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

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 · see citing works

A Coefficient of Agreement for Nominal Scales

Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398 · see citing works

Gaussian Processes for Machine Learning

Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705 · see citing works

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 · see citing works

Efficient Methods to Compute Genomic Predictions

VanRaden, P. M. (2008) · Journal of Dairy Science · doi:10.3168/jds.2007-0980 · Cited by 6,050 · see citing works

An Introduction to Applied Geostatistics

Bilonick, R. A. (1991) · Technometrics · doi:10.1080/00401706.1991.10484886 · Cited by 4,447 · see citing works

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 · see citing works

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 · see citing works

ANew Look at the Statistical Model Identification

AKAlKE, H. (2011) · Cited by 4,177 · see citing works

Best Linear Unbiased Estimation and Prediction under a Selection Model

Henderson, C. (1975) · Biometrics · doi:10.2307/2529430 · Cited by 2,513 · see citing works

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 · see citing works

Interpolation of Spatial Data

Stein, M. L. (1999) · Springer series in statistics · doi:10.1007/978-1-4612-1494-6 · Cited by 2,358 · see citing works

Coefficients of Inbreeding and Relationship

Wright, S. (1922) · The American Naturalist · doi:10.1086/279872 · Cited by 2,307 · see citing works

Semiparametric Regression

Ruppert, D., WAND, M. P., & Carroll, R. J. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511755453 · Cited by 2,153 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 1,415 · see citing works

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 · see citing works

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 · see citing works

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 1,268 · see citing works

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 · see citing works

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 940 · see citing works

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 898 · see citing works

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 803 · see citing works

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., Daniel Gianola, B., 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 801 · see citing works

[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 · see citing works

SYSTEMS OF MATING. I. THE BIOMETRIC RELATIONS BETWEEN PARENT AND OFFSPRING

Wright, S. (1921) · Genetics · doi:10.1093/genetics/6.2.111 · Cited by 722 · see citing works

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 721 · see citing works

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 714 · see citing works

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 695 · see citing works

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 · see citing works

Les mathématiques de l'hérédité

Malécot, G. (1948) · Masson eBooks · Cited by 669 · see citing works

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 650 · see citing works

SIRE EVALUATION AND GENETIC TRENDS

Henderson, C. (1973) · Journal of Animal Science · doi:10.1093/ansci/1973.symposium.10 · Cited by 648 · see citing works

Systems genetics approaches to understand complex traits

Civelek, M., & Lusis, A. J. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3575 · Cited by 621 · see citing works

Genomic and metabolic prediction of complex heterotic traits in hybrid maize

Riedelsheimer, C., Czedik‐Eysenberg, A., Grieder, C., Lisec, J., Technow, F., Ronan Sulpice, A., Altmann, T., Stitt, M., Willmitzer, L., & Melchinger, A. E. (2012) · Nature Genetics · doi:10.1038/ng.1033 · Cited by 605 · see citing works

Powerful SNP-Set Analysis for Case-Control Genome-wide Association Studies

Wu, M. C., Kraft, P., 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 589 · see citing works

Theory and Analysis of Threshold Characters

Gianola, D. (1982) · Journal of Animal Science · doi:10.2527/jas1982.5451079x · Cited by 507 · see citing works

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 · see citing works

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 492 · see citing works

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 · see citing works

Priors in whole-genome regression: the Bayesian alphabet returns

Gianola, D. (2013) · Genetics · doi:10.1534/genetics.113.151753 · Cited by 457 · see citing works

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 · see citing works

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 455 · see citing works

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 · see citing works

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 423 · see citing works

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 · see citing works

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 395 · see citing works

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 364 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 349 · see citing works

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 343 · see citing works

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 · see citing works

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 · see citing works

Ridge Regression and Extensions for Genomewide Selection in Maize

Piepho, H. (2009) · Crop Science · doi:10.2135/cropsci2008.10.0595 · Cited by 273 · see citing works

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 265 · see citing works

Systems of Mating

Templeton, A. R. (2006) · doi:10.1002/0470047356.ch3 · Cited by 259 · see citing works

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 253 · see citing works

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 · see citing works

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 · see citing works

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

Pérez‐Rodríguez, P., Daniel Gianola, B., González‐Camacho, J. M., Crossa, J. L., Manès, Y., & Dreisigacker, S. (2012) · G3 Genes Genomes Genetics · doi:10.1534/g3.112.003665 · Cited by 226 · see citing works

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 216 · see citing works

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 · see citing works

Genome-enabled prediction of genetic values using radial basis function neural networks

