In 2012 we published a method, now called Generation-Proxy Selection Mapping, to identify loci responding to current selection. In this analysis we fit birth date (as a surrogate to generation number) 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 relatedness and population structure within the data.

We have previously used this method in Angus cattle using approximately 45,000 SNPs. In 2021, we published results using this method in 3 cattle breeds using approximately 834,000 SNPs.

We are also interested in applying this method in other species.

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. We are approaching this problem investigating signatures of local adaptation, GxE GWAS, and ecoregion-specific genomic predictions.

One of the largest drivers of profitability in beef production is reproductive performance. However, genetic tools to improve fertility have been limited. We are working to address this problem through genomic investigations of new puberty and fertility phenotypes.

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, we are working to collect data to predict cow feed intake and basal metabolic rate using genomic and phenotypic data.

We use 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.

We use various computational methods to investigate the genetic histories of world-wide cattle breeds. We are interested in building the family tree of cattle breeds, as well as understanding the domestication of cattle.

We also have various other population genetic collaborations, such as in Brassica and mad toms.