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Statistical Genetics Using Sequence Data Dajiang J. Liu Department of Statistics
Why We Study Statistical Genetics <ul><li>Statistics is originated from genetics </li></ul><ul><li>R.A. Fisher: “ The Corr...
Statistical Genetics <ul><li>Disease gene mapping :   </li></ul><ul><ul><li>The determination of the sequence of genes and...
Statistical Genetics <ul><li>Modern era: </li></ul><ul><ul><li>Microsatellite Markers: </li></ul></ul><ul><ul><ul><li>Gene...
Statistical Genetics <ul><li>Sequencing Era: </li></ul><ul><li>Study of diseases due to rare variants is emerging </li></u...
Statistical Genetics <ul><li>Data we work with </li></ul>Human  Genome  Project Hap Map  Project 1000  Genome Project
Multi-facotorial Disease Etiology Hypothesis <ul><li>Common Disease Common Variants Hypothesis (CD/CV) hypothesis: </li></...
Multi-facotorial Disease Etiology Hypothesis <ul><li>Common Disease Rare Variants Hypothesis: </li></ul><ul><ul><li>Common...
Challenges on Statistical Methodologies <ul><li>Variants misclassification: </li></ul><ul><ul><li>Non-causal variants Incl...
Kernel Based Adaptive Clustering <ul><li>Combine variant classification with association testing into a coherent framework...
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10 Liu, Dajiang

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