González‐Camacho, J. M., Campos, G. D. L., Pérez‐Rodríguez, P., Daniel Gianola, B., Cairns, J. E., Mahuku, G., Babu, R., & Crossa, J. L. (2012) · Theoretical and Applied Genetics · doi:10.1007/s00122-012-1868-9 · Cited by 210 · see citing works

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 · see citing works

Genomic breeding value prediction: methods and procedures

Calus, M. P. L. (2009) · animal · doi:10.1017/s1751731109991352 · Cited by 200 · see citing works

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 · see citing works

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 194 · see citing works

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 · see citing works

WHY EPISTASIS IS IMPORTANT FOR SELECTION AND ADAPTATION

Hansen, T. F. (2013) · Evolution · doi:10.1111/evo.12214 · Cited by 186 · see citing works

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 · see citing works

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 · see citing works

On Marker-Assisted Prediction of Genetic Value: Beyond the Ridge

Daniel Gianola, B., Miguel Pérez-enciso, A., & Toro, M. Á. (2003) · Genetics · doi:10.1093/genetics/163.1.347 · Cited by 121 · see citing works

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 · see citing works

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 · see citing works

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 117 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 84 · see citing works

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 79 · see citing works

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 · see citing works

Statistical Learning from a Regression Perspective

Maindonald, J. H. (2009) · Journal of Statistical Software · doi:10.18637/jss.v029.b12 · Cited by 69 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Radial basis function regression methods for predicting quantitative traits using SNP markers

Long, N., Daniel Gianola, B., Rosa, G. J. M., WEIGEL, K. A., Kranis, A., & González-Recio, Ó. (2010) · Genetics Research · doi:10.1017/s0016672310000157 · Cited by 56 · see citing works

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 56 · see citing works

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 · see citing works

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 47 · see citing works

Genome-enabled prediction of quantitative traits in chickens using genomic annotation

Morota, G., Abdollahi-Arpanahi, R., Kranis, A., & Daniel Gianola, B. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-109 · Cited by 42 · see citing works

Genome-enabled methods for predicting litter size in pigs: a comparison

Tusell, L., Pérez‐Rodríguez, P., Forni, S., Wu, X. ‐., & Daniel Gianola, B. (2013) · animal · doi:10.1017/s1751731113001389 · Cited by 42 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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., & Daniel Gianola, B. (2013) · Genetics Selection Evolution · doi:10.1186/1297-9686-45-17 · Cited by 41 · see citing works

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 · see citing works

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., Daniel Gianola, B., & DeNise, S. K. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00056 · Cited by 30 · see citing works

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 · see citing works

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 · see citing works

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 23 · see citing works

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., & Daniel Gianola, B. (2014) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12070 · Cited by 21 · see citing works

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 · see citing works

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 · see citing works

Untitled work

(2014) · Proceedings of the World Congress on Genetics Applied to Livestock Production · Cited by 18 · see citing works

Statistical learning methods for genome‐based analysis of quantitative traits

Daniel Gianola, B. (2010) · Cited by 18 · see citing works

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 · see citing works

Efficiency of marker assisted selection

Visscher, P. M., Mackinnon, M. J., & Haley, C. S. (1997) · Animal Biotechnology · doi:10.1080/10495399709525872 · Cited by 15 · see citing works

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 · see citing works

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 · see citing works

Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants

Xu, Y., Liu, X., Fu, J., Wang, H., Wang, J., Huang, C., Prasanna, B. M., Olsen, M. S., Wang, G., & Zhang, A. (2019) · Plant Communications · doi:10.1016/j.xplc.2019.100005 · Cited by 297 · see citing works

Modeling Epistasis in Genomic Selection

Jiang, Y., & Reif, J. C. (2015) · Genetics · doi:10.1534/genetics.115.177907 · Cited by 264 · see citing works

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 · see citing works

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 · see citing works

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 241 · see citing works

Genomic prediction contributing to a promising global strategy to turbocharge gene banks

Yu, X., Li, X., Guo, T., Zhu, C., Wu, Y., Mitchell, S. E., Roozeboom, K. L., Wang, D., Wang, M. L., PEDERSON, G. A., et al. (2016) · Nature Plants · doi:10.1038/nplants.2016.150 · Cited by 239 · see citing works

Metabolomic selection for enhanced fruit flavor

Colantonio, V., Ferrão, L. F. V., Tieman, D. M., Bliznyuk, N., Sims, C. A., Klee, H. J., Muńoz, P., & Resende, M. F. R. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2115865119 · Cited by 219 · see citing works

Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models

Cuevas, J., Crossa, J., Soberanis, V., Pérez‐Elizalde, S., Pérez‐Rodríguez, P., Campos, G. D. L., Montesinos‐López, O. A., & Burgueño, J. (2016) · The Plant Genome · doi:10.3835/plantgenome2016.03.0024 · Cited by 181 · see citing works

Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials

Costa-Neto, G., Fritsche-Neto, R., & Crossa, J. (2021) · Heredity · doi:10.1038/s41437-020-00353-1 · Cited by 153 · see citing works

Genome-wide association study and genomic prediction in citrus: Potential of genomics-assisted breeding for fruit quality traits

Minamikawa, M. F., Nonaka, K., Kaminuma, E., Kajiya‐Kanegae, H., Onogi, A., Goto, S., Yoshioka, T., Imai, A., Hamada, H., Hayashi, T., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-05100-x · Cited by 160 · see citing works

Genomic selection in a commercial winter wheat population

He, S., Schulthess, A. W., Mirdita, V., Zhao, Y., Korzun, V. N., Bothe, R., Ebmeyer, E., Reif, J. C., & Jiang, Y. (2016) · Theoretical and Applied Genetics · doi:10.1007/s00122-015-2655-1 · Cited by 155 · see citing works

Prospects for Genomic Selection in Cassava Breeding

Wolfe, M. D., Carpio, D. P. D., Alabi, O., Ezenwaka, L., Ikeogu, U. N., Kayondo, S. I., Lozano, R. D., Okeke, U. G., Ozimati, A., Esuma, W., et al. (2017) · The Plant Genome · doi:10.3835/plantgenome2017.03.0015 · Cited by 147 · see citing works

The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics

de Vlaming, R., & Groenen, P. J. F. (2015) · BioMed Research International · doi:10.1155/2015/143712 · Cited by 113 · see citing works

Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials

Cuevas, J., Montesinos-López, O., Juliana, P., Guzmán, C., Pérez-Rodríguez, P., González-Bucio, J., Burgueño, J., Montesinos-López, A., & Crossa, J. (2019) · G3 Genes|Genomes|Genetics · doi:10.1534/g3.119.400493 · Cited by 97 · see citing works

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

Sousa, M. B. E., Cuevas, J., Couto, E. G. D. O., Pérez‐Rodríguez, P., Jarquín, D., Fritsche‐Neto, R., Burgueño, J. A., & Crossa, J. L. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.117.042341 · Cited by 105 · see citing works

Comparing estimates of genetic variance across different relationship models

Legarra, A. (2015) · Theoretical Population Biology · doi:10.1016/j.tpb.2015.08.005 · Cited by 94 · see citing works

Predicting Growth and Carcass Traits in Swine Using Microbiome Data and Machine Learning Algorithms

Maltecca, C., Lu, D., Schillebeeckx, C., McNulty, N. P., Schwab, C. R., Shull, C. M., & Tiezzi, F. (2019) · Scientific Reports · doi:10.1038/s41598-019-43031-x · Cited by 90 · see citing works

Optimal Designs for Genomic Selection in Hybrid Crops

Guo, T., Yu, X., Li, X., Zhang, H., Zhu, C., Flint-Garcia, S. A., McMullen, M. D., Holland, J. B., Szalma, S. J., Wisser, R. J., et al. (2019) · Molecular Plant · doi:10.1016/j.molp.2018.12.022 · Cited by 89 · see citing works

A review of machine learning models applied to genomic prediction in animal breeding

Chafai, N., Hayah, I., Houaga, I., & Badaoui, B. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1150596 · Cited by 96 · see citing works

Genome-wide association mapping and genomic prediction for CBSD resistance in Manihot esculenta

Kayondo, S. I., Carpio, D. P. D., Lozano, R. D., Ozimati, A., Wolfe, M. D., Baguma, Y., Gracen, V., Offei, S. K., Ferguson, M. E., Kawuki, R., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-19696-1 · Cited by 86 · see citing works

Potential and limits of whole genome prediction of resistance to Fusarium head blight and Septoria tritici blotch in a vast Central European elite winter wheat population

Mirdita, V., He, S., Zhao, Y., Korzun, V. N., Bothe, R., Ebmeyer, E., Reif, J. C., & Jiang, Y. (2015) · Theoretical and Applied Genetics · doi:10.1007/s00122-015-2602-1 · Cited by 83 · see citing works

Epistasis and covariance: how gene interaction translates into genomic relationship

Martini, J. W. R., Wimmer, V., Erbe, M., & Simianer, H. (2016) · Theoretical and Applied Genetics · doi:10.1007/s00122-016-2675-5 · Cited by 82 · see citing works

Deep Kernel and Deep Learning for Genome-Based Prediction of Single Traits in Multienvironment Breeding Trials

Crossa, J., Martini, J. W. R., Gianola, D., Pérez-Rodríguez, P., Jarquin, D., Juliana, P., Montesinos-López, O., & Cuevas, J. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01168 · Cited by 79 · see citing works

