Transcript DNA copy number variation and cancer risk

DNA copy number variation and
cancer risk
John F Pearson
Canterbury Statistics Open Day
University of Canterbury 2/10/2012
Breast Cancer
Foulkes WD. N Engl J Med 2008; 359:2143-2153
(Foulkes WD. N Engl J Med 2008; 359:2143-2153)
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Missing heritability
TA Manolio et al. Nature 461, 747-753 (2009) doi:10.1038/nature08494
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Evan E. Eichler.
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Copy number variation
Allele 1
Allele 2
Copy number loss
Copy number gain
Whole gene
Partial gene
Contiguous genes
Regulatory effects
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Copy number variants (CNVs)
16,000 copy number variant loci cover >50% of the human genome
CNVs are associated with cancer risk
• Rare CNVs detected in ~50% of familial cancer genes
eg. BRCA1, BRCA2
• Genome-wide association studies of cancer
• prostate cancer, hepatocarcinoma, nasopharyngeal carcinoma,
and neuroblastoma
• Increased CNV load
• Li Fraumeni Syndome (cancer related genes?)
• breast cancer (TP53 pathway, ESR1 pathway)
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SNP arrays
LRR𝑅= =
log2𝑋(R+
𝑌 /Rexpected)
observed
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𝑋
𝜃 = arctan
𝜋
𝑌
The B Allele Frequency (BAF) is a
somewhat confusing term that actually
refers to a normalized measure of
relative signal intensity ratio of the B
and A alleles
Wang et al Genome Res. 2007 November; 17(11): 1665–1674.
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Genomic location
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Copy number
Copy number
loss
Copy
neutral
LOH
Normal
BB
AB
AA
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Copy number gain
Copy number
gain
BBB
ABB
AAB
AAA
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CNV calling
Illumina bead arrays.
o CNVision
(workflow software)
o Gnosis
o PennCNV
CNV calling
algorithms
o QuantiSNP
o CNV Partition
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PennCNV, QuantiSNP
Hidden Markov Model
Estimate copy number at each SNP from
• Log R ratio
• B allele frequency
• transition probability at previous SNP.
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PennCNV
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PennCNV
ri
bi
zi
LRR
BAF
copy number state
at SNP i. ( 1 ≤ i ≤ M )
The likelihood of the observed data is:
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PennCNV
ri
bi
zi
LRR
BAF
copy number state
at SNP i. ( 1 ≤ i ≤ M )
The likelihood of the observed data is:
LRR emission probability
model includes a term for
chemical fluctuations and
misannotation/assembly
BAF emission probability complicated mixture
model
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PennCNV
ri
bi
zi
LRR
BAF
copy number state
at SNP i. ( 1 ≤ i ≤ M )
Transmission probabilities between 2 adjacent SNPs i -1 and i.
with copy numbers zi and zi-1 at distance di.
D = 100Mb for state 4, 100kb for other states.
p are unknowns, estimated by the Baum-Welch algorithm.
