Statistical Applications in Biology and Genetics

Statistical Applications in Biology and Genetics

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

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

Chromosomes and genes

Video from the Human Genome Project You can also find links to background readings at : http://www.stat.columbia.edu/~tzheng/research/statgen.html

Celebrating the 50th Anniversary of the discovery of DNA double-helix structure.

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

Computational Biology (2) Motif detection

Homology detection Bioinformatics/Genomics

Gene expression analysis (using DNA

chips or Microarray) Protein regulatory network inference Pedigree inference Phylogeny inference

Genetic Epidemiology

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

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

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

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