Download Copy Number Variation (CNV)

Global Variation in Copy Number
in the Human Genome
Nature, Genome Research, Genome Research,
2006.
Speaker: Yao-Ting Huang
References





Redon et al. Global variation in copy number in the human
genome. Nature, 2006.
Fiegler et al. Accurate and reliable high-throughput detection
of copy number variation in the human genome. Genome
Research, 2006.
Komura et al. Genome-wide detection of human copy number
variations using high-density DNA oligonucleotide arrays.
Genome Research, 2006.
Komura et al. Noise Reduction from genotyping microarrays
using probe level information. In Silico Biology, 2006.
Price et al. SW-Array: a dynamic programming solution for
the identification of copy-number changes in genomic DNA
using array comparative genome hybridization data. NAR,
2005.
2
Copy Number Variation (CNV)

Copy Number Variation (CNV) is a DNA
segment
with length at least 1kb and
 presents at variable copy number compared with a
reference genome.


The cause of a CNV is speculated due to nonallelic homologous recombination.

CNVs may disrupt genes, alter gene dosage, and
confer risk to complex diseases such as HIV-1.
3
Examples of CNVs (1)
…
Paternal Maternal
Copy # = 2 Copy # = 2
Paternal Maternal
Copy # = 2 Copy # = 2
A
T
A
A
T
A
C
…
Offspring
Copy # = 1
Offspring
Copy # = 3
Deletion
Duplication
4
Examples of CNVs (2)
Paternal Maternal
Copy # = 3 Copy # = 3

Hard to tell the actual
type of a CNV even
within a family.
Offspring Offspring
Copy # = 2 Copy # = 4
Mendelian inheritance,
deletion, duplication.
5
Use of Two Array Platforms

(1) Whole Genome TilePath array (93.7% of
euchromatin); (2) Affymetrix 500K SNP array.
6
Results

There are a total of 1,447 CNVs identified and
merged from these two arrays.
913 CNVs from tiling array and 980 CNVs from SNP
genotyping array.
 These CNVs cover 360Mb (12%) of the human
genome.


The mean sizes of CNVs are 341kb in tiling array
and 206 kb in SNP array.

The use of large insert clones (~170kb) on tiling array
tends to overestimate the size of CNV.
7
Strength and Weakness of these Two
Arrays

The 500k SNP array is better for detecting smaller
CNVs.

The tiling array has more power than SNP array in
segmental-duplicated region.
8
Location of CNVs

CNVs are preferentially located outside of genes and
ultra-conserved elements.
Types of Sequences
WGTP CNVs
500K CNVs
RefSeq (~25,000 genes)
2,561
1,139
OMIN (1,961 genes)
251
112
48
16
116,678
59,397
Ultra-conserved elements
(481 elements)
Conserved non-coding
elements
9
Other Results


48% of gaps in the human genome assembly
are flanked or overlapped by CNVs.
24% of 1,447 CNVs are associated with
segmental duplications.


A portion of segmental duplications are CNVs and
thus will not be fixed in the human genome.
12% of 1,447 CNVs are validated by locusspecific quantitative assay (e.g., quantitative
PCR).
10
Linkage Disequilibrium between biallelic CNVs and Tag SNPs

Linkage disequilibrium between bi-allelic CNVs and
flanking SNPs can guide the selection of tag SNPs.


e.g, the copy number of CNV1 can be predicted by SNP2.
A single SNP array is sufficient to detect both SNP and CNV.
SNP1
C
A
C
C
A
SNP2
A
C
A
C
A
CNV1
Copy # = 2
Copy # = 3
Copy # = 2
Copy # = 3
Copy # = 2
SNP3
C
G
C
G
C
SNP4
C
C
T
C
C
11
Linkage Disequilibrium between biallelic CNVs and Tag SNPs

Linkage disequilibrium between bi-allelic CNVs and
flanking SNPs can guide the selection of tag SNPs.

e.g., Suppose SNP2 is selected as tag SNP.
SNP1
C
A
C
C
A
SNP2
A
C
A
C
A
CNV1
Copy # = 2
Copy # = 3
Copy # = 2
Copy # = 3
Copy # = 2
SNP3 SNP
SNP42
C
CA
G
C
C
T
G Copy
C# = 2
C
C
SNP2
C
Copy # = 3
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Results of Linkage Disequilibrium
around bi-allelic CNVs

