Open Access

Copy Number Variation detection from 1000 Genomes project exon capture sequencing data

  • Jiantao Wu1,
  • Krzysztof R Grzeda1,
  • Chip Stewart1,
  • Fabian Grubert2,
  • Alexander E Urban2,
  • Michael P Snyder2 and
  • Gabor T Marth1Email author
Contributed equally
BMC Bioinformatics201213:305

DOI: 10.1186/1471-2105-13-305

Received: 14 June 2012

Accepted: 7 November 2012

Published: 17 November 2012

Abstract

Background

DNA capture technologies combined with high-throughput sequencing now enable cost-effective, deep-coverage, targeted sequencing of complete exomes. This is well suited for SNP discovery and genotyping. However there has been little attention devoted to Copy Number Variation (CNV) detection from exome capture datasets despite the potentially high impact of CNVs in exonic regions on protein function.

Results

As members of the 1000 Genomes Project analysis effort, we investigated 697 samples in which 931 genes were targeted and sampled with 454 or Illumina paired-end sequencing. We developed a rigorous Bayesian method to detect CNVs in the genes, based on read depth within target regions. Despite substantial variability in read coverage across samples and targeted exons, we were able to identify 107 heterozygous deletions in the dataset. The experimentally determined false discovery rate (FDR) of the cleanest dataset from the Wellcome Trust Sanger Institute is 12.5%. We were able to substantially improve the FDR in a subset of gene deletion candidates that were adjacent to another gene deletion call (17 calls). The estimated sensitivity of our call-set was 45%.

Conclusions

This study demonstrates that exonic sequencing datasets, collected both in population based and medical sequencing projects, will be a useful substrate for detecting genic CNV events, particularly deletions. Based on the number of events we found and the sensitivity of the methods in the present dataset, we estimate on average 16 genic heterozygous deletions per individual genome. Our power analysis informs ongoing and future projects about sequencing depth and uniformity of read coverage required for efficient detection.

Background

Copy Number Variations (CNVs) i.e. deletions and amplifications, are an essential part of normal human variability [1]. Specific CNV events have also been linked to various human diseases [2], including cancer [3, 4] autism [5, 6] and schizophrenia [7]. Historically, large CNV events can be observed using FISH [8] but systematic, genome-wide discovery of CNVs started with microarray-based methods [911] which can detect events down to 1 kb resolution. As with all hybridization based approaches, these methods are blind in repetitive and low complexity regions of the genome where probes cannot be designed. High throughput sequencing with next-generation technologies have enabled CNV detection at higher resolution (i.e. down to smaller event size), in whole-genome shotgun datasets [1214]. However, despite decreasing costs, deep-coverage (≥ 25×) whole-genome data is still prohibitively expensive for routine sequencing of hundreds of samples, and in low-coverage (2-6× base coverage) datasets detection sensitivity and resolution is limited to long genomic events [1].

Targeted DNA capture technologies combined with high-throughput sequencing now provide a reasonable balance between coverage and sequencing cost in a substantial portion of the genome, and full-exome sequencing projects are presently collecting ≥ 25× average sequence coverage in thousands of samples. CNV events in exonic regions are important because the deletions of one or both copies, or amplifications affecting exons, are likely to incur phenotypic consequences.

Current algorithms for detecting CNVs in whole-genome shotgun sequencing data use one of four signals as evidence for an event: (1) aberrantly mapped mate-pair reads (RP or read pair methods); (2) split-read mapping positions (SR); (3) de novo assembly (AS); and (4) a significant drop or increase of mapped read depth (RD methods). Unfortunately, these methods are not generally applicable for CNV detection in capture sequence data without substantial modifications. SR, RP, and AS based methods are sensitive only to CNVs in which mapped reads or fragments span the event breakpoint (s). In the case of exon capturing data, this restricts detection to CNV events where at least one breakpoint falls in a targeted exon. RD based methods suffer from large fluctuations of sequence coverage stemming from variability in probe-specific hybridization affinities across different capture targets (in this case: exons) and sets of such targets (in our case: genes), and from the over-dispersion of the read coverage distribution in the same target across different samples. Presumably because of the technical challenges, and despite the importance of deletion or amplification events within exons, there are currently no reported CNV detection algorithms for targeted DNA capture based exon-sequencing data (with the exception of methods for tumor-normal datasets [15] where the read depth measured in the normal sample can be used for normalization – signal not available in the case of population sequencing).

