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

Abstract

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 (...

Linkage Disequilibrium with Linkage Analysis of Multiline Crosses Reveals Different Multiallelic QTL for Hybrid Performance in the Flint and Dent Heterotic Groups of Maize

“…This is the case for PH and DtTAS and also indirectly for DMC since DMC at harvest of a genotype depends on its precocity and its drying speed. Interestingly, common DMC QTL between groups and most of the DMC QTL detected with the joint analysis and significant in both data sets were detected in regions also carrying QTL for flowering time (DtSILK or DtTAS).The few common QTL between dent and flint groups that we detected could explain the low predictive abilities of the prediction between dent and flint in genomic selection Jannink et al 2010) when dent are in the estimation set and flint in the test set and vice versa (Lehermeier et al 2014). The presence of a major effect QTL in the flint group might also partly explain this result.…”

“…Field trial design is described in Lehermeier et al (2014). Individual field plot measures were analyzed (Lehermeier et al 2014) to compute for each hybrid the adjusted means over the different trials that were used in this study. …”

“…This might be due to heterogeneity of withinfamily variances for some traits. For instance, for DtSILK, for the dent data set, genetic variances varied from 0.95 to 4.93 (see Lehermeier et al 2014 for an estimation of these variances). As for the separate data sets thresholds, for the joint data set, threshold values for the connected model were lower for DMC and higher for DtSILK and DtTAS.…”

An Equation to Predict the Accuracy of Genomic Values by Combining Data from Multiple Traits, Populations, or Environments

“…Therefore, combining different populations in one training population for estimating SNP effects is an appealing approach to increase the size of the training population and, thereby, the accuracy of predicting genomic values. The potential accuracy of combing different populations in one training population has been investigated by combining populations from different breeds (e.g., Hayes et al 2009a;Harris and Johnson 2010), lines (e.g., Zhong et al 2009;Calus et al 2014;Lehermeier et al 2014), subpopulations (e.g., De Los Campos et al 2013, or countries (e.g., Lund et al 2011;Haile-Mariam et al 2015). The increase in accuracy by adding individuals from another population to the training population is in most cases much lower than the increase in accuracy obtained by adding an equal number of individuals from the same population.…”

Recent Advances in QTL Mapping and Quantitative Disease Resistance Approach

“…One important question in genomic prediction, and generally in marker-based prediction, is how to design the training set (TS) for achieving high prediction accuracy. Major factors identified are the sample size and number of families in the TS and their relatedness to the validation set (VS, Riedelsheimer et al, 2013;Lehermeier et al, 2014). Multi-family QTL mapping offers the possibility to unveil the genetic basis of prediction accuracy in genomic prediction with different composition of the TS.…”