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TitleGenomics & Personalized Medicine
Author
LanguageEnglish
File Size6.2 MB
Total Pages134
Table of Contents
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Who is Personalized Medicine?
Personalized Medicine
Goals for today
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How long is all the DNA in your Body?
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Is family history important?
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Personalized Medicine
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Who will pay for all this?
Letter from Insurance Group
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Personalized Medicine
Would you like your genome sequenced?
Categorical Model of Choice
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Consumer Driven
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Challenges
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How do we help patients today?
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Genomic MedicineBig Data: Production, Analysis and Management
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Strategies for spanning gaps
Variant callers give different results on the same data
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Analytical considerations/challenges on the variant impact axis
e.g. Allele frequency data cannot be relied upon without review
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e.g. Many reported “rare mutations” are not so rare polymorphisms
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Solutions
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Teachers FIRSTFrom Interesting Research to Scientific Teaching
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Current State of Practice: Whole Genome Sequencing in clinical use today
Disclosures
Whole Genome Sequencing (WGS) & Whole Exome Sequencing (WES) is in clinical practice
Steps in clinical whole genome sequencing
Patient selection
Genetic counseling
WES vs WGS
Laboratory Considerations in WGS/WES
Analysis Challenges
Tertiary analysis – annotation considerations
A brief history of genetic testing
Writing the report
How can we improve the process?
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Extending Current State of Practice
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Document Text Contents
Page 1

Genomics & Personalized
Medicine:

Analysis & Clinical
Implementation

Page 2

Our vision
To create a borderless,

complementary and synergistic
research environment in southeast
Wisconsin to translate discoveries
into better health for our citizens;
while simultaneously providing

comprehensive educational and
training programs to develop the

next generation of clinical and
translational researchers.

Page 67

Analytical considerations/challenges
on the variant impact axis

• Existing WGS technologies produce many
sequencing errors

• Existing mapping algorithms in combination with
short read technology give rise to many mapping
errors

• Bioinformatics limitations with variant calling
(especially indels and SV)

• Data is incorrect/outdated - Allele Frequencies
• Data must be used appropriately - nucleotide or

amino acid conservation scores
• Data must be understood - SIFT, PolyPhen, Condel

Page 68

e.g. Allele frequency data cannot be
relied upon without review

The allele frequency reported for a particular variant can vary widely amongst commonly used data sources

These variants were not randomly selected – these variants are all associated
with disease in HGMD

Solution: compare multiple data sources and consider the sources in terms of
possible source disease status as well as technological aspects etc.

Page 133

Leveraging Existing and Emerging
Tools for Clinical Use

• Identifying needs
• Identifying tools – existing and

emerging
• Identifying potential collaborations
• Development and testing
• Implementation

Page 134

Genomics & Personalized
Medicine:

Analysis & Clinical
Implementation

Breakout Sessions 3 & 4

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