In this study, we set out to develop a CNV detection algorithm for capture sequencing data. This algorithm is based on RD measurement, and detects samples with non-normal copy number in the capture target regions. As participants of the 1000 Genomes Project, we took part in the data analysis of the “Exon Sequencing Pilot” dataset [16], where 12,475 exons from over 900 genes were targeted and sequenced with a variety of DNA capture and sequencing technologies.

Results and discussion

Brief algorithmic overview

Our algorithm is an extended version of RD-based CNV detection that aims to mitigate the vast target-to-target (and consequently gene-to-gene) heterogeneity of read coverage by normalization procedures roughly corresponding to those employed in CNV detection methods from microarray hybridization intensity data. The overall workflow of our method is shown in Figure 1 and described in greater detail in the Methods section. For a given gene in a given sample (we will use the abbreviation GSS: Gene-Sample Site throughout the paper), we define the read depth as the number of uniquely mapped reads whose 5’ end falls within any of the targeted exons within that gene. We compare this measurement with an expected read depth (Eq. 2, Methods), based on a “gene affinity” calculated from measured read depth for that gene across all samples (to account for across-target read coverage variance due to target-specific hybridization), and the overall read depth for the sample (to account for the variance of read coverage due to the overall sequence quantity collected for the sample under examination). We then use a Bayesian scheme to determine whether the measured coverage is consistent with normal copy number (e.g. CN = 2 for autosomes), or aberrant copy number (i.e. homozygous deletion: CN = 0, heterozygous deletion: CN = 1, or amplification: CN > 2). We have included two algorithmic variants: One is suitable for CNV events that occur at a low allele frequency (i.e. in a small fraction of the samples), and the other for capturing higher-frequency deletion events (see Methods).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-13-305/MediaObjects/12859_2012_Article_5642_Fig1_HTML.jpg
Figure 1

Workflow of the CNV detection method. A. Median Read Depth (MRD) is calculated for each sample, as a measure of sample coverage (NA18523 shown). B. The gene affinity is estimated for each gene as the slope of the least-square-error linear fit between MRD and RD for that gene (TRIM33 shown). C. Example of observed (magenta) and expected (green) read depth for three samples and four genes. The observed read depths were roughly half of the expected values for genes TRIM33 and NRAS, in sample NA18523, and detected as deletions.

Dataset

In this study we analyzed the exon capture sequencing dataset collected by the 1000 Genomes Project Exon Sequencing Pilot, including 931 genes processed with Agilent liquid-phase and Nimblegen solid-phase capture methods, and sequenced from 697 individuals with Illumina paired-end and/or 454 technologies. The samples in the dataset have been sequenced by four different data collection centers (Washington University, WU; Wellcome Trust Sanger Institute, SC; Broad Institute, BI; and Baylor College of Medicine, BCM) using different pairings of capture and sequencing technologies (Table 1 and Table 2). As our method relies on an estimate of the gene-specific hybridization affinity, it requires that such affinities are consistent across all samples analyzed simultaneously. According to Principal Component Analysis of the observed read depths, (Figure 2A, see Methods), target and genes affinities are inconsistent across data from different centers, and therefore we analyzed each dataset separately. We only considered datasets with at least 100 samples (SC, BI, BCM) so we can obtain sufficient sample statistics across genes. After filtering out genes and samples that didn’t meet our minimum read depth requirements (see Methods), we were left with the following datasets: SC (862 genes in 106 individuals sequenced with Illumina), BI (739 genes in 110 samples sequenced with Illumina), and BCM (439 genes in 349 samples sequenced with 454) (Table 1). The number of genes that passed our filters was substantially lower in the BCM dataset both due to lower overall 454 coverage (see below), and because the longer 454 reads result in lower RD (fewer reads) when compared to shorter Illumina reads, even at equivalent base coverage.
Table 1

Properties of datasets from different sequencing centers

 

