1. DNA Copy Number Analysis Qunyuan Zhang Division of Statistical Genomics Department of Genetics & Center for Genome Sciences Washington University School of Medicine 04 - 23 – 2010 GEMS Course: M 21-621 Computational Statistical Genetics 1

2. What is Copy Number ? Gene Copy Number The gene copy number (also "copy number variants" or CNVs) is the amount of copies of a particular gene in the genotype of an individual. Recent evidence shows that the gene copy number can be elevated in cancer cells. For instance, the EGFR copy number can be higher than normal in Non-small cell lung cancer. …Elevating the gene copy number of a particular gene can increase the expression of the protein that it encodes. From Wikipedia www.wikipedia.org 2

3. DNA Copy Number A Copy Number Variant (CNV) represents a copy number change involving a DNA fragment that is ~1 kilobases or larger. From Nature Reviews Genetics, Feuk et al. 2006 DNA Copy Number ≠ DNA Tandem Repeat Number (e.g. microsatellites) <10 bases DNA Copy Number ≠ RNA Copy Number RNA Copy Number = Gene Expression Level DNA transcription mRNA Copy Number is the amount of copies of a particular fragment of nucleic acid molecular chain. It refers to DNA Copy Number in most publications. 3

4. Why study Copy Number ? Motive 1: Genetic Polymorphisms - restriction fragment length polymorphism (RFLP) - amplified fragment length polymorphism (AFLP) - random amplification of polymorphic DNA (RAPD) - variable number of tandem repeat (VNTR; e.g., mini- and microsatellite) - single nucleotide polymorphism (SNP) - presence/absence of transportable elements … - structural alterations (deletions, duplications, insertions, inversions … ) - DNA copy number variant (CNV) Association with phenotypes/diseases genes/genetic factors 4

5. Motive 2: Genetic Aberrations in Tumor Cells Mutation, LOH, Copy Number Aberration (CNA) Homologous repeats Segmental duplications Chromosomal rearrangements Duplicative transpositions Non-allelic recombinations …… Normal cell Tumor cells deletion amplification CN=0 CN=1 CN=2 CN=3 CN=4 CN=2 5

6. How to measure/quantify Copy Number? Quantitative Polymerase Chain Reaction (Q-PCR) : DNA Amplification (dNTPs, primers, Taq polymerase, fluorescent dye) PCR less CN amplification less DNA low fluorescent intensity more CN amplification more DNA high fluorescent intensity (one fragment each time) Microarray : DNA Hybridization (dNTPs, primers, Taq polymerase, fluorescent dye) PCR less CN amplification less DNA arrayed probes low intensities more CN amplification more DNA arrayed probes high intensities (multiple/different fragments, mixed pool) Hybridization 6

7. Array Comparative Genomic Hybridization (CGH) Tumor: red intensity Normal: green intensity Red < Green: Deletion (CN<2) Red > Green: Amplification (CN>2) Red = Green: No Alteration (CN=2) more DNA copy number more DNA hybridization higher intensity 7

8. SNP Array TumorNormal Affymetrix Mapping 250K Sty- I chip ~250K probe sets ~250K SNPs CN=1 CN=0 CN>2 CN=2 probe set (24 probes) Deletion Amplification more DNA copy number more DNA hybridization higher intensity 8

9. Genotyping & Copy Number Calling CN=0 CN=1 CN=2 CN=3 CN=4 2 copy deletion, genotype (_//_) 1 copy deletion, genotype (_//B) 1 copy amplification, genotype (AA//B) Normal, genotype (A//B) 2 copy amplification, genotype (AA//BB) 9

10. BB BBBB AB AABB AA A_ 10

11. Copy Number Analysis Data Pre-processing Individual Sample Analysis Population Analysis 11

12. An Example Finished chips (scanner) Raw image data [.DAT files] (experiment info [.EXP]) (image processing software) Probe level raw intensity data [.CEL files] Background adjustment, Normalization, Summarization Summarized intensity data Raw copy number (CN) data [log ratio of tumor/normal intensities] Significance test of CN changes Estimation of CN Smoothing and boundary determination Concurrent regions among population Amplification and deletion frequencies among populations Association analysis Preprocessing : chip description file [.CDF] 12

13. Background Adjustment/Correction Reduces unevenness of a single chip Makes intensities of different positions on a chip comparable Before adjustment After adjustment Corrected Intensity (S’) = Observed Intensity (S) – Background Intensity (B) For each region i, B(i) = Mean of the lowest 2% intensities in region i AffyMetrix MAS 5.0 13

14. Eliminates non-specific hybridization signal Obtains accurate intensity values for specific hybridization Background Adjustment/Correction PM only, PM-MM, Ideal MM, etc. quartet probe set sense or antisense strands 25 oligonucleotide probes 14

15. Normalization Reduces technical variation between chips Makes intensities from different chips comparable Before normalization After normalization Base Line Array (linear); Quantile Normalization etc. S – Mean of S S’ = STD of S S’ ~ N(0,1 ) 15

16. Combines the multiple probe intensities for each probe set to produce a summarized value for subsequent analyses. Summarization Average methods: PM only or PM-MM, allele specific or non-specific Model based method : Li & Wong, 2001 Gene Expression Index 16

17. Raw Copy Number Data S : Summarized raw intensity S’ : Log transformation, S’ = log 2 (S) Log ratio of sample i / sample ref. CN_log2 = log 2 (S i /S ref ) CN = 2(S i /S ref ) before Log transformation S after Log transformation Log(S) Raw CN 17

18. Individual Level Analysis Individual Level Analysis  Smoothing  Significance test of amplification and deletion  Segmentation  CN estimation 18

