A dimensionality-reduction genomic prediction method without direct inverse of the genomic relationship matrix for large genomic data
Key message A new genomic prediction method (RHPP) was developed via combining randomized Haseman–Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. Computational efficiency is becoming a hot issue in the practical...
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| Vydané v: | Plant cell reports Ročník 42; číslo 11; s. 1825 - 1832 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.11.2023
Springer Nature B.V |
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| ISSN: | 0721-7714, 1432-203X, 1432-203X |
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| Abstract | Key message
A new genomic prediction method (RHPP) was developed via combining randomized Haseman–Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm.
Computational efficiency is becoming a hot issue in the practical application of genomic prediction due to the large number of data generated by the high-throughput genotyping technology. In this study, we developed a fast genomic prediction method RHPP via combining randomized Haseman–Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. The simulation results demonstrated similar prediction accuracy between RHPP and GBLUP, and significantly higher computational efficiency of the former with the increase of individuals. The results of real datasets of both bread wheat and loblolly pine demonstrated that RHPP had a similar or better predictive accuracy in most cases compared with GBLUP. In the future, RHPP may be an attractive choice for analyzing large-scale and high-dimensional data. |
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| AbstractList | KEY MESSAGE: A new genomic prediction method (RHPP) was developed via combining randomized Haseman–Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. Computational efficiency is becoming a hot issue in the practical application of genomic prediction due to the large number of data generated by the high-throughput genotyping technology. In this study, we developed a fast genomic prediction method RHPP via combining randomized Haseman–Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. The simulation results demonstrated similar prediction accuracy between RHPP and GBLUP, and significantly higher computational efficiency of the former with the increase of individuals. The results of real datasets of both bread wheat and loblolly pine demonstrated that RHPP had a similar or better predictive accuracy in most cases compared with GBLUP. In the future, RHPP may be an attractive choice for analyzing large-scale and high-dimensional data. A new genomic prediction method (RHPP) was developed via combining randomized Haseman-Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. Computational efficiency is becoming a hot issue in the practical application of genomic prediction due to the large number of data generated by the high-throughput genotyping technology. In this study, we developed a fast genomic prediction method RHPP via combining randomized Haseman-Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. The simulation results demonstrated similar prediction accuracy between RHPP and GBLUP, and significantly higher computational efficiency of the former with the increase of individuals. The results of real datasets of both bread wheat and loblolly pine demonstrated that RHPP had a similar or better predictive accuracy in most cases compared with GBLUP. In the future, RHPP may be an attractive choice for analyzing large-scale and high-dimensional data.KEY MESSAGEA new genomic prediction method (RHPP) was developed via combining randomized Haseman-Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. Computational efficiency is becoming a hot issue in the practical application of genomic prediction due to the large number of data generated by the high-throughput genotyping technology. In this study, we developed a fast