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

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. 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.

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

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

~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

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)

Significance test of CN amplification and deletion

Boundary finding (smoothing and segmentation)

CN estimation

Intensities and Raw CNs, Chr. 1 (Piar#101)Black: Normal, Red: Tumor, Green: Tumor- Normal

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)

Genome-wide Raw CN Changes (Piar#105)

Genome-wide Widow-based Test of CN Changes (Piar#105)

- Log (p)

SegmentationBioConductor 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=-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

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

Associations (with mutations, LOHs, clinical variables …)

Genome-wide Raw CN Changes(average over ~400 pairs )

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

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)

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

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)

Population Level Segmentation Analysis (~400 pairs)Circular Binary Segmentation approach, Bioconductor Package DNAcopy

Segmentation of Chr. 14(average result of ~400 pairs)

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

positions

samples

Group-specific AnalysisBlack: non-smokers, Red: non-smokers

Separate Tumor Samples from Normal Samples Using Six Chromosomal Peaks with Significant CN Changes

(Classification Based on RAW CN)

Tumor

Normal

Mapping Known Cancer-related Genes onto the Copy Number Map

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 ?

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 …

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