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Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP
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Many important traits in plant breeding are polygenic and therefore recalcitrant to traditional marker-assisted selection. Genomic selection addresses this complexity by including all markers in the prediction model. A key method for the genomic prediction of breeding values is ridge regression (RR), which is equivalent to best linear unbiased prediction (BLUP) when the genetic covariance between lines is proportional to their similarity in genotype space. This additive model can be broadened to include epistatic effects by using other kernels, such as the Gaussian, which represent inner products in a complex feature space. To facilitate the use of RR and nonadditive kernels in plant breeding, a new software package for R called rrBLUP has been developed. At its core is a fast maximum-likelihood algorithm for mixed models with a single variance component besides the residual error, which allows for efficient prediction with unreplicated training data. Use of the rrBLUP software is demonstrated through several examples, including the identification of optimal crosses based on superior progeny value. In cross-validation tests, the prediction accuracy with nonadditive kernels was significantly higher than RR for wheat (Triticum aestivum L.) grain yield but equivalent for several maize (Zea mays L.) traits.
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![Figure 2 Performance of the Gaussian model (GAUSS). The fi gure depicts the effect of the Gaussian scale parameter ( θ in Eq. [6]) on the restricted log-likelihood (LL), the training population accuracy ( r train ), and the cross-validation accuracy ( r pred ) when predicting sets 5 or 6 in environment 1. For set 5 the restricted maximum likelihood solution for θ ( θ REML ) = 0.5, and for set 6 θ REML = 0.4. In both cases r train approached 1 as θ → 0, but the trends for r pred were different. For set 5 r pred exhibited an interior maximum near θ REML , while for set 6 r pred increased monotonically with θ . Because GAUSS is approximately ridge regression (RR) when θ is large, the contrasting behavior in this fi gure illustrates why GAUSS had higher r pred than RR for set 5 but vice versa for set 6 (see Table 2).](/paper-figures/eb5657fa983040d490573ea2a9864980/ridge-regression-and-other-kernels-for-genomic-selection-with-r-package-rrblup-figure-2.png)
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“Endelman (2011) developed the R -package rrBLUP that allows convenient restricted maximum likelihood (REML) estimation of the bandwidth, but only in the context of LMMs with a single genetic variance component, i.e., no G × E or G × E × M interaction models.”
From REML implementations of kernel-based genomic prediction models for genotype x environment x management interactions (2025) · cites this paper as Endelman (2011)
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,934
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
TASSEL: software for association mapping of complex traits in diverse samples
Bradbury, P. J., Zhang, Z., Kroon, D. E., Casstevens, T. M., Ramdoss, Y., & Buckler, E. S. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm308 · Cited by 7,785
Ridge Regression: Biased Estimation for Nonorthogonal Problems
Hoerl, A. E., & Kennard, R. W. (2000) · Technometrics · doi:10.2307/1271436 · Cited by 7,001
Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Williams, C. K. I. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s269 · Cited by 4,390