Prediction of Plant Height in Arabidopsis thaliana Using DNA Methylation Data

Hu, Y., Morota, G., Rosa, G. J. M., & Daniel Gianola, B. (2015) · Genetics · doi:10.1534/genetics.115.177204 · Cited by 79 · see citing works

On the Use of the Pearson Correlation Coefficient for Model Evaluation in Genome-Wide Prediction

Waldmann, P. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00899 · Cited by 74 · see citing works

Genomic selection needs to be carefully assessed to meet specific requirements in livestock breeding programs

Jonas, E., & de Koning, D. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00049 · Cited by 71 · see citing works

Predictive ability of genome-assisted statistical models under various forms of gene action

Momen, M. M., Mehrgardi, A. A., Sheikhi, A., Kranis, A., Tusell, L., Morota, G., Rosa, G. J. M., & Daniel Gianola, B. (2018) · Scientific Reports · doi:10.1038/s41598-018-30089-2 · Cited by 68 · see citing works

Multi-trait genomic prediction for nitrogen response indices in tropical maize hybrids

Lyra, D. H., Mendonça, L. D. F., Galli, G., Alves, F. C., Granato, Í. S. C., & Fritsche‐Neto, R. (2017) · Molecular Breeding · doi:10.1007/s11032-017-0681-1 · Cited by 66 · see citing works

Predicting bull fertility using genomic data and biological information

Abdollahi-Arpanahi, R., Morota, G., & Peñagaricano, F. (2017) · Journal of Dairy Science · doi:10.3168/jds.2017-13288 · Cited by 68 · see citing works

Machine learning for predicting phenotype from genotype and environment

Guo, T., & Li, X. (2023) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2022.102853 · Cited by 61 · see citing works

Predicting male fertility in dairy cattle using markers with large effect and functional annotation data

Nani, J. P., de Rezende, F. M., & Peñagaricano, F. (2019) · BMC Genomics · doi:10.1186/s12864-019-5644-y · Cited by 60 · see citing works

Applications of Support Vector Machine in Genomic Prediction in Pig and Maize Populations

Zhao, W., Lai, X., Liu, D., Zhang, Z., Ma, P., Wang, Q., Zhang, Z., & Pan, Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.598318 · Cited by 60 · see citing works

Genome-wide prediction using Bayesian additive regression trees

Waldmann, P. (2016) · Genetics Selection Evolution · doi:10.1186/s12711-016-0219-8 · Cited by 58 · see citing works

Cassava yield traits predicted by genomic selection methods

de Andrade, L. R. B., Sousa, M. B. E., de Oliveira, E. J., de Resende, M. D. V., & Azevedo, C. F. (2019) · PLoS ONE · doi:10.1371/journal.pone.0224920 · Cited by 56 · see citing works

Evidence for the placenta-brain axis: multi-omic kernel aggregation predicts intellectual and social impairment in children born extremely preterm

Junior, H. P. D. O. S., Bhattacharya, A., Joseph, R. M., Smeester, L., Kuban, K., Marsit, C. J., O’Shea, T. M., & Fry, R. C. (2020) · Molecular Autism · doi:10.1186/s13229-020-00402-w · Cited by 50 · see citing works

Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction

Pérez‐Elizalde, S., Cuevas, J., Pérez‐Rodríguez, P., & Crossa, J. L. (2015) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-015-0229-y · Cited by 47 · see citing works

Multikernel linear mixed models for complex phenotype predictionPreprint

Weissbrod, O., Geiger, D., & Rosset, S. (2016) · Genome Research · doi:10.1101/gr.201996.115 · Cited by 48 · see citing works

Deep neural networks for genomic prediction do not estimate marker effects

Ubbens, J. R., Parkin, I. A. P., Eynck, C., Stavness, I., & Sharpe, A. (2021) · The Plant Genome · doi:10.1002/tpg2.20147 · Cited by 46 · see citing works

Interdisciplinary strategies to enable data-driven plant breeding in a changing climate

Kusmec, A., Zheng, Z., Archontoulis, S. V., Ganapathysubramanian, B., Hu, G., Wang, L., Yu, J., & Schnable, P. S. (2021) · One Earth · doi:10.1016/j.oneear.2021.02.005 · Cited by 46 · see citing works

Leveraging Breeding Values Obtained from Random Regression Models for Genetic Inference of Longitudinal Traits

Campbell, M. T., Momen, M. M., Walia, H., & Morota, G. (2019) · The Plant Genome · doi:10.3835/plantgenome2018.10.0075 · Cited by 46 · see citing works

