Download Gill: Human Disease Genomics

CS273A
Lecture 18: Human Disease Genomics
http://cs273a.stanford.edu [Bejerano Fall16/17]
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Announcements
• Hope my vocal chords last till 2:50 :)
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START WITH HYPERBOLE
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The Human Genome
Genome
1013 cells
≈
Human
Genome = The Operating System that runs every cell in our body
3*109 letters long, over the DNA alphabet = {A,C,G,T}
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The Biggest Challenge in Genomics…
… is computational:
How does this
Program
encode this
Output
This “coding” question has profound implications for our lives
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Match to the Next Three Slides…
Time
Negative Selection
Neutral Drift
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Positive Selection
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The Biggest Challenge in Genomics…
… is computational:
How does this
encode this
Program
Forks & re-merges
Where did we come from? How are we different from each other?
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The Biggest Challenge in Genomics…
… is computational:
How does this
encode this
Program
Suite of related products
What in our genomes make us different from other species?
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The Biggest Challenge in Genomics…
… is computational:
How does this
Program
encode this
Bugs
Output
What genomic mutations predispose us to disease?
http://cs273a.stanford.edu [Bejerano Fall16/17]
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The Biggest Challenge in Genomics…
… is computational:
How does this
Program
encode this
Bugs
Patching
What genomic mutations determine our drug response?
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The Biggest Challenge in Genomics…
… is computational:
How does this
Program
encode this
Bugs
Verification
We can eliminate suffering by not “booting” “buggy” embryos
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The Biggest Challenge in Genomics…
… is computational:
How does this
Program
encode this
Bugs
Debugging
We can eliminate suffering by fixing people’s “buggy” genomes
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Literally Save Lives From Your Keyboard!
Read
Understand
Fix
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Gene Therapy: We Design the Cure
Life saving
“code injection”
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Biomedicine is facing a phase transition
From an obsession with the interpreter,
Code
Interpreter
Output
To an obsession with the code.
(When your code has a bug what do you fix?..)
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Gene Therapy 3.0: Precise, Hereditary
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Every New Technology Raises Ethical Issues
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Human disease
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Even single basepair mutations can be
devastating (to the individual)
The Species Tree
S
S
Sampled Genomes
S
Speciation
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Time
Fixation, Positive & Negative Selection
Time
Negative Selection
Neutral Drift
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Positive Selection
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Mosaicism
germ
cells
offsprings
Mosaicism can be:
• somatic (ie in most body cells) or
• gonadal (confined solely to the gonads).
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Fundamental Changes in Cancer Cell Physiology
Exploitation of natural pathways
for cellular growth
• Growth Signals (e.g. TGF family)
• Angiogenesis
• Tissue Invasion & Metastasis
Evasion of anti-cancer control
mechanisms
• Apoptosis (e.g. p53)
• Antigrowth signals (e.g. pRb)
• Cell Senescence
Acceleration of Cellular Evolution
Via Genome Instability
• DNA Repair
• DNA Polymerase
Hanahan and Weinberg. 2000. The hallmarks of cancer. Cell 100: 57-70.
Many Paths Lead to Cancer Self-Sufficiency
Hanahan, Douglas, and Ra Weinberg. 2000. The hallmarks of cancer. Cell 100: 57-70.
Cancer Mutations
The relation between sporadic and inherited forms of the same tumour.
The target tissue contains n cells and the chance of one cell suffering a loss of
function mutation in the tumour suppressor gene is µ.
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Cancer: A Disease of the Genome
Cancer genomes have a really messed up genome.
Challenge in Treating Cancer:
 Every tumor is different
 Every cancer patient is different
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Cancer Genomics Goal
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Statistical Genetics
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Genome Wide Association Studies (GWAS)
Control
A/G
A/G
G/G
G/G
A/G
G/G
G/G
Disease
A/A
A/G
A/A
A/G
A/G
A/A
A/A
AA
0
4
AG
3
3
GG
4
0
p-value
Genome Semantics
• The Genome is ultimately a Programming Language
• Statistical approaches ignore genome semantics
• Population Genetics (neutral drift)
• Selective sweeps (positive selection)
• GWAS (negative selection)
• All ask questions about allele transmission
• Not allele meaning
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Consumer Genomics
1 Collect scientific literature
about all structural variant
correlations with human
disease & traits.
2 Genotype customers for as
many informative loci as is
commercially viable.
3 Offer counseling for your
findings, and their meaning.
4 Ask customers to
phenotype themselves.
5 Discover new associations!
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Knowledge = technology + pre-conceptions
Our knowledge is strongly influenced by what our
technology allows us to see. What it does not, we will with
pre-conceptions. And those may often be wrong.
Even after half the human genome was sequenced, people
thought there will be over 100,000 coding genes in it.
There are in fact ~20,000 (in par with the gene repertoire of
much simpler animals).
Before the next generation sequencing revolution people
thought the majority (99%) of human disease mutations
will be in the coding regions.
80-90% of human disease GWAS associated SNPs are in
fact non-coding. The majority is likely gene regulatory.
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Developmental Defects
gene
genome
protein
Limb Malformation
Over 300 genes already
implicated in limb malformations.
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Genetic Causes
gene
genome
NO protein
made
Limb Malformation
More and more cases are being discovered.
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Modularity: Disease Implications
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Critical regulatory sequences
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Cis-regulatory contribution to Human Disease
SHH
1Mb
LMBR1
Limb
Lettice et al. HMG 2003
12: 1725-35
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Mendelian Diseases
• Caused by 1 or 2 mutations in a patient’s gene
• Because of “extensive code reuse” in our genome
these few mutations result in complicated phenotypes
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Mendelian Diseases
• There are 8,000 known rare Mendelian diseases
• Each can cause over a dozen different phenotypes
of 10,000 known disease phenotypes
• Together rare Mendelian diseases affect 1 in 33 babies
• There are over 20,000 genes in the human genome
• Sequencing all genes is cheap, and getting cheaper
• We now know of thousands of genes that when mutated
cause thousands of different Mendelian diseases.
• Diagnosing a case is vital for patient & their family
• Because doctors rarely see each rare disease twice,
and because there are so many genes and so many
rare diseases, MANUAL DIAGNOSIS IS UNSUSTAINABLY
SLOW, INACCURATE AND EXPENSIVE.
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Diagnosis is theoretically straightforward
affected
variants
unaffected gene-phenotypes
associations
variants
causal
mutation
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WHAT NEXT?
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Beyond Coding
CODING
SNV
1-50bp
mutations
Exome
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Targeted Sequencing, or
looking under the lamp
Exome
Library
Shotgun
Library
Exon 1
Exon 2
Genomic DNA
Capture Methods vs. Shotgun
•
Targeted sequencing allows for
much higher coverage at less cost
• Will only capture known sites
• These methods also introduce
significant captures bias, including
0.1% SNP array $100 failure to capture sites that differ
significantly from the reference
2% Exome
$1000 genome. (analogous to microarrays)
100%* Genome $2000
http://cs273a.stanford.edu [Bejerano
Modified from Meyerson et al. . 2010. Advances in understanding cancer genomes through
Fall16/17]
second-generation sequencing. Nature Reviews Genetics 11, no. 10 (October): 685-696
Beyond Coding
CODING
SNV
1-50bp
mutations
SV
rest of
spectrum
NONCODING
Exome
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Beyond Coding
SNV
1-50bp
mutations
SV
rest of
spectrum
CODING
NONCODING
Exome
Genome
Genome
Genome
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