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

@article{Schrag2018BeyondGP,
  title={Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize.},
  author={Tobias A Schrag and Matthias Westhues and Wolfgang Schipprack and Felix Seifert and Alexander Thiemann and Stefan Scholten and Albrecht E Melchinger},
  journal={Genetics},
  year={2018},
  volume={208 4},
  pages={
          1373-1385
        }
}
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… CONTINUE READING
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