BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

175 related articles for article (PubMed ID: 29765161)

  • 1. Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits.
    Wang J; Zhou Z; Zhang Z; Li H; Liu D; Zhang Q; Bradbury PJ; Buckler ES; Zhang Z
    Heredity (Edinb); 2018 Dec; 121(6):648-662. PubMed ID: 29765161
    [TBL] [Abstract][Full Text] [Related]  

  • 2. GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction.
    Wang J; Zhang Z
    Genomics Proteomics Bioinformatics; 2021 Aug; 19(4):629-640. PubMed ID: 34492338
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Efficient weighting methods for genomic best linear-unbiased prediction (BLUP) adapted to the genetic architectures of quantitative traits.
    Ren D; An L; Li B; Qiao L; Liu W
    Heredity (Edinb); 2021 Feb; 126(2):320-334. PubMed ID: 32980863
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results.
    Meher PK; Rustgi S; Kumar A
    Heredity (Edinb); 2022 Jun; 128(6):519-530. PubMed ID: 35508540
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Using markers with large effect in genetic and genomic predictions.
    Lopes MS; Bovenhuis H; van Son M; Nordbø Ø; Grindflek EH; Knol EF; Bastiaansen JW
    J Anim Sci; 2017 Jan; 95(1):59-71. PubMed ID: 28177367
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Accuracy of whole-genome prediction using a genetic architecture-enhanced variance-covariance matrix.
    Zhang Z; Erbe M; He J; Ober U; Gao N; Zhang H; Simianer H; Li J
    G3 (Bethesda); 2015 Feb; 5(4):615-27. PubMed ID: 25670771
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Performances of Adaptive MultiBLUP, Bayesian regressions, and weighted-GBLUP approaches for genomic predictions in Belgian Blue beef cattle.
    Gualdrón Duarte JL; Gori AS; Hubin X; Lourenco D; Charlier C; Misztal I; Druet T
    BMC Genomics; 2020 Aug; 21(1):545. PubMed ID: 32762654
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Improving the accuracy of whole genome prediction for complex traits using the results of genome wide association studies.
    Zhang Z; Ober U; Erbe M; Zhang H; Gao N; He J; Li J; Simianer H
    PLoS One; 2014; 9(3):e93017. PubMed ID: 24663104
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Genomic BLUP decoded: a look into the black box of genomic prediction.
    Habier D; Fernando RL; Garrick DJ
    Genetics; 2013 Jul; 194(3):597-607. PubMed ID: 23640517
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Genome-wide prediction of traits with different genetic architecture through efficient variable selection.
    Wimmer V; Lehermeier C; Albrecht T; Auinger HJ; Wang Y; Schön CC
    Genetics; 2013 Oct; 195(2):573-87. PubMed ID: 23934883
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Improving Genomic Prediction Using High-Dimensional Secondary Phenotypes.
    Arouisse B; Theeuwen TPJM; van Eeuwijk FA; Kruijer W
    Front Genet; 2021; 12():667358. PubMed ID: 34108993
    [TBL] [Abstract][Full Text] [Related]  

  • 12. An efficient unified model for genome-wide association studies and genomic selection.
    Li H; Su G; Jiang L; Bao Z
    Genet Sel Evol; 2017 Aug; 49(1):64. PubMed ID: 28836943
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers.
    Moser G; Tier B; Crump RE; Khatkar MS; Raadsma HW
    Genet Sel Evol; 2009 Dec; 41(1):56. PubMed ID: 20043835
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.).
    Onogi A; Ideta O; Inoshita Y; Ebana K; Yoshioka T; Yamasaki M; Iwata H
    Theor Appl Genet; 2015 Jan; 128(1):41-53. PubMed ID: 25341369
    [TBL] [Abstract][Full Text] [Related]  

  • 15. GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction.
    Tang Y; Liu X; Wang J; Li M; Wang Q; Tian F; Su Z; Pan Y; Liu D; Lipka AE; Buckler ES; Zhang Z
    Plant Genome; 2016 Jul; 9(2):. PubMed ID: 27898829
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.).
    Resende MF; Muñoz P; Resende MD; Garrick DJ; Fernando RL; Davis JM; Jokela EJ; Martin TA; Peter GF; Kirst M
    Genetics; 2012 Apr; 190(4):1503-10. PubMed ID: 22271763
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino sheep.
    Zhu S; Guo T; Yuan C; Liu J; Li J; Han M; Zhao H; Wu Y; Sun W; Wang X; Wang T; Liu J; Tiambo CK; Yue Y; Yang B
    G3 (Bethesda); 2021 Oct; 11(11):. PubMed ID: 34849779
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Evaluation of RR-BLUP Genomic Selection Models that Incorporate Peak Genome-Wide Association Study Signals in Maize and Sorghum.
    Rice B; Lipka AE
    Plant Genome; 2019 Mar; 12(1):. PubMed ID: 30951091
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Parametric and nonparametric statistical methods for genomic selection of traits with additive and epistatic genetic architectures.
    Howard R; Carriquiry AL; Beavis WD
    G3 (Bethesda); 2014 Apr; 4(6):1027-46. PubMed ID: 24727289
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Salinity stress tolerance prediction for biomass-related traits in maize (Zea mays L.) using genome-wide markers.
    Singh V; Krause M; Sandhu D; Sekhon RS; Kaundal A
    Plant Genome; 2023 Dec; 16(4):e20385. PubMed ID: 37667417
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.