Replication Data for: Genome-based prediction of multiple wheat quality traits in multiple years

The use of genomic prediction could greatly help to increase the efficiency of selecting for wheat quality traits by reducing the cost and time required for this analysis. This study contains data used to evaluate the prediction performances of 13 wheat quality traits under two multi-trait models [Bayesian multi-trait multi-environment (BMTME) and multi-trait ridge regression (MTR)]. Separate files are provided for each year of data. An additional supplemental data file provides R code for running the analyses as well as a table describing the Average Pearson´s correlation (APC) and mean arctangent absolute percentage error (MAAPE) for the testing sets for each dataset and trait.

Bibliographic Details

Main Authors:	Ibba, Maria Itria, Crossa, Jose, Montesinos-López, Osval A., Montesinos-López, Abelardo, Juliana, Philomin, Guzman, Carlos, Dolorean, Emily, Dreisigacker, Susanne, Poland, Jesse
Other Authors:	Dreher, Kate
Format:	Phenotypic data biblioteca
Language:	English
Published:	CIMMYT Research Data & Software Repository Network 2020
Subjects:	Agricultural Sciences, Triticum aestivum, Agricultural research, Wheat, Grain weight, Grain test weight, Grain protein content, Grain hardness, Flour protein content, Flour SDS sedimentation, Dough mixograph mixing time, Dough mixograph torque, Dough alveograph value W, Dough alveograph P/L, Bread loaf volume, Thousand Kernel Weight,
Online Access:	https://hdl.handle.net/11529/10548423
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