Academic journal article Genetics

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

Academic journal article Genetics

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

Article excerpt

(ProQuest: ... denotes formulae omitted.)

IN the context of quantitative trait locus (QTL) mapping, multiparental populations have been suggested to be ad- vantageous over biparental families due to their greater al- lelic diversity and the possibility of evaluating allelic effects in multiple genetic backgrounds (Muranty 1996; Xu 1998; Verhoeven et al. 2006). Especially if the multiparental pop- ulation consists of several families connected by common parents, they can provide greater power of QTL detection and better resolution of QTL localization compared to in- dividual 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 max- imum 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 polymor- phism (SNP) effects were derived. Thus, as for QTL mapping, similar arguments in favor of multiparental populations hold in the context of genome-based prediction.

The accuracy of genome-based prediction based on multiparental populations has been reported for a number of species, traits, and statistical methods (Legarra et al. 2008; Asoro et al. 2011; Zhao et al. 2011; Ornella et al. 2012; Resende et al. 2012; Schulz-Streeck et al. 2012; Peiffer et al. 2013; Albrecht et al. 2014; Scutari et al. 2014). While these results are promising for the implementation of genome- based prediction in breeding, the optimum design of the population to be employed in model training is still an open question and more research needs to be put into this direction (Crossa et al. 2014). Recently, Riedelsheimer et al. (2013) addressed the effect of the composition of the estimation set on prediction of disease and kernel traits with five inter- connected biparental maize families. They found a strong de- crease in predictive abilities when full-sib lines were replaced by half-sib lines in model training despite the fact that their parental lines originated from the same breeding program and were highly related. Thus, an emerging question from their study was how prediction performance across full- and half-sib lines is affected when parental lines represent a wide spectrum of genetic diversity. It is expected that quantitative genetic parameters relevant for prediction accuracy such as genetic variances and heritabilities vary greatly between fam- ilies, as has been shown for the U.S. maize nested association mapping (NAM) population (Hung et al. 2012). Thus, the predictive power of individual families in a half-sib design might strongly depend on the magnitude and variation of these parameters.

From theory and empirical studies it is known that the sample size of the population employed in model training (estimation set) has a strong impact on prediction accuracy (Daetwyler et al. 2008; Lorenzana and Bernardo 2009; Zhong et al. 2009; Albrecht et al. 2011; Guo et al. 2012; Combs and Bernardo 2013; Wimmer et al. 2013). Restrict- ing genome-based prediction to within biparental families puts upper limits on sample sizes employed in model train- ing. While sample sizes of biparental families generated in the breeding process rarely exceed 100, the use of multi- parental populations permits increasing the size of the esti- mation set by adding progenies from connected crosses. To our knowledge, the question of how many half-sib lines are required to obtain the same predictive ability as a given sample of full-sib lines has empirically not been addressed in a formal manner. …

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