51% of CNVs in non-African populations have
tag SNPs, whereas only 22% of CNVs in
African population can be tagged.
Duplications would generate linkage disequilibrium at
acceptor locus instead of donor locus.
 The Phase I HapMap project has a paucity of SNPs
in segmental-duplicated regions, where their CNVs
are enriched.
 Given false-positive CNVs inside and the uncertainty
of CNV boundary, these results are bias (Conrad et al,
13
Nat. Genet., 2006).

Linkage Disequilibrium around multiallelic CNVs

Linkage disequilibrium between multi-allelic CNVs and
each flanking SNP are computed by square of Pearson’s
correlation coefficient.


No SNPs with strong linkage disequilibrium are found.
Mistakes in comparing bi-allelic SNP with multi-allelic CNV.
SNP1
C
A
CNV1
Copy # = 0
Copy # = 1
SNP2
C
G
SNP1
C or A
C
C
A
Copy # = 2
Copy # = 3
Copy # = 1
C
G
C
Copy # = 0, 1, 2, or 3
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Lunch Break - Method
Intensity preprocessing
CNV detection
Copy number inference
15
Intensity Preprocessing

The signal intensity could be skewed due to
length of restriction enzyme fragment,
 GC content of the probe sequence,
 GC content of the restriction fragment, or
 Affinity differences of different SNP genotypes (e.g.,
AA, AC, CC).


Probe selection, noise reduction, and
normalization are done at this stage (Komura
et al, In Silico Biology, 2006).
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CNV Detection
Log2 intensity ratio

For each pair of samples, we can test the
relative intensity ratio at each SNP position.
2
Relative gain of copy
1
No copy number change
0
-1
-2
Relative loss of copy
1 2 3 4 5 6 7 8 9 10
…
SNP position
45
…
65 …
17
CNV Detection
Log2 intensity ratio

CNV is detected by finding clusters of
sufficiently high (or low) ratios.
2
1
0
-1
-2
1 2 3 4 5 6 7 8 9 10
…
SNP position
45
…
65 …
18
CNV Detection

The intensity ratios at all SNPs can be
regarded as a sequence of real numbers.
We seek for a consecutive subsequence of
maximum sum.
Log2 intensity ratio

SNP position
0, 0.54, 1.21, 0.26, 2.34, …, 0, 0.1, -1.43, -0.2, …, -2.4, -2.6, -1.83 19
CNV Detection

A dynamic programming algorithm called SWArray is used to find the subsequence (NAR, 2005).

This algorithm has been proposed by Bentley in 1984.
S (i  1)
S (i)  Pi  max 
 0
0, 0.54, 1.21, 0.26, 2.34, …, 0, 0.1, -1.43, -0.2, …, -2.4, -2.6, -1.83
P1
P2
P3
P4
P5
…
20
Copy Number Inference

These clusters implies a putative CNV.
Log2 intensity ratio

But we still don’t know the exact copy number.
2
1
0
-1
-2
1 2 3 4 5 6 7 8 9 10
…
SNP position
45
…
65 …
21
Pairwise Comparison for All Samples

The above algorithm is repeated for each pair
of samples.
Sample a / Sample b
22
Copy Number Inference

The largest group of samples with the same
copy number is called a diploid group.
This diploid group is used as a reference
representing two copies.
 They assume the mutation events are rare, and thus
two copies should present highest frequency in the
population.

23
Steps of Copy Number Inference
24
Copy Number Inference

Samples c, d, and e are the largest group.
25
Copy Number Inference

The copy numbers of samples a and b are
inferred by comparing its intensity ratio with
the average ratio of the diploid group.
26
Concluding Remarks

The authors identify 1,447 CNVs using whole
genome tiling and SNP genotyping arrays.
Given the low resolution of their arrays and flawed
methods, I believe JJ’s results should be much more
promising.
 Linkage disequilibrium between CNVs and SNPs
requires more sophisticated statistics and algorithms.

27