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 mRNA cDNA DNA microarray samples

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 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 Brown and Botstein, 1999 Alphacdc15cdc28Elu M/G1 G1 S G2 M

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

8 - Cancer Diagnosis by Microarray 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

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 four- base overhang  Hybridization of four- base adapters used to read sequence  Number of times a particular sequence is found is measure of RNA abundance