Research

 

The LSU AgCenter Small Grains program focuses on variety development and quantitative genetics methods that support and accelerate the variety development pipeline. Primary efforts investigate optimal strategies for using molecular markers to improve breeding for pest and pathogen resistance using both marker assisted selection and genomic prediciton. While genetic resistance to these biotic stresses are generally repeatable from year to year, variation for grain yield is affected by those biotic stresses and all other events that occur in a plot's life history, inclulding abiotic stresses such as heat, flood, and drought that are impacted by the specific environmental conditions of a location and year. Modern database tools are being used to gather together far-ranging historic data sets on phenotypes, genotypes, and environmental variables to create data sets useful for investigating these challenges.  Better understanding the yield effects of major genes controlling wheat phenology, that through phenological variation generate environment-specific yield variation, is a major focus of this work. Beyond major genes, machine learning approaches that integrate interactions between environmental variables and genetic data allow for the prediction of site-specific yield, and have become a heavily-used tool within the LSU AgCenter wheat breeding program. Work continues on optimizing those models and the data that powers them by studying optimal early-generation, multi-location trial design.

Research Areas

Marker-based breeding for pest and pathogen resistance

comb disease

  • Marker development, fine-mapping, and validation of major Hessian fly resistance genes and development of adapted wheat lines carrying Hessian fly gene pyramids.
  • Genomic prediction and sparse testing approaches to improve quantitative field resistance of wheat to Hessian fly.
  • Optimizing genomic prediction models for Fusarium head blight resistance, including incorporation of information on known major resistance QTL.
  • Identification of novel resistance genes for oat crown rust and oat stem rust in wild oat relatives.

 


Wheat abiotic stress and phenology

GxE Figure Description below

  • Optimizing wheat phenology for double-cropping of wheat with soybeans, understanding the impact of varying wheat maturities with soybean planting date and joint profitability of the double-crop system.
  • Relationship of major wheat phenology genes (Vrn and Ppd) with environment-specific yield effects as mediated by environmental variables such as heat, freeze, and drought stress.


Optimizing genomic selection and field testing

Gxe Prediction Description below

  • Incorporating environmental variables into genomic predictions from historic data for wheat yield to target specific macroenvironments within the broader Southern United States.
  • Genomics-enabled sparse testing designs for multi-region breeding programs.
  • Optimizing within-family evaluation of early-generation breeding program lines for Fusarium head blight resistance.

 


Digital infrastructure for breeding programs

lsu db workflow description below

  • BrAPI-based tools for interfacing with Breedbase for applied breeding programs.
  • Database software for line-development portion of pedigreed breeding programs.
  • Recurring updates of family-based genomic predictions based on pedigree information.

 

Cooperative and Variety Testing Reports

 

 

Variety Releases

 

Publications