Yu, J., Pressoir, G., Briggs, W. H., Bi, I. V., Yamasaki, M., Doebley, J., McMullen, M. D., Gaut, B. S., Nielsen, D. M., Holland, J. B., et al. (2005) · Nature Genetics · doi:10.1038/ng1702 · Cited by 4,155
Browning, S. R., & Browning, B. L. (2007) · The American Journal of Human Genetics · doi:10.1086/521987 · Cited by 3,217
Efficient Control of Population Structure in Model Organism Association Mapping
Kang, H. M., Zaitlen, N., Wade, C. M., Kirby, A. W., Heckerman, D. E., Daly, M. J., & Eskin, E. (2008) · Genetics · doi:10.1534/genetics.107.080101 · Cited by 1,869
Genomic Selection for Crop Improvement
Heffner, E. L., Sorrells, M. E., & Jannink, J. (2009) · Crop Science · doi:10.2135/cropsci2008.08.0512 · Cited by 1,753
Research works printed but not linked: 3
- Bernardo, 2010, Quantitative traits in plant breeding
- SAS Institute, 1994, SAS 9.2 for Windows
- Searle, 2006, Variance components
Cited by
Sorted by citations
Items 201–300 of 1,001
The apple REFPOP—a reference population for genomics-assisted breeding in apple
Jung, M., Roth, M., Aranzana, M. J., Auwerkerken, A., Bink, M. C. A. M., Denancé, C., Dujak, C., Durel, C.-É., Forcada, C. F. I., Cantín, C. M., et al. (2020) · Horticulture Research · doi:10.1038/s41438-020-00408-8 · Cited by 90
An Evaluation of High-Throughput Approaches to QTL Mapping in Saccharomyces cerevisiae
Wilkening, S., Lin, G., Fritsch, E. S., Tekkedil, M. M., Anders, S., Kuehn, R., Nguyen, M., Aiyar, R. S., Proctor, M. J., Sakhanenko, N. A., et al. (2013) · Genetics · doi:10.1534/genetics.113.160291 · Cited by 86
Luo, Z., Wang, M., Long, Y., Huang, Y., Shi, L., Zhang, C., Liu, X., Fitt, B. D. L., Xiang, J., Mason, A. S., et al. (2017) · Theoretical and Applied Genetics · doi:10.1007/s00122-017-2911-7 · Cited by 80
Optimizing Genomic Selection for a Sorghum Breeding Program in Haiti: A Simulation Study
Muleta, K. T., Pressoir, G., & Morris, G. P. (2018) · G3 Genes Genomes Genetics · doi:10.1534/g3.118.200932 · Cited by 80
Alemu, A., Brazauskas, G., Gaikpa, D. S., Henriksson, T., Islamov, B., Jørgensen, L. N., Koppel, M., Koppel, R., Liatukas, Ž., Svensson, J. T., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.661742 · Cited by 79
Newell, M. A., Asoro, F. G., Scott, M. P., White, P. J., Beavis, W. D., & Jannink, J. (2012) · Theoretical and Applied Genetics · doi:10.1007/s00122-012-1945-0 · Cited by 79
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
Faville, M. J., Ganesh, S., Cao, M., Jahufer, M. Z. Z., Bilton, T. P., Easton, H. S., Ryan, D. L., Trethewey, J. A. K., Rolston, M. P., Griffiths, A. G., et al. (2017) · Theoretical and Applied Genetics · doi:10.1007/s00122-017-3030-1 · Cited by 79
Wang, J., Zhou, Z., Zhang, Z., Liu, D., Liu, D., Zhang, Q., Bradbury, P. J., Buckler, E. S., & Zhang, Z. (2018) · Heredity · doi:10.1038/s41437-018-0075-0 · Cited by 79
Bao, Y., Vuong, T. D., Meinhardt, C. G., Tiffin, P., Denny, R. L., Chen, S., Nguyen, H. T., Orf, J. H., & Young, N. D. (2014) · The Plant Genome · doi:10.3835/plantgenome2013.11.0039 · Cited by 78
Schmidt, M., Kollers, S., Maasberg-Prelle, A., Großer, J., Schinkel, B., Tomerius, A.-M., Graner, A., & Korzun, V. N. (2015) · Theoretical and Applied Genetics · doi:10.1007/s00122-015-2639-1 · Cited by 78
Genomic Prediction of Testcross Performance in Canola (Brassica napus)
Jan, H. U., Abbadi, A., Lücke, S., Nichols, R. A., & Snowdon, R. J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0147769 · Cited by 78