SC

BCM

BI

WU

Total sample count

117

352

161

93

Sample count after quality control

106

349

110

82

Technology

Illumina

454

Illumina

Illumina

Duplicate rate

0.21

0.30

0.50

0.72

Mapping quality (mean)

50

33

45

51

Base coverage(mean ± standard deviation)

56 ± 34

23 ± 12

70 ± 61

29 ± 9

Read depth per gene(mean ± standard deviation)

2309 ± 3166

106 ± 171

1329 ± 2053

977 ± 1382

MRD(mean ± standard deviation)

1710 ± 1073

97 ± 52

1070 ± 803

599 ± 164

Number of exons

8174

8174

8174

8174

Exons overlapped with segmental duplication regions

458 (5.6%)

458 (5.6%)

458 (5.6%)

458 (5.6%)

Number of genes (passing QC)

862

439

739

1

Genes overlapped with segmental duplication regions

29 (3.3%)

11(2.5%)

23(3.1%)

0(0.0%)

Over-dispersion factor(mean ± standard deviation)

7.9 ± 8.2

2.1 ± 1.1

6.4 ± 5.5

N/A

Quality index(mean ± standard deviation)

9.4 ± 8.8

5.5 ± 2.3

7.6 ± 5.6

N/A

Expected detection sensitivity based on quality index

0.46

0.20

0.41

N/A

Number of calls h = 0.65 either with or without a neighboring call

36

4

56

N/A

Number of calls h = 0.1 either with a neighboring call

17

0

11

N/A

Table 2

Data characteristized by sequencing center and population

 

SC

 

CEU

CHB

 

JPT

 

TSI

YRI

Number of samples

18

14

 

9

 

51

14

Male/Female

9/9

5/9

 

5/4

 

24/27

2/12

Average read depth per gene

1679

1701

 

1597

 

1617

1865

Read length

36

36

 

36

 

36

36

 

BCM

 

CEU

CHB

CHD

JPT

LWK

 

YRI

Number of samples

40

62

78

16

108

 

45

Male/Female

20/20

15/47

38/40

5/11

51/57

 

22/23

Average read depth per gene

178

131

171

243

128

 

165

Read length

258

323

339

300

336

 

322

 

BI

 

CEU

CHB

CHD

JPT

  

YRI

Number of samples

16

13

28

34

  

19

Male/Female

9/7

11/2

12/16

16/18

  

12/7

Average read depth per gene

1623

1631

1675

1104

  

1612

Read length

73

75

74

75

  

76

Population abbreviations:

CEU – Utah residents with Northern and Western European ancestry.

CHB – Han Chinese in Beijing.

CHD – Chinese in Denver, Colorado.

JPT – Japanese in Tokyo, Japan.

LWK – Luhya in Webuye, Kenya.

TSI – Toscans in Italy.

YRI – Yoruba in Ibadan, Nigeria.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-13-305/MediaObjects/12859_2012_Article_5642_Fig2_HTML.jpg
Figure 2

Data characteristics for the 1000 genomes exon sequencing pilot datasets. A. Principal component analysis of a “mixed” read depth matrix built with data from 3 different sequencing centers, SC (Wellcome Trust Sanger Institute), BI (Broad Institute) and BCM (Baylor College of Medicine). Each sample is represented as a point in the plot, with the first principal component plotted vs. the second principal component. Samples from different sequencing centers cluster separately from each other within this space, suggesting significant differences in the gene affinities among these three datasets. B. Distributions of MRD for each of the BCM, BI and SC samples C. Histogram of RD across all GSSs in the three datasets. D. Histogram of gene affinities across genes within each of the three datasets. E. Distributions of the RD over-dispersion factor (ODF) in our data.

Sample coverage and gene affinities

As a metric of coverage for each sample, we calculated the sample-specific median gene RD, referred to as “Median Read Depth” (MRD); see Figure 1A and Methods. MRD was highest for the SC samples (1,710 ± 1,073, median 1,491 reads/gene; data presented as mean±standard deviation), see Figure 2B. MRD was somewhat lower for the BI samples (1,070 ± 803, median 860 reads/gene), and much lower in the BCM dataset (97 ± 52, median 87 reads/gene). As mentioned above, RD (distributed as in Figure 2C) is not determined by base coverage alone. Base coverage was highest in the BI data (70 ± 61, median 56 reads/base), followed by SC (56 ± 34, median 50 reads/base). The much lower RD in the 454 reads from BCM corresponds to only somewhat lower base coverage (23 ± 12, median 21 reads/base).