Genomic Prediction Using Multi-trait Weighted GBLUP Accounting for Heterogeneous Variances and Covariances Across the Genome

Karaman, E., Lund, M. S., Anche, M. T., Janss, L., & Su, G. (2018) · G3 Genes Genomes Genetics · doi:10.1534/g3.118.200673 · Cited by 45 · see citing works

Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E Interactions

Souza, L. M., Francisco, F. R., Gonçalves, P. S., Scaloppi, E. J., Guen, V. L., Fritsche‐Neto, R., & de Souza, A. P. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.01353 · Cited by 42 · see citing works

Modeling host-microbiome interactions for the prediction of meat quality and carcass composition traits in swine

Khanal, P., Maltecca, C., Schwab, C. R., Fix, J., Bergamaschi, M., & Tiezzi, F. (2020) · Genetics Selection Evolution · doi:10.1186/s12711-020-00561-7 · Cited by 42 · see citing works

Genomic prediction for yields, processing and nutritional quality traits in cultivated potato ( Solanum tuberosum L.)

Habyarimana, E., Parisi, B., & Mandolino, G. (2017) · Plant Breeding · doi:10.1111/pbr.12461 · Cited by 41 · see citing works

Implementation of Genomic Selection in the CIMMYT Global Wheat Program, Findings from the Past 10 Years

Dreisigacker, S., Crossa, J. L., Pérez‐Rodríguez, P., Montesinos-López, O. A., Rosyara, U. R., Juliana, P., Mondal, S., Crespo‐Herrera, L., Govindan, V., Singh, R. P., et al. (2021) · Crop Breeding Genetics and Genomics · doi:10.20900/cbgg20210005 · Cited by 41 · see citing works

Genetic Dissection of Grain Yield of Maize and Yield-Related Traits Through Association Mapping and Genomic Prediction

Ma, J., & Cao, Y. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.690059 · Cited by 38 · see citing works

A Multiple-Trait Bayesian Lasso for Genome-Enabled Analysis and Prediction of Complex Traits

Daniel Gianola, B., & Fernando, R. L. (2019) · Genetics · doi:10.1534/genetics.119.302934 · Cited by 36 · see citing works

Response to Early Generation Genomic Selection for Yield in Wheat

Bonnett, D. G., Li, Y., Crossa, J. L., Dreisigacker, S., Basnet, B. R., Pérez‐Rodríguez, P., Alvarado, G., Jannink, J., Poland, J., & Sorrells, M. E. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2021.718611 · Cited by 38 · see citing works

A predictive assessment of genetic correlations between traits in chickens using markers

Momen, M. M., Mehrgardi, A. A., Sheikhy, A., Esmailizadeh, A., Fozi, M. A., Kranis, A., Valente, B. D., Rosa, G. J. M., & Daniel Gianola, B. (2017) · Genetics Selection Evolution · doi:10.1186/s12711-017-0290-9 · Cited by 38 · see citing works

Bayesian optimization for genomic selection: a method for discovering the best genotype among a large number of candidates

Tanaka, R., & Iwata, H. (2017) · Theoretical and Applied Genetics · doi:10.1007/s00122-017-2988-z · Cited by 38 · see citing works

Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens

Abdollahi-Arpanahi, R., Morota, G., Valente, B. D., Kranis, A., Rosa, G. J. M., & Daniel Gianola, B. (2016) · Genetics Selection Evolution · doi:10.1186/s12711-016-0187-z · Cited by 37 · see citing works

Phantom Epistasis in Genomic Selection: On the Predictive Ability of Epistatic Models

Schrauf, M. F., Martini, J. W. R., Simianer, H., Campos, G. D. L., Cantet, R. J. C., Freudenthal, J. A., Korte, A., & Munilla, S. (2020) · G3 Genes Genomes Genetics · doi:10.1534/g3.120.401300 · Cited by 36 · see citing works

The utility of genomic prediction models in evolutionary genetics

McGaugh, S. E., Lorenz, A. J., & Flagel, L. (2021) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2021.0693 · Cited by 35 · see citing works

Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance

Fonseca, J. M. O., Klein, P. E., Crossa, J. L., Pacheco, Á., Pérez‐Rodríguez, P., Perumal, R., Klein, R. R., & Rooney, W. L. (2021) · The Plant Genome · doi:10.1002/tpg2.20127 · Cited by 36 · see citing works

Genome-wide analysis of acute low salinity tolerance in the eastern oyster Crassostrea virginica and potential of genomic selection for trait improvement

McCarty, A. J., Allen, S. K., & Plough, L. V. (2021) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkab368 · Cited by 38 · see citing works

Gene mapping and genomic prediction of bull fertility using sex chromosome markers

