Genomic and metabolic prediction of complex heterotic traits in hybrid maize

Abstract

Maize is both an exciting model organism in plant genetics and also the most important crop worldwide for food, animal feed and bioenergy production. Recent genome-wide association and metabolic profiling studies aimed to resolve quantitative traits to their causal genetic loci and key metabolic regulators. Here we present a complementary approach that exploits large-scale genomic and metabolic information to predict complex, highly polygenic traits in hybrid testcrosses. We crossed 285 diverse Dent inbred lines from worldwide sources with two testers and predicted their combining abilities for seven biomass- and bioenergy-related traits using 56,110 SNPs and 130 metabolites. Whole-genome and metabolic prediction models were built by fitting effects for all SNPs or metabolites. Prediction accuracies ranged from 0.72 to 0.81 for SNPs and from 0.60 to 0.80 for metabolites, allowing a reliable screening of large collections of diverse inbred lines for their potential to create superior hybrids.

Marker-Based Estimation of Heritability in Immortal Populations

“…Here we investigate the feasibility of marker-based estimation of heritability with one-and two-stage approaches and look at how heritability estimates affect genomic prediction with the best linear unbiased predictor (G-BLUP) and GWAS. Although our analysis of observed phenotypes focuses on the model plant Arabidopsis thaliana, the asymptotic variances of different heritability estimators were also computed for diverse panels of Zea mays (Riedelsheimer et al 2012;Van Heerwaarden et al 2012) and Oryza sativa (Zhao et al 2011).In both published data and new experiments, we found very large standard errors and sometimes unrealistically high estimates of heritability, which could not be explained by varying linkage disequilibrium . Much better heritability estimates were obtained when mixed-model analysis was performed at the individual plant level.…”

“…These differences were hence largest in the structured RegMap and smallest in the HapMap ( Figure S1 and Figure S2). For the asymptotic distributions of heritability estimators given below, we also considered marker-based kinship matrices for three populations of crop plants: the panel described in Van Heerwaarden et al (2012) (Z. mays, 400 accessions), the panel described in Riedelsheimer et al (2012) (Z. mays, 280 accessions), and the panel from Zhao et al (2011) (O. sativa, 413 accessions).…”

“…VH, Van Heerwaarden et al (2012). RH, Riedelsheimer et al (2012). Figure 1 Heritability estimates for 5000 simulated traits for random samples of 200 accessions drawn from the structured RegMap (top) and the HapMap (bottom).…”

“…Here we investigate the feasibility of marker-based estimation of heritability with one-and two-stage approaches and look at how heritability estimates affect genomic prediction with the best linear unbiased predictor (G-BLUP) and GWAS. Although our analysis of observed phenotypes focuses on the model plant Arabidopsis thaliana, the asymptotic variances of different heritability estimators were also computed for diverse panels of Zea mays (Riedelsheimer et al 2012;Van Heerwaarden et al 2012) and Oryza sativa (Zhao et al 2011).…”

Discovering variation of secondary metabolite diversity and its relationship with disease resistance inCornus floridaL.

“…In contrast to the research presented in this article, few studies have evaluated broader relationships between chemical diversity and genetic diversity within species (focusing on the diversity of chemical compound composition among individuals in a population) while examining a select group of metabolites diagnostic of health versus disease (otherwise known as biomarkers) and associated with SNPs under selection. Recent innovations in next‐generation sequencing coupled with untargeted chemical profiling provide unique opportunities to examine these relationships in plant systems (Eckert et al., 2012; Gomez‐Casati, Zanor, & Busi, 2013; Raguso et al., 2015; Riedelsheimer et al., 2012). Integrating next‐generation sequencing technologies with population genomics and community ecology permits identification of chemical compounds and associated SNPs related to disease resistance or other ecologically functional traits.…”