New Gene Tool May Unlock Root Causes of Disease
Copyright 2005 Daily News Central
Genetic researchers have made substantial advances in
understanding the root causes of common diseases and the history
of human evolution, according to a series of reports published
in scientific journals this week.
Chief among these accomplishments is the work of an
international consortium of more than 200 scientists from
Canada, China, Japan, Nigeria, the United Kingdom and the United
States published in the October 27 issue of the journal Nature.
The team studied DNA samples from four different parts of the
world and concluded that genetic variants located physically
close to each other are inherited collectively as groups, called
haplotypes. The comprehensive catalog of all of these blocks is
known as the "HapMap."
"Built upon the foundation laid by the human genome sequence,
the HapMap is a powerful new tool for exploring the root causes
of common diseases," says David Altshuler, MD, PhD, director of
the program in Medical and Population Genetics at the Broad
Institute of Harvard and MIT.
"Such understanding is required for researchers to develop new
and much-needed approaches to understand the still-elusive root
causes of common diseases, such as diabetes, bipolar disorder,
cancer and many others," he adds.
Altshuler and Peter Donnelly, PhD, of the University of Oxford
in England are the corresponding authors of the Nature paper.
Greatest Information in Most Efficient Manner
It has been known for a long time that diseases run in families,
with perhaps half the risk of any given common disease explained
by genetic differences inherited from one's parents. Inheritance
also can play a role in different responses to a drug or to an
environmental factor.
Because the underlying causes of these common diseases and
therapeutic responses remain largely unknown -- and because
knowing this information is necessary for successful development
of new approaches to prevention, diagnosis and treatment --
identifying the genetic contributors to human health is a
fundamental goal of biomedicine.
A new genomics-based approach to human genetics was proposed
nearly a decade ago to catalog common human DNA sequence
variations comprehensively and to test them systematically for
their association to disease in human populations.
Although it is theoretically possible to capture all of this
information by sequencing every individual human genome, this is
neither technically nor financially feasible.
"The data from the HapMap project allows scientists to select
the particular DNA variants that provide the greatest
information in the most efficient manner, lowering the costs and
increasing the power of genetic research to identify the origin
of disease," says Mark Daly, an associate member of the Broad
Institute of Harvard and MIT. Daly led the Boston team's
statistical and analytical work, and was a member of the writing
group for the Nature paper.
Millions of SNPs a Day
Moreover, the HapMap project helped spur a remarkable advance in
the technology for testing genetic variations in DNA, making it
possible to undertake comprehensive studies in large patient
samples.
A single nucleotide polymorphism, or SNP (pronounced "snip"), is
a small genetic change, or variation, that can occur within a
person's DNA sequence.
"When we started doing this work a number of years ago,
determining the genotype of a SNP in a patient cost nearly a
dollar, and we could do hundreds a day," notes Stacey Gabriel,
director of the Broad Institute's Genetic Analysis platform and
an author of the Nature paper.
"Today the prices have dropped in many cases to a fraction of a
penny per genotype, and we can do millions a day," Gabriel
notes. "This is the difference between not being able to do the
studies, and getting them done rapidly and well."
Tag SNPs
The HapMap provides excellent power to capture most human
variation and link it to disease or other traits, according to a
related paper published in the November issue of Nature Genetics.
Paul de Bakker, Roman Yalensky and their colleagues demonstrated
this finding by developing and evaluating methods to select "tag
SNPs" that capture the genetic variation in each neighborhood
with a minimum amount of work.
Using these tags, scientists can compare the SNP patterns of
people affected by a disease with those unaffected far more
efficiently than previously has been possible.
"Compared to directly genotyping all common SNPs in the genome
in all individuals of a disease study, we observe that selected
tag SNPs based on HapMap can save genotyping costs by almost an
order of magnitude without losing much power to detect a true
association," says de Bakker, a postdoctoral fellow in Altshuler
and Daly's group at the Broad Institute.
The widely used tool for tag SNP selection was developed by de
Bakker and colleagues.
Previous Computer Models Too Simplistic
Another important observation revealed by the availability of
the HapMap data is that previous computer models of human
genetics are too simplistic and can lead to false conclusions
about the role of genes or genetic loci in different diseases.
Stephen Schaffner, Altshuler and their colleagues at the Broad
Institute describe the limitations of these prior models in a
paper published in the November issue of Genome Research. They
also provide the entire scientific community with updated models
that more closely approximate reality, based on the empirical
data generated by the HapMap Consortium.
"Better computer models can be valuable tools in understanding
the nature of human DNA variation, past changes in human
populations size, and evolutionary selection," says Schaffner, a
computational biologist in Broad's program in Medical and
Population Genetics.
Candidates for Natural Selection
The public availability of HapMap's genome-wide variation data
set also makes it possible for scientists to make systematic
examinations of potential natural selection sites in the human
genome, as well as to re-evaluate previous claims for such
selection.
Pardis Sabeti, Eric Lander and their colleagues at the Broad
Institute, together with Stephen O'Brien and his colleagues at
the National Cancer Institute, used the HapMap data to examine a
prominent reported case of natural selection related to HIV
infection.
A genetic variation in a T-cell receptor called CCR5-?32, which
confers strong resistance to infection by HIV and has been
implicated in resistance to the bubonic plague, did not arise
recently in the human population, they report in the November
issue of PLoS Biology.
"With the benefit of greater genotyping and empirical
comparisons from the HapMap, we were able to show that the
pattern of genetic variation seen at CCR5-?32 does not stand out
as exceptional relative to other loci across the genome and is
consistent with neutral evolution," says Sabeti, a postdoctoral
fellow at the Broad Institute.
"In fact, the CCR5-?32 allele is likely to have arisen more than
5,000 years ago, rather than during the last 1,000 years as was
previously thought," Sabeti adds.
In addition to allowing the re-examination of previous claims of
selection, the HapMap data give scientists a new way to identify
novel candidates for natural selection.
Attainment of Goal
The successful completion of the HapMap has its roots not only
in the completion of the human genome sequence in 2001, but also
in the massive effort to characterize and catalog the millions
of SNPs across the genome.
Based on these initial data, the haplotype structure of the
human genome was recognized as early as 2001, leading directly
to the formation of the International HapMap Consortium.
Finally, methods for identifying the influence of natural
selection on the human genome were described in 2003.
Altshuler, Lander, Gabriel, Daly and many other Broad Institute
scientists led or contributed significantly to all of these
efforts, in addition to their role in the completion of the
HapMap and demonstrations of its utility, as outlined above.
In October 2002, the International HapMap Consortium set the
ambitious goal of creating the HapMap within three years. The
Nature paper marks the attainment of that goal with its detailed
description of the Phase I HapMap, consisting of more than 1
million SNPs.
The consortium also is nearing completion of the Phase II
HapMap, which will contain nearly three times more SNPs than the
initial version and will enable researchers to focus their gene
searches even more precisely on specific regions of the genome.
In line with the Broad Institute's commitment to building
critical resources for the scientific community, HapMap data are
freely available in several public databases, including the
HapMap Data Coordination Center (http://www.hapmap.org) the
NIH-funded National Center for Biotechnology Information's dbSNP
(http://www.ncbi.nlm.nih.gov/SNP/index.html) and the JSNP
Database (http://snp.ims.u-tokyo.ac.jp) in Japan.