The impact of population structure on genomic prediction in stratified populations

Guo, Zhigang; Tucker, David M.; Basten, Christopher J.; Gandhi, Harish; Ersoz, Elhan S.; Guo, B.; Xu, Zhanyou; Wang, Daolong; Gay, Gilles

doi:10.1007/s00122-013-2255-x

Cited by 164 publications

(175 citation statements)

References 55 publications

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“…This was not the case in the dent families, which were evaluated in fewer locations and had a much larger range of family-specific heritabilities. However, for both half-sib panels we also observed a positive correlation of predictive ability (and accuracy) with family-specific trait heritabilities in the estimation set of CwC, which confirms the desirability to use estimation sets with high heritability and high genetic variance (Guo et al 2014). However, it was not possible to predict the genetic variance of the progenies of a specific cross from the molecular similarity of the respective founder and the central line.…”

Section: Impact Of Quantitative Genetic Parameters On Prediction Perfmentioning

confidence: 74%

Usefulness of Multiparental Populations of Maize (Zea mays L.) for Genome-Based Prediction

et al. 2014

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The efficiency of marker-assisted prediction of phenotypes has been studied intensively for different types of plant breeding populations. However, one remaining question is how to incorporate and counterbalance information from biparental and multiparental populations into model training for genome-wide prediction. To address this question, we evaluated testcross performance of 1652 doubled-haploid maize (Zea mays L.) lines that were genotyped with 56,110 single nucleotide polymorphism markers and phenotyped for five agronomic traits in four to six European environments. The lines are arranged in two diverse half-sib panels representing two major European heterotic germplasm pools. The data set contains 10 related biparental dent families and 11 related biparental flint families generated from crosses of maize lines important for European maize breeding. With this new data set we analyzed genome-based best linear unbiased prediction in different validation schemes and compositions of estimation and test sets. Further, we theoretically and empirically investigated marker linkage phases across multiparental populations. In general, predictive abilities similar to or higher than those within biparental families could be achieved by combining several half-sib families in the estimation set. For the majority of families, 375 half-sib lines in the estimation set were sufficient to reach the same predictive performance of biomass yield as an estimation set of 50 full-sib lines. In contrast, prediction across heterotic pools was not possible for most cases. Our findings are important for experimental design in genome-based prediction as they provide guidelines for the genetic structure and required sample size of data sets used for model training. IN the context of quantitative trait locus (QTL) mapping, multiparental populations have been suggested to be advantageous over biparental families due to their greater allelic diversity and the possibility of evaluating allelic effects in multiple genetic backgrounds (Muranty 1996;Xu 1998;Verhoeven et al. 2006). Especially if the multiparental population consists of several families connected by common parents, they can provide greater power of QTL detection and better resolution of QTL localization compared to individual families (Rebai and Goffinet 1993;Jannink and Jansen 2001;Blanc et al. 2006;Yu et al. 2008;Bardol et al. 2013;Mackay et al. 2014). In the context of genome-based prediction (Meuwissen et al. 2001), accuracies achieved within large biparental families are assumed to be the maximum that can be obtained with a given sample size (Crossa et al. 2014), because of medium allele frequencies, absence of genetic substructure, and equal linkage phases between markers and functional polymorphisms. However, prediction accuracies of newly generated progenies from different crosses will be poor. This is especially true if the respective germplasm exhibits broad allelic diversity and is unrelated to the biparental family from which single nucleotide polymorphism (...

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Section: Impact Of Quantitative Genetic Parameters On Prediction Perfmentioning

confidence: 74%

Usefulness of Multiparental Populations of Maize (Zea mays L.) for Genome-Based Prediction

et al. 2014

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“…In stratified sampling, the calibration set is divided into subpopulations and then a proportion of genotypes is randomly selected from each subpopulation (Guo et al 2014; Albrecht et al 2014; Janss et al 2012; Daetwyler et al 2012). However, subpopulations are sometimes not clearly defined or they are internally heterogeneous (Crossa et al 2013).…”

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confidence: 99%

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

Bustos-Korts

Malosetti

Chapman

et al. 2016

G3 Genes|Genomes|Genetics

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Genome-enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome-enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under-represented in the training set, whereas other parts will be over-represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTL and GBLUPs, and Reproducing Kernel Hilbert Space (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction methods led to similar predictive ability, a reflection of the very strong population structure in this panel.

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“…HE impact of population structure has been investigated for genome-wide association studies (GWAS) and genome-based prediction, where it was found to lead to false positive detected quantitative trait loci (QTL) and to have important influences on prediction accuracy (Windhausen et al 2012;Albrecht et al 2014;Guo et al 2014). Population structure occurs naturally in animal, plant, and human populations due to geographic adaptation and natural selection.…”

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confidence: 99%

Assessment of Genetic Heterogeneity in Structured Plant Populations Using Multivariate Whole-Genome Regression Models

2015

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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.

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The impact of population structure on genomic prediction in stratified populations

Cited by 164 publications

References 55 publications

Usefulness of Multiparental Populations of Maize (Zea mays L.) for Genome-Based Prediction

Usefulness of Multiparental Populations of Maize (Zea mays L.) for Genome-Based Prediction

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

Assessment of Genetic Heterogeneity in Structured Plant Populations Using Multivariate Whole-Genome Regression Models

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