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Computational and Statistical Challenges in Association Studies

Computational and Statistical Challenges in Association Studies. Eleazar Eskin University of California, Los Angeles. The Human Genome Project. “What we are announcing today is that we have reached a milestone…that is, covering the genome in…a working draft of the human sequence.”.

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Computational and Statistical Challenges in Association Studies

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  1. Computational and Statistical Challenges in Association Studies Eleazar Eskin University of California, Los Angeles

  2. The Human Genome Project “What we are announcing today is that we have reached a milestone…that is, covering the genome in…a working draft of the human sequence.” “I would be willing to make a prediction 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.

  3. Disease Risk “genetic” factors account for 20%-80% of disease risk. Many genes contribute to “complex” diseases. Mother Risk Factors Risk Factors Child Human Genetics Mother Father Child • Personalized Medicine • Treatment decisions influenced by diagnostics • Understanding Disease Biology • New drug targets. • Understanding of mechanism of disease. Where are the risk factors? (Genetic Basis of Disease)

  4. Disease Association StudiesThe search for genetic factors • Comparing the DNA contents of two populations: • Cases - individuals carrying the disease. • Controls - background population. Differences within a gene between the two populations is evidence the gene is involved in the disease.

  5. Single Nucleotide Polymorphisms(SNPs) AGAGCCGTCGACAGGTATAGCCTA AGAGCCGTCGACATGTATAGTCTA AGAGCAGTCGACAGGTATAGTCTA AGAGCAGTCGACAGGTATAGCCTA AGAGCCGTCGACATGTATAGCCTA AGAGCAGTCGACATGTATAGCCTA AGAGCCGTCGACAGGTATAGCCTA AGAGCCGTCGACAGGTATAGCCTA • Human Variation • Humans differ by 0.1% of their DNA. • A significant fraction of this variation is accounted by SNPs.

  6. Associated SNP Single Nucleotide PolymorphismsAssociation Analysis Cases: (Individuals with the disease) AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCC AGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTC AGAGCAGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCC AGAGCAGTCGACAGGTATAGCCTACATGAGATCAACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGCC AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCAACATGATAGCC AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGCC AGAGCCGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGTC AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCC Controls: (Healthy individuals) AGAGCAGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGCC AGAGCAGTCGACATGTATAGTCTACATGAGATCAACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCC AGAGCAGTCGACATGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGCCGTGAGATCAACATGATAGCC AGAGCCGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGTC AGAGCCGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCTGTAGAGCCGTGAGATCAACATGATAGCC AGAGCAGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCTGTAGAGCAGTGAGATCGACATGATAGCC AGAGCCGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCC AGAGCCGTCGACAGGTATAGTCTACATGAGATCAACATGAGATCTGTAGAGCAGTGAGATCGACATGATAGTC

  7. Correlations between SNPs • Millions of Common SNPs False Positives Challenges: Single Nucleotide Polymorphisms Association Analysis Cases: AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCAACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCAACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA Controls: AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCAACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCAACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA

  8. Single Nucleotide Polymorphisms(SNPs) Cases: AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCAACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCAACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA Controls: AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCAACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCAACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCCGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACATGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCCGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCAACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA AGAGCAGTCGACAGGTATAGCCTACATGAGATCGACATGAGATCTGTAGAGCCGTGAGATCGACATGATAGCCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTAGAGCAGTGAGATCGACATGATAGTCAGAGCCGTCGACATGTATAGTCTACATGAGATCGACATGAGATCGGTA • Millions of Common SNPs • Correlations between SNPs • SNP locations unknown False Positives Challenges:

  9. Successor to the Human Genome Project • International consortium that aims in genotyping the genome of 270 individuals from four different populations. • Launched in 2002. First phase was finished in October (Nature, 2005). • Collected genotypes for 3.9 million SNPs. • Location and correlation structure of many common SNPs.

  10. HapMap Phase 2 5,000,000+ SNPs 600,000,000+ genotypes TSC Data Nucleic Acids Research 35,000 SNPs 4,500,000 genotypes Perlegen Data Science 1,570,000 SNPs 100,000,000 genotypes NCBI dbSNP Genome Research 3,000,000 SNPs 286,000,000 genotypes Daly et al. Nature Genetics 103 SNPs 40,000 genotypes Gabriel et al. Science 3000 SNPs 400,000 genotypes 2001 2002 2003 2004 2005 2006 Public Genotype Data Growth • More SNPs increase genome coverage in association studies. • More genotypes allow for discovery of weaker associations.

