The National Institutes of Health (NIH) is taking a leadership role in a new research approach that involves scanning the human genome looking for variations, or markers, that raise the risk of disease. Results of these genome-wide association studies (GWAS) are being posted in a database accessible to qualified researchers and will be used to come up with better detection, treatment, and prevention strategies, says Emily Harris, PhD, epidemiologist in the new Office of Population Genomics within the NIH's National Human Genome Research Institute.

Until completion of the Human Genome Project about five years ago, GWAS weren't even possible. The latest, whole-genome genotyping technology has now made them more financially feasible to carry out, says Harris.

The markers chosen are relatively common in the general population, "five percent or higher," to keep sample sizes manageable, says Harris. "We want to find the location [of genetic variations], not necessarily the gene itself." Follow-up studies can further narrow the search to suspect genes, what the genetic variations do, and how they relate to disease risk changes.

Results of GWAS are to be a "community resource," says Harris. It's not just a cost issue. "NIH wants to encourage investigators to share data more quickly and broadly, and...new statistical methods are available to better elucidate some associations."

The results of 14 GWAS are currently available through the Database of Genotype of Phenotype (dbGaP) that was created last year by the National Center for Biotechnology Information, a part of the NIH's National Library of Medicine, says Harris.

Data from the 9,000-participant Framingham Genetic Research Study is also being shared through dbGaP, says Harris. Information from another eight studies under the NIH's Genes, Environment and Health Initiative (GEI) will be added starting later this year.

GWAS are more like epidemiological studies than traditional clinical research in that they look for clues to disease among large populations, says Harris. The clues form the basis of subsequent research on different, increasingly targeted patient groups. But first, researchers want to ensure they aren't following "false clues" by replicating results across several like studies.

The end goal for the Office of Population Genomics is to facilitate the application of genomic knowledge to health. Currently, the eight-employee Office is providing leadership to GAIN and GEI and helping to cross-educate epidemiologists and geneticists, says Harris. It is involved with projects to improve statistical methods as well as explore the use of electronic medical records and associated bio-repositories in population-based studies.

Once a disease's "causal variant" has been identified, the Office will begin funding studies looking at the distribution of that disease and variant in the population, says Harris.  To ensure consistency, the Office will be developing a "toolkit" regarding how to measure ethnicity, blood pressure, height, and other phenotypic data. "The other purpose is to look at the different ways people measure things and see if they can be combined in a way that makes sense so we have some harmonization between data [already and yet to be] collected."

The chief computational challenge of GWAS is how to collectively analyze genetic variations and non-genetic factors, like environment and family history, "to understand what is really going on in the data and biologically," says Harris. "Now, we tend to analyze each marker one at a time."
-----------------------
This article first appeared in Bio-IT World’s eCliniqua newsletter. Click here for a free subscription to eCliniqua.