsma, H. W. (2009) · Genetics Selection Evolution · doi:10.1186/1297-9686-41-56 · Cited by 201
Reproducing kernel Hilbert spaces regression: A general framework for genetic evaluation1
Campos, G. D. L., Daniel Gianola, B., & Rosa, G. J. M. (2009) · Journal of Animal Science · doi:10.2527/jas.2008-1259 · Cited by 194
Self-citation
Generalized Genomic Distance–Based Regression Methodology for Multilocus Association Analysis
Wessel, J., & Schork, N. J. (2006) · The American Journal of Human Genetics · doi:10.1086/508346 · Cited by 190
WHY EPISTASIS IS IMPORTANT FOR SELECTION AND ADAPTATION
Hansen, T. F. (2013) · Evolution · doi:10.1111/evo.12214 · Cited by 189
Genomewide predictions from maize single-cross data
Massman, J. M., Gordillo, A., Lorenzana, R. E., & Bernardo, R. (2012) · Theoretical and Applied Genetics · doi:10.1007/s00122-012-1955-y · Cited by 151
Best Linear Unbiased Prediction of Breeding Values Not in the Model for Records
Henderson, C. (1977) · Journal of Dairy Science · doi:10.3168/jds.s0022-0302(77)83935-0 · Cited by 137
On Marker-Assisted Prediction of Genetic Value: Beyond the Ridge
Gianola, D., Miguel Pérez-enciso, A., & Toro, M. Á. (2003) · Genetics · doi:10.1093/genetics/163.1.347 · Cited by 121
Nonparametric Methods for Incorporating Genomic Information Into Genetic Evaluations: An Application to Mortality in Broilers
González-Recio, Ó., Daniel Gianola, B., Long, N., WEIGEL, K. A., Rosa, G. J. M., & Avendaño, S. (2008) · Genetics · doi:10.1534/genetics.107.084293 · Cited by 121
Application of support vector regression to genome-assisted prediction of quantitative traits
Long, N., Daniel Gianola, B., Rosa, G. J. M., & WEIGEL, K. A. (2011) · Theoretical and Applied Genetics · doi:10.1007/s00122-011-1648-y · Cited by 121
Mixed Model Methods for Genomic Prediction and Variance Component Estimation of Additive and Dominance Effects Using SNP Markers
Da, Y., Wang, C., Wang, S., & Hu, G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087666 · Cited by 120
MENDEL'S LAWS AND THEIR PROBABLE RELATIONS TO INTRA‐RACIAL HEREDITY.
Yule, G. U. (1902) · New Phytologist · doi:10.1111/j.1469-8137.1902.tb07336.x · Cited by 103
Comparison of whole-genome prediction models for traits with contrasting genetic architecture in a diversity panel of maize inbred lines
Riedelsheimer, C., Technow, F., & Melchinger, A. E. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-452 · Cited by 94
Multivariate Phenotype Association Analysis by Marker‐Set Kernel Machine Regression
Maity, A., Sullivan, P. F., & Tzeng, J. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21663 · Cited by 87
Congruence of Additive and Non-Additive Effects on Gene Expression Estimated from Pedigree and SNP Data
Powell, J. E., Henders, A. K., McRae, A. F., Kim, J., Hemani, G., Martin, N. G., Dermitzakis, E. T., Gibson, G. C., Montgomery, G. W., & Visscher, P. M. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003502 · Cited by 87
Benefits from marker-assisted selection under an additive polygenic genetic model1
Villanueva, B., Pong‐Wong, R., Fernández, J., & Toro, M. Á. (2005) · Journal of Animal Science · doi:10.2527/2005.8381747x · Cited by 86
Genomic-enabled prediction with classification algorithms
Ornella, L., Pérez‐Rodríguez, P., Tapia, E., González‐Camacho, J. M., Burgueño, J. A., Zhang, X., Singh, S., Vicente, F. S., Bonnett, D. G., Dreisigacker, S., et al. (2014) · Heredity · doi:10.1038/hdy.2013.144 · Cited by 82
A note on the rationale for estimating genealogical coancestry from molecular markers
Toro, M. Á., García‐Cortés, L. A., & Legarra, A. (2011) · Genetics Selection Evolution · doi:10.1186/1297-9686-43-27 · Cited by 70
Statistical Learning from a Regression Perspective
Maindonald, J. H. (2009) · Journal of Statistical Software · doi:10.18637/jss.v029.b12 · Cited by 69
Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens
González-Recio, Ó., Daniel Gianola, B., Rosa, G. J. M., WEIGEL, K. A., & Kranis, A. (2009) · Genetics Selection Evolution · doi:10.1186/1297-9686-41-3 · Cited by 66
Why Breeding Values Estimated Using Familial Data Should Not Be Used for Genome-Wide Association Studies
Ekine-Dzivenu, C. C., Rowe, S. J., Bishop, S. C., & de Koning, D. (2013) · G3 Genes Genomes Genetics · doi:10.1534/g3.113.008706 · Cited by 61
The missing story behind Genome Wide Association Studies: single nucleotide polymorphisms in gene deserts have a story to tell
Schierding, W. S., Cutfield, W. S., & O'Sullivan, J. M. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00039 · Cited by 60
A Function Accounting for Training Set Size and Marker Density to Model the Average Accuracy of Genomic Prediction
Erbe, M., Gredler, B., Seefried, F. R., Bapst, B., & Simianer, H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081046 · Cited by 59
Predicting Genetic Values: A Kernel-Based Best Linear Unbiased Prediction With Genomic Data
Ober, U., Erbe, M., Long, N., Porcu, E., Schlather, M., & Simianer, H. (2011) · Genetics · doi:10.1534/genetics.111.128694 · Cited by 57