  11. HAP SAT Tagger WHAP Some Computational Challenges • Genetics - identifying disease genes • Haplotype phasing - preprocessing SNPs • Association study design • Association study analysis • Population stratification • Inferring evolutionary processes (recombination rates, selection, haplotype ancestry). • Etc… • Genomics - functions of disease genes • Predicting functional effect of variation • Understanding disease effect on gene regulation • Understanding disease effect on metabolic pathways • Combining systems biology with genetics • Etc…

  12. Haplotype Phasing using Imperfect Phylogeny

  13. Genotype T C C mother chromosome father chromosome A CG G A A ATACGA AGCCGC AGACGA ATCCGC Possible phases: …. Haplotype Phasing Haplotypes • High throughput cost effective sequencing technology gives genotypes and not haplotypes. ATCCGA AGACGC

  14. Haplotype Limited Diversity • Previous studies on local haplotype structure: • (Daly et al., 2001) chromosome 5q31. • (Patil et al., 2001) chromosome 21. • Study findings: • The SNPs on each haplotype are correlated. • SNPs can be separated into blocks of limited diversity. • Local regions have few haplotypes.

  15. Haplotype Data in a Block (Daly et al., 2001) Block 6 from Chromosome 5q31

  16. 1st Possible resolution 11111110 2 00000001 7 11000001 3 00000001 7 11011000 2 00000001 7 11111110 2 00000001 7 11000001 3 00000001 7 11011000 2 00000001 7 11000001 3 00000001 7 2nd Possible resolution 11100110 1 00011001 1 10000001 2 01000001 2 01011001 1 10000000 1 10101110 1 01010001 1 11000001 1 00000001 1 11001000 1 00010001 1 01000001 2 10000001 2 1 1 1 0 0 0 ? ? 0 2 1 or Maximum Likelihood Criterion ExamplePhasing Genotypes 22222222 22000001 22022002 22222222 22000001 22022002 22000001 Maximum Likelihood Haplotype Inference is a NP-Hard Problem

  17. Narrowing the Search:PerfectPhylogeny 00000 • A directed phylogenetic tree. • {0,1} alphabet. • Each site mutates at mostonce. • No recombination. 2 01000 1 5 11000 01001 3 11100 4 11110

  18. The Perfect Phylogeny Haplotype Problem (PPH) • Given genotypes over a short region. • Find compatible haplotypes which correspond to a perfect phylogeny tree. • [Gusfield 02’]. • PPH deficiency – the data does not fit the model.

  19. Solving PPH • A very simple o(nm2) algorithm for PPH problem. (Also Gusfield 02, Bafna et al., 2003) But – in practice, we do not expect to see perfect phylogeny in biological data. We extend our algorithms to the case where the data is almost perfect phylogeny. Eskin, Halperin, Karp ``Large Scale Reconstruction of Haplotypes from Genotype Data.'’ RECOMB 2003.

  20. HAP Algorithm • HAP Local Predictions • http://research.calit2.net/hap/ • Over 6,000 users of webserver. • Main Ideas: • Imperfect Phylogeny • Maximum Likelihood Criterion • Extremely efficient. • Orders of magnitude faster than other algorithms. Eskin, Halperin, Karp ``Large Scale Reconstruction of Haplotypes from Genotype Data.'’ RECOMB 2003.

  21. HapMap Phase 2 5,000,000+ SNPs 600,000,000+ genotypes TSC Data Nucleic Acids Research 35,000 SNPs 4,500,000 genotypes Perlegen Data Science 1,570,000 SNPs 100,000,000 genotypes NCBI dbSNP Genome Research 3,000,000 SNPs 286,000,000 genotypes Daly et al. Nature Genetics 103 SNPs 40,000 genotypes Gabriel et al. Science 3000 SNPs 400,000 genotypes 2001 2002 2003 2004 2005 2006 HAP Timeline : Public Genotype Data Growth Eskin, Halperin, Karp RECOMB 2003

  22. Phasing Methods • HAP is one of many phasing algorithms. • Clark, 1990, Excoffier and Slatkin, 1995, PHASE – Stephens et al., 2001, HAPLOTYPER - Niu et al., 2002. Gusfield, 2000, Lancia et al. 2001. Many more… • Algorithms were designed for only 4-12 SNPs! • How do we phase entire chromosomes? • HAP “tiling” extension phasing for long regions. • Leverages the speed of HAP.

  23. Scaling to Whole GenomesHAP-TILE genotypes Local predictions • For each window we compute the haplotypes using HAP • We tile the windows using dynamic programming

  24. 001000 110111 010000 101111 011111 100000 000101 111010 000011 111100 100110 011001 (minimum number of conflicts) 00100000110 11011111001 Haplotype Tiling Problem (ignoring homozygous positions) 001000 110111 010000 101111 011111 100000 000101 111010 000011 111100 100110 011001 00100000110 11011111001 • NP-Hard Problem • Dynamic Programming Solution • (Eskin et al. 2004.)

  25. HAP is over 1000x faster than PHASE. Phasing Running Time Comparison(Phaseoff Competition) Marchini et al. American Journal of Human Genetics, 2006.

