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Lecture 26 GWAS Based on chapter 9 Functional and Comparative Genomics Copyright © 2010 Pearson Education Inc. 1 - RNA Expression Analysis – Determining Genomewide RNA Expression Levels • Genomewide RNA expression analysis • Types of microarrays • Making microarrays • Hybridization to microarrays 7 - Genomic Expression Analysis Methods 1. Microarrays a. Hybridization based 2. SAGE – Serial analysis of gene expression 3. MPSS – Massively parallel signature sequencing 12 - Microarray Hybridization 1. Usually comparative a. Ratio between two samples 2. Examples a. Tumor vs. normal tissue b. Drug treatment vs. no treatment c. Embryo vs. adult samples mRNA cDNA DNA microarray 25 - Labels 1. Cy3 and Cy5 a. Fluoresce at different wavelengths b. Used for competitive hybridization 2. Biotin a. Binds to fluorescently labeled avidin b. Used with Affymetrix GeneChips 28 - Analysis of Hybridization 1. Results given as ratios 2. Images use colors: Cy3 = Green Cy5 = red Yellow 3. Yellow is equal intensity or no change in expression 29 - Example of Spotted Microarray 1. RNA from irradiated cells (red) 2. Compare with untreated cells (green) 3. Most genes have little change (yellow) 4. Gene CDKN1A: red = increase in expression 5. Gene Myc: green = decrease in expression CDKNIA MYC 2 – Yeast Cell Cycle Experimental 3 - Analysis of cell-cycle regulation 1. Yeast cells stopped at different stages of cell cycle G1, S, G2, and M 2. RNA extracted from each stage 3. Control RNA from unsynchronized culture 4 - Results of cell-cycle analysis 1. 800 genes identified whose expression changes during cell cycle 2. Grouped by peak expression a. M/G1, G1, S, G2, and M 3. Four different treatments used to synchronize cells a. All gave similar results 4. Results from Spellman et al., 1998; Cho et al., 1998 5 - Cell-cycle regulated genes Each gene is a line on the longitudinal axis Treatments in different panels Cell-cycle stages are color coded at top Vertical axis groups genes by stage in which expression peaks Alpha cdc15 cdc28 Elu M/G1 G1 S G2 M Brown and Botstein, 1999 7 - Profiling tumors 1. Image portrays gene expression profiles showing differences between different tumors 2. Tumors: a. MD (medulloblastoma) b. Mglio (malignant glioma) c. Rhab (rhabdoid) d. PNET (primitive neuroectodermal tumor) 3. Ncer: normal cerebella 1. Gene expression differences for medulloblastoma correlated with response to chemotherapy 2. Those who failed to respond had a different profile from survivors 3. Can use this approach to determine treatment 60 different samples 8 - Cancer Diagnosis by Microarray 9 - Analysis of microarray results 1. Inherent variability: need for repetition a. Biological and technical replicates 2. Analysis algorithms a. Based on statistical models 3. Means of generating hypotheses that need to be tested 10 – Serial Analysis of Gene Expression (SAGE) 1. Serial analysis of gene expression 2. Concept: sequence a small piece of each cDNA in a library a. Gives measure of abundance of each RNA species 3. Method a. Cut off “tag” from each cDNA b. Ligate tags together into a concatemer c. Sequence the concatemer 13 - SAGE IV 1. Sequence the concatemers 2. Identify tag borders a. Size of tag and restriction-enzyme sites 3. Compare tag sequences to database 4. Abundance of tag is measure of abundance of that RNA species 14 - MPSS I 1. Massively parallel signature sequencing 2. Means of determining abundance of RNA species 3. Unique tags added to cDNAs 4. Tags hybridized to oligonucleotides on microbeads Slide 15 – MPSS I Sequencing performed in glass chamber Initiated by restriction enzyme revealing fourbase overhang Hybridization of fourbase adapters used to read sequence Number of times a particular sequence is found is measure of RNA abundance