Use of family structure information in interaction with environments for leveraging genomic prediction models

作     者：Reyna Persa Hiroyoshi Iwata Diego Jarquin Reyna Persa;Hiroyoshi Iwata;Diego Jarquin

作者机构：Department of Agronomy and HorticultureUniversity of Nebraska-LincolnLincolnNE 68583USA Graduate School of Agricultural and Life SciencesThe University of TokyoBunkyoTokyo 113-8657Japan 

出 版 物：《The Crop Journal》 (作物学报（英文版）)

年 卷 期：2020年第8卷第5期

页      面：843-854页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0711[理学-系统科学] 07[理学] 08[工学] 

基　　金：This project was supported by the Agriculture and Food Research Initiative Grant(NEB-21-176)from the USDA National Institute of Food and Agriculture Plant Health and Production and Plant Products:Plant Breeding for Agricultural Production A1211 Accession No.1015252.We are also thankful to two anonymous reviewers and the assigned editor for their valuable comments suggestions and positive criticisms to a previous version of the manuscript 

主　　题：interaction structure instead 

摘      要：The characterization of genomes with great detail offered by the modern genotyping platforms have opened a venue for accurately predicting the genotype-by-environment interaction(GE)effects of untested genotypes in different environmental *** developed statistical models have shown the advantages of including the GE interaction component in the prediction context using molecular markers,pedigree,or *** order to leverage the family information of highly structured populations when pedigree data is not available,we developed a model that uses the family membership *** proposed model extends the reaction norm model by including the interaction between families and environments(FE).A representative fraction of a soybean Nested Association Mapping population(16,187 grain yield records)comprising 38 bi-parental families(1358 genotypes)observed in 18 environments(2011,2012,and 2013)was used to contrast the proposed model with three conventional prediction *** crossvalidation scenarios(prediction of tested[CV2]and untested[CV1]genotypes)with a twofold design(50%for training and testing sets)were used for mimicking prediction situations that breeders face in *** showed that the family factor in interaction with environments explains a sizable amount of the phenotypic *** helped to improve the predictive ability with respect to the main effects model(GBLUP)around 41%(CV2)and 49%(CV1),and about 17%with respect to the conventional reaction norm *** inclusion of the FE term not only improved the global results but also significantly increased the prediction accuracy of those environments where the conventional models showed a very poor *** results show the importance of taking into consideration the family structure existing in breeding programs for improving the selection strategies in multi-parental populations.