Efficiency of genomic selection for breeding population design and phenotype prediction in tomato
Yamamoto, E., Matsunaga, H., Onogi, A., Ohyama, A., Miyatake, K., Yamaguchi, H., Nunome, T., Iwata, H., & Fukuoka, H. (2016) · Heredity · doi:10.1038/hdy.2016.84 · Cited by 76
Ji, Y., Zhao, D., Chen, X., Zhang, Y., & Wang, J. (2018) · The Crop Journal · doi:10.1016/j.cj.2018.05.003 · Cited by 76
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Lipka, A. E., Lü, F., Cherney, J. H., Buckler, E. S., Casler, M. D., & Costich, D. E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112227 · Cited by 76
Muqaddasi, Q. H., Brassac, J., Ebmeyer, E., Kollers, S., Korzun, V. N., Argillier, O., Stiewe, G., Plieske, J., Ganal, M. W., & Röder, M. S. (2020) · Scientific Reports · doi:10.1038/s41598-020-69381-5 · Cited by 75
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Grinberg, N. F., Lovatt, A., Hegarty, M., Lovatt, A., Skøt, K. P., Kelly, R., Blackmore, T. M., Thorogood, D., King, R. D., Armstead, I. P., et al. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.00133 · Cited by 75
Genomic assessment of local adaptation in dwarf birch to inform assisted gene flow
Borrell, J. S., Zohren, J., Nichols, R. A., & Buggs, R. J. A. (2019) · Evolutionary Applications · doi:10.1111/eva.12883 · Cited by 78
Onogi, A., Ideta, O., Inoshita, Y., Ebana, K., Yoshioka, T., Yamasaki, M., & Iwata, H. (2014) · Theoretical and Applied Genetics · doi:10.1007/s00122-014-2411-y · Cited by 80
Odilbekov, F., Armonienė, R., Koc, A., Svensson, J. T., & Chawade, A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01224 · Cited by 74
Principles and approaches of association mapping in plant breeding
Ibrahim, A., Zhang, L., Niyitanga, S., Afzal, M. Z., Xu, Y., Zhang, L., Zhang, L., & Qi, J. (2020) · Tropical Plant Biology · doi:10.1007/s12042-020-09261-4 · Cited by 79
Adak, A., Murray, S. C., Božinović, S., Lindsey, R., Nakasagga, S., Chatterjee, S., Anderson, S. L., & Wilde, S. C. (2021) · Remote Sensing · doi:10.3390/rs13112141 · Cited by 73
Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations
Duarte, J. L. G., Cantet, R. J. C., Bates, R. O., Ernst, C. W., Raney, N. E., & Steibel, J. P. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-246 · Cited by 73
A genome-wide association study of malting quality across eight U.S. barley breeding programs
Mohammadi, M., Blake, T. K., Budde, A. D., Chao, S., Hayes, P. M., Horsley, R. D., Obert, D. E., Ullrich, S. E., & Smith, K. P. (2015) · Theoretical and Applied Genetics · doi:10.1007/s00122-015-2465-5 · Cited by 73
Ma, Y., Reif, J. C., Jiang, Y., Wen, Z., Wang, D., Liu, Z., Guo, Y., Wei, S., Wang, S., Yang, C., et al. (2016) · Molecular Breeding · doi:10.1007/s11032-016-0504-9 · Cited by 74
Thistlethwaite, F. R., Ratcliffe, B., Klápště, J., Porth, I. M., Chen, C., Stoehr, M., & El-Kassaby, Y. A. (2017) · BMC Genomics · doi:10.1186/s12864-017-4258-5 · Cited by 73
Schulthess, A. W., Zhao, Y., Longin, C. F. H., & Reif, J. C. (2017) · Theoretical and Applied Genetics · doi:10.1007/s00122-017-3029-7 · Cited by 73
Minamikawa, M. F., Takada, N., Terakami, S., Saito, T., Onogi, A., Kajiya‐Kanegae, H., Hayashi, T., Yamamoto, T., & Iwata, H. (2018) · Scientific Reports · doi:10.1038/s41598-018-30154-w · Cited by 73
A framework for transcriptome-wide association studies in breast cancer in diverse study populations
Bhattacharya, A., García‐Closas, M., Olshan, A. F., Perou, C. M., Troester, M. A., & Love, M. I. (2020) · Genome biology · doi:10.1186/s13059-020-1942-6 · Cited by 74
