DNA Copy Number Analysis

DNA Copy Number AnalysisDNA Copy Number Analysis

Qunyuan Zhang,Ph.D.

Division of Statistical Genomics

Department of Genetics & Center for Genome Sciences

Washington University School of Medicine

03 - 25 – 2008

GEMS Course: M 21-621 Computational Statistical Genetics

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?

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

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. 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.

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

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

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

SNP Array: From Image to Copy Number

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

Array CGH : 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

How to Analyze Copy How to Analyze Copy Number?Number?

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]

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

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

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 )

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

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

Individual Level AnalysisIndividual Level Analysis

Analysis for each individual sample (or each sample pair)

Smoothing

Significance test of CN amplification and deletion

Boundary finding (smoothing and segmentation)

CN estimation

Smoothing via 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 30 consecutive SNPs (k, k+1, k+2, k+3, …, k+29)

Smoothing Smoothing (sliding window=30 snps)(sliding window=30 snps)

Affymetrix

IlluminaChrom. 7

Chrom. 7 Chrom. 7

Mbp

CN

Mbp

Chrom. 7

CN

Mbp

CN

Mbp

CN

Significance Test of CN ChangesSignificance Test of CN Changes

An ExampleAn Example

Sliding Window SmoothingC

N

CN

Mbp Mbp

NormalizationC

N

MbpSD

Mbp

P-value calculationSD

Mbp

-log P

Mbp

Calculate FDR for each window

-log F

DR

Mbp

-log P

Mbp

Select window (FDR < 0.05)

CN

Mbp

-log F

DR

Mbp

Another Example Intensities and Raw CNs, Chr. 1 (Piar#101)

Black: Normal, Red: Tumor, Green: Tumor- Normal

Significance Test for Copy Number Changes: -log(p) values, TSP data, 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)

Segmentation (break chrom. into CN-homologous pieces)BioConductor R Packages (www.bioconductor.org)GLAD package, adaptive weights smoothing (AWS) methodDNAcopy package, circular binary segmentation method

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 n

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

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

Raw CN Changes of Chr. 14(average over ~400 pairs )

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

Sliding Window Test of Significance of CN Changes -log(p) values, based on ~ 400 pairs

Visualization of Concurrent Regions of Chr. 14(~400 pairs)

positions

samples

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