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Genome to phenome: tools to hit the target in soybean (Glycine max) and maize (Zea mays) testing
  • +2
  • Pat Morgan,
  • Jennifer Rinehart,
  • Madhav Bhatta,
  • Yurong Chen,
  • Charles Dietrich
Pat Morgan

Corresponding Author:[email protected]

Author Profile
Jennifer Rinehart
Madhav Bhatta
Yurong Chen
Charles Dietrich


With increasing demands for sustainable food production, expediting innovation within the development of agricultural products is paramount for Bayer and similar companies. We used a novel gene-editing protocol that generates numerous events to increase our gene-editing capacity. TREDMIL was used create 800 distinct edits in soybeans at three Dt1 gRNA targets in over 1500 events sites distributed across 100 soy lines. With the ability to produce great numbers of edit events, our phenotypic testing had to evolve to keep pace. Using a hypothesis-based approach, we have refined phenotypic testing to measure relevant plant traits that impact yield. Our in-field phenotyping of the target set of plant traits feeds a machine learning model that adjusts small plot yield. Testing in both corn and soy has demonstrated small plots are predictive of large-scale yield testing (84% agreement) and that modeling with additional traits improved the predictive capacity to 93%.
06 Nov 2023Submitted to NAPPN 2024
06 Nov 2023Published in NAPPN 2024