Download Statistical Applications in Biology and Genetics

Statistical Applications in
Biology and Genetics
Tian Zheng
Wednesday, March 12, 2003
Outline
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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
Chromosomes and genes
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Video from the Human Genome Project
You can also find links to background
readings at :
http://www.stat.columbia.edu/~tzheng/research/statgen.html
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Celebrating the 50th Anniversary of the
discovery of DNA double-helix structure.
Biology: Science of 21st century
Everybody talks about it!
Computational Biology (1)
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Sequence to function
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Sequence alignment using wet-lab results
Model aligned sequences
Predict function to sequence with unknown
function using model fitted
Sequence to structure of proteins
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Significance:
sequence  structure  function
Computational Biology (2)
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Motif detection
Homology detection
Bioinformatics/Genomics
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Gene expression analysis (using DNA
chips or Microarray)
Protein regulatory network inference
Pedigree inference
Phylogeny inference
Genetic Epidemiology
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Linkage mapping
Association mapping
Mapping for complex traits: quantitative
traits, epistasis etc.
Linkage and Association
Gene, alleles;
Haplotype
Transmission
Cross-over and
recombination
Linkage
Sample Project: Bayesian
Regression Analysis
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Mike West et al (2000) Bayesian
Regression Analysis in the “large p,
small n” Paradigm with application in
DNA Microarray studies.
What is a Microarray/DNA chip
How Chips Work?
Oligonucleotide Arrays
Current “Golden Standard”!
Affymetrix GeneChip System
An Affymetrix GeneChip
Gene Expression Data
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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