ABSTRACT
Genomewide association (GWA) studies, a hypothesis-free study design to associate
complex diseases to particular genotypes, have come into use only very recently. They
are increasingly seen to offer a more efficient strategy for identifying disease genes and
overcoming bias in the more traditional candidate gene approach. Some major successes
in the use of GWA studies have already been documented. This commentary
summarizes the recent rise of GWA studies, identifies some key characteristics, and
points to aspects of their methodology susceptible to improving their efficiency,
particularly phenotype classification. A number of excellent reviews of genetic
epidemiology methods have been published, but for the most part they predate the
widespread use of GWA studies (1-6). A workshop on GWA studies (7) considered
some ways of making these designs more efficient, and Brookes, writing in 2001,
commented, “As statistical genetic, genomic and computational technologies improve, it
is likely that within one or two decades a corresponding ‘hypothesis free,’ comprehensive,
and highly automated research strategy could turn out to be the most effective (although
still limited) way to unravel the molecular basis of human disease” (8). As we shall see,
rather than decades into the future, it was the following year that saw publication of the
first hypothesis-free GWA study.