genomic prediction method RHPP via combining randomized Haseman-Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. The simulation results demonstrated similar prediction accuracy between RHPP and GBLUP, and significantly higher computational efficiency of the former with the increase of individuals. The results of real datasets of both bread wheat and loblolly pine demonstrated that RHPP had a similar or better predictive accuracy in most cases compared with GBLUP. In the future, RHPP may be an attractive choice for analyzing large-scale and high-dimensional data. Key messageA new genomic prediction method (RHPP) was developed via combining randomized Haseman–Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm.Computational efficiency is becoming a hot issue in the practical application of genomic prediction due to the large number of data generated by the high-throughput genotyping technology. In this study, we developed a fast genomic prediction method RHPP via combining randomized Haseman–Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. The simulation results demonstrated similar prediction accuracy between RHPP and GBLUP, and significantly higher computational efficiency of the former with the increase of individuals. The results of real datasets of both bread wheat and loblolly pine demonstrated that RHPP had a similar or better predictive accuracy in most cases compared with GBLUP. In the future, RHPP may be an attractive choice for analyzing large-scale and high-dimensional data. Key message A new genomic prediction method (RHPP) was developed via combining randomized Haseman–Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. Computational efficiency is becoming a hot issue in the practical application of genomic prediction due to the large number of data generated by the high-throughput genotyping technology. In this study, we developed a fast genomic prediction method RHPP via combining randomized Haseman–Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. The simulation results demonstrated similar prediction accuracy between RHPP and GBLUP, and significantly higher computational efficiency of the former with the increase of individuals. The results of real datasets of both bread wheat and loblolly pine demonstrated that RHPP had a similar or better predictive accuracy in most cases compared with GBLUP. In the future, RHPP may be an attractive choice for analyzing large-scale and high-dimensional data. |
| Author | Yu, Shizhou Liu, Hailan |
| Author_xml | – sequence: 1 givenname: Hailan orcidid: 0000-0001-5737-9240 surname: Liu fullname: Liu, Hailan email: lhlzju@hotmail.com organization: Maize Research Institute, Sichuan Agricultural University – sequence: 2 givenname: Shizhou surname: Yu fullname: Yu, Shizhou email: yusz@nwafu.edu.cn organization: Molecular Genetics Key Laboratory of China Tobacco, Guizhou Academy of Tobacco Science |
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| Cites_doi | 10.1016/j.ajhg.2010.11.011 10.1016/j.xplc.2019.100005 10.3389/fpls.2021.728567 10.1186/1297-9686-42-2 10.1186/s12711-016-0261-6 10.2135/cropsci2011.06.0297 10.3389/fgene.2018.00078 10.1007/s00122-019-03493-z 10.3168/jdsc.2021-0097 10.1186/s12711-017-0369-3 10.1534/genetics.113.152207 10.1534/g3.118.200311 10.1093/aob/mcs109 10.2135/cropsci2011.11.0592 10.3389/fpls.2022.1089937 10.1071/AN15538 10.1016/j.cj.2021.09.001 10.3168/jds.2019-17332 10.2527/af.2016-0002 10.1534/genetics.111.137026 10.3168/jds.2007-0980 10.1186/s12711-022-00767-x 10.1186/s12711-018-0373-2 10.1038/s41467-020-18404-w 10.1093/bioinformatics/bty253 10.1534/genetics.112.147983 10.2135/cropsci2008.08.0512 10.1186/s12711-022-00756-0 10.1534/g3.120.401172 10.1007/s00122-015-2555-4 10.3168/jds.2019-17684 10.1038/s41437-018-0078-x 10.1007/s00122-017-2887-3 10.1186/s12711-014-0060-x 10.1186/s13059-021-02370-7 10.1111/j.1439-0388.2007.00702.x 10.1016/j.animal.2023.100766 10.1007/s11434-011-4632-7 10.2527/af.2011-0027 10.1038/s41437-018-0099-5 10.1186/s12711-014-0082-4 10.1038/ng.3920 10.1146/annurev-animal-031412-103705 10.1534/genetics.115.177907 10.1534/genetics.115.182089 10.1017/S1751731116002366 10.1371/journal.pone.0210442 10.1038/hdy.2013.16 10.1534/genetics.116.187013 10.1093/genetics/157.4.1819 10.1186/1753-6561-5-S3-S11 |
