Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices
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Kevin P. Smith | Malachy T. Campbell | M. Gore | M. Sorrells | J. Jannink | Trevor H. Yeats | L. Gutiérrez | Haixiao Hu | L. Brzozowski | M. Caffe‐Treml
[1] Wenzhong Liu,et al. Efficient weighting methods for genomic best linear-unbiased prediction (BLUP) adapted to the genetic architectures of quantitative traits , 2020, Heredity.
[2] Kevin P. Smith,et al. Translating insights from the seed metabolome into improved prediction for healthful compounds in oat (Avena sativa L.) , 2020, bioRxiv.
[3] A. Malmendal,et al. Prediction of complex phenotypes using the Drosophila metabolome , 2020, bioRxiv.
[4] Daniel E. Runcie,et al. MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits , 2020, Genome Biology.
[5] I Misztal,et al. Current status of genomic evaluation , 2020, Journal of animal science.
[6] Samuel B. Fernandes,et al. Multi-Trait Genome-wide Association Studies Reveal Loci Associated with Maize Inflorescence and Leaf Architecture. , 2020, Plant & cell physiology.
[7] Timothy M. Beissinger,et al. Genomic Prediction Informed by Biological Processes Expands Our Understanding of the Genetic Architecture Underlying Free Amino Acid Traits in Dry Arabidopsis Seeds , 2019, bioRxiv.
[8] R. Fernando,et al. Inferring trait-specific similarity among individuals from molecular markers and phenotypes with Bayesian regression. , 2019, Theoretical population biology.
[9] Koji Noshita,et al. Comparison of shape quantification methods for genomic prediction, and genome-wide association study of sorghum seed morphology , 2019, PloS one.
[10] T. Mackay,et al. Systems genetics of the Drosophila metabolome , 2019, Genome research.
[11] T. Mackay,et al. Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits , 2019, bioRxiv.
[12] M. Lund,et al. Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits , 2019, Proceedings of the National Academy of Sciences.
[13] M. Gore,et al. Multivariate Genome-Wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativa L.) , 2019, G3: Genes, Genomes, Genetics.
[14] H. Simianer,et al. Integrating Gene Expression Data Into Genomic Prediction , 2019, Front. Genet..
[15] Harkamal Walia,et al. Genomic Bayesian Confirmatory Factor Analysis and Bayesian Network To Characterize a Wide Spectrum of Rice Phenotypes , 2018, G3: Genes, Genomes, Genetics.
[16] Suchismita Mondal,et al. Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat , 2018, G3: Genes, Genomes, Genetics.
[17] M. Gore,et al. Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays , 2018, G3: Genes, Genomes, Genetics.
[18] M. Lund,et al. Genomic Prediction Using Multi-trait Weighted GBLUP Accounting for Heterogeneous Variances and Covariances Across the Genome , 2018, G3: Genes, Genomes, Genetics.
[19] C. Bastien,et al. Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar , 2018, G3: Genes, Genomes, Genetics.
[20] Phenomic selection: a low-cost and high-throughput alternative to genomic selection , 2018 .
[21] A. Melchinger,et al. Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize , 2018, Genetics.
[22] J. Jannink,et al. Genomic prediction in a large African maize population , 2017 .
[23] Omics-based hybrid prediction in maize , 2017, Theoretical and Applied Genetics.
[24] M. Magwire,et al. Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize , 2016, Theoretical and Applied Genetics.
[25] Jean-Luc Jannink,et al. Evaluating Imputation Algorithms for Low-Depth Genotyping-By-Sequencing (GBS) Data , 2016, PloS one.
[26] Jean-Luc Jannink,et al. The Triticeae Toolbox: Combining Phenotype and Genotype Data to Advance Small‐Grains Breeding , 2016, The plant genome.
[27] G. Covarrubias-Pazaran. Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer , 2016, PloS one.
[28] Stefan M. Edwards,et al. Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster , 2016, Genetics.
[29] I. M. MacLeod,et al. Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits , 2016, BMC Genomics.
[30] M. Gore,et al. Closing the Divide between Human Nutrition and Plant Breeding , 2015 .
[31] Christian Maltecca,et al. Accounting for trait architecture in genomic predictions of US Holstein cattle using a weighted realized relationship matrix , 2015, Genetics Selection Evolution.
[32] Han Xu,et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. , 2014, American journal of human genetics.
[33] M. Lund,et al. Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances. , 2014, Journal of dairy science.
[34] G. de los Campos,et al. Genome-Wide Regression and Prediction with the BGLR Statistical Package , 2014, Genetics.
[35] Doug Speed,et al. MultiBLUP: improved SNP-based prediction for complex traits , 2014, Genome research.
[36] R. Fernando,et al. Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor , 2013, PLoS genetics.
[37] D. Gianola. Priors in Whole-Genome Regression: The Bayesian Alphabet Returns , 2013, Genetics.
[38] Sayan Mukherjee,et al. Dissecting High-Dimensional Phenotypes with Bayesian Sparse Factor Analysis of Genetic Covariance Matrices , 2012, Genetics.
[39] J. Ohlrogge,et al. Acyl-Lipid Metabolism , 2013, The arabidopsis book.
[40] Dorian J. Garrick,et al. A Fast EM Algorithm for BayesA-Like Prediction of Genomic Breeding Values , 2012, PloS one.
[41] I Misztal,et al. Efficient computation of the genomic relationship matrix and other matrices used in single-step evaluation. , 2011, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.
[42] N. Palacios-Rojas,et al. Near-infrared reflectance spectroscopy (NIRS) for protein, tryptophan, and lysine evaluation in quality protein maize (QPM) breeding programs. , 2011, Journal of agricultural and food chemistry.
[43] Zhe Zhang,et al. Best Linear Unbiased Prediction of Genomic Breeding Values Using a Trait-Specific Marker-Derived Relationship Matrix , 2010, PloS one.
[44] J. Rafferty,et al. Fatty Acid Biosynthesis in Plants — Metabolic Pathways, Structure and Organization , 2009 .
[45] P. VanRaden,et al. Efficient methods to compute genomic predictions. , 2008, Journal of dairy science.
[46] M. Hamberg,et al. Analysis of oil composition in cultivars and wild species of oat (Avena sp.). , 2008, Journal of agricultural and food chemistry.
[47] M. Goddard,et al. Prediction of total genetic value using genome-wide dense marker maps. , 2001, Genetics.
[48] J. Ohlrogge,et al. REGULATION OF FATTY ACID SYNTHESIS. , 1997, Annual review of plant physiology and plant molecular biology.
[49] A. Melchinger,et al. Evaluation of Near Infra‐red Reflectance Spectroscopy for Predicting Grain and Stover Quality Traits in Maize , 1986 .