Roth, M., Muranty, H., Guardo, M. D., Guerra, W., Patocchi, A., & Costa, F. (2020) · Horticulture Research · doi:10.1038/s41438-020-00370-5 · Cited by 72
Xie, J., Fernandes, S. B., Mayfield‐Jones, D., Erice, G., Choi, M. S., Lipka, A. E., & Leakey, A. D. B. (2021) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiab299 · Cited by 72
Crossa, J. L., Montesinos-López, O. A., Pérez‐Rodríguez, P., Costa‐Neto, G., Fritsche‐Neto, R., Ortiz, R. O., Martini, J. W. R., Lillemo, M., Montesinos‐López, A., Jarquín, D., et al. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2205-6_9 · Cited by 83
Bao, Y., Kurle, J. E., Anderson, G., & Young, N. D. (2015) · Molecular Breeding · doi:10.1007/s11032-015-0324-3 · Cited by 72
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GMStool: GWAS-based marker selection tool for genomic prediction from genomic data
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Merrick, L. F., Burke, A. B., Chen, X., & Carter, A. H. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.713667 · Cited by 71
Hu, G., Zhao, L., Lu, Y., Li, C., Shichen, G., Yan, S., Li, G., Wang, M., Ren, H., Guan, H., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-11318-6 · Cited by 71
Ward, B., Brown‐Guedira, G., Tyagi, P., Kolb, F. L., Sanford, D. A. V., Sneller, C., & Griffey, C. A. (2019) · Crop Science · doi:10.2135/cropsci2018.03.0189 · Cited by 82
Genetic architecture of maize kernel row number and whole genome prediction
Liu, L., Du, Y., Huo, D., Wang, M., Shen, X., Yue, B., Qiu, F., Zheng, Y., Yan, J., & Zhang, Z. (2015) · Theoretical and Applied Genetics · doi:10.1007/s00122-015-2581-2 · Cited by 72
Juliana, P., Singh, R. P., Singh, P. K., Crossa, J. L., Rutkoski, J. E., Poland, J., Bergstrom, G. C., & Sorrells, M. E. (2017) · The Plant Genome · doi:10.3835/plantgenome2016.08.0082 · Cited by 70
Laricchia, K. M., Zdraljevic, S., Cook, D. E., & Andersen, E. C. (2017) · Molecular Biology and Evolution · doi:10.1093/molbev/msx155 · Cited by 73
Bonnafous, F., Fiévet, G., Blanchet, N., Boniface, M., Carrère, S., Gouzy, J., Legrand, L., Marage, G., Bret-Mestries, E., Muños, S., et al. (2017) · Theoretical and Applied Genetics · doi:10.1007/s00122-017-3003-4 · Cited by 69
Robert, P., Auzanneau, J., Goudemand, E., Oury, F., Rolland, B., Heumez, E., Bouchet, S., Gouis, J. L., & Rincent, R. (2022) · Theoretical and Applied Genetics · doi:10.1007/s00122-021-04005-8 · Cited by 69
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Huang, M. F., Balimponya, E. G., Mgonja, E. M., McHale, L. K., Luzi-Kihupi, A., Wang, G., & Sneller, C. (2019) · Molecular Breeding · doi:10.1007/s11032-019-1023-2 · Cited by 68
Gill, H. S., Halder, J., Zhang, J., Brar, N. K., Rai, T. S., Hall, C., Bernardo, A., Amand, P. S., Bai, G., Olson, E. L., et al. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.709545 · Cited by 68
Steiner, B., Michel, S., Maccaferri, M., Lemmens, M., Tuberosa, R., & Buerstmayr, H. (2018) · Theoretical and Applied Genetics · doi:10.1007/s00122-018-3253-9 · Cited by 68
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Zhou, H., Xia, D., Li, P., Ao, Y., Xu, X., Wan, S., Lǐ, Y., Wu, B., Shi, H., Wang, K., et al. (2020) · Molecular Plant · doi:10.1016/j.molp.2020.12.001 · Cited by 67
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Yoshida, H., Hirano, K., Yano, K., Wang, F., Mori, M., Kawamura, M., Koketsu, E., Hattori, M., Ordonio, R. L., Huang, P., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33318-5 · Cited by 66
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Gao, P., Zhao, H., Luo, Z., Lin, Y.-T., Feng, W., Li, Y., Kong, F., Li, X., Fang, C., & Wang, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad349 · Cited by 66
Schopp, P., Müller, D., Technow, F., & Melchinger, A. E. (2016) · Genetics · doi:10.1534/genetics.116.193243 · Cited by 64
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Evaluation of Genomic Prediction for Pasmo Resistance in Flax
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Valluru, R., Gazave, É., Fernandes, S. B., Ferguson, J. N., Lozano, R. D., Hirannaiah, P., Zuo, T., Brown, P. J., Leakey, A. D. B., Gore, M. A., et al. (2019) · Genetics · doi:10.1534/genetics.118.301742 · Cited by 64
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Divilov, K., Barba, P., Cadle-Davidson, L. E., & Reisch, B. I. (2018) · Theoretical and Applied Genetics · doi:10.1007/s00122-018-3065-y · Cited by 63
Muleta, K. T., Bulli, P., Zhang, Z., Chen, X., & Pumphrey, M. (2017) · The Plant Genome · doi:10.3835/plantgenome2016.12.0124 · Cited by 62
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Manickavelu, A., Hattori, T., Yamaoka, S., Yoshimura, K., Kondou, Y., Onogi, A., Matsui, M., Iwata, H., & Ban, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169416 · Cited by 62
Fiedler, J. D., Salsman, E., Liu, Y., de Jiménez, M. M., Hegstad, J. B., Chen, B., Manthey, F. A., Chao, S., Xu, S. S., Elias, E. M., et al. (2017) · The Plant Genome · doi:10.3835/plantgenome2017.05.0038 · Cited by 62
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Qin, J., Shi, A., Song, Q., Li, S., Wang, F., Cao, Y., Ravelombola, W., Song, Q., Yang, C., & Zhang, M. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.01445 · Cited by 61
Henry, P. M., Pincot, D. D. A., Jenner, B. N., Borrero, C., Avilés, M., Nam, M., Epstein, L., Knapp, S. J., & Gordon, T. R. (2020) · New Phytologist · doi:10.1111/nph.17141 · Cited by 61
Li, J., Zhang, D., Yang, F., Zhang, Q., Pan, S., Zhao, X., Zhang, Q., Han, Y., Yang, J., Wang, K., et al. (2024) · Plant Communications · doi:10.1016/j.xplc.2024.100975 · Cited by 66
Thavamanikumar, S., Dolferus, R., & Thumma, B. R. (2015) · G3 Genes Genomes Genetics · doi:10.1534/g3.115.019745 · Cited by 61
Singh, N., Wu, S., Tiwari, V. K., Sehgal, S. K., Raupp, J. W., Wilson, D., Abbasov, M., Gill, B. S., & Poland, J. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.00009 · Cited by 68
Evaluation of genomic selection methods for predicting fiber quality traits in Upland cotton
Islam, M. S., Fang, D. D., Jenkins, J. N., Guo, J. X., McCarty, J. C., & Jones, D. C. (2019) · Molecular Genetics and Genomics · doi:10.1007/s00438-019-01599-z · Cited by 60
Xu, S., Xu, X., Blacker, C., Gaulton, R. G., Zhu, Q., Yang, M., Yang, G., Zhang, J., Yang, Y., Yang, M., et al. (2023) · Remote Sensing · doi:10.3390/rs15030854 · Cited by 60
Bhattacharya, A., Vo, D., Jops, C. T., Kim, M., Wen, C. H., Hervoso, J. L., Paşaniuc, B., & Gandal, M. J. (2023) · Nature Genetics · doi:10.1038/s41588-023-01560-2 · Cited by 61
Kwong, Q. B., Ong, A. L., Teh, C.-K., Chew, F. T., Tammi, M. T., Mayes, S., Kulaveerasingam, H., Yeoh, S. H., Harikrishna, J. A., & Appleton, D. R. (2017) · Scientific Reports · doi:10.1038/s41598-017-02602-6 · Cited by 60
Strategies for Selecting Crosses Using Genomic Prediction in Two Wheat Breeding Programs
Lado, B., Battenfield, S. D., Guzmán, C., Quincke, M., Singh, R. P., Dreisigacker, S., Peña, R. J., Fritz, A. K., Silva, P., Poland, J., et al. (2017) · The Plant Genome · doi:10.3835/plantgenome2016.12.0128 · Cited by 60
Zdraljevic, S., Strand, C., Seidel, H. S., Cook, D. E., Doench, J. G., & Andersen, E. C. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006891 · Cited by 61
