About

Stephen Moose is a Professor in the Department of Crop Sciences at the University of Illinois. His research focuses on corn functional genomics, utilizing genomics and genetic approaches to improve crop traits. His work involves studying the genetics of maize, including the inheritance patterns of small RNAs and their association with phenotypes and heterosis, as well as using genome editing techniques such as CRISPR Cas9 to explore gene functions related to protein accumulation and other traits. Dr. Moose's research aims to enhance understanding of the genetic basis of important agronomic traits in maize, contributing to crop improvement through advanced genomic and biotechnological methods.

Research topics

• Computer Science
• Biology
• Genetics
• Agronomy
• Ecology
• Machine Learning
• Artificial Intelligence
• Data science
• Botany
• Geography
• World Wide Web
• Biotechnology
• Medicine

Selected publications

• 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

  • Computer Science
  • Machine Learning
  • Biology

  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 could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.

  PublisherDOI

• 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

  • Computer Science
  • Biology
  • Ecology

  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. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

  PublisherOA PDFDOI

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

  BMC Research Notes · 2020 · 75 citations

  • Computer Science
  • Artificial Intelligence
  • Data science

  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. This report covers the public release of datasets for 2014-2017. DATA DESCRIPTION: Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

  PublisherOA PDFDOI

• Genome biology of the paleotetraploid perennial biomass crop Miscanthus

  Nature Communications · 2020 · 126 citations

  • Biology
  • Agronomy
  • Botany

  Miscanthus is a perennial wild grass that is of global importance for paper production, roofing, horticultural plantings, and an emerging highly productive temperate biomass crop. We report a chromosome-scale assembly of the paleotetraploid M. sinensis genome, providing a resource for Miscanthus that links its chromosomes to the related diploid Sorghum and complex polyploid sugarcanes. The asymmetric distribution of transposons across the two homoeologous subgenomes proves Miscanthus paleo-allotetraploidy and identifies several balanced reciprocal homoeologous exchanges. Analysis of M. sinensis and M. sacchariflorus populations demonstrates extensive interspecific admixture and hybridization, and documents the origin of the highly productive triploid bioenergy crop M. × giganteus. Transcriptional profiling of leaves, stem, and rhizomes over growing seasons provides insight into rhizome development and nutrient recycling, processes critical for sustainable biomass accumulation in a perennial temperate grass. The Miscanthus genome expands the power of comparative genomics to understand traits of importance to Andropogoneae grasses.

  PublisherOA PDFDOI

Recent grants

• Gene Discovery for Maize Responses to Nitrogen

  NSF · $2.6M · 2005–2011

• NIH Grant F32GM018577

  NIH · $25k

Frequent coauthors

• Kankshita Swaminathan

  HudsonAlpha Institute for Biotechnology

  29 shared

• Matthew E. Hudson

  University of Illinois Urbana-Champaign

  28 shared

• Stephen P. Long

  18 shared

• Ray Ming

  Fujian Agriculture and Forestry University

  14 shared

• Archie R. Portis

  13 shared

• Daniel S. Rokhsar

  Innovative Genomics Institute

  11 shared

• Rebekah Wood

  HudsonAlpha Institute for Biotechnology

  11 shared

• Dafu Wang

  11 shared

Labs

• The Stephen Moose Corn Functional Genomics Lab At IllinoisPI

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