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Call for Papers: Linking Genotypes to Clinical Phenotypes

Francisco M.De La Vega fvega at computer.org
Tue Apr 25 00:20:55 EST 2000

Human Genome Variation: Linking Genotypes to Clinical

A session of the Pacific Symposium on Biocomputing 2001,
Honolulu, Hawaii, January 3-7, 2001


This year the first draft of the human genome sequence will
be available as well as a large sample of our species'
genetic variability in the form of large polymorphisms
databases. Lying buried in human genetic variability is the
source not only of all genetic disease, but the entire
range of normal phenotypic variation, including
susceptibilities to pathogens and environmental factors,
and individual differences in response to drug treatment.
At the same time, the microarray technology is enabling for
the first time large-scale genotyping and gene expression
profiling of human populations. Databases comprising gene
expression profiles of normal and diseased tissues at
several stages of pathogenesis or from different clinical
states are also now becoming available.

What are the challenges in the integration of these
databases and their clinical phenotypic annotation? To
date, most genotyping/expression experiments have used
characterizations of animal or human data sets that were
defined by the investigators. As high-throughput genotyping
and expression-measurement systems are applied to large
populations, the opportunity will soon arise to use
existing clinical phenotypic annotations: that is, in
humans, the extended medical record. This poses several
technical challenges.  Among them: To what extent can
clinical databases be used?  Are existing clinical data
models and vocabularies sufficient for the purposes of
clinical annotations of genomic databases? If not, how can
they be improved. Are genomic data models adequate in their
present form to add to existing individual medical record
systems?  How do machine-learning methodologies manage data
types in which clinicians determine the time and nature of
samples as part of clinical care?

Tools for managing the impending data flow, for class
discovery and prediction within combined data sets, and for
visualizing large amounts of results will be vitally
important for realizing the full potential of microarray
results. How to extract evolutionary features of population
genetic data poses additional challenges. Dramatic advances
will be required for theory and model development,
population genetic parameter estimation, and the
development of provably good statistical tests of
evolutionary models. Can these population genetic models
then be put to use in making the link between genotype and
complex phenotype?  What are the challenges for obtaining
the data for these studies if the necessary data sets are
fragmented between the public and private sectors?

Call for Papers and Participation

The PSB 2001 session "Human Genome Variation: Linking
Genotypes to Clinical Phenotypes" aims to provide a timely
forum on the computational challenges to correlate the
impending flood of high-throughput genotyping and gene
expression profiling data with clinical phenotypes, and to
address the forthcoming problems in the utilization of this
information in human genetics, pharmacogenetics,
populational genetics, and clinical studies.

We encourage academic, industrial and government scientists
to submit manuscripts. In addition to a session for oral
presentation of novel peer-reviewed contributions, there
will be a panel discussion devised to foster exchange
between industry and academic scientists. Participants are
invited to discuss their issues with other peers in this
panel session. Posters and computer demonstrations are also
requested to complement the session.


The contributions should pose and discuss a specific
problem that the biocomputing community will need to
address in the upcoming post-genomic era. The accepted
contributions are expected to describe models, propose
specific solutions, or address computational or theoretical
aspects of topics related to the questions posed above.

Among the anticipated topics are:

+ Design of and algorithms/methods for analyzing
association/LD/QTL studies using SNPs
+ Algorithm/methods for molecular classification from
genotypic/phenotypic databases
+ Visualization of large-scale genotypic/gene expression
+ Ontologies, control vocabularies, data exchange formats,
and data models for genotypic/gene expression/phenotypic
+ Correlation finding and knowledge discovery in
genotypic/phenotypic databases
+ Data integration of microarray genotyping and gene
expression data
+ Evolutionary models of genome variability, LD extent, and
SNP analysis.
+ Theoretical aspects of sequence variation origins and


PSB will publish accepted full papers in an archival
proceedings indexed in MEDLINE. All contributed papers will
be rigorously peer-reviewed by at least three referees. A
limited number of papers will be selected for a 30-minute
oral presentation to the full assembled conference.
Accepted poster abstracts will be distributed at the
conference separately from the archival Proceedings. Please
prepare your submission according to the instructions found
at the Web page:


Dates & Deadlines

Paper submissions due: July 17, 2000
Notification of paper acceptance: August 28, 2000
Abstract deadline: November 6, 2000
Meeting: January 3-7, 2001

Conference Information

The Pacific Symposium on Biocomputing (PSB 2001) is an
international, multidisciplinary conference for the
presentation and discussion of current research in the
theory and application of computational methods in problems
of biological significance. PSB 2001 will be held January
3-7, 2001, in Honolulu, Hawaii at the Sheraton Waikiki. For
more information see the official PSB 2001 Web page at :


Session Chairs

Francisco M. De La Vega, Synthesis and Arrays R&D,
PE Biosystems, Foster City, CA, USA.
E-mail: DelaveFM at pebio.com

Martin Kreitman, Department of Ecology and Evolution,
University of Chicago, Chicago, IL, USA.
E-mail: mkre at midway.uchicago.edu

Isaac Kohane, Children's Hospital of Boston and Harvard
Medical School, Cambridge, MA, USA.
E-mail: isaac_kohane at harvard.edu

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