03937naa a2200325 a 450000100080000000500110000800800410001902200140006002400350007410000130010924501710012226000090029350011000030252018340140265000100323665300200324665300160326665300240328265300280330665300200333465300290335465300200338365300210340370000170342470000160344170000140345770000160347170000190348777301050350610591412022-02-24       2018    bl uuuu     u00u1 u    #d  a0040-57527 a10.1007/s00122-018-3186-32DOI1 aLADO, B.  aResource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article].h[electronic resource]  c2018  aArticle history: Received: 29 January 2018 / Accepted: 10 September 2018 / Published online: 19 September 2018. Supplementary materials. Acknowledgements: We express our appreciation for the effort of the technical personnel of INIA La Estanzuela from ?Laboratorio de calidad industrial de granos.? Support for doctoral work of BL was provided by Agencia Nacional de Investigación e Innovación (ANII), Uruguay, through Grant POS_NAC_2013_1_11261 and by Comisión Sectorial de Investigación Científica (CSIC), Uruguay, through grants in the program internships abroad. We would like to thank two anonymous reviewers for their comments that improved the manuscript. Open Access Copyright information: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  aKEY MESSAGE: Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters.  ABSTRACT: Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped for both the training and testing sets was the best model considering phenotyping resources and the gain in predictive ability. The inclusion of correlated traits in the training and testing lines is a strategic approach to replace phenotyping of labor-intensive and high cost traits in a breeding program.  © 2018, The Author(s).  aGENES  aABILITY TESTING  aFORECASTING  aGENOMIC PREDICTIONS  aPLANT BREEDING PROGRAMS  aPLANTS (BOTANY)  aPLATAFORMA AGROALIMENTOS  aQUALITY CONTROL  aSOFTWARE TESTING1 aVÁZQUEZ, D.1 aQUINCKE, M.1 aSILVA, P.1 aAGUILAR, I.1 aGUTIÉRREZ, L.  tTheoretical and Applied Genetics, 1 December 2018, Volume 131, Issue 12, pp. 2719-2731. OPEN ACCESS.