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| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. |
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| Keywords | Genomic prediction Principal component regression GBLUP Preconditioned conjugate gradient algorithm Randomized Haseman–Elston regression |
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| References | Ødegård, Indahl, Strandén, Meuwissen (CR32) 2018; 50 Wang, Miao, Chang, Xia, An, Li, Xu, Zhang, Gao, Li, Gao (CR47) 2019; 14 VanRaden (CR43) 2008; 91 Xu, Liu, Fu, Wang, Wang, Huang, Prasanna, Olsen, Wang, Zhang (CR50) 2020; 1 Ogutu, Piepho, Schulz-Streeck (CR33) 2011; 5 Heslot, Yang, Sorrells, Jannink (CR14) 2012; 52 Yang, Lee, Goddard, Visscher (CR51) 2011; 88 Tsuruta, Lourenco, Masuda, Lawlor, Misztal (CR42) 2021; 2 Habier, Fernando, Garrick (CR12) 2013; 194 Xiao, Jiang, Cheng, Wang, Yan, Zhang, Qiao, Ma, Luo, Li, Liu, Yang, Song, Meng, Warburton, Zhao, Wang, Yan (CR49) 2021; 22 Zhang, Zhang, Ding (CR52) 2011; 56 Liu, Chen (CR21) 2022; 10 Meuwissen, Hayes, Goddard (CR24) 2001; 157 Varona, Legarra, Toro, Vitezica (CR45) 2018; 9 Gholami, Wimmer, Sansaloni, Petroli, Hearne, Covarrubias-Pazaran, Rensing, Heise, Pérez-Rodrĺguez, Dreisigacker, Crossa, Martini (CR10) 2021; 12 Liu, Chen (CR20) 2018; 121 Resende, Muñoz, Resende, Garrick, Fernando, Davis, Jokela, Martin, Peter, Kirst (CR39) 2012; 190 Christensen, Lund (CR3) 2010; 42 Pocrnic, Lourenco, Masuda, Legarra, Misztal (CR35) 2016; 203 Nakaya, Isobe (CR30) 2012; 110 VanRaden (CR44) 2020; 103 Sansaloni, Franco, Santos, Percival-Alwyn, Singh, Petroli, Campos, Dreher, Payne, Marshall, Kilian, Milne, Raubach, Shaw, Stephen, Carling, Pierre, Burgueño, Crosa, Li, Guzman, Kehel, Amri, Kilian, Wenzl, Uauy, Banziger, Caccamo, Pixley (CR41) 2020; 11 Ghafouri-Kesbi, Rahimi-Mianji, Honarvar, Nejati-Javaremi (CR9) 2017; 57 Cesarani, Bermann, Dimauro, Degano, Vicario, Lourenco, Macciotta (CR2) 2023; 17 Liu, Xia, Lan (CR22) 2022; 13 Crossa, Pérez, Hickey, Burgueño, Ornella, Cerón-Rojas, Zhang, Dreisigacker, Babu, Li, Bonnett, Mathews (CR4) 2014; 112 Pocrnic, Lindgren, Tolhurst, Herring, Gorjanc (CR37) 2022; 54 Meuwissen, Hayes, Goddard (CR25) 2013; 1 Piepho, Ogutu, Schulz -Streeck T, Estaghvirou B, Gordillo A, Technow F (CR34) 2012; 52 Eggen (CR8) 2012; 2 Goddard, Hayes (CR11) 2007; 124 Misztal, Legarra (CR29) 2017; 11 Meuwissen, Hayes, Goddard (CR26) 2016; 6 Ros-Freixedes, Johnsson, Whalen, Chen, Valente, Herring, Gorjanc, Hickey (CR40) 2022; 54 Daetwyler, Calus, Pong-Wong, de los Campos, Hickey (CR6) 2013; 193 Mageto, Crossa, Pérez-Rodrĺguez, Dhliwayo, Palacios-Rojas, Lee, Guo, Vicente, Zhang, Hindu (CR23) 2020; 10 Ceballos, Kawuki, Gracen, Yencho, Hershey (CR1) 2015; 128 Liu, Chen (CR19) 2017; 130 Jiang, Reif (CR16) 2015; 201 Jiang, Cheng, Yan, Fu, Wang (CR17) 2020; 133 Hickey, Chiurugwi, Mackay, Powell (CR15) 2017; 49 Heffner, Sorrells, Jannink (CR13) 2009; 49 Meuwissen, Indahl, Ødegård (CR27) 2017; 49 Misztal (CR28) 2016; 202 (CR38) 2017 Dadousis, Veerkamp, Heringstad, Pszczola, Calus (CR5) 2014; 46 Pocrnic, Lourenco, Masuda, Misztal (CR36) 2016; 48 Norman, Taylor, Edwards, Kuchel (CR31) 2018; 8 Du, Wei, Wang, Jia (CR7) 2018; 121 Wang, Chen, Goddard, Meuwissen, Kemper, Hayes (CR46) 2015; 47 Wu, Sankararaman (CR48) 2018; 34 Li, VanRaden, Guduk, O’Connell, Null, Connor, VandeHaar, Tempelman, Weigel, Cole (CR18) 2020; 103 F Ghafouri-Kesbi (3069_CR9) 2017; 57 A Norman (3069_CR31) 2018; 8 B Li (3069_CR18) 2020; 103 H Liu (3069_CR22) 2022; 13 PM VanRaden (3069_CR44) 2020; 103 THE Meuwissen (3069_CR24) 2001; 157 C Sansaloni (3069_CR41) 2020; 11 H Liu (3069_CR21) 2022; 10 I Misztal (3069_CR29) 2017; 11 I Pocrnic (3069_CR37) 2022; 54 T Meuwissen (3069_CR26) 