title: Assessment of Genetic Heterogeneity in Structured Plant Populations Using Multivariate Whole-Genome Regression Models.
abstract: Plant breeding populations exhibit varying levels of structure and admixture; these features are likely to induce heterogeneity of marker effects across subpopulations. Traditionally, structure has been dealt with as a potential confounder, and various methods exist to "correct" for population stratification. However, these methods induce a mean correction that does not account for heterogeneity of marker effects. The animal breeding literature offers a few recent studies that consider modeling genetic heterogeneity in multibreed data, using multivariate models. However, these methods have received little attention in plant breeding where population structure can have different forms. In this article we address the problem of analyzing data from heterogeneous plant breeding populations, using three approaches: (a) a model that ignores population structure [A-genome-based best linear unbiased prediction (A-GBLUP)], (b) a stratified (i.e., within-group) analysis (W-GBLUP), and (c) a multivariate approach that uses multigroup data and accounts for heterogeneity (MG-GBLUP). The performance of the three models was assessed on three different data sets: a diversity panel of rice (Oryza sativa), a maize (Zea mays L.) half-sib panel, and a wheat (Triticum aestivum L.) data set that originated from plant breeding programs. The estimated genomic correlations between subpopulations varied from null to moderate, depending on the genetic distance between subpopulations and traits. Our assessment of prediction accuracy features cases where ignoring population structure leads to a parsimonious more powerful model as well as others where the multivariate and stratified approaches have higher predictive power. In general, the multivariate approach appeared slightly more robust than either the A- or the W-GBLUP.
authors.affiliation: Plant Breeding, Technische Universität München, 85354 Freising, Germany christina.lehermeier@tum.de.
authors.forename: Christina
authors.initials: C
authors.lastname: Lehermeier
authors.affiliation: Plant Breeding, Technische Universität München, 85354 Freising, Germany.
authors.forename: Chris-Carolin
authors.initials: CC
authors.lastname: Schön
authors.affiliation: Department of Epidemiology and Biostatistics and Department of Statistics, Michigan State University, East Lansing, Michigan 48824.
authors.forename: Gustavo
authors.initials: G
authors.lastname: de Los Campos
mesh_terms: D018740:Genetic Heterogeneity; D018745:Genome, Plant; D008957:Models, Genetic; D015999:Multivariate Analysis; D012275:Oryza / Q000235:genetics*; D000069600:Plant Breeding; D040641:Quantitative Trait Loci; D012044:Regression Analysis; D014908:Triticum / Q000235:genetics*; D003313:Zea mays / Q000235:genetics*
keywords: GBLUP; GenPred; genomic selection; multivariate models; plant breeding; population structure; shared data resource