HAP Webserver: Early step towards personalized medicine
by Grace Shaw
Udpated August 1, 2005
Science magazine recently published "125 big questions that face scientific inquiry over
the next quarter-century." The question, "To What Extent Are Genetic Variation and
Personal Health Linked?" is raised among the top 25.
Genetic variation explains individuals’ response to drugs and susceptibility to specific
diseases, including cancer and several mental illnesses. Researchers suggest that
genetic variation can explain why some people develop a disease while others do not.
Carriers of certian genetic variants may have up to twice the risk of disease compared
to non-carriers. Some of these variants occur in genes that increases the risk of type 2
diabetes or the risk of Alzheimer’s.
By identifying variation that affects drug response, doctors could potentially prescribe
personalized medicine based on an individual’s genetic variation, which would be more
beneficial to each individual than standardized medicine. Exposure to dangerous side
effects or even ineffectiveness of the drug would likely decrease for the individual.
Genetic association studies determine the relations between diseases and genetic
variations. These studies involve the determiniation of variant frequencies among
healthy and diseased populations. However, these studies currently face several
limitations including costly expenses for collecting data and the lack of tools for analysis.
To confront these limitations, groups including the International HapMap Project are
developing techniques for genotyping analysis. In addition, several biotech companies
including Perlegen Sciences, Inc., Illumina, and Affymetrix are developing
highthroughput genotyping technologies.
Researchers at UCSD are working on these tools, including the HAP webserver, funded
by the California Institute for Telecommunications and Technology (Calit2). These
researchers include graduate and undergraduate students under the direction of Dr.
Eleazar Eskin, assistant professor in the Computer Science and Engineering (CSE)
Department and a Calit2 researcher.
The students, although working in the CSE Department, come from many disciplines
including computer science, bioinformatics, biochemistry and other biology-related
fields.
Whole genome association studies are more effective than candidate gene analysis in
its analysis of variation and relation to disease. Dr. Eskin coauthored a paper regarding
the HapMap and human variation with other researchers at UC Berkeley and Perlegen
Sciences, Inc. The paper, "Whole Genome Patterns of Common DNA Variation in Three
Diverse Human Populations," published on the front cover of the February 28, 2005
issue of Science, describes genetic variation in European Americans, African
Americans, and Han Chinese. The study analyzed genetic variation between the
different population groups and also within each population group. Additionally, it
showed that a whole-genome association study is possible.
“HAP” is derived from the term haplotype. A haplotype is a set of single nucleotide
polymophims (SNPs) on a chromosome that aid the investigation of diseases and
disease susceptibility. Haplotypes are usually inherited as a unit. A SNP is a small
genetic change, that can occur within a person’s DNA sequence. SNPs are important,
because they can change a protein’s biological function. However, haplotype analysis
often gives a clearer picture of thevariation than SNPs. Whole genome association
studies can show which haplotypes are associated with disease or disease response.
The HAP webserver would provide preliminary work in the identification of human
variation related to disease. Hyun Min Kang, a PhD student in Eskin’s lab, describes the
project as “an intergrated tool for genetic association analysis which leverages interplay
between haplotype reconstruction, statistical analysis, and predictions of functional
SNPs”. The user inputs genotype, phenotype, and optional SNP data into the
webserver. The data is partitioned into haplotype blocks based on the SNPs, where
haplotype predictions will be returned to the user.
The haplotype map deduced from technologies including the webserver would, as
Science claims, “further accelerate the search for disease genes.” Kang additionally
commented that the webserver “is efficient enough to be scaled up to high-density
genome wide association studies.” Most tools already developed are usually used for a
single candidate gene analysis and not applicable to the current large datatsets.
Researchers at UCSD anticipate the release of the HAP webserver by then end of
Summer, 2005.