Data about DNA differences, gene expression, or methylation can each tell epidemiologists something about the link between genomics and disease. A new statistical model that can integrate all those sources provides a markedly improved analysis, according to two new papers.
PROVIDENCE, R.I. [Brown University] — The difference between merely throwing around buzzwords like “personalized medicine” and “big data” and delivering on their medical promise is in the details of developing methods for analyzing and interpreting genomic data. In a pair of new papers, Brown University epidemiologist Yen-Tsung Huang and colleagues show how integrating different kinds of genomic data could improve studies of the association between genes and disease. (more…)