1. Collecting quantitative CNV information using
aCGH in a Cytogenetic laboratory
Anton Petrov, Ph.D.
infoQuant
December 2009

2. Overview
Copy number analysis of array CGH data
Overview of an array-­‐based Cytogenetic test
Looking for clinically relevant aberrations
Samples from healthy individuals and building CNV tracks
Aberration frequency data
HapMap project, release 3: Affymetrix SNP 6.0 data
Population-­‐specific CNV profiles
HapMap data: 11 populations
Probe coverage issue
CNV profile variation from one array platform to another

3. Array-based copy number tests: data analysis
Data pre-­‐processing
Raw measurements extracted from a slide
Data normalized between two channels (experiment/reference)
Log-­‐ratio measurements built and arranged along genome
Detection of copy number changes
Detection of regions where log-­‐ratios significantly deviate from zero (gains and losses)
A robust binary segmentation approach for high-­‐res data in infoQuant software
Reporting of detected anomalies
In a Cytogenetic test need to determine clinical relevance of detected aberrations

4. Looking for clinically relevant anomalies
Known genes
Look for genes overlapping with detected anomalies
Find genes, associated with a specific class of disorders
Known CNVs
Publicly available CNV databases: Database of Genomic Variants (Toronto)
Lack of control over source
Different array platforms used
In-­‐house CNV tracks using chosen platform

5. Building CNV tracks in copy number software
In-­‐house CNV track
Perform copy number analysis on aCGH data from individual samples (control group)
Collect CNV regions for a cohort of control samples and visualize in routine Cyto tests
Keep updating CNV tracks as new samples get analyzed
CNV frequency profile
Compute frequency of CNVs along genome based on accumulated samples
Use quantitative information that CNV frequencies provide to interpret relevance of
detected chromosomal anomalies in newly acquired samples

6. Using CNV tracks in Cytogenetic tests
Visualize accumulated CNV information during routine tests
Visualize regions of frequent CNVs when reviewing data for a new sample
to determine clinical relevance of detected anomalies
Further increase insight into possible clinical relevance of a detected
anomaly using quantitative information provided by CNV frequencies

7. Cohorts of healthy individuals: the HapMap project
The HapMap project
The International HapMap Project is a partnership of scientists and funding agencies
from various countries www.hapmap.org
The goal of the International HapMap Project is to compare the genetic sequences of
different individuals to identify chromosomal regions where genetic variants are
shared
Genetic data are being gathered from different human populations >1000 samples in
the latest release
aCGH and SNP data were obtained using different array platforms from medium
resolution to high resolution to ultra-­‐high resolution over the years

8. Building CNV frequencies across HapMap samples
A large, powerful pool of data
High-­‐density information provided by Affymetrix SNP 6.0 arrays
CNV frequencies across HapMap samples provide a useful insight into how
frequently a certain anomaly may be observed in healthy individuals
Separate gain frequencies and loss frequencies
Useful addition to the Database of Genomic Variants

9. Filtering HapMap CNV data
Control group may produce CNVs that are clinically relevant
HapMap sample below demonstrates a large copy number loss confirmed by
both sets of measurements
The region includes cancer-­‐related gene NRAS
Such anomalies need to be isolated using copy number analysis software
Filter CNVs by size
When computing CNV frequency profiles pre-­‐set software to disregard CNVs
larger than 2Mbp, for instance

10. CNV frequency profiles specific to sample attribute
Demographic attributes may be important
For example: 11 different populations in HapMap
Individual CNV frequency plots may be built for the populations by aCGH
software and used in Cytogenetic tests for more targeted reference

11. Probe coverage issue
Different array platforms may produce slightly different CNV profiles
-­‐scale and complex architecture of human copy-­‐number
Affymetrix SNP 6.0 arrays (HapMap release 3)
Platforms may concentrate their probe coverage on different areas, hence different CNV
profiles. This is typical for performing copy number analysis across array platforms.

12. Other sources of high-quality CNV data
Other studies
CHOP: High-­‐resolution mapping of copy number variations in 2,026 healthy individuals
http://cnv.chop.edu/
Wellcome Trust: Ultra-­‐high resolution CNV study. 42 M probe coverage, custom 2.1 M
array designs based on Roche-­‐Nimblegen aCGH.
http://www.sanger.ac.uk/humgen/cnv/42mio/