My role involves using molecular analytics, statistical modeling, and quantitative genetics to inform decisions in the breeding pipeline.

My current NIH-funded research project involves investigating the biological mechanisms underlying resistance to oxidative stress in a Drosophila population using a systems genetics approach. More specifically, I am currently regressing resistance to oxidative stress trait data onto whole genome sequence data and whole genome expression profiles. This will allow me to identify causative transcriptional regulatory networks, cell signaling pathways, genes and variants involved in the resistance to oxidative stress, a trait that has been implicated in human health concerns such as aging, the progression of cardiovascular and neurodegenerative disease, and the disruption of cell signaling processes that control cell growth and death.

Using candidate-gene-based association mapping in two different teosinte association mapping panels I was able to identify many genes contributing to teosinte complex trait variation, including domestication genes such as tb1 and zagl1 (Weber et al. 2007; Weber et al. 2008). In the case of zagl1, a gene of unknown function, we found evidence for its role in domestication by identifying associations between variation in this gene and a domestication trait, ear shattering. Lastly, using this same method I found that genetic variation in genes identified as putative targets of selection by large-scale selection screens in maize (selection-candidates) were no more likely to contribute to trait variation in teosinte than genes selected at random from the genome (Weber et al., 2009).