DNA Copy Number Analysis Qunyuan Zhang Division of Statistical Genomics Department of Genetics &...
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DNA Copy Number AnalysisDNA 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
What is Copy Number ?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
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
Why study Copy Number ?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
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
How to measure/quantify Copy Number?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)
Hybridization6
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
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SNP Array
Tumor NormalAffymetrix Mapping
250K Sty-I chip
~250K probe sets
~250K SNPs
CN=1
CN=0
CN>2
CN=2
CN=2
CN=2
probe set (24 probes)
Deletion
Deletion
Amplification
more DNA copy number more DNA hybridization higher intensity 8
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
BB
BBBB
AB
AABB
AAA_
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Copy Number AnalysisCopy Number Analysis
Data Pre-processing
Individual Sample Analysis
Population Analysis
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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 changesEstimation of CN
Smoothing and boundary determination Concurrent regions among population
Amplification and deletion frequencies among populationsAssociation analysis
Preprocessing :
chip description file [.CDF]
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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
Eliminates non-specific hybridization signalObtains 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
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 )
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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
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Raw Copy Number Data
S : Summarized raw intensity
S’ : Log transformation, S’ = log2(S)Log ratio of sample i / sample ref.
CN_log2 = log2(Si/Sref)
CN = 2(Si/Sref )
before Log transformation
S
after Log transformation
Log(S)
Raw CN
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Individual Level Individual Level AnalysisAnalysis
Smoothing
Significance test of amplification and deletion
Segmentation
CN estimation
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Sliding Window Sliding Window
… .. … … . . . . .. …… …… .. … … . . . . .. …… … .. …… … ..
Window 1Window 2
Window 3Window 4
Window 5Window 6
Window 7Window 8
Window 9Window 10
Window N
Window k
………..
………..
Each window (k) contains n consecutive SNPs (k, k+1, k+2, k+3, …, k+n-1)
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Smoothing (sliding window=30 snps)Smoothing (sliding window=30 snps)
Affymetrix
IlluminaChrom. 7
Chrom. 7 Chrom. 7
Mbp
CN
Mbp
Chrom. 7
CN
Mbp
CN
Mbp
CN
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Significance Test of CN ChangesSignificance Test of CN Changes
CN
SD
Mbp
CN
CN
Mbp Mbp
-log F
DR
Mbp
-log P
Mbp 21
Window Selection (FDR < 0.05)
CN
Mbp
-log F
DR
Mbp
epidermal growth factor receptor (EGFR) 22
SegmentationSegmentation
(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
CBS AlgorithmCBS Algorithm
1,2,3, ….,i-1, i, i+1,…,j-1,j, j+1,...n ijC
ijnijij
n
kkn
j
kkj
i
kki
ZZ
ijnij
ijnSSSijSSZ
xSxSxS
njianyGiven
max
)(1)(1
)()()()(
,,
,,
111
Iterate until Zc is not significant.
Olshen et al. Biostatistics. 2004 Oct;5(4):557-72.24
CN Estimation: Hidden Markov Model (HMM) CNAT(www.affymetrix.com); dChip (www.dchip.org) ; CNAG (www.genome.umin.jp)
CN=?
CN=?
CN=?
CN=?
CN=?
log ratio
log ratio
log ratio
log ratio
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=xi | CN_i=xj); 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 n25
HMM Results (An Example)Black: Normal Intensities, Red: Tumor Intensities, Green: Tumor- Normal
Blue: HMM estimated CNs in Tumor Tissue
CN=2 CN=1
CN=4CN=3
26
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
Population Level AnalysisPopulation Level AnalysisCommon/Reocurrent Region Identification
samples
28
Nature 2007, 450, 893-898
Genome-wide Raw Copy Number Changes(sliding window plot, averaged over ~400 pairs )
29
Frequency Test
30
Diskin et al. 2006. STAC, Genome Res 16: 1149-1158. Permutation test
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
Population-based One-step Analysis
32
CMDS MethodQ Zhang et al. Bioinformatics, 2009 doi:10.1093/bioinformatics/btp708
Referencesfor 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
Sequencing Datacoverage/depth based analysis
34Nature Genetics 41, 1061 - 1067 (2009)
Sequencing Datapaired-end data based analysis
35
Science 2007:Vol. 318. pp. 420 - 426DOI: 10.1126/science.1149504
HomeworkDownload 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 [email protected] in two weeks36