Omics-based hybrid prediction in maize

生物 基因组学 上位性 计算生物学 数量性状位点 特质 转录组 遗传建筑学 组学 生物技术 基因组 遗传学 基因 计算机科学 基因表达 程序设计语言
作者
Matthias Westhues,Tobias A. Schrag,Claas Heuer,Georg Thaller,H. Friedrich Utz,Wolfgang Schipprack,Alexander Thiemann,Felix Seifert,Anita Ehret,Armin Schlereth,Mark Stitt,Zoran Nikoloski,Lothar Willmitzer,C. C. Schön,Stefan Scholten,A. E. Melchinger
出处
期刊:Theoretical and Applied Genetics [Springer Science+Business Media]
卷期号:130 (9): 1927-1939 被引量:99
标识
DOI:10.1007/s00122-017-2934-0
摘要

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.
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