1. Statistical Applications in Biology and Genetics
Tian Zheng
Wednesday, March 12, 2003

2. Outline Biological Background
Overview of quantitative research area related to genetics
Sample project I: Bayesian Regression Analysis with application to Microarray studies
Sample project II: BHTA algorithm for complex traits

3. Chromosomes and genes Video from the Human Genome Project
You can also find links to background readings at :
Celebrating the 50th Anniversary of the discovery of DNA double-helix structure.

4. Biology: Science of 21st century
Everybody talks about it!

5. Computational Biology (1)
Sequence to function
Sequence alignment using wet-lab results
Model aligned sequences
Predict function to sequence with unknown function using model fitted
Sequence to structure of proteins
Significance:
sequence  structure  function

6. Computational Biology (2)
Motif detection
Homology detection

7. Bioinformatics/Genomics
Gene expression analysis (using DNA chips or Microarray)
Protein regulatory network inference
Pedigree inference
Phylogeny inference

9. Genetic Epidemiology Linkage mapping Association mapping
Mapping for complex traits: quantitative traits, epistasis etc.

10. Linkage and Association
Gene, alleles;
Haplotype
Transmission
Cross-over and recombination
Linkage

11. Sample Project: Bayesian Regression Analysis
Mike West et al (2000) Bayesian Regression Analysis in the “large p, small n” Paradigm with application in DNA Microarray studies.

12. What is a Microarray/DNA chip
How Chips Work?

21. Oligonucleotide Arrays
Current “Golden Standard”!

22. Affymetrix GeneChip System

23. An Affymetrix GeneChip

27. Gene Expression Data
n experiments (patients, types of cell lines, types of cancer tissues, etc)
p genes on one array
Subtracted and normalized gene expression data is a n by p matrix