2016; 6 HD Daetwyler (3069_CR6) 2013; 193 HP Piepho (3069_CR34) 2012; 52 S Jiang (3069_CR17) 2020; 133 X Wang (3069_CR47) 2019; 14 T Meuwissen (3069_CR25) 2013; 1 ME Goddard (3069_CR11) 2007; 124 J Ødegård (3069_CR32) 2018; 50 H Liu (3069_CR19) 2017; 130 I Pocrnic (3069_CR36) 2016; 48 N Heslot (3069_CR14) 2012; 52 A Eggen (3069_CR8) 2012; 2 OF Christensen (3069_CR3) 2010; 42 Y Wu (3069_CR48) 2018; 34 Y Xu (3069_CR50) 2020; 1 L Varona (3069_CR45) 2018; 9 I Misztal (3069_CR28) 2016; 202 M Gholami (3069_CR10) 2021; 12 J Yang (3069_CR51) 2011; 88 H Liu (3069_CR20) 2018; 121 T Wang (3069_CR46) 2015; 47 PM VanRaden (3069_CR43) 2008; 91 C Du (3069_CR7) 2018; 121 A Cesarani (3069_CR2) 2023; 17 H Ceballos (3069_CR1) 2015; 128 A Nakaya (3069_CR30) 2012; 110 J Crossa (3069_CR4) 2014; 112 THE Meuwissen (3069_CR27) 2017; 49 C Dadousis (3069_CR5) 2014; 46 R Ros-Freixedes (3069_CR40) 2022; 54 EL Heffner (3069_CR13) 2009; 49 S Tsuruta (3069_CR42) 2021; 2 D Habier (3069_CR12) 2013; 194 JM Hickey (3069_CR15) 2017; 49 Y Xiao (3069_CR49) 2021; 22 Y Jiang (3069_CR16) 2015; 201 EK Mageto (3069_CR23) 2020; 10 Z Zhang (3069_CR52) 2011; 56 JO Ogutu (3069_CR33) 2011; 5 I Pocrnic (3069_CR35) 2016; 203 R Core Team (3069_CR38) 2017 MFR Resende Jr (3069_CR39) 2012; 190 |
| References_xml | – volume: 88 start-page: 76 year: 2011 end-page: 82 ident: CR51 article-title: GCTA: a tool for genome-wide complex trait analysis publication-title: Am J Hum Genet doi: 10.1016/j.ajhg.2010.11.011 – volume: 1 year: 2020 ident: CR50 article-title: Enhancing genetic gain through genomic selection: from livestock to plants publication-title: Plant Commun doi: 10.1016/j.xplc.2019.100005 – volume: 12 year: 2021 ident: CR10 article-title: A comparison of the adoption of genomic selection across different breeding institutions publication-title: Front Plant Sci doi: 10.3389/fpls.2021.728567 – volume: 42 start-page: 2 year: 2010 ident: CR3 article-title: Genomic prediction when some animals are not genotyped publication-title: Genet Sel Evol doi: 10.1186/1297-9686-42-2 – volume: 48 start-page: 82 year: 2016 ident: CR36 article-title: Dimensionality of genomic information and performance of the algorithm for Proven and Young for different livestock species publication-title: Genet Sel Evol doi: 10.1186/s12711-016-0261-6 – volume: 52 start-page: 146 year: 2012 end-page: 160 ident: CR14 article-title: Genomic selection in plant breeding: a comparison of models publication-title: Crop Sci doi: 10.2135/cropsci2011.06.0297 – volume: 9 start-page: 78 year: 2018 ident: CR45 article-title: Non-additive effects in genomic selection publication-title: Front Genet doi: 10.3389/fgene.2018.00078 – volume: 133 start-page: 1491 year: 2020 end-page: 1502 ident: CR17 article-title: Genome optimization for improvement of maize breeding publication-title: Theor Appl Genet doi: 10.1007/s00122-019-03493-z – volume: 2 start-page: 356 year: 2021 end-page: 360 ident: CR42 article-title: Reducing computational cost of large-scale genomic evaluation by using indirect genomic prediction publication-title: JDS Commun doi: 10.3168/jdsc.2021-0097 – volume: 49 start-page: 94 year: 2017 ident: CR27 article-title: Variable selection models for genomic selection using whole-genome sequence data and singular value decomposition publication-title: Genet Sel Evol doi: 10.1186/s12711-017-0369-3 – volume: 194 start-page: 597 year: 2013 end-page: 607 ident: CR12 article-title: Genomic BLUP decoded: a look into the black box of genomic prediction publication-title: Genetics doi: 10.1534/genetics.113.152207 – volume: 8 start-page: 2889 year: 2018 end-page: 2899 ident: CR31 article-title: Optimising genomic selection in wheat: effect of marker density, population size and population structure on prediction accuracy publication-title: G3 Genes Genomes Genet doi: 10.1534/g3.118.200311 – volume: 110 start-page: 1303 year: 2012 end-page: 1316 ident: CR30 article-title: Will genomic selection be a practical method for plant