There are three broad interest areas of the group.
- Quantitative Evolutionary Genetics.After 15 years working on genomic approaches to complex traits in Drosophila, my group has spent much of the past 10 years focusing on human quantitative genetics. We start with the conviction that genotype-by-environment and genotype-by-genotype interactions are important influences at the individual level (even though they are almost impossible to detect at the population level). We use a combination of simulation studies and integrative genomics approaches to study phenomena such as cryptic genetic variation (context-dependent genetic effects) and canalization (evolved robustness) with the main focus currently on disease susceptibility.
- Immuno-Transcriptomics. As one of the early developers of statistical approaches to analysis of gene expression data, we have a long-term interest in applications of transcriptomics in ecology, evolution, and lately disease progression. Since blood is the most accessible human tissue, we’ve examined how variation is distributed within and among populations, across inflammatory and auto-immune states, and asked how it relates to variation in immune cell types. Our axes-of-variation framework provides a new way of monitoring lymphocyte, neutrophil, monocyte and reticulocyte profiles from whole peripheral blood. Most recently we have also been collaborating on numerous studies of specific tissues or purified cell types in relation to such diseases as malaria, inflammatory bowel disease, juvenile arthritis, lupus, and coronary artery disease.
- Predictive Health Genomics. Personalized genomic medicine can be divided into two domains: precision medicine and predictive health. We have been particularly interested in the latter, asking how environmental exposures and gene expression, metabolomic and microbial metagenomics profiles can be integrated with genome sequencing or genotyping to generate health risk assessments. A future direction is incorporation of electronic health records into genomic analyses of predictive health. Right now it is easier to predict the weather ten years in advance than loss of well-being, but we presume that preventative medicine is a big part of the future of healthcare.