Omics-based hybrid prediction in maize

Abstract

Complementing genomic data with other "omics" predictors can increase the probability of success for predicting the best hybrid combinations using complex agronomic traits. Accurate prediction of traits with complex genetic architecture is crucial for selecting superior candidates in animal and plant breeding and for guiding decisions in personalized medicine. Whole-genome prediction has revolutionized these areas but has inherent limitations in incorporating intricate epistatic interactions. Downstream "omics" data are expected to integrate interactions within and between different biological strata and provide the opportunity to improve trait prediction. Yet, predicting traits from parents to progeny has not been addressed by a combination of "omics" data. Here, we evaluate several "omics" predictors-genomic, transcriptomic and metabolic data-measured on parent lines at early developmental stages and demonstrate that the integration of transcriptomic with genomic data leads to higher success rates in the correct prediction of untested hybrid combinations in maize. Despite the high predictive ability of genomic data, transcriptomic data alone outperformed them and other predictors for the most complex heterotic trait, dry matter yield. An eQTL analysis revealed that transcriptomic data integrate genomic information from both, adjacent and distant sites relative to the expressed genes. Together, these findings suggest that downstream predictors capture physiological epistasis that is transmitted from parents to their hybrid offspring. We conclude that the use of downstream "omics" data in prediction can exploit important information beyond structural genomics for leveraging the efficiency of hybrid breeding.

Evaluating maize phenotypic variance, heritability, and yield relationships at multiple biological scales across agronomically relevant environments

“…Repeatability metrics averaged across these different levels of biology indicate that the metabolite levels are among the least impacted directly by genetics, and that whole plant phenotypes and transcripts both have an average approximately double the repeatability level of leaf metabolites, similar to the previous results in silage maize (Westhues et al, ). Metabolites are known to vary greatly in response to relatively rapid environmental condition changes (Caldana et al, ).…”

“…The weather conditions in all three years of the trial were within the normal range for a farm‐belt corn‐growing environment, suggesting environmental conditions do not need to be extreme to result in observable phenotypic differences in a set of commercially‐relevant maize hybrids. Some traits, such as KRN, have been observed to respond to more extreme stress conditions, or wider, non‐commercially relevant germplasm (Flint‐Garcia et al, , Westhues et al, ), but the conditions required to drive these changes may not be encountered frequently by farmers in the corn‐belt.…”

“…Understanding how genes impact phenotypic and/or metabolic traits and how those traits impact yield under a variety of the environmental conditions will enable identification of more efficient ways to improve yield (Flint‐Garcia et al, ). Westhues et al () have previously examined the predictive nature of “omics” technologies toward complex heterotic traits, such as dry matter yield in publicly available maize silage lines. The study has shown that transcript abundance has higher predictive power than metabolites.…”

“…Further examinations have begun to characterize the breadth of diversity of various inbreds and hybrids across typical agronomic environments particularly emphasizing these effectors on grain and forage composition (Asiago, Hazebroek, Harp, & Zhong, ; Baniasadi, Vlahakis, Hazebroek, Zhong, & Asiago, ; Benevenuto et al, ; Hall et al, ; Tang et al, ; Venkatesh et al, ). There have also been several “omic” analyses to understand the relationship of transcripts and metabolites to critical phenotypic traits including: yield (Asiago et al, ; Hazebroek, Janni, & Lightner, ; Wang, Xue, & Wang, ; Xu, Xu, & Xu, ), dry matter accumulation (Westhues et al, ), and silking (Yesbergenova‐Cuny et al, ). However, it is resource intensive to collect data for a suite of traits in a consistent manner across diverse germplasm to understand the environmental and genetic control of traits.…”

Using yield quantitative trait locus targeted SSR markers to study the relationship between genetic distance and yield heterosis in upland cotton (Gossypium hirsutum)

“…In maize, exploitation of heterosis has produced a many-fold increment in yield in the United States since the 1930s (Duvick, 2001). The genetic basis of heterosis is complex and has been researched for almost a century using a variety of approaches, for example, physiological biochemistry (Feng et al, 2016), genetics (Patil, Deosarkar, & Kalyankar, 2017), molecular biology (Zhu, Jiang, Zhang, Guo, & Zhang, 2011) and "-omics" approaches (Westhues et al, 2017). However, the inheritance mechanism of heterosis is still not clear.…”