For each target we define a quantity, the “target affinity”, intended to describe the number of reads (RD) being mapped to a given target, relative to the sample-specific MRD over all capture targets. Analogously, we define the gene-specific affinity as the ratio of the number of reads (RD) mapped to the targets (exons) belonging to that gene and the gene-specific MRD for that same sample (see Methods, Figure 2D). In general, tighter distributions of affinities, with mean and median as close to 1 as possible, are desirable because these correspond to more even target coverage. The observed gene affinities for our datasets (Figure 2D) were as follows: SC (1.40 ± 1.43, median 1.04), BI (1.58 ± 1.59, median 1.20), and BCM (2.63 ± 3.03, median 1.73). Because of the more favorable gene affinities, we used the SC data as our primary dataset for method development and experimental validations.

CNV candidates detected in the data

According to our Bayesian detection scheme, we call a heterozygous deletion event in a gene if the posterior probability value of CN = 1, i.e. P(CN=1 | RD) ≥ h where h is a pre-defined probability cutoff value. Similarly, a homozygous deletion is where P(CN=0 | RD) ≥ h. Although we detected both deletions and amplifications in the analyzed datasets, deletion events (even when in a heterozygous state) provide easier detectable signal than amplifications. For this reason we only discuss deletion events here and report candidate amplifications in Table 3.
Table 3

Gene duplication calls in the SC dataset

Population

Sample

Gene name

Gene function

Chr

Start [bp]

End [bp]

Posterior probablity

RD observed

RD expected

CEU

NA12348

CD300LB

CD300 molecule-like family member b

17

70030472

70039195

1.000

638

420

TSI

NA20533

CLDN10

claudin 10

13

95003009

95028269

1.000

2108

1582

CHB

NA18526

SNRNP27

small nuclear ribonucleoprotein 27 kDa (U4/U6.U5)

2

69974621

69977184

1.000

530

383

CHB

NA18532

CES1

carboxylesterase 1 (monocyte/macrophage serine esterase 1)

16

54401930

54424468

1.000

501

337

TSI

NA20752

NOM1

nucleolar protein with MIF4G domain 1

7

156435193

156455158

1.000

1335

966

TSI

NA20796

AHNAK

AHNAK nucleoprotein

11

62040792

62059238

1.000

7330

5169

TSI

NA20796

ZNF264

zinc finger protein 264

19

62408577

62416161

0.999

1276

888

TSI

NA20801

GPR128

G protein-coupled receptor 128

3

101811391

101896535

0.998

14747

8265

TSI

NA20772

STX16

syntaxin 16

20

56660469

56684753

0.998

2101

1605

TSI

NA20769

MRPS6

mitochondrial ribosomal protein S6

21

34419511

34436770

0.998

1585

1203

TSI

NA20774

ELAVL4

ELAV (embryonic lethal, abnormal vision, Drosophila)-like 4 (Hu antigen D)

1

50383216

50439437

0.998

782

567

TSI

NA20804

CYP2A13

cytochrome P450, family 2, subfamily A, polypeptide 13

19

46291375

46293686

0.997

1289

984

TSI

NA20774

CREB5

cAMP responsive element binding protein 5

7

28494318

28825421

0.996

1435

954

TSI

NA20796

ZNF32

zinc finger protein 32

10

43459504

43461587

0.996

911

646

TSI

NA20520

C6orf145

chromosome 6 open reading frame 145

6

3668852

3683381

0.995

2015

1601

CEU

NA12348

GDNF

glial cell derived neurotrophic factor

5

37851510

37870647

0.994

306

217

CHB

NA18561

PSMB4

proteasome (prosome, macropain) subunit, beta type, 4

1

149638688

149640730

0.986

3461

2216

CEU

NA12546

DAZAP2

DAZ associated protein 2

12

49920394

49922509

0.985

2265

1651

TSI

NA20752

AATF

apoptosis antagonizing transcription factor

17

32380539

32488077

0.976

1157

843

CEU

NA12749

PAQR5

progestin and adipoQ receptor family member V

15

67439474

67483215

0.976

1684

1239

TSI

NA20769

BCL2L11

BCL2-like 11 (apoptosis facilitator)

