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A Pedigree‐Based Reaction Norm Model for Prediction of Cotton Yield in Multienvironment Trials
Author(s) -
PérezRodríguez Paulino,
Crossa José,
Bondalapati Krishna,
De Meyer Geert,
Pita Fabiano,
Campos Gustavo de los
Publication year - 2015
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2014.08.0577
Subject(s) - pedigree chart , biology , variance (accounting) , gene–environment interaction , selection (genetic algorithm) , grain yield , covariate , statistics , microbiology and biotechnology , yield (engineering) , computational biology , biological system , genotype , genetics , computer science , agronomy , mathematics , machine learning , gene , materials science , metallurgy , business , accounting
Genotype × environment interaction (G × E) plays a fundamental role in important agricultural traits such as grain yield or disease resistance. Therefore, modeling G × E is essential for the selection of high yielding and well‐adapted varieties. The availability of new sources of genetic and environmental information (e.g., dense panels for molecular markers coupled with large numbers of environmental covariates [EC]) provides important opportunities for studying and exploiting G × E. However, incorporating high‐dimensional genetic and environmental data and accounting for potential interactions is not an easy task. Recently we developed a genomic model that incorporates molecular markers, EC, and the interactions between them using co‐variance functions. In this paper we demonstrate how the same approach can be applied in cases where genetic information is based on pedigrees instead of molecular markers. We evaluated the models using a collection of 7809 grain yield records obtained from 582 cotton lines evaluated in 2 yr (2011 and 2012) over nine locations. A total of 76 EC were available and used to model main and interaction effects. Estimates of variance components indicated that G × E explained a sizable proportion of the phenotypic variance, and two cross‐validation analyses indicated that modeling G × E increases prediction accuracy by a considerable margin. To the best of our knowledge, this is the first study considering both pedigree and EC for the analysis of cotton yield. The models described here can be used for prediction of genetic merit and for selection for target environments.