Jared E. Decker

Portrait of Jared Decker in white button-up shirt and dark suit coat

Ph.D.

Wurdack Chair of Animal Genomics

Animal Sciences

Associate Professor

Animal Sciences

Research at a glance

Area(s) of Expertise

Research Summary

Decker's research group has various research interests, combining quantitative genomics, population genomics, and evolutionary biology. All research projects use computational genomics approaches. Projects include: Genomic prediction, Mapping loci responding to selection, Genotype-by-environment interactions, Genetics of fertility, Population structure of cattle breeds, and Demographic modelling.

IDENTIFYING LOCI RESPONDING TO SELECTION
In 2012 Decker published a method, now called Generation-Proxy Selection Mapping, to identify loci responding to current selection. In this analysis, birth date (as a surrogate to generation number) is fit as the dependent variable in a mixed model equation. Variants that have changed in frequency rapidly due to selection are strongly associated with birth date, thus the method identifies regions under selection. The mixed model equations correct for demography, relatedness, and population structure within the data.

We have previously used this method in Angus cattle using approximately 45,000 SNPs. In 2021, Decker’s group published results using this method in 3 cattle breeds using approximately 834,000 SNPs and in 2023, Decker’s group published results in 3 swine breeds.

Decker’s group is also applying this method in other species.
   
GENOTYPE-BY-ENVIRONMENT INTERACTIONS
Cattle are a tremendous model organism for studying genotype-by-environment-by-management interactions, as they are subject to a variety of stressful environments, we can estimate the effects of management, and there are datasets of hundreds of thousands of phenotyped and genotyped animals available. Decker’s group is approaching this problem investigating signatures of local adaptation, GxE GWAS, and ecoregion-specific genomic predictions.
   
GENOMICS OF FERTILITY
One of the largest drivers of profitability in beef production is reproductive performance. However, genetic tools to improve fertility have been limited. Decker’s group is working to address this problem through genomic investigations of new puberty and fertility phenotypes.
   
GENOMICS OF METABOLISM
Feed costs are the largest expense in beef production. The beef industry does not currently have tools to measure feed intake or metabolic efficiency of mature cows grazing forage. It collaborative projects, Decker’s group is working to collect data to predict cow feed intake and basal metabolic rate using genomic and phenotypic data.
   
GENOMIC PREDICTION
Decker’s group uses mixed model equations to create genomic predictions for economically important traits in beef cattle. Two of the limitations of application of genomic prediction in beef cattle are the price of the assay and the accuracy of the prediction. Our research aims to overcome these limitations.
   
POPULATION STRUCTURE OF CATTLE BREEDS
Decker’s group uses various computational methods to investigate the genetic histories of world-wide cattle breeds. Decker’s group is interested in building the family tree of cattle breeds, as well as understanding the domestication of cattle.
   
POPULATION GENETICS
Decker’s group also has various other population genetic collaborations, such as in Brassica, fish, and humans.

Educational background

  • Ph.D. Genetics, Ph.D. Minor Statistics, University of Missouri, 2012

Courses taught

  • Animal Sciences 4323: Applied Livestock Genetics