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.
The kinds of data Huang integrates are single-nucleotide differences in DNA, called SNPs, data on gene expression, which is how the body puts genes into action, and methylation, a chemical alteration related to expression. All are potentially relevant to whether a person gets sick, but most analyses that connect genomics to disease account for only one. In papers now online in the journals Biostatistics and Annals of Applied Statistics, Huang describes the results of testing the model in analyses of asthma and brain cancer data.
"Our integrated approach outperforms single-platform approaches," Huang said. "Applied to real data sets, it works."
The statistical model Huang developed with Tyler VanderWeele and Xihong Lin of Harvard, co-authors on the Annals paper, isn't purely statistical. Its structure and assumptions are informed by the underlying biology. SNPs can be directly associated with disease, or that association can be mediated by whether genes, including the ones in which the SNPs reside, are expressed in healthy or sick patients.
The Annals paper describes the model witih SNPs and expression in detail and its application to data connecting the gene ORMDL3 to asthma. Using the model, the authors found 15 SNPs in the gene with significant associations with disease, compared to only five that have been apparent analyzing SNPs alone. The researchers also found that their "p-values," (a measure of an associat
|Contact: David Orenstein|