Pacheco, H. A., de Rezende, F. M., & Peñagaricano, F. (2020) · Journal of Dairy Science · doi:10.3168/jds.2019-17767 · Cited by 34 · see citing works

Genomic prediction of bull fertility in US Jersey dairy cattle

de Rezende, F. M., Nani, J. P., & Peñagaricano, F. (2019) · Journal of Dairy Science · doi:10.3168/jds.2018-15810 · Cited by 33 · see citing works

Genomic Prediction of Additive and Non-additive Effects Using Genetic Markers and Pedigrees

Filho, J. E. D. A., Guimarães, J. F. R., Silva, F. F. E., de Resende, M. D. V., Muńoz, P., Kirst, M., & Resende, M. F. R. (2019) · G3 Genes Genomes Genetics · doi:10.1534/g3.119.201004 · Cited by 32 · see citing works

Incorporating Genome Annotation Into Genomic Prediction for Carcass Traits in Chinese Simmental Beef Cattle

Xu, L., Gao, N., Wang, Z., Xu, L., Liu, Y., Chen, Y., Xu, L., Gao, X., Zhang, L., Gao, H., et al. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00481 · Cited by 31 · see citing works

Heritable and Nonheritable Rumen Bacteria Are Associated with Different Characters of Lactation Performance of Dairy Cows

Zang, X., Sun, H., Xue, M., Zhang, Z., Plastow, G. S., Yang, T., Guan, L. L., & Liu, J. (2022) · mSystems · doi:10.1128/msystems.00422-22 · Cited by 33 · see citing works

Epistasis Detection and Modeling for Genomic Selection in Cowpea (Vigna unguiculata L. Walp.)

Olatoye, M. O., Hu, Z., & Aikpokpodion, P. O. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00677 · Cited by 29 · see citing works

Genome-wide association mapping and genomic prediction of yield-related traits and starch pasting properties in cassava

Phumichai, C., Aiemnaka, P., Nathaisong, P., Hunsawattanakul, S., Fungfoo, P., Rojanaridpiched, C., Vichukit, V., Kongsil, P., Kittipadakul, P., Wannarat, W., et al. (2021) · Theoretical and Applied Genetics · doi:10.1007/s00122-021-03956-2 · Cited by 30 · see citing works

High-Dimensional Mediation Analysis with Applications to Causal Gene Identification

Zhang, Q. (2021) · Statistics in Biosciences · doi:10.1007/s12561-021-09328-0 · Cited by 28 · see citing works

An experimental approach for estimating the genomic selection advantage for Fusarium head blight and Septoria tritici blotch in winter wheat

Herter, C. P., Ebmeyer, E., Kollers, S., Korzun, V. N., & Miedaner, T. (2019) · Theoretical and Applied Genetics · doi:10.1007/s00122-019-03364-7 · Cited by 30 · see citing works

Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates

Washburn, J. D., Varela, J. I., Xavier, A., Chen, Q., Ertl, D., Gage, J. L., Holland, J. B., Lima, D. C., Romay, M. C., Lopez-Cruz, M. A., et al. (2024) · Genetics · doi:10.1093/genetics/iyae195 · Cited by 30 · see citing works

Multiple-trait analyses improved the accuracy of genomic prediction and the power of genome-wide association of productivity and climate change-adaptive traits in lodgepole pine

Cappa, E. P., Chen, C., Klutsch, J. G., Sebastian‐Azcona, J., Ratcliffe, B., Wei, X., Ros, L. M. D., Ullah, A., Liu, Y., Benowicz, A., et al. (2022) · BMC Genomics · doi:10.1186/s12864-022-08747-7 · Cited by 30 · see citing works

A spectrum of explainable and interpretable machine learning approaches for genomic studies

Conard, A. M., DenAdel, A., & Crawford, L. A. (2023) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1617 · Cited by 30 · see citing works

Performance of whole genome prediction for growth traits in a crossbred chicken population

Teng, J., Gao, N., Zhang, H., Li, X., Li, J., Zhang, H., Zhang, X., & Zhang, Z. (2019) · Poultry Science · doi:10.3382/ps/pey604 · Cited by 28 · see citing works

Impact of imputation methods on the amount of genetic variation captured by a single-nucleotide polymorphism panel in soybeans

Xavier, A., Muir, W. M., & Rainey, K. M. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0899-7 · Cited by 27 · see citing works

Effectiveness of genomic selection for improving provitamin A carotenoid content and associated traits in cassava

Esuma, W., Ozimati, A., Kulakow, P., Gore, M. A., Wolfe, M. D., Nuwamanya, E., Egesi, C. N., & Kawuki, R. (2021) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkab160 · Cited by 25 · see citing works

Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling

Ortiz, R. O., Crossa, J. L., Reslow, F., Pérez‐Rodríguez, P., & Cuevas, J. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.785196 · Cited by 25 · see citing works

Genomic selection in tropical perennial crops and plantation trees: a review

Seyum, E. G., Bille, N. H., Abtew, W. G., Munyengwa, N., Bell, J. M., & Cros, D. (2022) · Molecular Breeding · doi:10.1007/s11032-022-01326-4 · Cited by 27 · see citing works

A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice

Jacquin, L. J., Cao, T., & Ahmadi, N. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00145 · Cited by 24 · see citing works

Multi-task Gaussian process for imputing missing data in multi-trait and multi-environment trials

Hori, T., Montcho, D., Agbangla, C., Ebana, K., Futakuchi, K., & Iwata, H. (2016) · Theoretical and Applied Genetics · doi:10.1007/s00122-016-2760-9 · Cited by 22 · see citing works

Transcriptome‐wide association and prediction for carotenoids and tocochromanols in fresh sweet corn kernels

Hershberger, J. L., Tanaka, R., Wood, J. C., Kaczmar, N. S., Wu, D., Hamilton, J. P., DellaPenna, D., Buell, C. R., & Gore, M. A. (2022) · The Plant Genome · doi:10.1002/tpg2.20197 · Cited by 23 · see citing works

Integrating genomic and infrared spectral data improves the prediction of milk protein composition in dairy cattle

Baba, T., Pegolo, S., Mota, L. F. M., Peñagaricano, F., Bittante, G., Cecchinato, A., & Morota, G. (2021) · Genetics Selection Evolution · doi:10.1186/s12711-021-00620-7 · Cited by 21 · see citing works

lme4GS: An R-Package for Genomic Selection

Caamal-Pat, D., Pérez‐Rodríguez, P., Crossa, J. L., Velasco‐Cruz, C., Pérez‐Elizalde, S., & Peña, M. A. V. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.680569 · Cited by 21 · see citing works

Gene × gene and genotype × environment interactions in wheat

Raffo, M. A., & Jensen, J. (2023) · Crop Science · doi:10.1002/csc2.20986 · Cited by 18 · see citing works

Quantifying genomic connectedness and prediction accuracy from additive and non-additive gene actions

Momen, M. M., & Morota, G. (2018) · Genetics Selection Evolution · doi:10.1186/s12711-018-0415-9 · Cited by 18 · see citing works

Genome-Enabled Prediction Methods Based on Machine Learning

Reinoso-Peláez, E. L., Daniel Gianola, B., & González-Recio, Ó. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2205-6_7 · Cited by 19 · see citing works

Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction

Puglisi, D., Delbono, S., Visioni, A., Özkan, H., İbrahim, K. A. R. A., Casas, A. M., Igartua, E., Valè, G., Piero, A. R. L., Cattivelli, L., et al. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.664148 · Cited by 17 · see citing works

Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

Lopez-Cruz, M. A., Beyene, Y., Gowda, M., Crossa, J. L., Pérez‐Rodríguez, P., & Campos, G. D. L. (2021) · Heredity · doi:10.1038/s41437-021-00474-1 · Cited by 19 · see citing works

Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits

Chen, C., Powell, O. M., Dinglasan, E. G., Ross, E. M., Yadav, S., Wei, X., Atkin, F., Deomano, E. C., & Hayes, B. J. (2023) · The Plant Genome · doi:10.1002/tpg2.20390 · Cited by 17 · see citing works

Joint Use of Genome, Pedigree, and Their Interaction with Environment for Predicting the Performance of Wheat Lines in New Environments

Howard, R., Daniel Gianola, B., Montesinos-López, O., Juliana, P., Singh, R. P., Poland, J., Shrestha, S. K., Pérez‐Rodríguez, P., Crossa, J. L., & Jarquín, D. (2019) · G3 Genes Genomes Genetics · doi:10.1534/g3.119.400508 · Cited by 16 · see citing works

Accuracy of whole genome prediction with single-step GBLUP in a Chinese yellow-feathered chicken population

Gao, N., Teng, J., Pan, R., Li, X., Ye, S., Li, J., Zhang, H., Zhang, X., & Zhang, Z. (2019) · Livestock Science · doi:10.1016/j.livsci.2019.103817 · Cited by 16 · see citing works

Innovative Approaches in the Breeding of Climate‐Resilient Crops

Cvejić, S., Jocić, S., Mitrović, B., Bekavac, G., Mirosavljević, M., Marjanović‐Jeromela, A., Zorić, M., Radanović, A., Kondić‐Špika, A., & Miladinović, D. (2022) · Wiley eBooks · doi:10.1002/9781119789789.ch6 · Cited by 16 · see citing works

