Daniel Runcie

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Assistant Professor

University of California, Davis

United States

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About

Daniel Runcie is an Assistant Professor at the University of California Davis, USA. His research areas include advanced genomic prediction methods, genotype-by-environment interactions, and the molecular regulation of flowering in plants. He has published recent articles focusing on Bayesian regression techniques, adaptation in maize, and the impact of environmental factors on plant phenology.

Recent Grants

Grant: Close

Mechanisms of Malleability and Resilience of Flowering Responses to Current and Future Variability in Seasonal Cues in a Geographically-widespread Species

Open Date: 2016-05-01

Close Date: 2020-04-01

Grant: Close

NSF Postdoctoral Fellowship in Biology FY 2012

Open Date: 2013-01-01

Close Date: 2014-12-31

Articles (16)

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2025

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2021

Collaborators (9)

Liana Burghardt

Assistant Professor

Pennsylvania State University

UNITED STATES

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Renaud Rincent

Université Clermont Auvergne

FRANCE

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Matthew B. Hufford

UNITED STATES

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Valerio Hoyos‐Villegas

CANADA

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Hao Cheng

Associate Professor

University of California, Davis

UNITED STATES

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Mitchell J Feldmann

Assistant Professor

University of California, Davis

UNITED STATES

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Katrina McGuigan

Associate Professor

University of Queensland

AUSTRALIA

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Johanna Schmitt

UNITED STATES

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Ruairidh James Hay Sawers

Assistant Professor

Pennsylvania State University

UNITED STATES

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Daniel Runcie

Research Interests

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About

Recent Grants

Articles (16)

Why usefulness is rarely useful

Megavariate methods capture complex genotype-by-environment interactions

MegaLMM improves genomic predictions in new environments using environmental covariates

Exploring the molecular regulation of vernalization‐induced flowering synchrony in Arabidopsis

Precipitation timing and soil substrate drive phenology and fitness of Arabidopsis thaliana in a Mediterranean environment

Mega-scale Bayesian regression methods for genome-wide prediction and association studies with thousands of traits

Improving predictive ability in sparse testing designs in soybean populations

Two teosintes made modern maize

Maintenance of quantitative genetic variance in complex, multitrait phenotypes: the contribution of rare, large effect variants in 2 Drosophila species

Demonstration of local adaptation in maize landraces by reciprocal transplantation

Analysis of genotype-by-environment interactions in a maize mapping population

Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci

MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

Corrigendum to: “Social network analysis of the genealogy of strawberry: retracing the wild roots of heirloom and modern cultivars”

Social network analysis of the genealogy of strawberry: retracing the wild roots of heirloom and modern cultivars

Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials

Collaborators (9)

Liana Burghardt

Renaud Rincent

Matthew B. Hufford

Valerio Hoyos‐Villegas

Hao Cheng

Mitchell J Feldmann

Katrina McGuigan

Johanna Schmitt

Ruairidh James Hay Sawers

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