Multi-environment analysis of sorghum breeding trials using additive and dominance genomic relationships

[1]  Ky L. Mathews,et al.  Genomic selection in multi-environment plant breeding trials using a factor analytic linear mixed model. , 2019, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[2]  Shizhong Xu,et al.  Hybrid breeding of rice via genomic selection , 2019, Plant biotechnology journal.

[3]  B. Cullis,et al.  Plant breeding selection tools built on factor analytic mixed models for multi-environment trial data , 2018, Euphytica.

[4]  Yusheng Zhao,et al.  Exploiting the Rht portfolio for hybrid wheat breeding , 2018, Theoretical and Applied Genetics.

[5]  C. T. Guimarães,et al.  Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials , 2018, Heredity.

[6]  D. Butler,et al.  Molecular Marker Information in the Analysis of Multi‐Environment Trials Helps Differentiate Superior Genotypes from Promising Parents , 2016 .

[7]  Robin Thompson,et al.  Genomic Selection in Multi-environment Crop Trials , 2016, G3: Genes, Genomes, Genetics.

[8]  B. Hayes,et al.  Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits , 2016, Genetics Selection Evolution.

[9]  Gustavo de Los Campos,et al.  Unraveling Additive from Nonadditive Effects Using Genomic Relationship Matrices , 2014, Genetics.

[10]  L. Varona,et al.  On the Additive and Dominant Variance and Covariance of Individuals Within the Genomic Selection Scope , 2013, Genetics.

[11]  M. Calus,et al.  Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding , 2013, Genetics.

[12]  P. Lichtner,et al.  The impact of genetic relationship information on genomic breeding values in German Holstein cattle , 2010, Genetics Selection Evolution.

[13]  Ben J Hayes,et al.  Accuracy of genomic breeding values in multi-breed dairy cattle populations , 2009, Genetics Selection Evolution.

[14]  Didier Boichard,et al.  GSE is now an open access journal published by BioMed Central , 2009, Genetics Selection Evolution.

[15]  P. VanRaden,et al.  Efficient methods to compute genomic predictions. , 2008, Journal of dairy science.

[16]  B. Cullis,et al.  Joint modeling of additive and non-additive (genetic line) effects in multi-environment trials , 2007, Theoretical and Applied Genetics.

[17]  Brian R. Cullis,et al.  On the design of early generation variety trials with correlated data , 2006 .

[18]  Brian R. Cullis,et al.  Prediction in linear mixed models , 2004 .

[19]  D. G. León,et al.  Genetic Diversity, Specific Combining Ability, and Heterosis in Tropical Maize under Stress and Nonstress Environments , 2003 .

[20]  Robin Thompson,et al.  Analyzing Variety by Environment Data Using Multiplicative Mixed Models and Adjustments for Spatial Field Trend , 2001, Biometrics.

[21]  H. Piepho Empirical best linear unbiased prediction in cultivar trials using factor-analytic variance-covariance structures , 1998, Theoretical and Applied Genetics.

[22]  Brian R. Cullis,et al.  Accounting for natural and extraneous variation in the analysis of field experiments , 1997 .

[23]  H. D. Patterson,et al.  Variability of yields of cereal varieties in U. K. trials , 1977, The Journal of Agricultural Science.

[24]  H. Akaike A new look at the statistical model identification , 1974 .

[25]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[26]  Hsiao-Pei Yang,et al.  Genomic Selection in Plant Breeding: A Comparison of Models , 2012 .

[27]  L. D. Van Vleck Variance of prediction error with mixed model equations when relationships are ignored. , 1993, TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik.