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Genotyping’s
Golden Age?
New
data and tools add sizzle to once-maligned field
By Malorye A.
Branca, Editor-in-Chief, Pharma DD
November/December
2006
Pfizer, the National Human Genome Research
Institute (NHGRI), and a growing number of other labs are suddenly investing
more in association studies, thanks to a dramatic trend—a critical data and
tool infusion that could finally allow genotyping to deliver real medical
breakthroughs.
"The race to uncover the genetic
underpinnings of common diseases is on," says Jay Flatley, President
and CEO of Illumina, one of the leading vendors in this space. Big players
agree. "We've been watching the field for ten years, and this is a real
tipping point," says Pfizer's Patrice Milos. NHGRI head Francis Collins
concurs. "Everyone is fired up to uncover real genetic
associations," he says.
No Pain, No GAIN
Pfizer and NHGRI happen to be working together on one of the major genetic
association projects in progress—the Genetic Association Information Network
(GAIN). A $25 million plus public/private partnership, GAIN recently made
six grants to researchers doing genome-wide scans for the genetic roots of
depression, psoriasis, schizophrenia, and other common conditions. Pfizer
has pumped at least $5 million into the project. Affymetrix donated a few
million dollars worth of resources, and Perlegen will perform whole genome
scans. The plan is to make data from these, as well as other studies,
available to more researchers.
The reward for Pfizer and other private
contributors? "We're impatient to unravel the complexity of human
disease," says Milos. "Doing this by ourselves would take too
long." Thanks to the partnership, Pfizer hopes that as much data will
be generated over the next couple of years as the company could have gotten
by itself in a decade. That much acceleration apparently makes it worth
sharing the labor's fruit.
If you still doubt the field has a new look,
just examine pioneer Illumina's financials. Not too long ago the company's
stock was below $3 per share. Today, its value has risen to more than ten
times that amount.
Flatley attributes his company's success to
continued strong sales of high-end systems as well as a burgeoning middle-
and low-end market. "This whole market was created during the last
12-18 months," he says. Established Illumina customers such as DeCode
Genetics have beefed up their labs with more scanners and robots. Meanwhile,
Illumina is also selling lots of its newer, lower-end instruments to
scientists who have become attracted to genotyping as it gets increasingly
cost-effective and respectable. (For more on genotyping tools see the
article in this issue by Nina Flanagan, "New Genotyping Technologies
Key to Drug Discovery & Development.")
Genotyping first surged in the late '90s, but
interest dipped after genomania cooled in 2000. The problem was that despite
better new tools, association studies continued to be plagued by weak or
iffy data. To find disease-causing genes, scientists look across many
individuals' genomes for signals that explain why some people get a disease
and others don't. The most common of these signals are called single
nucleotide polymorphisms (SNPs). Problems arise when the studies are badly
designed, poorly executed, or simply underpowered because they don't include
enough samples. The cost of genotyping, which was about 50 cents per SNP a
few years ago, has thus had a huge impact on the quality of the studies.
A New Attitude
Things have changed a lot. One big step forward was completion of the
International HapMap project, which created a deep and wide database of
human genetic variation. "The HapMap laid out the landscape of
variation across all chromosomes, in multiple ethnicities," says
Collins, whose division led that charge. "Now, scientists can do scans
across the whole genome but using just a few thousand SNPs."
Scientists must sift through millions of SNPs
to find those that matter. Before, it was tough to tell whether a SNP was a
real variation or not, let alone whether it was one that actually played a
role in disease. Now, there is at least a well-established starting point
for every study. "The HapMap gives us a catalogue," says Flatley.
Vendors, such as Illumina and Applied Biosystems, quickly provided reagents
to match the SNPs turned up in the HapMap hunt.
On top of that, the technology for doing
genotyping is now "So efficient and so amenable to high throughput that
once-unthinkable studies have zoomed into the affordable range," says
Collins, who adds, "I give a lot of credit to the companies who have
made that possible, such as Illumina, Affymetrix, and Perlegen."
According to Collins, the cost of genotyping is now down to about 1/3 of a
cent per SNP.
Work still needs to be done, particularly in
designing better data-handling tools. (See sidebar, "Turning Genotypes
into Gold."). "For example, we need more statistical tools to sort
out hits from false positives," says Pfizer's John Thompson. Concerns
about genetic privacy also linger. The NIH recently announced plans to
create a central, standardized database of all genotypic and phenotypic data
from NIH-funded genome-wide studies. As part of that effort, the agency aims
to develop new policies on the use and sharing of genetic data.
Data from GAIN is expected to start coming
out as early as the first part of 2007. "It's the foundation for a new
future," says Milos, adding that each study will use several thousand
patient samples and look at an "unparalleled" density of SNPs.
Other HapMap-fueled studies are already bearing fruit. Having been burned
before, many scientists will continue to be skeptical of association
studies, but that should be good for the field. "People will set a high
bar for the statistical evidence for any association," says Collins.
Hence, fewer spurious associations will get in the way of the real ones.
It's a rewarding turn of events, particularly
for those who have lived through the down years. Despite the huge progress,
researchers on the front lines are hardly complacent. "The first
technological hurdles have been overcome," says Milos. "Now, we
have to do the experiments."
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