New insights into genomic selection through population-based non-parametric prediction methods
Lima, Leísa Pires; Azevedo, Camila Ferreira; Resende, Marcos Deon Vilela de; Silva, Fabyano Fonseca e; Suela, Matheus Massariol; Nascimento, Moysés; Viana, José Marcelo Soriano.
; 76(4): 290-298, July-Aug. 2019. tab
Artigo em Inglês | VETINDEXEXPRESS | ID: vti-740882
Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.).
Genome-wide association study of salt tolerance at the seed germination stage in rice.
Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates.
Metabolomic prediction of yield in hybrid rice.
Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China).
A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa).
Prediction and identification of the effectors of heterotrimeric G proteins in rice (Oryza sativa L.).
Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines.
Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II.
Genome-wide association study of agronomic traits in rice cultivated in temperate regions.