Genetics of Complex Diseases

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Human Genetic Variation

• Genetics of Complex Diseases

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The Human Genome Project - Goals

• Determine the sequences of the 3 billion base
  pairs that make up human DNA

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The Human Genome Project - Goals

• Determine the sequences of the 3 billion chemical
  base pairs that make up human DNA
• Improve tools for data analysis

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The Human Genome Project
What we are announcing today is that we have
reached a milestonethat is, covering the genome
ina working draft of the human sequence.
But our work previously has shown that having
one genetic code is important, but it's not all
that useful.
I would be willing to make a predication that
within 10 years, we will have the potential of
offering any of you the opportunity to find out
what particular genetic conditions you may be at
increased risk for
Washington, DC June, 26, 2000
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The Vision of Personalized Medicine
Genetic and epigenetic variants measurable
environmental/behavioral factors would be used
for a personalized treatment and diagnosis
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Example Warfarin
An anticoagulant drug, useful in the prevention
of thrombosis.
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Example Warfarin
Warfarin was originallyused as rat poison.
Optimal dose variesacross the
population Genetic variants (VKORC1 and CYP2C9)
affect the variation of the personalized optimal
dose.
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Association Studies

• Studying complex diseases by comparing cases
  to controls

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Where should we look first?
SNP Single Nucleotide Polymorphism
person 1 .AAGCTAAATTTG. person 2
.AAGCTAAGTTTG. person 3 .AAGCTAAGTTTG. person
4 .AAGCTAAATTTG. person 5 .AAGCTAAGTTTG.
Most common SNPs have only two possible alleles.
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Disease Association Studies
Cases
AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGA
GCCGTGAGATCGACATGATAGCC AGAGCCGTCGACATGTATAGTCTACA
TGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTC AG
AGCAGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGC
CGTGAGATCGACATGATAGCC AGAGCAGTCGACAGGTATAGCCTACATG
AGATCAACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGCC AGAG
CCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCG
TGAGATCAACATGATAGCC AGAGCCGTCGACATGTATAGCCTACATGAG
ATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGCC AGAGCC
GTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTG
AGATCAACATGATAGTC AGAGCAGTCGACAGGTATAGCCTACATGAGAT
CGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCC
Associated SNP
Controls
AGAGCAGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGA
GCAGTGAGATCAACATGATAGCC AGAGCAGTCGACATGTATAGTCTACA
TGAGATCAACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCC AG
AGCAGTCGACATGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGC
CGTGAGATCAACATGATAGCC AGAGCCGTCGACAGGTATAGCCTACATG
AGATCGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGTC AGAG
CCGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCTGTAGAGCCG
TGAGATCAACATGATAGCC AGAGCAGTCGACAGGTATAGTCTACATGAG
ATCGACATGAGATCTGTAGAGCAGTGAGATCGACATGATAGCC AGAGCC
GTCGACAGGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGCCGTG
AGATCGACATGATAGCC AGAGCCGTCGACAGGTATAGTCTACATGAGAT
CAACATGAGATCTGTAGAGCAGTGAGATCGACATGATAGTC
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Genotyping technology
AGACTAACC. ACGAATCCT. GGACTTACC. GCACAACCT. GG
GATTAAC.
DNA
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Genotype technologies

• Cost of genotyping technologies has reduced
  dramatically in the last decade.
• Genotyping one SNP per one individual was gt 1 in
  the beginning of the decade.
• Price now is at 0.03 cents.
• Exponential growth doubles every 10 months
• Faster than Moores law doubling every 18
  months.

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Public Genotype Data Growth
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Association Studies

• Genetic variants such as Single Nucleotide
  Polymorphisms (SNPs) are tested for association
  with the trait.

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Published Genome-Wide Associations through
6/2009, 439 published GWA at p lt 5 x 10-8
NHGRI GWA Catalog www.genome.gov/GWAStudies
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Preliminary Definitions

• SNP single nucleotide polymorphism. A genetic
  variant which may carry different alleles for
  different individuals.
• Most SNPs are bi-allelic. There are only two
  observed alleles in the populations.
• Risk allele the allele which is more common in
  cases than in controls (denoted R)
• Nonrisk allele the allele which is more common
  in the controls (denoted N)

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Other Structural Variants
Inversion
Deletion
Copy number variant
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Chance or Real Association?
Cases
AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGA
GCCGTGAGATCGACATGATAGCC AGAGCCGTCGACATGTATAGTCTACA
TGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTC AG
AGCAGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGC
CGTGAGATCGACATGATAGCC AGAGCAGTCGACAGGTATAGCCTACATG
AGATCAACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGCC AGAG
CCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCG
TGAGATCAACATGATAGCC AGAGCCGTCGACATGTATAGCCTACATGAG
ATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGCC AGAGCC
GTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTG
AGATCAACATGATAGTC AGAGCAGTCGACAGGTATAGCCTACATGAGAT
CGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCC
Associated SNP
Controls
AGAGCAGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGA
GCAGTGAGATCAACATGATAGCC AGAGCAGTCGACATGTATAGTCTACA
TGAGATCAACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCC AG
AGCAGTCGACATGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGC
CGTGAGATCAACATGATAGCC AGAGCCGTCGACAGGTATAGCCTACATG
AGATCGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGTC AGAG
CCGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCTGTAGAGCCG
TGAGATCAACATGATAGCC AGAGCAGTCGACAGGTATAGTCTACATGAG
ATCGACATGAGATCTGTAGAGCAGTGAGATCGACATGATAGCC AGAGCC
GTCGACAGGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGCCGTG
AGATCGACATGATAGCC AGAGCCGTCGACAGGTATAGTCTACATGAGAT
CAACATGAGATCTGTAGAGCAGTGAGATCGACATGATAGTC
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Hypothesis testing

• We want to distinguish between two hypotheses
• Null hypothesis the allele frequency in the
  cases and the controls is the same (the SNP has
  nothing to do with the disease)
• Alternative hypothesis the allele frequency in
  the cases and in the controls is different (the
  SNP is correlated with the disease).
• Intuitively, we want to ask how likely is the
  null hypothesis.

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How does it work?

• For every SNP we can construct a contingency
  table
• From the table we construct a statistic
  .
• The likelihood that under the null hypothesis we
  get T or a bigger number is a p-value.

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Example

• For every SNP we can construct a contingency
  table
• T 0.02.
• The p-value is 0.8875 (88 chance of getting T gt
  0.02)

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Example

• For every SNP we can construct a contingency
  table
• T 11.11
• The p-value is low 0.001 10-3

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Example

• For every SNP we can construct a contingency
  table
• T 83.33
• The p-value is extremely low 10-19

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Results Manhattan Plots
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Challenges
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Challenge 1 Corrections of multiple testing

• In a typical Genome-Wide Association Study
  (GWAS), we test millions of SNPs.
• If we set the p-value threshold for each test to
  be 0.05, by chance we will find about 5 of the
  SNPs to be associated with the disease.
• This needs to be corrected. Different statistical
  methods are used.

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Challenge 2 Correcting genotyping errors

• How can we detect genotyping errors?
• Hardy-Weinberg Equilibrium
• If we have Mother-father-child trios we can check
  Mendelian consistency.