Adjusting for Spatial Effects in Genomic Prediction

Mao, X., Dutta, S., Wong, R. K. W., & Nettleton, D. S. (2020) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-020-00396-1 · Cited by 17 · see citing works

An alternative covariance estimator to investigate genetic heterogeneity in populations

Heslot, N., & Jannink, J. (2015) · Genetics Selection Evolution · doi:10.1186/s12711-015-0171-z · Cited by 14 · see citing works

Do stronger measures of genomic connectedness enhance prediction accuracies across management units?1

Yu, H., Spangler, M. L., Lewis, R. M., & Morota, G. (2018) · Journal of Animal Science · doi:10.1093/jas/sky316 · Cited by 14 · see citing works

Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program

Peixoto, M. A., Leach, K. A., Jarquín, D., Flannery, P., Zystro, J., Tracy, W. F., Bhering, L. L., & Resende, M. F. R. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1293307 · Cited by 13 · see citing works

Toward Capturing Genetic Epistasis from Multivariate Genome-Wide Association Studies Using Mixed-Precision Kernel Ridge Regression

Ltaief, H., Alomairy, R., Cao, Q., Ren, J., Slim, L., Kurth, T., Dorschner, B., Bougouffa, S., Abdelkhalak, R., & Keyes, D. E. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41406.2024.00012 · Cited by 13 · see citing works

Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge

Hem, I. G., Selle, M. L., Gorjanc, G., Fuglstad, G., & Riebler, A. (2021) · Genetics · doi:10.1093/genetics/iyab002 · Cited by 13 · see citing works

Statistical and Computational Challenges in Whole Genome Prediction and Genome-Wide Association Analyses for Plant and Animal Breeding

Tempelman, R. J. (2015) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-015-0225-2 · Cited by 13 · see citing works

Evaluation of Nonparametric Models for Genomic Prediction of Early‐Stage Single Crosses in Maize

Kadam, D. C., & Lorenz, A. J. (2019) · Crop Science · doi:10.2135/cropsci2017.11.0668 · Cited by 13 · see citing works

Genomic selection for agronomic traits in a winter wheat breeding program

Ficht, A., Konkin, D. J. F., Cram, D., Sidebottom, C. H. D., Tan, Y., Pozniak, C. J., & Rajcan, I. (2023) · Theoretical and Applied Genetics · doi:10.1007/s00122-023-04294-1 · Cited by 12 · see citing works

A liquid biomarker signature of inflammatory proteins accurately predicts early pancreatic cancer progression during FOLFIRINOX chemotherapy

van Eijck, C. W. F., Sabroso‐Lasa, S., Strijk, G. J., Mustafa, D. A. M., Fellah, A., Koerkamp, B. G., Malats, N., & van Eijck, C. H. J. (2024) · Neoplasia · doi:10.1016/j.neo.2024.100975 · Cited by 12 · see citing works

A first characterization of the microbiota-resilience link in swine

Mancin, E., Maltecca, C., Huang, Y., Mantovani, R., & Tiezzi, F. (2024) · Microbiome · doi:10.1186/s40168-024-01771-7 · Cited by 11 · see citing works

Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging

Roudbar, M. A., Mousavi, S., Ardestani, S. S., Lopes, F., Momen, M., Gianola, D., & Khatib, H. (2021) · G3 · doi:10.1093/g3journal/jkab112 · Cited by 11 · see citing works

Genome-wide association and genomic prediction for iron and zinc concentration and iron bioavailability in a collection of yellow dry beans

Izquierdo, P., Sadohara, R., Wiesinger, J. A., Glahn, R. P., Urrea, C. A., & Cichy, K. A. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1330361 · Cited by 10 · see citing works

Lost in Translation: On the Problem of Data Coding in Penalized Whole Genome Regression with Interactions

Martini, J. W. R., Rosales, F., Ha, N.-T., Heise, J., Wimmer, V., & Kneib, T. (2019) · G3 Genes Genomes Genetics · doi:10.1534/g3.118.200961 · Cited by 10 · see citing works

Untitled work

(2014) · Proceedings of the World Congress on Genetics Applied to Livestock Production · Cited by 18 · see citing works

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., Daniel Gianola, B., & DeNise, S. K. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00056 · Cited by 30 · see citing works

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 · see citing works

Machine learning methods and predictive ability metrics for genome-wide prediction of complex traits

González-Recio, Ó., Rosa, G. J. M., & Daniel Gianola, B. (2014) · Livestock Science · doi:10.1016/j.livsci.2014.05.036 · Cited by 178 · see citing works

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 · see citing works