Old-fashioned gene hunting wasn’t terribly efficient.
Geneticists typically pursued
one gene at a time, armed only
with guesses — usually wrong — about
which chunks of genetic code might be
linked to human disease.
Geneticists managed to bag a few
trophies anyway — genes for Huntington’s chorea and cystic fibrosis, for example — mostly in rare diseases caused by a
problem in a single, high-powered gene.
Unfortunately, most of the more common diseases, such as type II diabetes,
are instead controlled by a whole crowd
of gene variants, each playing a small and
often subtle role in the path to disease.
To spot these quiet genes lying in the
genomic underbrush, disease geneticists
realized they’d better try a new tack. In the
mid-1990s, the most foresighted among
them asked, “What if someday we could
take a bunch of unrelated people and
compare their genetic blueprint in lots of
different places, all at once? Could it revolutionize the study of human disease?”
International partnerships soon formed
to figure out if this was even possible.
It was. Now, new technology, buttressed by new analytical methods and
enhanced knowledge of the genome,
allows scientists to do just that: Researchers can test up to a million of the most
important spots across the entire genome
at one time. These “genome-wide association” studies excel at detecting the subtle
effects from common versions or variants
of genes that went unnoticed before.
Researchers can now put their guesswork
aside and watch as a single study hauls in
thousands of potential gene suspects.
Not surprisingly, geneticists are
cheered by the prospect of leaving behind
their days of hapless gene-hunt bumbling.
“After years as ‘Keystone Cops,’ complex-trait geneticists can now find culprits not
previously suspected and establish guilt
beyond a reasonable doubt,” geneticists
David Altshuler and Mark Daly of the
Broad Institute in Cambridge, Mass.,
wrote last July in Nature Genetics.
In the past two years alone, genome-wide association studies have found about
100 new genetic variants linked to 40 common diseases, including type II diabetes,
prostate cancer and heart disease. These
studies point to genes that researchers
never suspected of being involved with
certain diseases, or to uncharted regions
known as “gene deserts” where genes are
not known — at least yet — to exist.
Researchers hope the new studies
will help explain how common diseases
develop and also will help guide the search
for new treatments and drugs. “I think it
is absolutely clear that we have learned a
tremendous amount about a whole range
of complex, common, genetic diseases
in the human population, and we have
much greater knowledge than we did just
a very short time ago,” says biostatisti-cian Michael Boehnke of the University
of Michigan in Ann Arbor. But for all the
promise and hype, finding genes with the
new methods may not prove as easy as
shooting DNA ducks in a genomic pond.
Genome-wide association studies come
booby-trapped with potential pitfalls.
Ironically, some of these problems
stem from the studies’ biggest strength:
an unprecedented avalanche of data.
Other challenges arise from the lingering genetic effects of migrations out of
Africa 60,000 years ago. Ignoring these
issues might cause scientists to waste
valuable time investigating innocent sus-
Sorting out SNPs
Most human cells contain a complete set of
genetic instructions — the genome — comprising about 20,000 to 25,000 genes packaged in
bundles called chromosomes. the locations of
those genes can be mapped to specific regions
along those chromosomes.
from person to person, those genes are
substantially the same, consisting of nearly
identical strands of dNa. But many genes are
found in variant forms that may differ by as little
as one letter of the dNa alphabet. an alteration
in one of those letters is known as a single
nucleotide polymorphism, or sNp (pronounced
“snip” and shown by red flag). sNps give people their genetic individuality, and some sNps
lie within or near genes that predispose their
owners to certain diseases. scientists compare
the sNps of a healthy person to that of someone with a disease (far right, top and bottom).