Course Description:
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<br>Current biological and medical studies make serious use of data from various high-throughput genomic experiments.  In addition to those generated in specific research projects, there are massive valuable data in the public domain. These data presents challenges to the science community. The statistical methods discussed in this course will allow the students to handle such data related to genetic studies. In addition to basic molecular genetics, genotyping technologies and classical statistical genetics, we will emphasize statistical approaches to genome-wide association studies (GWAS) and its follow-up functional studies.  These include methods to detect hidden population substructure, multiple testing issues, eQTL and pathway analysis.  
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<br>Text Books and references:
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<br>A statistical approach to genetic epidemiology: concepts and applications. Second edition.  By Ziegler and Konig.
<br>A list of references from recent literature will be prepared.
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<br>Teaching Method:
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<br>Mostly by lecture.  Students are encouraged to make presentations based on their interests/expertise.
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<br>Syllabus:
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<br>1. Basic molecular biology for statistical genetics
<br>2. Segregation analysis and Hardy-Weinberg equilibrium
<br>3. Genetic markers and genotyping methods
<br>4. Genotype data quality check methods
<br>5. Genetic map distance and linkage disequilibrium
<br>6. Family study methods and heritability
<br>7. Basics of genetic association studies
<br>8. Association studies based on unrelated individuals
<br>9. Family based association studies
<br>10. Genome-wide association studies (GWAS)
<br>11. Bayesian approaches to GWAS
<br>12. Linkage analysis
<br>13. Quantitative traits
<br>14. Meta-analysis of genetic studies 
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<br>Evaluation:
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<br>Homework (40%), midterm exam. (30%), Final exam.(30%).
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