Genetics of Complex Diseases

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Human Genetic Variation

• Genetics of Complex Diseases

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Challenges
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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.

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Challenge 3 Population Substructure

• Imagine that all the cases are collected from
  Africa, and all the controls are from Europe.
• Many association signals are going to be found
• The vast majority of them are false

Why ???
Different evolutionary forces drift, selection,
mutation, migration, population bottleneck.
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Shaping Genetic Variation

• Mutations add to genetic variation
• Natural Selection controls the frequency of
  certain traits and alleles
• Genetic drift

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Ancestral population
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Ancestral population
migration
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• different allele frequencies

Ancestral population
Genetic drift
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Population Substructure

• Imagine that all the cases are collected from
  Africa, and all the controls are from Europe.
• Many association signals are going to be found
• The vast majority of them are false

What can we do about it?
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Ancestry Inference

• To what extent can population structure be
  detected from SNP data?
• What can we learn from these inferences?
• Can we build the tree of life?
• How do we analyze complexpopulations (mixed)?

Novembre et al., Nature, 2008
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Principal Component Analysis

• Dimensionality reduction
• Based on linear algebra
• Intuition find the most important features of
  the data.

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Principal Component Analysis
Plotting the data on a onedimensional line for
which the spread is maximized.
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Principal Component Analysis

• In our case, we want to look at two dimensions at
  a time.
• The original data points have many dimensions
  each SNP corresponds to one dimension.

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Data Available
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International consortium that aims in
genotyping the genome of 270 individuals from
four different populations.
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• Launched in 2002.
• First phase (2005)
• 1 million SNPs for 270 individuals from four
  populations
• Second phase (2007)
• 3.1 million SNPs for 270 individuals from four
  populations
• Third phase (ongoing)
• gt 1 million SNPs for 1115 individuals across 11
  populations

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HapMap Populations
MKK
LWK
YRI
GIH
ASW
JPT
MEX
CHB
CHD
CEU
TSI
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HapMap PCA 1-2
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HapMap PCA 1-3
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HapMap PCA 1,2,4
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Lessons from the HapMap

• African populations have higher genetic diversity
  than other populations
• Evidence for bottlenecks or founder effect in the
  other population
• Evidence for the out-of-Africa theory
• HapMap was used to detect
• Common deletions across the genome
• Regions under selection
• Recombination rates, hotspots
• Associations of SNPs with disease

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Example detection of deletions using SNPs
Conrad et al., Nature Genetics, 2006
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Example detection of deletions using SNPs

• Conrad et al. applied the method on the HapMap
  and found
• Typical individuals have roughly 30-50 deletions
  larger than 5kb (500kb-750kb total sequence
  length).
• Deletions tend to be gene-poor.
• The deletions detected in the HapMap span 267
  known and predicted genes.
• Deletions were found to be related to different
  conditions such as Schizophrenia (Steffanson et
  al., 2008), lupus glomerulonephritis (Aitman et
  al., Nature, 2006), and others.

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Distribution of deletion length
Conrad et al., Nature Genetics, 2006
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Significant Region

• Why do we have differences between data1 and
  data2?
• How come so many SNPs seem to be associatedin
  this region?
• Maybe there are multiple causal SNPs?
• Or maybe there are correlations between the
  SNPs ?

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Linkage Disequilibrium

• Signatures of History

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Linkage Disequilibrium
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Haplotypes vs. Genotypes
Haplotypes
ATCCGA AGACGC

• Cost effective genotyping technology gives
  genotypes and not haplotypes.

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Haplotypes cluster naturally
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Haplotypes cluster naturally