breeding? publication-title: Ann Bot doi: 10.1093/aob/mcs109 – volume: 52 start-page: 1093 year: 2012 end-page: 1104 ident: CR34 article-title: Efficient computation of ridge-regression best linear unbiased prediction in genomic selection in plant breeding publication-title: Crop Sci doi: 10.2135/cropsci2011.11.0592 – volume: 13 start-page: 1089937 year: 2022 ident: CR22 article-title: An efficient genomic prediction method without the direct inverse of the genomic relationship matrix publication-title: Front Plant Sci doi: 10.3389/fpls.2022.1089937 – volume: 57 start-page: 229 year: 2017 end-page: 236 ident: CR9 article-title: Predictive ability of random forests, boosting, support vector machines and genomic best linear unbiased prediction in different scenarios of genomic evaluation publication-title: Anim Prod Sci doi: 10.1071/AN15538 – volume: 10 start-page: 550 year: 2022 end-page: 554 ident: CR21 article-title: A novel genomic prediction method combining randomized Haseman–Elston regression with a modified algorithm for Proven and Young for large genomic data publication-title: Crop J doi: 10.1016/j.cj.2021.09.001 – volume: 103 start-page: 2477 year: 2020 end-page: 2486 ident: CR18 article-title: Genomic prediction of residual feed intake in US Holstein dairy cattle publication-title: J Dairy Sci doi: 10.3168/jds.2019-17332 – volume: 6 start-page: 6 year: 2016 end-page: 14 ident: CR26 article-title: Genomic selection: A paradigm shift in animal breeding publication-title: Anim Front doi: 10.2527/af.2016-0002 – volume: 190 start-page: 1503 year: 2012 end-page: 1510 ident: CR39 article-title: Accuracy of genomic selection methods in a standard data set of loblolly pine ( L.) publication-title: Genetics doi: 10.1534/genetics.111.137026 – volume: 91 start-page: 4414 year: 2008 end-page: 4423 ident: CR43 article-title: Efficient methods to compute genomic predictions publication-title: J Dairy Sci doi: 10.3168/jds.2007-0980 – volume: 54 start-page: 76 year: 2022 ident: CR37 article-title: Optimisation of the core subset for the APY approximation of genomic relationships publication-title: Genet Sel Evol doi: 10.1186/s12711-022-00767-x – volume: 50 start-page: 6 year: 2018 ident: CR32 article-title: Large-scale genomic prediction using singular value decomposition of the genotype matrix publication-title: Genet Sel Evol doi: 10.1186/s12711-018-0373-2 – volume: 11 start-page: 4572 year: 2020 ident: CR41 article-title: Diversity analysis of 80,000 wheat accessions reveals consequences and opportunities of selection footprints publication-title: Nat Commun doi: 10.1038/s41467-020-18404-w – volume: 34 start-page: i187 year: 2018 end-page: i194 ident: CR48 article-title: A scalable estimator of SNP heritability for biobank-scale data publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty253 – volume: 193 start-page: 347 year: 2013 end-page: 365 ident: CR6 article-title: Genomic prediction in animals and plants: simulation of data, validation, reporting, and benchmarking publication-title: Genetics doi: 10.1534/genetics.112.147983 – volume: 49 start-page: 1 year: 2009 end-page: 12 ident: CR13 article-title: Genomic selection for crop improvement publication-title: Crop Sci doi: 10.2135/cropsci2008.08.0512 – volume: 54 start-page: 65 year: 2022 ident: CR40 article-title: Genomic prediction with whole-genome sequence data in intensely selected pig lines publication-title: Genet Sel Evol doi: 10.1186/s12711-022-00756-0 – volume: 10 start-page: 2629 year: 2020 end-page: 2639 ident: CR23 article-title: Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm publication-title: G3 Genes Genomes Genet doi: 10.1534/g3.120.401172 – volume: 128 start-page: 1647 year: 2015 end-page: 1667 ident: CR1 article-title: Conventional breeding, marker-assisted selection, genomic selection and inbreeding in clonally propagated crops: a case study for cassava publication-title: Theor Appl Genet doi: 10.1007/s00122-015-2555-4 – volume: 103 start-page: 5291 year: 2020 end-page: 5301 