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PennCNV
ri
bi
zi
LRR
BAF
copy number state
at SNP i. ( 1 ≤ i ≤ M )
• Baum-Welch used to train the model
• Viterbi algorithm used to infer most likely path
• CNV called whenever a stretch of states is different from
normal ( usually state 3 or 4)
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Copy number gain
Copy number
gain
BBB
ABB
AAB
AAA
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Noisy data
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Breast cancer
A characteristic of breast tumour cells is genomic instability
BRCA1, BRCA2
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BRCA1: known large deletions
Detected
Sample ID
EMB0001242
EMB0001532
EMB0001222
EMB0001425
EMB0001439
EMB0001458
EMB0001477
GEM0002463
PAD0005718
EMB0001770
EMB0001057
KCO0003228
EMB0001082
GEM0002430
BRCA1 mutation
del exons 2-24
del exons 3-19
del exons 1-23
del exons1-21
del exons 1-23
del exons 1-23
del exons1-21
del exons 16-23
del exons 9-19
del exons 1-17
del exons 1-17
del exons 1-17
del exons 8-13
del exons 8-13
Not detected
Sample ID
EMB0001530
EMB0001689
BRCA1 mutation
del exons 3-19
del exons 1-17
CNV prediction summary:
• cnvPartition - 25% (4/16)
• GNOSIS
- 19% (3/16)
• PennCNV - 88% (14/16)
• QuantiSNP - 81% (13/16)
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Endometrial cancer
1343 cases
ANECS, SEARCH
QC(1) – GWAS criteria
CNV calling by 4 algorithms
655 female controls
Hunter Community Study
619 controls
1279 cases
1210 cases
612 controls
Case vs. control
analyses
Want to find:
1. CNVs overlapping known susceptibility genes
2. novel CNVs in the mismatch repair pathway
3. common or rare CNVs associations
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CNV frequency: all
Case
1,210
Control
612
Difference
P
Total CNVs
26.7
26.5
0.2
NS
Deletions
17.7
18.1
-0.4
NS
Duplications
8.9
8.4
0.5
NS
Exons
7.1
6.9
0.2
NS
Mean CNV per sample
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CNV frequency: rare (< 1%)
Case
1,210
Control
612
Difference
P
6
3.3
2.7
4.0E-05
Deletions
3.8
1.4
2.4
3.0E-06
Duplications
2.2
1.9
0.3
NS
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3.3
2.7
2.0E-04
Total CNVs
Exons
Mean rare CNV per sample
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CNV frequency: rare (< 1%)
Case
1,210
Control
612
Difference
P
6
3.3
2.7
4.0E-05
Deletions
3.8
1.4
2.4
3.0E-06
Duplications
2.2
1.9
0.3
NS
6
3.3
2.7
2.0E-04
Total CNVs
Exons
Mean rare CNV per sample
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Association study
CNV Regions
Case
Chr
0
1
3
X
0
1
0
X
0 30
7
X
0
2
0
X
0
0
0
6
9 10
0
16
0 125 127
X
0
0
0
6
812 203 438
2
0
2
2
7
0
0
0
11
0 38 32
X
0
1
0
Control
4
0
1
3
0
0 57
0
0
0 78
0
0
0 34
0
0
0 24
0
0
4 35
0
0
0 10 19
0
0 14
0
20 477 184 276
0
0 14 16
0
0 12
4
0
0
1
3
0
0
0 11
4
0
0
0
0
0
0
0
14
0
0
0
0
P
adjusted
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.003
0.006
0.006
0.010
0.016
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Association study
CNV overlapping genes
Case
Chr
X
1
1
7
1
1
1
1
1
1
7
X
0
0
0
0
0
0
0
0
0
0
0
0
0
1
2
37
35
0
36
36
34
33
31
31
4
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Control
3
0
2
2
1
2
2
2
2
1
1
32
6
4
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
53
0
0
13
0
0
0
0
0
0
0
36
3
0
0
0
5
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
P
adjusted
0.000
0.004
0.004
0.004
0.004
0.004
0.005
0.008
0.011
0.011
0.011
0.021
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Acknowledgements
University of Otago
• Gemma Moir-Meyer
• Logan Walker
• Mackenzie Cancer Research Group
University of Cambridge
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Queensland Institute of Medical
Research
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Mandy Spurdle
Felicity Lose
Yen Tan
Alex Metcalf
Australian National Endometrial
Cancer Study
• Bryony Thompson
Deborah Thompson
Paul Pharoah
Alison Dunning
Douglas Easton
Studies of Epidemiology and Risk
Factors in Cancer Heredity (SEARCH)
University of Newcastle
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Rodney Scott
Mark McEvoy
John Attia
Elizabeth Holliday
The Hunter Community Study
CIMBA consortium
MAYO clinic
• Fergus Couch
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