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10 Liu, Dajiang

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  • 1. Statistical Genetics Using Sequence Data Dajiang J. Liu Department of Statistics
  • 2. Why We Study Statistical Genetics
    • Statistics is originated from genetics
    • R.A. Fisher: “ The Correlation Between Relatives on the Supposition of Mendelian Inheritance”
      • Introduced the concept of variance in this article
    • Francis Galton : Regression of human height toward the mean:
      • Introduced correlation and regression
    • Karl Pearson:
      • “ Mendelism and the problem of mental defect”
      • “ Tuberculosis, heredity and environment ”
    • Why don’t we seek our roots?
    • In order to find disease genes in the genome, statistics is a must
  • 3. Statistical Genetics
    • Disease gene mapping :
      • The determination of the sequence of genes and their relative distances from one another on a specific chromosome
      • Technology driven field :
      • Mendel’s era: Segregation Analysis
      • - Patience : peas, fruit fly: inbreeding is necessary
    Experimental Design
  • 4. Statistical Genetics
    • Modern era:
      • Microsatellite Markers:
        • Genetic linkage analysis
          • Extremely successful for mapping and identifying Mendelian traits
      • Single nucleotide polymorphism (SNP) marker
        • Case control studies:
          • Genome Wide Association Studies: To identify common variants involved in complex traits
    Computational Techniques for likelihood in Pedigrees Statistics play a major role
  • 5. Statistical Genetics
    • Sequencing Era:
    • Study of diseases due to rare variants is emerging
    ABI SOLiD sequencer Statistics is ALL for sequencing data
  • 6. Statistical Genetics
    • Data we work with
    Human Genome Project Hap Map Project 1000 Genome Project
  • 7. Multi-facotorial Disease Etiology Hypothesis
    • Common Disease Common Variants Hypothesis (CD/CV) hypothesis:
      • Common diseases are caused by a few common variants with moderate effect
      • E.g. Age-related Macular Degeneration:
    • Common variants are likely to have lower odds ratio than rare variants:
  • 8. Multi-facotorial Disease Etiology Hypothesis
    • Common Disease Rare Variants Hypothesis:
      • Common diseases are caused by multiple rare variants with large effect size:
      • The discovery of rare variants will have high impact on public health since they will aid in risk prediction and treatment
        • E.g. Multiple Rare Alleles Contribute to Low Plasma Levels of HDL Cholesterol
        • E.g. Colorectal Adenomas
  • 9. Challenges on Statistical Methodologies
    • Variants misclassification:
      • Non-causal variants Included:
        • Huge number of mutations on the genome:
          • Most of them are not causing the disease under study
      • Causal Variants Excluded:
        • Intronic mutations:
        • Intergenic regions:
    • Unknown patterns of interactions:
      • Within gene interactions: e.g. Hirschsprung’s disease (RET gene)
      • Gene x gene interactions: e.g. breast cancer genes (BRCA 1 BRCA2 x CHEK2)
      • Adaptive methods are needed
    1. 2. x
  • 10. Kernel Based Adaptive Clustering
    • Combine variant classification with association testing into a coherent framework
    • Applicable to population based case/control studies using unrelated individuals
    • Robust against variants misclassifications
    • Can handle gene x gene interactions and gene x environment interactions

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