Genome-wide association studies (GWAS) help us determine which of hundreds
of thousands of genetic variants tested are associated with specific
traits or diseases. Scientists perform GWAS by analyzing the genetic
profiles of many individuals exhibiting distinct phenotypes and comparing
their genetic profiles to those of the individuals that do not show that
trait. These studies are becoming more and more popular – partly because
of their affordability and somewhat because they enable us to analyze
population genetics data of many individuals, which makes them more
valuable in finding answers to questions like:
»What genetics variants did we observe in a group of people who
responded positive or negative to a drug in a trial?«, »Are genetic
variants associated with heart disease the same in all populations?«,
»What genetic variants associated with breast cancer do people of
different ethnicities have in common – is it an appropriate target for
the drug development?«.
The questions scientists are trying to answer with this
approach are numerous and vital. However, the collected data is dispersed
through many publications, and the parameters needed to conduct a
meta-analysis of collected data are often missing from publications.
Life-science community needs a centralized database to publish and
organize GWAS studies and provide experimental guidelines to allow for
automatized integration of collected data into a centralized database.
Until then, we compiled a list of the most extensive and commonly used
GWAS databases.