ident: CR44 article-title: Symposium review: how to implement genomic selection publication-title: J Dairy Sci doi: 10.3168/jds.2019-17684 – volume: 121 start-page: 12 year: 2018 end-page: 23 ident: CR7 article-title: Genomic selection using principal component regression publication-title: Heredity doi: 10.1038/s41437-018-0078-x – year: 2017 ident: CR38 publication-title: R: A language and environment for statistical computing – volume: 130 start-page: 1277 year: 2017 end-page: 1284 ident: CR19 article-title: A fast genomic selection approach for large genomic data publication-title: Theor Appl Genet doi: 10.1007/s00122-017-2887-3 – volume: 46 start-page: 60 year: 2014 ident: CR5 article-title: A comparison of principal component regression and genomic REML for genomic prediction across populations publication-title: Genet Sel Evol doi: 10.1186/s12711-014-0060-x – volume: 22 start-page: 148 year: 2021 ident: CR49 article-title: The genetic mechanism of heterosis utilization in maize improvement publication-title: Genome Biol doi: 10.1186/s13059-021-02370-7 – volume: 124 start-page: 323 year: 2007 end-page: 330 ident: CR11 article-title: Genomic selection publication-title: J Anim Breed Genet doi: 10.1111/j.1439-0388.2007.00702.x – volume: 17 year: 2023 ident: CR2 article-title: Strategies for choosing core animals in the algorithm for proven and young and their impact on the accuracy of single-step genomic predictions in cattle publication-title: Animal doi: 10.1016/j.animal.2023.100766 – volume: 56 start-page: 2655 year: 2011 end-page: 2663 ident: CR52 article-title: Advances in genomic selection in domestic animals publication-title: Chin Sci Bull doi: 10.1007/s11434-011-4632-7 – volume: 2 start-page: 10 year: 2012 end-page: 15 ident: CR8 article-title: The development and application of genomic selection as a new breeding paradigm publication-title: Anim Front doi: 10.2527/af.2011-0027 – volume: 121 start-page: 196 year: 2018 end-page: 204 ident: CR20 article-title: A new genomic prediction method with additive-dominance effects in the least-squares framework publication-title: Heredity doi: 10.1038/s41437-018-0099-5 – volume: 47 start-page: 34 year: 2015 ident: CR46 article-title: A computationally efficient algorithm for genomic prediction using a Bayesian model publication-title: Genet Sel Evol doi: 10.1186/s12711-014-0082-4 – volume: 49 start-page: 1297 year: 2017 end-page: 1303 ident: CR15 article-title: Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery publication-title: Nat Genet doi: 10.1038/ng.3920 – volume: 1 start-page: 221 year: 2013 end-page: 237 ident: CR25 article-title: Accelerating improvement of livestock with genomic selection publication-title: Annu Rev Anim Biosci doi: 10.1146/annurev-animal-031412-103705 – volume: 201 start-page: 759 year: 2015 end-page: 768 ident: CR16 article-title: Modeling epistasis in genomic selection publication-title: Genetics doi: 10.1534/genetics.115.177907 – volume: 202 start-page: 401 year: 2016 end-page: 409 ident: CR28 article-title: Inexpensive computation of the inverse of the genomic relationship matrix in populations with small effective population size publication-title: Genetics doi: 10.1534/genetics.115.182089 – volume: 11 start-page: 731 year: 2017 end-page: 736 ident: CR29 article-title: Invited review: efficient computation strategies in genomic selection publication-title: Animal doi: 10.1017/S1751731116002366 – volume: 14 year: 2019 ident: CR47 article-title: Evaluation of GBLUP, BayesB and elastic net for genomic prediction in Chinese Simmental beef cattle publication-title: PLoS ONE doi: 10.1371/journal.pone.0210442 – volume: 112 start-page: 48 year: 2014 end-page: 60 ident: CR4 article-title: Genomic prediction in CIMMYT maize and wheat breeding programs publication-title: Heredity doi: 10.1038/hdy.2013.16 – volume: 203 start-page: 573 year: 2016 end-page: 581 ident: CR35 article-title: The Dimensionality of genomic information and its effect on genomic prediction