Radial basis function regression methods for predicting quantitative traits using SNP markers
Long, N., Gianola, D., Rosa, G. J. M., WEIGEL, K. A., Kranis, A., & González-Recio, Ó. (2010) · Genetics Research · doi:10.1017/s0016672310000157 · Cited by 55
Poly-Omic Prediction of Complex Traits: OmicKriging
Wheeler, H. E., Aquino-Michaels, K., Gamazon, E. R., Trubetskoy, V., Dolan, M. E., Huang, R. S., Cox, N. J., & Im, H. K. (2014) · Genetic Epidemiology · doi:10.1002/gepi.21808 · Cited by 51
Enhancing Genome-Enabled Prediction by Bagging Genomic BLUP
Daniel Gianola, B., WEIGEL, K. A., Krämer, N., Stella, A., & Schön, C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0091693 · Cited by 47
Genome-enabled prediction of quantitative traits in chickens using genomic annotation
Morota, G., Abdollahi-Arpanahi, R., Kranis, A., & Gianola, D. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-109 · Cited by 45
Self-citation
Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data
Morota, G., Koyama, M., Rosa, G. J. M., WEIGEL, K. A., & Gianola, D. (2013) · Genetics Selection Evolution · doi:10.1186/1297-9686-45-17 · Cited by 44
Self-citation
What was Fisher’s fundamental theorem of natural selection and what was it for?
Plutynski, A. (2006) · Studies in History and Philosophy of Science Part C Studies in History and Philosophy of Biological and Biomedical Sciences · doi:10.1016/j.shpsc.2005.12.004 · Cited by 42
Genome-enabled methods for predicting litter size in pigs: a comparison
Tusell, L., Pérez‐Rodríguez, P., Forni, S., Wu, X. ‐., & Gianola, D. (2013) · animal · doi:10.1017/s1751731113001389 · Cited by 41
Cross-validation in association mapping and its relevance for the estimation of QTL parameters of complex traits
Würschum, T., & Kraft, T. (2013) · Heredity · doi:10.1038/hdy.2013.126 · Cited by 41
Accuracy of Genome-Enabled Prediction in a Dairy Cattle Population using Different Cross-Validation Layouts
Pérez-Cabal, M. Á., Vazquez, A. I., Daniel Gianola, B., Rosa, G. J. M., & WEIGEL, K. A. (2012) · Frontiers in Genetics · doi:10.3389/fgene.2012.00027 · Cited by 41
P-values in genomics: Apparent precision masks high uncertainty
Lazzeroni, L. C., Lü, Y., & Belitskaya‐Lévy, I. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.184 · Cited by 36
Kernel-based variance component estimation and whole-genome prediction of pre-corrected phenotypes and progeny tests for dairy cow health traits
Morota, G., Boddhireddy, P., Vukasinovic, N., Gianola, D., & DeNise, S. K. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00056 · Cited by 33
Self-citation
Accuracy of direct genomic values for functional traits in Brown Swiss cattle
Kramer, M., Erbe, M., Seefried, F. R., Gredler, B., Bapst, B., Bieber, A., & Simianer, H. (2014) · Journal of Dairy Science · doi:10.3168/jds.2013-7054 · Cited by 27
Toyama Kametaro and Vernon Kellogg: Silkworm Inheritance Experiments in Japan, Siam, and the United States, 1900–1912
Onaga, L. (2010) · Journal of the History of Biology · doi:10.1007/s10739-010-9222-z · Cited by 26
Combining Genomic and Genealogical Information in a Reproducing Kernel Hilbert Spaces Regression Model for Genome-Enabled Predictions in Dairy Cattle
Rodríguez‐Ramilo, S. T., García‐Cortés, L. A., & González-Recio, Ó. (2014) · PLoS ONE · doi:10.1371/journal.pone.0093424 · Cited by 24
Model averaging for genome‐enabled prediction with reproducing kernel H ilbert spaces: a case study with pig litter size and wheat yield
Tusell, L., Pérez‐Rodríguez, P., Forni, S., & Gianola, D. (2014) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12070 · Cited by 21
Nonparametric Method for Genomics-Based Prediction of Performance of Quantitative Traits Involving Epistasis in Plant Breeding
Sun, X., Ma, P., & Mumm, R. H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0050604 · Cited by 20
Genome-wide selection by mixed model ridge regression and extensions based on geostatistical models
Schulz‐Streeck, T., & Piepho, H. (2010) · BMC Proceedings · doi:10.1186/1753-6561-4-s1-s8 · Cited by 20
Untitled work
(2014) · Proceedings of the World Congress on Genetics Applied to Livestock Production · Cited by 18
Self-citation
Statistical learning methods for genome‐based analysis of quantitative traits
Daniel Gianola, B. (2010) · Cited by 18
The limits of p-values for biological data mining
Malley, J. D., Dasgupta, A., & Moore, J. H. (2013) · BioData Mining · doi:10.1186/1756-0381-6-10 · Cited by 17
Efficiency of marker assisted selection
Visscher, P. M., Mackinnon, M. J., & Haley, C. S. (1997) · Animal Biotechnology · doi:10.1080/10495399709525872 · Cited by 15
A Bayesian Mixed Regression Based Prediction of Quantitative Traits from Molecular Marker and Gene Expression Data
Bhattacharjee, M., & Sillanpää, M. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0026959 · Cited by 8
Nonlinear dimension reduction with Wright–Fisher kernel for genotype aggregation and association mapping
Zhu, H., Li, L., & Zhou, H. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts406 · Cited by 4

