Application of the genome topographer system\\ to the study of
complex human disease.
Thomas G. Marr (firstname.lastname@example.org)
Cold Spring Harbor Laboratory
1 Bungtown Road, Cold Spring Harbor, NY 11724, U.S.A.
The next phase of the Human Genome Project (HGP) will be
characterized by the availability of (1) significant amounts of DNA
sequence from human and model organisms, (2) physical maps of increasing
resolution of the human and mouse genomes using standardized, verifiable
mapping technologies, (3) high density maps of polymorphic genetic markers
for the human and mouse genomes, and (4) major advances in methods for high
throughput genotyping, positional cloning and/or exon trapping, and
The methods and materials resulting from the HGP are well suited to
meet the challenges in the study of complex human genetic diseases, which
require the ability to detect and sort out heterogeneous, polygenic, and
epistatic genetic effects, modulated by environmental factors. With these
issues in mind, we have developed the Genome Topographer (GT) informatics
system. The main focus of system development currently is:
In order to test the effectiveness of the design of GT, we have
begun application of GT to study two complex genetic diseases, Manic
Depressive disorder and cancer.
- To optimize the ability of users of GT to gather and merge
highly detailed genetic, molecular and biochemical data from the major
genome centers, various public databases (e.g. GenBank, GDB, CHLC, Mouse
Genome Database, various ACeDB-based files), and the scientific literature.
- To integrate a state-of-the-art sequence analysis and
interpretation tool within the GT framework, called Sequence Analyst (SA).
SA is designed to help the user perform and interpret sensitive sequence
analyses using methods that are known to be mathematically optimal, while
remaining compatible with existing approaches, such as BLAST and
- To integrate a new tool called Genetic Analyst (GA). GA will
provide a way to store, display, analyze, and manipulate data required for
genetic segregation and linkage analyses.
- To maximize the effectiveness of annotation of data as an aid in
the design of key experiments needed to understand gene function and
Note: While I will be presenting this work, it is the result of work by
many individuals in my laboratory, including Lisa Catapano, William Chang,
Steven Cozza, Martha Hiller, Rebecca Koskela, Moira Mallison, Corp Reed,
Jacqueline Salit, Adam Tracy.