19. Sliding Window Sliding Window ….. … …...... …… …….. … …...... …… ….. …… ….. Window 1 Window 2 Window 3 Window 4 Window 5 Window 6 Window 7 Window 8 Window 9 Window 10 Window N Window k ……….. Each window (k) contains n consecutive SNPs (k, k+1, k+2, k+3, …, k+n-1) 19

20. Smoothing (sliding window=30 snps) Smoothing (sliding window=30 snps) Affymetrix Illumina Chrom. 7 Mbp CN Mbp Chrom. 7 CN Mbp CN Mbp CN 20

21. Significance Test of CN Changes Significance Test of CN Changes CN SD Mbp CN Mbp -log FDR Mbp -log P Mbp 21

22. Window Selection (FDR < 0.05) CN Mbp -log FDR Mbp epidermal growth factor receptor (EGFR) 22

23. Segmentation Segmentation (Break chrom. into CN-homologous pieces) BioConductor R Packages (www.bioconductor.org) DNAcopy package, circular binary segmentation (CBS) GLAD package, adaptive weights smoothing (AWS) 23

24. CBS Algorithm CBS Algorithm 1,2,3, ….,i-1, i, i+1,…,j-1,j, j+1,...n Iterate until Zc is not significant. Olshen et al. Biostatistics. 2004 Oct;5(4):557-72. 24

25. CN Estimation: Hidden Markov Model (HMM) CNAT(www.affymetrix.com); dChip (www.dchip.org) ; CNAG (www.genome.umin.jp) CN=? log ratio … SNP_i SNP_i+1 SNP_i+2 SNP_i+3 SNP_i+4 … position hidden status (unknown CN ) observed status (raw CN = log ratio of intensities) CN estimation: finding a sequence of CN values which maximizes the likelihood of observed raw CN. Algorithm: Viterbi algorithm (can be Iterative) Information/assumptions below are needed Background probabilities: Overall probabilities of possible CN values. P(CN=x); x=0,1,2,3,4,…, n (usually,n<10) Transition probabilities: Probabilities of CN values of each SNP conditional on the previous one. P(CN_i+1=x i | CN_i=x j ); x=0,1,2,3,4,…, or n Emission probabilities: Probabilities of observed raw CN values of each SNP conditional on the hidden/unknown/true CN status. P(log ratio<x|CN=y)=f(x|CN=y); x=one of real numbers; y=0,1,2,3,4, …, or n 25

26. HMM Results (An Example) Black: Normal Intensities, Red: Tumor Intensities, Green: Tumor- Normal Blue: HMM estimated CNs in Tumor Tissue CN=2CN=1 CN=4 CN=3 26

27. References for Single Sample Analysis Hsu et al. 2005. Denoising array-based comparative genomic hybridization data using wavelets. Biostatistics 6: 211-226. Hupe et al. Analysis of array CGH data: from signal ratio to gain and loss of DNA regions. Bioinformatics 20: 3413-3422. Jong et al. 2004. Breakpoint identification and smoothing of array comparative genomic hybridization data. Bioinformatics 20: 3636-3637. Lai et al. 2005. Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics 21: 3763-3770. Lai et al. 2005. A statistical method to detect chromosomal regions with DNA copy number alterations using SNP-array-based CGH data. Comput Biol Chem 29: 47-54. Olshen et al. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5: 557-572. Picard et al. 2005. A statistical approach for array CGH data analysis. BMC Bioinformatics 6: 27. Shah et al. 2007. Modeling recurrent DNA copy number alterations in array CGH data. Bioinformatics 23: i450-458. Nilsson et al. Bioinformatics. 2009 Apr 15;25(8):1078-9. Epub 2009 Feb 19. 27

28. Population Level Analysis Population Level Analysis Common/Reocurrent Region Identification samples 28 Nature 2007, 450, 893-898

29. Genome-wide Raw Copy Number Changes (sliding window plot, averaged over ~400 pairs ) 29

30. Frequency Test 30 Diskin et al. 2006. STAC, Genome Res 16: 1149-1158. Permutation test

31. Amplitude Test 31 GISTIC Beroukhim et al. 2007. Proc Natl Acad Sci U S A 104: 20007-20012 Weir et al. Nature 2007, 450, 893-898

32. Population-based One-step Analysis 32 CMDS Method Q Zhang et al. Bioinformatics, 2009 doi:10.1093/bioinformatics/btp708

33. References for Multiple Sample Analysis (GISTIC ) Beroukhim et al. 2007. Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc Natl Acad Sci U S A 104: 20007-20012. (STAC) Diskin et al. 2006. STAC: A method for testing the significance of DNA copy number aberrations across multiple array-CGH experiments. Genome Res 16: 1149-1158. (MSA) Guttman et al. 2007. Assessing the significance of conserved genomic aberrations using high resolution genomic microarrays. PLoS Genet 3: e143. (GFA) Lipson et al. 2006. Efficient calculation of interval scores for DNA copy number data analysis. J Comput Biol 13: 215-228. (MAR) Rouveirol et al. 2006. Computation of recurrent minimal genomic alterations from array-CGH data. Bioinformatics 22: 849-856. (CMDS) Zhang et al. CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data. Bioinformatics, 2009 doi:10.1093/bioinformatics/btp708 33

34. Sequencing Data coverage/depth based analysis 34 Nature Genetics 41, 1061 - 1067 (2009)

35. Sequencing Data paired-end data based analysis 35 Science 2007:Vol. 318. pp. 420 - 426 DOI: 10.1126/science.1149504

36. Homework Download the data file dsgweb.wustl.edu/qunyuan/data/cn_data.csv Use any published or self-developed method/software to analyze/present the data Write a report of your analysis Send to qunyuan@wustl.edu in two weeksqunyuan@wustl.edu 36