Research works printed but not linked: 2

Harville, 1983, Discussion on A Section on Interpolation and Estimation. in Statistics an Appraisal

Sorted by citations

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Modeling Epistasis in Genomic Selection
Jiang, Y., & Reif, J. C. (2015) · Genetics · doi:10.1534/genetics.115.177907 · Cited by 271
BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture1
Morota, G., Ventura, R. V., Silva, F. F. E., Koyama, M., & Fernando, S. C. (2018) · Journal of Animal Science · doi:10.1093/jas/sky014 · Cited by 245
Self-citation
Non-additive Effects in Genomic Selection
Varona, L., Legarra, A., Toro, M. Á., & Vitezica, Z. G. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00078 · Cited by 242
Genomic and environmental determinants and their interplay underlying phenotypic plasticity
Li, X., Guo, T., Mu, Q., Li, X., & Yu, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1718326115 · Cited by 256

See all 162

Authors: Morota, G., & Gianola, D.
Affiliations: University of Nebraska–Lincoln (US)
Corresponding author: Morota, Gota
ORCID: Morota, Gota
Venue: Frontiers in Genetics
Publisher: Frontiers Media SA
Published: 2014-10-16
Volume: 5
Pages: 363-363
ISSN: 1664-8021 (ISSN-L)
DOI: 10.3389/fgene.2014.00363
PMC: PMC4199321
OpenAlex: W2061187129
Cited by: 162
References: 119
Citations by year: 2026: 7 · 2025: 11 · 2024: 13 · 2023: 14 · 2022: 16 · 2021: 22 · 2020: 14 · 2019: 20 · 2018: 12 · 2017: 11 · 2016: 11 · 2015: 10
FWCI: 1.41
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: Frontiers in Genetics (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Lincoln (University of Nebraska) (submittedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords: kernel methods, whole-genome prediction, complex traits, non-additive genetic effects, epistasis, quantitative genetics, animal breeding, plant breeding, molecular markers, genome annotation, predictive performance, high-dimensional genomic data

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Kernel-based whole-genome prediction of complex traits: a review

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