The eMERGE network is a consortium formed to leverage biorepository samples linked to electronic medical records to reduce the cost and time in genetic research. Typically it is necessary to collect a new group of individuals for each disease a researcher wishes to study, which is a time-consuming and expensive process. But when DNA and tissue can be linked to a subject's electronic medical record, it becomes possible to study many diseases in the same group. eMERGE is charged with developing informatic and statistical methods to accommodate the unique nature of electronic medical record data, while addressing the bioethical and privacy concerns that use of a medical record could cause.
Carlson Studies collaborates with the Group Health Research Institute and the University of Washington as the Seattle eMERGE site, studying a population of elderly individuals with Alzheimer's Disease.
C-Reactive Protein is a marker for inflammatory response in the blood. Elevated levels of CRP are implicated in the development of cardiovascular disease. We are trying to more thoroughly characterize how genetic variation influences CRP levels. We are resequencing the gene in 2500 individuals to better elucidate genetic variation in the gene and to test for correlations between the genetic variations and CRP levels. For variants that seem to be linked to CRP levels, we are constructing cells that show CRP levels by florescence to see if the variant affects expression levels in human cells.
Although genome-wide association studies have found common genetic variations associated with many common diseases, collectively these variants explain very little of the genetic component of disease. We hypothesis that common traits, like obesity, may also be influenced by rare or idiosyncratic genetic variants. By looking to the tails of traits in large study population, the Women's Health Initiative, we hope to find carriers of these rare, but high penetrance variants.
The technology to assess rare variants are large numbers of individuals has only recently become feasible. We are refining and building computing tools to cope with challenges in exome sequencing. Currently we are building a Quality Control (QC) pipeline for the sequencing data. Rare variants that pass our QC thresholds will then be interrogated for genotype-phenotype associations. Since most statistical methods for genotype-phenotype associations are most appropriate for common risk alleles, we will be refining the statistical methodology to deal with rare variants. This work is funded through an American Re-investment and Recovery Act (ARRA) Grand Opportunity (GO) grant to the WHI.