Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize

Genetics: A Periodical Record of Investigations Bearing on Heredity and Variation

Matthias Westhues^1¹  Tobias A. Schrag^1² 

[1] Biocenter Klein Flottbek, Developmental Biology and Biotechnology, University of Hamburg, 22609 Hamburg, Germany^2;Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599 Stuttgart, Germany^1

关键词: BLUP;    hybrid performance;    maize;    omics;    genomic prediction;    genomic selection;    GenPred;    Shared Data Resources;    Genomic Selection;   

DOI  :  10.1534/genetics.117.300374

学科分类：医学（综合）

来源: Genetics Society of America

【 摘 要 】

The ability to predict the agronomic performance of single-crosses with high precision is essential for selecting superior candidates for hybrid breeding. With recent technological advances, thousands of new parent lines, and, consequently, millions of new hybrid combinations are possible in each breeding cycle, yet only a few hundred can be produced and phenotyped in multi-environment yield trials. Well established prediction approaches such as best linear unbiased prediction (BLUP) using pedigree data and whole-genome prediction using genomic data are limited in capturing epistasis and interactions occurring within and among downstream biological strata such as transcriptome and metabolome. Because mRNA and small RNA (sRNA) sequences are involved in transcriptional, translational and post-translational processes, we expect them to provide information influencing several biological strata. However, using sRNA data of parent lines to predict hybrid performance has not yet been addressed. Here, we gathered genomic, transcriptomic (mRNA and sRNA) and metabolomic data of parent lines to evaluate the ability of the data to predict the performance of untested hybrids for important agronomic traits in grain maize. We found a considerable interaction for predictive ability between predictor and trait, with mRNA data being a superior predictor for grain yield and genomic data for grain dry matter content, while sRNA performed relatively poorly for both traits. Combining mRNA and genomic data as predictors resulted in high predictive abilities across both traits and combining other predictors improved prediction over that of the individual predictors alone. We conclude that downstream “omics” can complement genomics for hybrid prediction, and, thereby, contribute to more efficient selection of hybrid candidates.

【 授权许可】

CC BY