  26. HapMap Phase 2 5,000,000+ SNPs 600,000,000+ genotypes TSC Data Nucleic Acids Research 35,000 SNPs 4,500,000 genotypes Perlegen Data Science 1,570,000 SNPs 100,000,000 genotypes NCBI dbSNP Genome Research 3,000,000 SNPs 286,000,000 genotypes Daly et al. Nature Genetics 103 SNPs 40,000 genotypes Gabriel et al. Science 3000 SNPs 400,000 genotypes (12 hours) (24 hours) (48 hours) 2001 2002 2003 2004 2005 2006 HAP Timeline : Perlegen collaboration NCBI dbSNP collaboration Public Genotype Data Growth Eskin, Halperin, Karp RECOMB 2003

  27. Only 103 SNPs, 0.02% of the genome! RECOMB 2003 Submission

  28. Weighted Haplotype Association

  29. Association Statistics • Assume we are given N/2 cases and N/2 control individuals. • Since each individual has 2 chromosomes, we have a total of N case chromosomes and N control chromosomes. • At SNP A, let p+A and p-A be the observed case and control frequencies respectively. • We know that: p+A ~N(p+A,p+A(1-p+A)/N). p-A ~ N(p-A,p-A(1-p-A)/N). ^ ^ ^ ^

  30. Association Statistics ^ p+A ~N(p+A,p+A(1-p+A)/N). p-A ~ N(p-A,p-A(1-p-A)/N). p+A- p-A ~ N(p+A- p-A,(p+A(1-p+A)+p-A(1-p-A))/N) We approximate p+A(1-p+A)+p-A(1-p-A) ≈ 2 pA(1-pA) then if p+A =p-A ^ ^ ^ ^ ^

  31. Association Statistic • Under the null hypothesis p+A- p-A=0 • We compute the statistic SA. • If SA< -1(/2) or SA>--1(/2) then the association is significant at level . -

  32. Association Power • Lets assume that SNP A is causal and p+A ≠ p-A • Given the true p+A and p-A, if we collect N individuals, and compute the statistic SA, the probability that SA has a significance level of  is the power. • Power is the chance of detecting an association of a certain strength with a certain number of individuals.

  33. Association Statistic • Lets assume that p+A ≠ p-A then

  34. Association Power Power of association test Threshold for significance Non-centrality parameter.

  35. Association Power • Statistical Power of an association with N individuals, non-centrality parameter and significance threshold  is P(, )= • Note that if =0, power is always .

  36. Indirect Association • Now lets assume that we have 2 markers, A and B. Let us assume that marker B is the causal mutation, but we are observing marker A. • If we observed marker B directly our statistic would be

  37. Indirect Association • However, we are observing A where our statistic is • What is the relation between SA and SB?

  38. Indirect Association • We want to relate • to

  39. Indirect Association • We assume conditional probability distributions are equal in case and control samples

  40. Indirect Association • Then

  41. Indirect Association • Note that

  42. Indirect Association • How many individuals, NA, do we need to collect at marker A to achieve the same power as if we collected NB markers at marker B?

  43. Visualization in terms of Power Power of association test Threshold for significance Non-centrality parameters.

  44. Correlating Haplotypes with the Disease • The disease may be correlated with a SNP not in the panel. • The disease may be more correlated with a haplotype (group of SNPs) than with any single SNP in the panel. • Haplotype tests: • Which haplotypes should we test? • Which blocks should we pick?

  45. Key Problem: Indirect Association • We have the HapMap. • Information on 4,000,000 SNPs. • AffyMetrix gene chip collects information on 500,000 SNPs. • What about the remaining 3,500,000 SNPs? • So far, we have designed studies by picking tag SNPs with high r2. • Can we use the HapMap when performing association? • Multi-Tag methods.

  46. Haplotypes as Proxies for Hidden SNPs (de Bakker 2005)

  47. WHAP - Weighted Haplotypes A 0.71AA + 0.29AG

  48. Basic MultiMarker Method • For each SNP in HapMap, find haplotype among genotyped SNPs that has highest r2 to the SNP. • Perform association at each SNP and each added haplotype. • Now instead of performing 500,000 tests, we perform 4,000,000 tests.

  49. Weighted Haplotype Test • For each haplotype h, we assign a weight wh • We use a “weighted” allele frequency statistic: • This statistic is the weighted numerator in SA. • What is the variance of this statistic? • Complication: Haplotype frequencies are not independent!

  50. Weighted Haplotype Example • Assume we have 4 haplotypes AB, Ab, aB and ab. • If we set the weights so that wAB=wAb=1 and waB=wab=0, this is equivalent to looking at the single SNP A. • If we set the weights so that wAB=1 and wAb=waB=wab=0, this is equivalent to looking at the single haplotype AB. • Other weights are can be something in between.

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