Page 92 - ASHG 2013 Program Guide

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Wednesday, October 23
Concurrent Invited Session I (3-9)
SESSION 6 – Evidence-Based Genetic Counseling for
Clinical Genome Sequencing
Grand Ballroom West, Level 3, Convention Center
Shelin Adam, Univ. of British Columbia;
Myra Roche, Univ. of North Carolina at Chapel Hill
The current standard of care in providing clinical
genetic counseling continues to rely on face-to-face
encounters, occurring at specific times and places,
usually prior to, and/or following, genetic testing.
Yet there is little empirical evidence to demonstrate
that this model is an optimal one for enhancing
informed decision-making and communicating
genetic test results; two critical goals of genetic
counseling. Attempts to study the effectiveness of
this traditional model have often focused on recall
of quantitative information such as recurrence risk,
which is disappointingly poor. However, aside from
knowledge, other factors have also been suggested
as important outcomes. Furthermore, serious doubts
have been raised about the appropriateness of the
current model and its ability to be scaled up to the
volume, complexity and potential implications of
clinical sequencing. Alternative models such as
e-learning platforms are now beginning to be used
for both education and counseling. Such computer-
based systems can be designed to present flexibility
in literacy level, prior knowledge of genomics and
interest level, have the advantage of accessibility at
locations, times and circumstances that are most
convenient for the family, and provide information
that is consistent with current science. However, are
we sacrificing effectiveness for practicality and cost?
This session will summarize the current counseling
model as well as alternative, multi-media options for
providing genetic counseling for genome sequencing.
We will discuss the need for more evidence, and
provide examples of validated methods that can be
used to gather robust outcome data regarding the
effectiveness of genetic counseling.
Evidence-based genomic counseling for
st century medicine.
M. J. Khoury. Ctrs. for Dis.
Control and Prevent.
Measuring patient benefits from genetic
counseling and testing interventions: Is patient
empowerment a useful outcome?
M. McAllister.
Cardiff Univ.
Can an e-learning platform provide
adequate genetic counseling?
P. Birch. Univ. of
British Columbia.
Online genetic education and
counseling — Lessons from a direct-access
genotyping service.
U. Francke. 23andMe Inc.,
Mountain View, CA.
Wednesday, October 23
Concurrent Invited Session I (3-9)
SESSION 7 – Functional Interpretation of Genomes
Using Biological Networks
Room 205, Level 2, Convention Center
Kasper Lage, Massachusetts Gen. Hosp.
The recent explosion in genome-wide association
studies, exome sequencing projects, and epigenetic
data sets, have revealed many genetic variants
likely to be involved in disease processes, but the
composition and function of the molecular systems
they affect remain largely obscure. This limits our
progress towards biological understanding and
therapeutic intervention. Computational analyses
that systematically integrate biological networks
i.e., networks in which genes are connected if they
are functionally associated in some experimental
system) with genetic data have emerged as a powerful
approach to functionally interpret large genomic
data sets by enabling the identification of de novo
pathways perturbed in disease. This session will
highlight algorithms, statistics, and web portals
being developed in this area, and exemplify how
different network-based methods have been used to
analyze common and rare genetic variants, as well
as epigenetic data sets. We will introduce specific
methods being developed and applied by leaders in
the field, which will enable the audience to use these
tools in their work. Furthermore, we will exemplify how
draft molecular systems involved in immunological,
metabolic, muscular, cardiovascular, and psychiatric
disorders have been elucidated by coupling genetic
data and biological networks through rigorous
statistical frameworks.
Integrating biological networks and
genetics to reverse engineer molecular systems
driving diseases.
K. Lage. Massachusetts Gen. Hosp.
Cell-type specificity of gene networks
to understand disease biology.
S. Raychaudhuri.
Brigham and Women’s Hosp.
Using networks, functional genomics
resources and signals of selection to understand
hundreds of complex disease loci.
J. Barrett.
Wellcome Trust Sanger Inst., Hinxton, U.K.
Network-based association models for
exome-sequencing data. S. Purcell.
Mount Sinai
Sch. of Med.