Publications by authors named "David Ertl"

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023, the first open-to-the-public Genomes to Fields initiative Genotype by Environment prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements, and field management notes gathered by the project over 9 years.

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  • - Predicting how genetic and environmental factors influence traits (phenotypes) is a critical challenge in biology, with potential benefits like improved health, food security, and environmental care.
  • - The Genomes to Fields (G2F) initiative hosted a competition in 2022 and 2023, inviting global participants from various disciplines to develop models using a comprehensive dataset gathered over nine years, including genetic and environmental data.
  • - Winning methods combined machine learning with traditional breeding techniques, showcasing a variety of approaches such as quantitative genetics and deep learning, indicating that no single strategy was universally superior in predicting phenotypes in this context.
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  • The paper emphasizes the need for simultaneous advancements in genomics (measuring genetic variation) and phenomics (measuring trait variation) for agricultural populations to improve agricultural productivity.
  • It discusses the Agricultural Genome to Phenome Initiative (AG2PI) and its efforts to coordinate with government agencies and stakeholders to improve agricultural outcomes through research collaboration.
  • A workshop was held to identify challenges and innovation opportunities in AG2P research, resulting in a vision for future advancements and six specific goals for immediate implementation.
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  • - The Maize GxE project, part of the Genomes to Fields Initiative, studies how different genetic types (genotypes) of maize interact with varying environmental conditions to improve resource use and predictability in crop performance.
  • - Data collected from 30 locations in the US and one in Germany during 2020-2021 include phenotypic details, soil and climate measurements, and other relevant metadata, all of which are being made publicly accessible.
  • - Collaborators at each site collected and submitted data, which was then verified and compiled by a coordination team, ensuring accuracy before releasing a minimally filtered version to the public.
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Objectives: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data.

Data Description: This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022.

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Objectives: This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions.

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Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models.

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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.

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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 identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments.

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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 inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project.

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Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids.

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Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments.

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  • Phytic acid in grains and oilseeds is hard for monogastric animals to digest, harming both nutrition and the environment.
  • Breeding efforts have struggled with traits linked to reducing phytic acid, particularly in maize, due to other negative agronomic traits.
  • Researchers found that silencing a specific transporter gene in maize leads to seeds with low phytic acid and normal growth, offering a simpler way to improve crops without the downside of recessive mutations.
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  • Phytic acid (Ins P6) is the most common myo-inositol phosphate in plants, but how it’s made is not well understood, especially concerning myo-inositol's role in its production.
  • A low-phytic acid mutant in maize, labeled lpa3, was found to have reduced phytic acid levels, increased myo-inositol, and minimal myo-inositol phosphate intermediates in its seeds, linked to a mutation in the myo-inositol kinase gene.
  • The maize MIK protein, crucial for phytic acid biosynthesis in developing seeds, phosphorylates myo-inositol to create various monophosphates, suggesting multiple pathways to phytic acid exist and hinting at
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Reduced phytic acid content in seeds is a desired goal for genetic improvement in several crops. Low-phytic acid mutants have been used in genetic breeding, but it is not known what genes are responsible for the low-phytic acid phenotype. Using a reverse genetics approach, we found that the maize (Zea mays) low-phytic acid lpa2 mutant is caused by mutation in an inositol phosphate kinase gene.

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