Genomic approaches to the genetics of common disease

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Genomic approaches to the genetics of common
disease

• David Altshuler, MD,PhD
• Harvard Medical School
• Massachusetts General Hospital
• Whitehead Institute/MIT Center for Genome
  Research

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Genetics the inherited contribution to
phenotypic variation
Genetic identity
Genetic diversity
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Correlating biological variation and variation
in DNA sequence
agaatttca atT/Cgt aagaggaca
3.2 billion letters of human DNA
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Potential impact of genetics

• Understand fundamental basis of disease
• Target research to the root causes of disease
• Risk-stratify for pre-clinical intervention
• Tailor treatment to individual risk and response

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Human genetics has been very successful at
explaining diseases caused by a single gene...
Genotype
Phenotype
Environment
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but most common diseases are caused by a
combination of multiple genes and environment
Genotype
Phenotype
Environment
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The traditional approach linkage analysis in
multiplex families
Very successful for single gene
disorders Limited success for common diseases
(e.g., heart disease, diabetes, psychiatric
disorders )
Power is reduced because of imperfect correlation
of any given gene with disease
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A statistically more powerful approachgenetics
by direct association

• Correlate individual variants with disease

Normal
Disease
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Critical challenges

• Characterize and catalogue human genome sequence
  variation (SNPs and haplotypes)
• Collect DNA from patients with appropriate study
  design and phenotype information
• Create high-throughput tools for testing the
  catalogue of variants in patient samples
• Develop robust statistical methods to interpret
  the resulting data!

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Most DNA variants change a single DNA
letterSingle Nucleotide Polymorphism (SNP)
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A genome-wide catalogue of SNPs

• The SNP Consortium
• 13 corporate partners
• The Welcome Trust
• 5 academic centers
• Goal
• 300,000 SNPs by spring 2001
• All placed in public domain

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Novel methods for SNP discoveryRRS and the
SNPfinder algorithm
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These tools have contributed to an explosion in
publicly available SNPs
Number of SNPs
GOAL
Altshuler et al., Nature, 2000 and
http//snp.cshl.org
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High-throughput tools for typingvariants in
patient populations
Automated capillary DNA sequencers
Multiplex SBE
DNA chips
MALDI-TOF Mass Spectometry
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Studying type 2 diabetes with SNPsthe need for
care in interpretation
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Where we stand in December, 2000

• The genome sequence of most genes is known
• 1.4 million common variants freely available
• Improved tools allow rapid genotyping
• Developing standards can provide robust
  interpretation of association reports
• What will it take to make this happen?

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Genomics
Organizational challenges
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How has biological research traditionally been
performed?

• Small research groups
• Single disciplinary focus
• Manual experimentation
• Data analysis on paper

• Progress is limited by the isolated capabilities
  of each individual team
• Knowledge remains local

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Genomics draws on many disciplines...
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and is leveraged by automation, computation, and
networked distribution
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Genomics presents a challenge to the traditional
structure of academic biomedical research

• Genomics requires broad multidisciplinary
  collaboration over extended periods of time
• Genomics is capital infrastructure intensive,
  with a technological half-life of 1-2 years
• Companies can organize to reward collaboration
  and will invest in technology and scale
• Genomics may continue its move from academia into
  the for-profit sector
• Genomic tools only available for profitable
  projects?

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A positive example The SNP Consortium
GOAL
An idea
Proof of concept
Process technology
Genome-wide map available
Algorithm development
TSC formed
Validation
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Genomics global perspectives on biology
Sequence databases
Genetics
Model systems, structure, etc.
Expression monitoring