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Journal Abstract Search

163 related items for PubMed ID: 29748895

  • 1. Genomic selection of agronomic traits in hybrid rice using an NCII population.
    Xu Y, Wang X, Ding X, Zheng X, Yang Z, Xu C, Hu Z.
    Rice (N Y); 2018 May 10; 11(1):32. PubMed ID: 29748895
    [Abstract] [Full Text] [Related]

  • 2. Hybrid breeding of rice via genomic selection.
    Cui Y, Li R, Li G, Zhang F, Zhu T, Zhang Q, Ali J, Li Z, Xu S.
    Plant Biotechnol J; 2020 Jan 10; 18(1):57-67. PubMed ID: 31124256
    [Abstract] [Full Text] [Related]

  • 3. Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II.
    Wang X, Li L, Yang Z, Zheng X, Yu S, Xu C, Hu Z.
    Heredity (Edinb); 2017 Mar 10; 118(3):302-310. PubMed ID: 27649618
    [Abstract] [Full Text] [Related]

  • 4. Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice.
    Xu Y, Zhao Y, Wang X, Ma Y, Li P, Yang Z, Zhang X, Xu C, Xu S.
    Plant Biotechnol J; 2021 Feb 10; 19(2):261-272. PubMed ID: 32738177
    [Abstract] [Full Text] [Related]

  • 5. Metabolomic prediction of yield in hybrid rice.
    Xu S, Xu Y, Gong L, Zhang Q.
    Plant J; 2016 Oct 10; 88(2):219-227. PubMed ID: 27311694
    [Abstract] [Full Text] [Related]

  • 6. Genomic Prediction of Yield Traits in Single-Cross Hybrid Rice (Oryza sativa L.).
    Labroo MR, Ali J, Aslam MU, de Asis EJ, Dela Paz MA, Sevilla MA, Lipka AE, Studer AJ, Rutkoski JE.
    Front Genet; 2021 Oct 10; 12():692870. PubMed ID: 34276796
    [Abstract] [Full Text] [Related]

  • 7. Genome-wide association study and genomic prediction for yield and grain quality traits of hybrid rice.
    Yu P, Ye C, Li L, Yin H, Zhao J, Wang Y, Zhang Z, Li W, Long Y, Hu X, Xiao J, Jia G, Tian B.
    Mol Breed; 2022 Apr 10; 42(4):16. PubMed ID: 37309463
    [Abstract] [Full Text] [Related]

  • 8. Identification of optimal prediction models using multi-omic data for selecting hybrid rice.
    Wang S, Wei J, Li R, Qu H, Chater JM, Ma R, Li Y, Xie W, Jia Z.
    Heredity (Edinb); 2019 Sep 10; 123(3):395-406. PubMed ID: 30911139
    [Abstract] [Full Text] [Related]

  • 9. Predicting hybrid performance in rice using genomic best linear unbiased prediction.
    Xu S, Zhu D, Zhang Q.
    Proc Natl Acad Sci U S A; 2014 Aug 26; 111(34):12456-61. PubMed ID: 25114224
    [Abstract] [Full Text] [Related]

  • 10. Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.).
    Zhang Y, Zhang M, Ye J, Xu Q, Feng Y, Xu S, Hu D, Wei X, Hu P, Yang Y.
    Mol Breed; 2023 Nov 26; 43(11):81. PubMed ID: 37965378
    [Abstract] [Full Text] [Related]

  • 11. Genomic prediction of rice mesocotyl length indicative of directing seeding suitability using a half-sib hybrid population.
    Chen L, Liu J, He S, Cao L, Ye G.
    PLoS One; 2023 Nov 26; 18(4):e0283989. PubMed ID: 37018326
    [Abstract] [Full Text] [Related]

  • 12. Metabolic prediction of important agronomic traits in hybrid rice (Oryza sativa L.).
    Dan Z, Hu J, Zhou W, Yao G, Zhu R, Zhu Y, Huang W.
    Sci Rep; 2016 Feb 24; 6():21732. PubMed ID: 26907211
    [Abstract] [Full Text] [Related]

  • 13. A metabolome-based core hybridisation strategy for the prediction of rice grain weight across environments.
    Dan Z, Chen Y, Xu Y, Huang J, Huang J, Hu J, Yao G, Zhu Y, Huang W.
    Plant Biotechnol J; 2019 May 24; 17(5):906-913. PubMed ID: 30321482
    [Abstract] [Full Text] [Related]

  • 14. Predicting Growth Traits with Genomic Selection Methods in Zhikong Scallop (Chlamys farreri).
    Wang Y, Sun G, Zeng Q, Chen Z, Hu X, Li H, Wang S, Bao Z.
    Mar Biotechnol (NY); 2018 Dec 24; 20(6):769-779. PubMed ID: 30116982
    [Abstract] [Full Text] [Related]

  • 15. Predicting hybrid rice performance using AIHIB model based on artificial intelligence.
    Sabouri H, Sajadi SJ.
    Sci Rep; 2022 Jun 11; 12(1):9709. PubMed ID: 35690641
    [Abstract] [Full Text] [Related]

  • 16. Realized genomic selection across generations in a reciprocal recurrent selection breeding program of Eucalyptus hybrids.
    Simiqueli GF, Resende RT, Takahashi EK, de Sousa JE, Grattapaglia D.
    Front Plant Sci; 2023 Jun 11; 14():1252504. PubMed ID: 37965018
    [Abstract] [Full Text] [Related]

  • 17. Genomic Selection for F1 Hybrid Breeding in Strawberry (Fragaria × ananassa).
    Yamamoto E, Kataoka S, Shirasawa K, Noguchi Y, Isobe S.
    Front Plant Sci; 2021 Jun 11; 12():645111. PubMed ID: 33747025
    [Abstract] [Full Text] [Related]

  • 18. Improving Genomic Selection With Quantitative Trait Loci and Nonadditive Effects Revealed by Empirical Evidence in Maize.
    Liu X, Wang H, Hu X, Li K, Liu Z, Wu Y, Huang C.
    Front Plant Sci; 2019 Jun 11; 10():1129. PubMed ID: 31620155
    [Abstract] [Full Text] [Related]

  • 19. Effects of marker density and population structure on the genomic prediction accuracy for growth trait in Pacific white shrimp Litopenaeus vannamei.
    Wang Q, Yu Y, Yuan J, Zhang X, Huang H, Li F, Xiang J.
    BMC Genet; 2017 May 17; 18(1):45. PubMed ID: 28514941
    [Abstract] [Full Text] [Related]

  • 20. Dataset on the agronomic characteristics and combining ability of new parental lines in the two-line hybrid rice systems in Vietnam.
    Tran QV, Tran LT, Nguyen DTK, Ta LH, Nguyen LV, Nguyen TT.
    Data Brief; 2021 Jun 17; 36():107069. PubMed ID: 34026969
    [Abstract] [Full Text] [Related]

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