Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers

Genomic selection (GS) has become an important aid in plant and animal breeding. Multienvironment (multitrait) models allow borrowing of information across environments (traits), which could enhance prediction accuracy. This study presents multienvironment (multitrait) models for GS and compares the predictive accuracy of these models with: (i) multienvironment analysis without pedigree and marker information, and (ii) multienvironment pedigree or/and marker-based models. A statistical framework for incorporating pedigree and molecular marker information in models for multienvironment data is described and applied to data that originate from wheat (Triticum aestivum L.) multienvironment trials. Two prediction problems relevant to plant breeders are considered: (CV1) predicting the performance of untested genotypes (“newly” developed lines), and (CV2) predicting the performance of genotypes that have been evaluated in some environments but not in others. Results confi rmed the superiority of models using both marker and pedigree information over those based on pedigree information only. Models with pedigree and/or markers had better predictive accuracy than simple linear mixed models that do not include either of these two sources of information. We concluded that the evaluation of such trials can benefi t greatly from using multienvironment GS models. J. Burgueño and J. Crossa, Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, México D.F., México; G. de los Campos, Dep. of Biostatistics, Univ. of Alabama at Birmingham, Ryals Public Health Bldg. 443, Birmingham, AL 35294; K. Weigel, Dep. of Dairy Science, Univ. of Wisconsin, Madison, WI 53706. Received 3 June 2011. *Corresponding author ( [email protected]). Abbreviations: Bayesian LASSO, Bayesian Least Absolute Shrinkage and Selection Operator; CV, cross-validation; D, diagonal; FA, factor analytic model; GE, genotype × environment interaction; GS, genomic selection; I, identity matrix; M, molecular marker; P, pedigree; PM, pedigree + molecular marker; REML, restricted maximum likelihood; UN, unstructured. Published in Crop Sci. 52:707–719 (2012). doi: 10.2135/cropsci2011.06.0299 Freely available online through the author-supported open-access option. © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. Published March, 2012