publication-title: Genetics doi: 10.1534/genetics.116.187013 – volume: 157 start-page: 1819 year: 2001 end-page: 1829 ident: CR24 article-title: Prediction of total genetic value using genome-wide dense marker maps publication-title: Genetics doi: 10.1093/genetics/157.4.1819 – volume: 5 start-page: S11 year: 2011 ident: CR33 article-title: A comparison of random forests, boosting and support vector machines for genomic selection publication-title: BMC Proc doi: 10.1186/1753-6561-5-S3-S11 – volume: 46 start-page: 60 year: 2014 ident: 3069_CR5 publication-title: Genet Sel Evol doi: 10.1186/s12711-014-0060-x – volume: 9 start-page: 78 year: 2018 ident: 3069_CR45 publication-title: Front Genet doi: 10.3389/fgene.2018.00078 – volume: 12 year: 2021 ident: 3069_CR10 publication-title: Front Plant Sci doi: 10.3389/fpls.2021.728567 – volume: 201 start-page: 759 year: 2015 ident: 3069_CR16 publication-title: Genetics doi: 10.1534/genetics.115.177907 – volume: 1 year: 2020 ident: 3069_CR50 publication-title: Plant Commun doi: 10.1016/j.xplc.2019.100005 – volume: 54 start-page: 76 year: 2022 ident: 3069_CR37 publication-title: Genet Sel Evol doi: 10.1186/s12711-022-00767-x – volume: 88 start-page: 76 year: 2011 ident: 3069_CR51 publication-title: Am J Hum Genet doi: 10.1016/j.ajhg.2010.11.011 – volume: 50 start-page: 6 year: 2018 ident: 3069_CR32 publication-title: Genet Sel Evol doi: 10.1186/s12711-018-0373-2 – volume: 48 start-page: 82 year: 2016 ident: 3069_CR36 publication-title: Genet Sel Evol doi: 10.1186/s12711-016-0261-6 – volume: 128 start-page: 1647 year: 2015 ident: 3069_CR1 publication-title: Theor Appl Genet doi: 10.1007/s00122-015-2555-4 – volume: 11 start-page: 731 year: 2017 ident: 3069_CR29 publication-title: Animal doi: 10.1017/S1751731116002366 – volume: 14 year: 2019 ident: 3069_CR47 publication-title: PLoS ONE doi: 10.1371/journal.pone.0210442 – volume-title: R: A language and environment for statistical computing year: 2017 ident: 3069_CR38 – volume: 8 start-page: 2889 year: 2018 ident: 3069_CR31 publication-title: G3 Genes Genomes Genet doi: 10.1534/g3.118.200311 – volume: 5 start-page: S11 year: 2011 ident: 3069_CR33 publication-title: BMC Proc doi: 10.1186/1753-6561-5-S3-S11 – volume: 52 start-page: 146 year: 2012 ident: 3069_CR14 publication-title: Crop Sci doi: 10.2135/cropsci2011.06.0297 – volume: 190 start-page: 1503 year: 2012 ident: 3069_CR39 publication-title: Genetics doi: 10.1534/genetics.111.137026 – volume: 124 start-page: 323 year: 2007 ident: 3069_CR11 publication-title: J Anim Breed Genet doi: 10.1111/j.1439-0388.2007.00702.x – volume: 194 start-page: 597 year: 2013 ident: 3069_CR12 publication-title: Genetics doi: 10.1534/genetics.113.152207 – volume: 133 start-page: 1491 year: 2020 ident: 3069_CR17 publication-title: Theor Appl Genet doi: 10.1007/s00122-019-03493-z – volume: 103 start-page: 2477 year: 2020 ident: 3069_CR18 publication-title: J Dairy Sci doi: 10.3168/jds.2019-17332 – volume: 49 start-page: 1297 year: 2017 ident: 3069_CR15 publication-title: Nat Genet doi: 10.1038/ng.3920 – volume: 203 start-page: 573 year: 2016 ident: 3069_CR35 publication-title: Genetics doi: 10.1534/genetics.116.187013 – volume: 22 start-page: 148 year: 2021 ident: 3069_CR49 publication-title: Genome Biol doi: 10.1186/s13059-021-02370-7 – volume: 157 start-page: 1819 year: 2001 ident: 3069_CR24 publication-title: Genetics doi: 10.1093/genetics/157.4.1819 – volume: 47 start-page: 34 year: 2015 ident: 3069_CR46 publication-title: Genet Sel Evol doi: 10.1186/s12711-014-0082-4 – volume: 42 start-page: 2 year: 2010 ident: 3069_CR3 publication-title: Genet Sel Evol doi: 10.1186/1297-9686-42-2 – volume: 52 start-page: 1093 year: 2012 ident: 3069_CR34 publication-title: Crop Sci doi: 10.2135/cropsci2011.11.0592 – volume: 91 start-page: 4414 year: 2008 ident: 3069_CR43 publication-title: J Dairy Sci doi: 10.3168/jds.2007-0980 – volume: 112 start-page: 48 year: 2014 ident: 3069_CR4 publication-title: Heredity doi: 10.1038/hdy.2013.16 – volume: 49 start-page: 1 year: 2009 ident: 3069_CR13 publication-title: Crop Sci doi: 10.2135/cropsci2008.08.0512 – volume: 17 year: 2023 ident: 3069_CR2 publication-title: Animal doi: 10.1016/j.animal.2023.100766 – volume: 6 start-page: 6 year: 2016 ident: 3069_CR26 publication-title: Anim Front doi: 10.2527/af.2016-0002 – volume: 2 start-page: 356 year: 2021 ident: 3069_CR42 publication-title: JDS Commun doi: 10.3168/jdsc.2021-0097 – volume: 202 start-page: 401 year: 2016 ident: 3069_CR28 publication-title: Genetics doi: 10.1534/genetics.115.182089 – volume: 10 start-page: 2629 year: 2020 ident: 3069_CR23 publication-title: G3 Genes Genomes Genet doi: 10.1534/g3.120.401172 – volume: 56 start-page: 2655 year: 2011 ident: 3069_CR52 publication-title: Chin Sci Bull doi: 10.1007/s11434-011-4632-7 – volume: 130 start-page: 1277 year: 2017 ident: 3069_CR19 publication-title: Theor Appl Genet doi: 10.1007/s00122-017-2887-3 – volume: 13 start-page: 1089937 year: 2022 ident: 3069_CR22 publication-title: Front Plant Sci doi: 10.3389/fpls.2022.1089937 – volume: 103 start-page: 5291 year: 2020 ident: 3069_CR44 publication-title: J Dairy Sci doi: 10.3168/jds.2019-17684 – volume: 49 start-page: 94 year: 2017 ident: 3069_CR27 publication-title: Genet Sel Evol doi: 10.1186/s12711-017-0369-3 – volume: 193 start-page: 347 year: 2013 ident: 3069_CR6 publication-title: Genetics doi: 10.1534/genetics.112.147983 – volume: 121 start-page: 12 year: 2018 ident: 3069_CR7 publication-title: Heredity doi: 10.1038/s41437-018-0078-x – volume: 110 start-page: 1303 year: 2012 ident: 3069_CR30 publication-title: Ann Bot doi: 10.1093/aob/mcs109 – volume: 54 start-page: 65 year: 2022 ident: 3069_CR40 publication-title: Genet Sel Evol doi: 10.1186/s12711-022-00756-0 – volume: 34 start-page: i187 year: 2018 ident: 3069_CR48 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty253 – volume: 10 start-page: 550 year: 2022 ident: 3069_CR21 publication-title: Crop J doi: 10.1016/j.cj.2021.09.001 – volume: 1 start-page: 221 year: 2013 ident: 3069_CR25 publication-title: Annu Rev Anim Biosci doi: 10.1146/annurev-animal-031412-103705 – volume: 11 start-page: 4572 year: 2020 ident: 3069_CR41 publication-title: Nat Commun doi: 10.1038/s41467-020-18404-w – volume: 2 start-page: 10 year: 2012 ident: 3069_CR8 publication-title: Anim Front doi: 10.2527/af.2011-0027 – volume: 57 start-page: 229 year: 2017 ident: 3069_CR9 publication-title: Anim Prod Sci doi: 10.1071/AN15538 – volume: 121 start-page: 196 year: 2018 ident: 3069_CR20 publication-title: Heredity doi: 10.1038/s41437-018-0099-5 |
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A new genomic prediction method (RHPP) was developed via combining randomized Haseman–Elston regression (RHE-reg), PCR based on genomic information... Key messageA new genomic prediction method (RHPP) was developed via combining randomized Haseman–Elston regression (RHE-reg), PCR based on genomic information... A new genomic prediction method (RHPP) was developed via combining randomized Haseman-Elston regression (RHE-reg), PCR based on genomic information of core... KEY MESSAGE: A new genomic prediction method (RHPP) was developed via combining randomized Haseman–Elston regression (RHE-reg), PCR based on genomic... |
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| SubjectTerms | Accuracy Algorithms Biomedical and Life Sciences Biotechnology Cell Biology Computational efficiency Computer applications Computing time Conjugate gradient method data collection Efficiency Genomics Genotyping Life Sciences Machine learning Methods Original Article Pine trees Pinus taeda Plant Biochemistry Plant Sciences prediction Predictions wheat |
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| Title | A dimensionality-reduction genomic prediction method without direct inverse of the genomic relationship matrix for large genomic data |
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| Volume | 42 |
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