Genomic Predictability of Interconnected Biparental Maize Populations

Abstract

Intense structuring of plant breeding populations challenges the design of the training set (TS) in genomic selection (GS). An important open question is how the TS should be constructed from multiple related or unrelated small biparental families to predict progeny from individual crosses. Here, we used a set of five interconnected maize (Zea mays L.) populations of doubled-haploid (DH) lines derived from four parents to systematically investigate how the composition of the TS affects the prediction accuracy for lines from individual crosses. A total of 635 DH lines genotyped with 16,741 polymorphic SNPs were evaluated for five traits including Gibberella ear rot severity and three kernel yield component traits. The populations showed a genomic similarity pattern, which reflects the crossing scheme with a clear separation of full sibs, half sibs, and unrelated groups. Prediction accuracies within full-sib families of DH lines followed closely theoretical expectations, accounting for the influence of sample size and heritability of the trait. Prediction accuracies declined by 42% if full-sib DH lines were replaced by half-sib DH lines, but statistically significantly better results could be achieved if half-sib DH lines were available from both instead of only one parent of the validation population. Once both parents of the validation population were represented in the TS, including more crosses with a constant TS size did not increase accuracies. Unrelated crosses showing opposite linkage phases with the validation population resulted in negative or reduced prediction accuracies, if used alone or in combination with related families, respectively. We suggest identifying and excluding such crosses from the TS. Moreover, the observed variability among populations and traits suggests that these uncertainties must be taken into account in models optimizing the allocation of resources in GS.G ENOMIC prediction or selection, initially proposed and rapidly implemented in animal breeding (Meuwissen et al. 2001), is increasingly applied in plant breeding (Bernardo and Yu 2009;Lorenz et al. 2011;Morrell et al. 2011). However, with more investigations in plant breeding applications, new challenges emerge, mainly as the result of the greater possibilities of genetic manipulation and reproduction modes in plants compared to animals. Genomic predictions within diverse populations (Crossa et al. 2010; Riedelsheimer et al. 2012a,b;Windhausen et al. 2012) largely overlap with the scenarios in animal breeding. Predicting crossbred performance in animals also has some similarities with the prediction of maize hybrids from fully homozygous inbred lines drawn from genetically distant heterotic pools (Technow et al. 2012). Apart from using mixtures of close and distant relatives, little overlap with the field of animal breeding however exists in the case of multiple related or unrelated biparental families of inbred lines with a size #200. The latter situation is investigated in this study, because it covers the most rele...

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

“…As all dent families are equally connected through one common parent, no relevant differences in linkage phases were observed between UH304 and the other dent families. Thus, differences in linkage phases as it has been suggested in other outlying cases of low prediction performance (Riedelsheimer et al 2013;Würschum et al 2013) cannot be a reason for the poor prediction performance here. UH304 was the only Iodent founder line within the dent pool and thus less related to the other founder lines.…”

“…Including additional lines from the opposite pool (i.e., unrelated families with different linkage phases) to the estimation set of LOCO-CV had no impact on predictive abilities in our study (results not shown). This might be surprising as one might expect that including unrelated families to the estimation set should lead to a decrease of prediction performance as different linkage phases between markers and QTL might exist that in turn lead to wrongly estimated marker effects (Riedelsheimer et al 2013). However, as the relationship between dent and flint lines observed by markers is close to zero, as seen on the heatmap of the realized kinship, the GBLUP model does not borrow information from lines of the other pool for prediction of lines from the same pool.…”

Modeling Epistasis in Genomic Selection

“…Previous studies have shown that prediction accuracy is impaired when performing genomic selection across connected biparental populations (Zhao et al 2012;Riedelsheimer et al 2013). This may be explained at least partially by epistatic effects as the genetic relatedness across connected populations may be better exploited by modeling epistasis in addition to additive effects.…”

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

“…In addition, the efficiency and accuracy with which superior lines can be predicted also depend on the size of the reference population (Jannink et al, 2010;Lorenz et al, 2011). The genetic relatedness or population structure (Saatchi et al, 2011;Riedelsheimer et al, 2013;Wray et al, 2013) may result in overestimating the heritability of the traits (Price et al, 2010;Visscher et al, 2012;Wray et al, 2013). The population structure of the training population can be determined with greater accuracy using genome wide SNPs compared with the simple sequence repeats and SNP arrays (Isidro et al, 2015).…”