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Daniel E. Runcie on Muck Rack

Daniel E. Runcie

As seen in: British Ecological Society (Wiley), Nature, New Phytologist, Genome Biology, BioRxiv, Cell Press
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Articles

Genetic variation at transcription factor binding sites largely explains phenotypic heritability in maize - Nature Genetics

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By Julia Engelhorn, Samantha J. Snodgrass, Amelie Kok, Arun S Seetharam, Michael Schneider, Ayush Singh, Daniel E. Runcie, Victor A. Sánchez-Camargo, Rechien Bader, Guangchao Sun, Maike Stam, Fabio Fiorani, Sebastian Beier, James C. Schnable, Hank W. Bass, Matthew B. Hufford, Benjamin Stich, Wolf Frommer, Jeffrey Ross-Ibarra
| Nature Verified
Abstract Comprehensive maps of functional variation at transcription factor (TF) binding sites (cis-elements) are crucial for elucidating how genotype shapes phenotype. Here, we report the construction of a pan-cistrome of the maize leaf under well-watered and drought conditions.
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Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software

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By José Crossa, Johannes Martini, Paulino Pérez-Rodríguez, Germano Costa-Neto, Roberto Fritsche-Neto, Daniel E. Runcie, Jaime cuevas, Fernando Toledo Verified, H. Li, Pasquale De Vita, Guillermo Gerard, Susanne Dreisigacker, Leonardo Crespo-Herrera, Carolina Saint Pierre, Alison R Bentley, Morten Lillemo, Rodomiro Ortiz, Osval A. Montesinos-López, Abelardo Montesinos-López
| Cell Press Verified
Feature ReviewOnline nowOpen access 1International Maize and Wheat Improvement Center (CIMMYT), Carretera México – Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico 2Colegio de Postgraduados, Montecillos, Edo.
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Why Accuracy Metrics Fall Short in Comparing Phenomic and Genomic Prediction Models

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By Fangyi Wang, Mitchell Feldmann, Daniel E. Runcie
| BioRxiv
Abstract Phenomic Selection is a new paradigm in plant breeding that uses high-throughput phenotyping technologies and machine learning models to predict traits of new individuals and make selections. This can allow breeders to evaluate more plants in higher throughput more accurately, resulting in faster rates of gain and reduced labor costs.
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