2016 ngs health_lecture

GENOMICS IN MEDICINEThe Future of Healthcare

Goal of Genomic Medicine

• Identify genetic variation that causes or contributes to disease (diagnostic)• Inform treatment options or patient care (therapeutic/prognostic)• Provides other (potentially) useful clinical information

Innovation Cycle in Healthcare

Application

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• Reduced diagnostic odyssey

• Cancer• Classification• Treatment Guidance• Targeted Panels

• Infectious disease• Epidemiology/Outbreak monitoring• Strain discrimination

What Drives Genomic Innovation in Medicine?

Cost

Knowledge Utility

The Players

Illumina Sequencing-By-Synthesis

Glass Plate (Flowcell)

Adapter Primers


Genomic DNA Fragment

Adapter Sequence


Bridge Amplification

Nucleotides

Enzyme to initiate Bridge Amplification


Cluster Generation


dNTPs

What We Get

Reference Human Genome

Millions of 'Short Read' Sequences:

Typically 75 – 300 bp in size

What We Need To Do

Reference Human Genome

Millions of 'Short Read' Sequences:

Typically 75 – 300 bp in size

How We Do It• Fast Computers with lots of memory• Fancy Math

How We Do It• Fast Computers with lots of memory• Fancy Math

Some details later


Cost

Knowledge Utility

Composition of the Human Genome

Exome Sequencing – Baited Capture

Amplicon Sequencing

Targeted Sequencing Panels

Bioinformatics Roles•Support/Maintain Computational Infrastructure•Process Data and Generate Reports•Quality Control•Report to Stake Holders (Clinicians, Fellow Scientists)

Typical Bioinformatics WorkflowQC 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 BioinformaticsValidate, validate, validate!

Typical Bioinformatics WorkflowQC 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

genes10,000

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 War34th President of the USA



genes10,000

possibly of disease

causing type


Knowledge Required

Variant

Gene

Population

Frequency

Pathways

Functions

Tissues

Variant Type

Impact on

Protein

Populations are Important

2001 – Present: 15 years of Knowledge Building

Exome Variant Server

Exome Aggregation Consortium

2001 – Present: 15 years of Knowledge Building

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

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 ExampleCalled

Genomic

Variants

Annotation PipelineAnnotat

ed Genomic Variants

1000 Genome

s

Exome Variant Server

ExAcExomes

UK10KGenome

s

Etc...

Ensembl

UCSC

NCBI

Gencode

SIFT

PolyPhen

CADD OMIM

COSMIC

HGMD

Locus and

Disease Specific Databas

esDrug DBs

Genetic Variant Reporting

Genetic Variant Reporting



genes10,000

possibly of disease

causing type


Clinically Relevant Genetic Variants


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

The Missing Pieces?

The Missing Pieces?

Exon 1 Intron 1 Exon 2Reference

Patient

StartTAAStopmRNA coding for protein

Exon 1 Intron 1 Exon 2

TACTyrSplice Site

Loss

Missense/Frameshift Stop Gain

Where Are We Going?

Direct-to-Consumer

New Technologies: Oxford Nanopore

Summary of Key Points

•Clinical application possible when cost and applicable knowledge reach critical point• Personalized/Precision 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 are required for clinical interpretation

2016 ngs health_lecture

Science