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Discover the latest advancements in computational biology and genetics research, including projects on Bayesian regression analysis and genetic epidemiology. Learn about DNA microarray studies, protein regulatory networks, and more. Dive into the world of genomics and genetic mapping to unlock new insights into complex traits and inheritance patterns.
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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”!
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