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GENOMICS IN
MEDICINE
The Future of Healthcare
Goal of Genomic Medicine
Identify genetic variation that causes or contributes to
disease (diagnostic), informs treatment options or patient
care (therapeutic/prognostic), or provides other useful
clinical information
Research Drives Innovation in Healthcare
Healthcare
Research
Innovation
Human Genome Project 1st Draft
Personalized Medicine: Expectations and Reality
Primary Clinical Applications
• Severe childhood genetic disorders
• Clinical Exome or Targeted Disease Panel
• Cheaper than 4 or 5 sequential gene tests
• Cystic Fibrosis Testing
• Oncology
• Classification
• Treatment Guidance
• Infectious disease
• Epidemiology/Outbreak monitoring
• Strain discrimination
What Drives Genomic Innovation in Medicine?
Cost
Knowledge Utility
2016 Dal Human Genetics - Genomics in Medicine Lecture
2016 Dal Human Genetics - Genomics in Medicine Lecture
2016 Dal Human Genetics - Genomics in Medicine Lecture
The Players
2016 Dal Human Genetics - Genomics in Medicine Lecture
2016 Dal Human Genetics - Genomics in Medicine Lecture
Total Cost of Sequencing
• Whole Genome:
• Approximately $5000 - $10,000
• Technically $1000 Genome is “here” with Illumina 10X
Total Cost of Sequencing
• Whole Genome:
• Approximately $5000 - $10,000
• Technically $1000 Genome is “here” with Illumina 10X
• Analysis cost >> Sequencing Cost
Total Cost of Sequencing
• Whole Genome:
• Approximately $5000 - $10,000
• Technically $1000 Genome is “here” with Illumina 10X
• Analysis cost >> Sequencing Cost
• But, do we need the whole genome?
Composition of the Human Genome
Exome Sequencing
Targeted Sequencing Panels
What Drives Genomic Innovation in Medicine?
Cost
Knowledge Utility
Bioinformatics Roles
• Support/Maintain Computational Infrastructure
• Raw data -> Genome/Exome
• Identify genetic variation
• Annotate genetic variation
• Quality Control
• Report to Stake Holders (Clinicians, Fellow
Scientists)
Typical Bioinformatics Workflow
QC of Raw Data
Map to Reference
QC
Find Variants
QC
Annotate
Filter
It Sounds simple but…
• For every stage there are multiple programs available and
published in the literature
It Sounds simple but…
• For every stage there are multiple programs available and
published in the literature
• For every program there are a wide-variety of parameter
values and options. Defaults often “good enough” but
not always
It Sounds simple but…
• For every stage there are multiple programs available and
published in the literature
• For every program there are a wide-variety of parameter
values and options. Defaults often “good enough” but
not always
• Best combinations of programs and options not well
understood
It Sounds simple but…
• For every stage there are multiple programs available and
published in the literature
• For every program there are a wide-variety of parameter
values and options. Defaults often “good enough” but
not always
• Best combinations of programs and options not well
understood
• Protocols changing rapidly as new technologies and
methods developed
Clinical Bioinformatics
Validate, validate, validate!
Typical Bioinformatics Workflow
QC of Raw Data
Map to Reference
QC
Find Variants
QC
Annotate
Filter
Clinical Genomics: Identify Clinically Relevant
Genetic Variation
Discovering Disease-Causing Genetic Variants
4 million genetic variants
2 million associated with
protein-coding genes
10,000 possibly
of disease
causing type
1500 <1%
frequency in
population
Clinically
Relevant Genetic
Variants
If a problem cannot be
solved, enlarge it.
--Dwight D. Eisenhower
Supreme Commander Allied Forces:
Second World War
34th President of the USA
4 million genetic variants
2 million associated with
protein-coding genes
10,000 possibly
of disease
causing type
1500 <1%
frequency in
population
Knowledge Required
Variant
Gene
Population
Frequency
Pathways
Functions
Tissues
Variant
Type
Impact on
Protein
Populations are Important
2001 – Present: 14 years of Knowledge Building
Exome Variant Server
Exome Aggregation Consortium
2001 – Present: 14 years of Knowledge Building
2001 – Present: 14 years of Knowledge Building
Building Knowledge Take-Away
• Clinical utility relies on:
• Knowledge of background variation from well
sampled populations
• Knowledge of function of as much genomic
sequence as possible
• Well defined workflows
• Knowledge of sources of error
Variant Annotation Pipeline Example
Variant Annotation Pipeline Example
Genetic Variant Reporting
Genetic Variant Reporting
Genetic Variation Reporting
Genetic Variation Reporting
Genetic Variation Reporting
Potential Pitfalls with Annotation Sources
• Databases often overlap and agree, but there may be
disagreements
• Source of information: Predicted versus experimental
• Incorrect and out-of-date information
• Large-scale un-validated versus manually curated datasets
What Drives Genomic Innovation in Medicine?
Cost
Knowledge Utility
Genomic Medicine: In the Clinic
• Rapid diagnosis of genetic disease in NICU cases
• Quicker and cheaper than sequential genetic testing (traditional
method)
• 50 hour diagnosis
Genomic Medicine: In the Clinic
Genomic Medicine: In the Clinic
Genomic Medicine: In the Clinic
Genomic Medicine: In the Clinic
Genomic Medicine: In the Clinic
Types of Next-Generation Sequencing
Experiments
• DNA-Seq
• RNA-Seq
• Methyl-Seq
• ChIP-Seq
• CLIP-Seq
The Missing Pieces?
The Missing Pieces?
The Missing Pieces?
The Missing Pieces?
The Missing Pieces?
The Missing Pieces?
The Missing Pieces?
The Missing Pieces?
Exon 1 Intron 1 Exon 2Reference
Patient
Start
TAA
StopmRNA coding for protein
Exon 1 Intron 1 Exon 2
TAC
TyrSplice Site Loss
Missense/Frameshift Stop Gain
Where Are We Going?
Where Are We Going?
Do whole genome anyway, use bioinformatics to filter
down to reportable/actionable information
4 million genetic variants
2 million associated with
protein-coding genes
10,000 possibly
of disease
causing type
1500 <1%
frequency in
population
Clinically
Relevant Genetic
Variants
Where Are We Going?
Direct-to-Consumer
New Technologies: Oxford Nanopore
2016 Dal Human Genetics - Genomics in Medicine Lecture
Summary of Key Points
• Clinical application possible when cost and applicable
knowledge reach critical point
• Personalized genomic medicine is here already
• The genome alone isn’t enough
• Large population surveys of healthy individuals
• Sample from diverse human populations globally
• Large-scale surveys of genes, genetic elements, and their
functions
• Data, data, and more data required

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2016 Dal Human Genetics - Genomics in Medicine Lecture