Genome-wide association studies burst onto the scene in the mid-2000s, and they are showing their worth for predicting disease risk, classifying disease, and examining gene-drug interactions, writes Muin Khoury, the director of the public health genomics office at the US Centers for Disease Control and Prevention, at the Genomics and Health Impact Blog.
While GWAS have their limitations — namely their inability explain the full genetic risk of disease and to uncover rare variants, as well as the small effect sizes of associations found — they are increasingly leading to new insights, Khoury says.
For example, he points to the role of GWAS in finding gene variants that put people at high risk of developing type 1 diabetes and in identifying subsets of people with a certain disease who may respond to a different treatment. In addition, GWAS have aided in monitoring adverse response to a hepatitis C treatment.
"Even as science moves on to other technologies such as whole genome sequencing and other 'omics', the already collected GWAS information worldwide is truly a phenomenal amount of 'big data '. This information will be analyzed for years to come in order to gain further insight into gene-gene and gene-environment interactions and response to treatment," Khoury adds, though he cautions that "we also need to have realistic expectations about the public health impact of GWAS."