Warmerdam, S., Sterken, M. G., van Schaik, C., Oortwijn, M. E. P., Sukarta, O. C. A., Lozano‐Torres, J. L., Dicke, M., Helder, J., Kammenga, J. E., Goverse, A., et al. (2018) · New Phytologist · doi:10.1111/nph.15034 · Cited by 62
Genomic Informed Breeding Strategies for Strawberry Yield and Fruit Quality Traits
Cockerton, H. M., Karlström, A., Johnson, A. W., Li, B., Stavridou, E., Hopson, K. J., Whitehouse, A. B., & Harrison, R. J. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.724847 · Cited by 59
Zhao, L., Walkowiak, S., & Fernando, W. G. D. (2023) · Plants · doi:10.3390/plants12091852 · Cited by 59
Genomic selection to resistance to Stenocarpella maydis in maize lines using DArTseq markers
dos Santos, J. P. R., Pires, L. P. M., Vasconcellos, R. C. D. C., Pereira, G. S., Pinho, R. G. V., & Balestre, M. (2016) · BMC Genetics · doi:10.1186/s12863-016-0392-3 · Cited by 59
Granato, Í. S. C., Cuevas, J., Luna-Vázquez, F. J., Crossa, J. L., Montesinos-López, O. A., Burgueño, J. A., & Fritsche‐Neto, R. (2018) · G3 Genes Genomes Genetics · doi:10.1534/g3.118.200435 · Cited by 66
Sugihara, Y., Abe, Y., Takagi, H., Abe, A., Shimizu, M., Ito, K., Kanzaki, E., Oikawa, K., Kourelis, J., Langner, T., et al. (2023) · PLOS Biology · doi:10.1371/journal.pbio.3001945 · Cited by 59
Yabe, S., Hara, T., Ueno, M., Enoki, H., Kimura, T., Nishimura, S., Yasui, Y., Ohsawa, R., & Iwata, H. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.00276 · Cited by 58
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Hong, J.-P., Ro, N., Lee, H., Kim, G., Kwon, J., Yamamoto, E., & Kang, B. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.570871 · Cited by 58
Ravelombola, W., Shi, A., & Huynh, B. (2021) · Horticulture Research · doi:10.1038/s41438-021-00462-w · Cited by 58
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Mangin, B., Bonnafous, F., Blanchet, N., Boniface, M., Bret-Mestries, E., Carrère, S., Cottret, L., Legrand, L., Marage, G., Pegot-Espagnet, P., et al. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.01633 · Cited by 58
Zdraljevic, S., Fox, B. W., Strand, C., Panda, O., Tenjo-Castaño, F., Brady, S. C., Crombie, T. A., Doench, J. G., Schroeder, F. C., & Andersen, E. C. (2019) · eLife · doi:10.7554/elife.40260 · Cited by 62
Genomic selection for lentil breeding: Empirical evidence
Haile, T. A., Heidecker, T., Wright, D. M., Neupane, S., Ramsay, L., Vandenberg, A., & Bett, K. E. (2020) · The Plant Genome · doi:10.1002/tpg2.20002 · Cited by 58
Metadata
- Authors
- Endelman, J. B.
- Affiliations
- Washington State University (US)
- Corresponding author
- Endelman, Jeffrey B.
- ORCID
- Endelman, Jeffrey B.
- Venue
- The Plant Genome
- Publisher
- Wiley
- Published
- 2011-11
- Volume
- 4
- Issue
- 3
- Pages
- 250-255
- ISSN
- 1940-3372 (ISSN-L)
- OpenAlex
- W2064013109
- Cited by
- 2,094
- References
- 29
- Citations by year
- 2026: 198 · 2025: 202 · 2024: 204 · 2023: 206 · 2022: 226 · 2021: 234 · 2020: 204 · 2019: 144 · 2018: 110 · 2017: 112 · 2016: 99 · 2015: 82 · 2014: 36 · 2013: 23 · 2012: 9
- FWCI
- 14.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
- Locations
- The Plant Genome (publishedVersion, cc-by-nc-nd)
- 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 › Agricultural and Biological Sciences › Plant Science › Genetics and Plant Breeding
- Keywords
- genomic selection, ridge regression, BLUP, Gaussian kernel, breeding value estimation, plant breeding, wheat grain yield, cross-validation
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