genome-wide copy number analysis qunyuan zhang,ph.d. division of statistical genomics department of...
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Genome-wide Copy Number AnalysisGenome-wide Copy Number Analysis
Qunyuan Zhang,Ph.D.
Division of Statistical Genomics
Department of Genetics & Center for Genome Sciences
Washington University School of Medicine
02 - 08 – 2006
Course: M 21-621 Computational Statistical Genetics
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Four QuestionsFour Questions
What is Copy Number ?What is Copy Number ?
What can Copy Number tell us?What can Copy Number tell us?
How to measure/quantify Copy Number?How to measure/quantify Copy Number?
How to analyze Copy Number?How to analyze Copy Number?
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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
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DNA Copy Number A Copy Number Variant (CNV) represents a copy number change involving a D
NA fragment that is ~1 kilobases or larger. From Nature Reviews Genetics, Feuk et al. 2006
DNA Copy Number ≠ DNA Tandem Repeat Number (e.g. micro satellites) <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.
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What can Copy Number tell us?What can Copy Number tell us?
Genetic Diversity/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 (e.g., deletions, duplications, inversions … )- DNA copy number variant (CNV)
Association with phenotypes/diseases genes/genetic factors
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Genetic Alterations in Tumor Cells (DNA Copy Number Changes)
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
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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)
Hybridization
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Microarray: From Image to Copy Number
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
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~400 cancer patients
Normal tissue & tumor tissue (~400 pairs, ~800 DNA samples)
Affymetrix 250K Sty-I Human Mapping SNP Array
DNA hybridization signals (intensities on chip images)
Genotype calling
SNP genotypes
LOH analysis DNA copy number analysis (genotypic changes) (DNA copy number changes)
How to Analyze Copy Number?How to Analyze Copy Number?
?
A Real Example
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General Procedures for Copy Number Analysis
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
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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
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NormalizationReduces technical variation between chips Makes intensities from different chips comparable
Before normalization After normalization
Base Line Array (linear); Quantile Normalization;Contrast 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)Raw CN: Log ratio of tumor / normal intensities
CN = S’tumor - S’normal = log2(Stumor/Snormal)
Pair design
Snormal = S of the paired normal sampleGroup design
Snormal = average S of the group of normal samples
before Log transformation
S
after Log transformation
Log(S)
Raw CN
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Individual Level AnalysisIndividual Level Analysis
Analysis for each individual sample (or each sample pair)
Significance test of CN amplification and deletion
Boundary finding (smoothing and segmentation)
CN estimation
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Intensities and Raw CNs, Chr. 1 (Piar#101)Black: Normal, Red: Tumor, Green: Tumor- Normal
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Significance Test for Copy Number Changes: -log(p) values, chr. 1, pair#101
Window-based t test
Window size = 0.5 Mbp (~30 SNPs); N = SNP number in window
Mean CN of window t = X N ~ t (df=N -1) SD of widow
-log(p)
Window Position (Mbp)
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Genome-wide Raw CN Changes (Piar#105)
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Genome-wide Widow-based Test of CN Changes (Piar#105)
- Log (p)
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SegmentationBioConductor R Packages (www.bioconductor.org)GLAD package, adaptive weights smoothing (AWS) methodDNAcopy package, circular binary segmentation method
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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=-2,-1,0,1,2,3,…, n (usually,n<10)
Transition probabilities: Probabilities of CN values of each SNP conditional on the previous one.
P(CN_i+1=x|CN_i=y); x=-2,-1,0,1,2,3,…, or n; y=-2,-1,0,1,2,3, …, 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=-2,-1,0,1,2,3, …, or n
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HMM Estimation of CN for Chr. 1 (Piar#101)Black: Normal Intensities, Red: Tumor Intensities, Green: Tumor- Normal
Blue: HMM estimated CNs in Tumor Tissue
CN=2 CN=1
CN=4CN=3
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Population Level AnalysisPopulation Level Analysis
Analysis for the whole group (or sub-group) of samples
Overall significance test
Amplification and deletion frequencies summarization
Common/concurrent region finding
Associations (with mutations, LOHs, clinical variables …)
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Genome-wide Raw CN Changes(average over ~400 pairs )
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Raw CN Changes of Chr. 14(average over ~400 pairs )
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Sliding Window Analysis
… .. … … . . . . .. …… …… .. … … . . . . .. …… … .. …… … ..
Window 1Window 2
Window 3Window 4
Window 5Window 6
Window 7Window 8
Window 9Window 10
Window N
Window k
………..
………..
Each window (k) contains 30 consecutive SNPs (k, k+1, k+2, k+3, …, k+29)
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Genome-wide Raw Copy Number Changes(sliding window plot, averaged over ~400 pairs )
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Sliding Window Test of Significance of CN Changes -log(p) values, based on ~ 400 pairs
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CN Change Frequencies in Population ( Chr.14,~400 pairs)Black: Freq.(CN>0) Red: Freq.(CN>0, significant amplification at 0.01 level) Green: Freq.(CN<0, significant deletion at 0.01 level)
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Population Level Segmentation Analysis (~400 pairs)Circular Binary Segmentation approach, Bioconductor Package DNAcopy
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Segmentation of Chr. 14(average result of ~400 pairs)
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Visualization of Concurrent Regions of Chr. 14(~400 pairs)
positions
samples
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Group-specific AnalysisBlack: non-smokers, Red: non-smokers
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Separate Tumor Samples from Normal Samples Using Six Chromosomal Peaks with Significant CN Changes
(Classification Based on RAW CN)
Tumor
Normal
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Mapping Known Cancer-related Genes onto the Copy Number Map
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Software
Affymetrix Chips (www.affymetrix.com)Illumina Chips (www.illumina.com)
CNAT(www.affymetrix.com); dChip (www.dchip.org) ;CNAG (www.genome.umin.jp)
GenePattern www.broad.mit.edu/cancer/software/genepattern/
BioConductor R Packages (www.bioconductor.org)GLAD package, adaptive weights smoothing (AWS) methodDNAcopy package, circular binary segmentation method
Widows ?Unix ?Parallel Computation ?
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References
• R Gentlemen et al. Bioinformatics and computational biology solutions using R and Bioconductor. Springer, 2005
• JL Freeman et al. Genome Research 2006; 16:949-961
• J Huang et al. Hum Genomics. 2004;1(4):287-99
• X Zhao et al. Cancer Research 2004; 64:3060-3071
• Y Nannya et al. Cancer Research 2005, 65: 6071-6079
• … see google …
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Acknowledgements
Aldi Kraja Li DingIngrid Borecki John OsborneMichael Province Ken Chen
Division of Statistical Genomics Medical Sequencing Group
Center for Genome SciencesWashington University School of Medicine