Natalia de Leon

About

Natalia de Leon is a researcher prominently involved in maize genetics and breeding, with a focus on quantitative trait dissection, genomic analyses, and the genetic basis of agronomic and compositional traits in maize. Her work includes the development and application of methods for identifying genomic traces of selection and the genetic analysis of traits relevant to maize silage yield, quality, and cell wall composition. She has contributed to studies on the genetic diversity of maize populations, the effects of artificial selection on seed size, and the genetic architecture underlying developmental timing and phenotypic variation in maize. De Leon's research also encompasses the evaluation of maize traits beneficial for bioenergy production, including cellulosic ethanol, and the genetic factors influencing maize endosperm vitreousness and hardness. Her collaborations with graduate students and postdoctoral researchers have resulted in significant advancements in understanding maize genomics, breeding strategies, and the integration of high-throughput sequencing technologies for genotyping and genomic selection. Through her work, de Leon has contributed to the improvement of maize germplasm and the elucidation of genetic mechanisms that support enhanced crop performance and biofeedstock quality.

Research topics

• Computer Science
• Biology
• Genetics
• Machine Learning
• Artificial Intelligence
• Ecology
• Engineering
• Data science
• Pulp and paper industry
• Organic chemistry

Selected publications

• Genetic modification can improve crop yields — but stop overselling it

  Nature · 2023-09-20 · 116 citations

  articleOpen access
  PublisherOA PDFDOI

• The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment

  G3 Genes Genomes Genetics · 2021 · 101 citations

  High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to…

  PublisherOA PDFDOI

• Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project

  Frontiers in Genetics · 2021 · 92 citations

  Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information…

  PublisherDOI

• Maize genomes to fields (G2F): 2014–2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets

  BMC Research Notes · 2020 · 75 citations

  OBJECTIVES: Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available…

  PublisherOA PDFDOI

• Assessing the Viability of Recovery of Hydroxycinnamic Acids from Lignocellulosic Biorefinery Alkaline Pretreatment Waste Streams

  ChemSusChem · 2020 · 75 citations

  Invited for this month's cover is the research team from the D.O.E. Great Lake Bioenergy Research Center (GLBRC) at the University of Wisconsin-Madison. The cover image shows how a diverse team with expertise in many different fields works together in an integrated fashion to address complex problems. Only when the whole system, from field to the liquid fuels and co-products, is assessed, can we identify the key parameters needed to design an economically viable biorefinery-based economy. Cover…

  DOI

Frequent coauthors

• Shawn M. Kaeppler

  University of Wisconsin–Madison

  227 shared

• C. Robin Buell

  85 shared

• Candice N. Hirsch

  University of Minnesota

  63 shared

• Rajandeep S. Sekhon

  Clemson University

  55 shared

• Brieanne Vaillancourt

  Applied Genetic Technologies (United States)

  45 shared

• Nathan M. Springer

  Bayer (United States)

  40 shared

• Edward S. Buckler

  Cornell University

  36 shared

• Sherry Flint‐Garcia

  United States Department of Agriculture

  36 shared

Labs

• Cornbreeding - UW Silage Breeding and Biofeedstock Breeding ProgramPI

  Former Members

Similar researchers at University of Wisconsin-Madison

• Philipp Simonh-64
• Yiqun Wengh-60