2

111597794

111638279

0.965

1813

1435

TSI

NA20804

PILRA

paired immunoglobin-like type 2 receptor alpha

7

99809603

99835466

0.909

962

752

TSI

NA20589

C8orf85

chromosome 8 open reading frame 85

8

118019664

118024121

0.903

147

91

TSI

NA20752

CCKAR

cholecystokinin A receptor

4

26092358

26100987

0.902

712

532

JPT

NA18973

HBG2

hemoglobin, gamma G

11

5278820

5523329

0.901

4151

3094

TSI

NA20774

HIPK1

homeodomain

1

114298778

114317657

0.900

2374

1626

TSI

NA20774

ODC1

ornithine decarboxylase 1

2

10498301

10502609

0.897

1489

935

TSI

NA20796

STBD1

starch binding domain 1

4

77446947

77450177

0.885

978

664

TSI

NA20589

CRIPAK

cysteine-rich PAK1 inhibitor

4

1378300

1379640

0.877

76

38

YRI

NA19189

PSMB4

proteasome (prosome, macropain) subunit, beta type, 4

1

149638688

149640730

0.853

2622

2090

TSI

NA20774

STX16

syntaxin 16

20

56660469

56684753

0.811

949

704

JPT

NA18980

CES1

carboxylesterase 1 (monocyte/macrophage serine esterase 1)

16

54401930

54424468

0.788

1679

1036

TSI

NA20774

PAQR5

progestin and adipoQ receptor family member V

15

67439474

67483215

0.788

1048

676

CHB

NA18561

CRNN

cornulin

1

150648694

150651333

0.778

4845

3172

TSI

NA20774

DKK4

dickkopf homolog 4 (Xenopus laevis)

8

42350775

42353720

0.760

493

362

TSI

NA20589

NOM1

nucleolar protein with MIF4G domain 1

7

156435193

156455158

0.740

1052

801

TSI

NA20769

RNF122

ring finger protein 122

8

33525813

33535831

0.734

2574

2004

TSI

NA20796

ZNF521

zinc finger protein 521

18

20896674

21184908

0.721

3536

2738

TSI

NA20769

VLDLR

very low density lipoprotein receptor

9

2625453

2631499

0.676

2092

1624

Using a cutoff value h = 0.65, we detected deletion 96 events in the three datasets (36 in SC, 56 in BI, and 4 in BCM), all heterozygous deletions (Table 4, Table 5 and Table 6). The top ranked deletions are shown in Figure 3A. Most of the events were found in the Tuscan population, which constituted about half of the sample set. 10 of 36 gene deletions in the SC dataset were found in two samples (NA18523 and NA20533), clustered in a contiguous string of deleted genes extending approximately 3 Mb on chromosome 1 and 17, respectively, a genomic deletion event that we were also able to find in the 1000 Genomes Project whole-genome Low Coverage Pilot data [16] from the same samples (data not shown).
Table 4

Gene deletion calls in the SC dataset

Population

Sample

Gene name

Gene function

Chr

Start [bp]

End [bp]

Posterior probability

RD observed

RD expected

YRI

NA18523

BCL2L15

BCL2-like 15

1

114225268

114231520

1.000

533

1158

YRI

NA18523

HIPK1

homeodomain interacting protein kinase 1

1

114298778

114317657

1.000

2539

5272

TSI

NA20533

GLOD4

glyoxalase domain containing 4

17

610163

632245

1.000

1322

2295

TSI

NA20533

C1QBP

complement component 1, q subcomponent binding protein

17

5277059

5282317

1.000

793

1416

TSI

NA20533

C17orf91

chromosome 17 open reading frame 91

17

1562414

1563890

1.000

369

574

YRI

NA18523

NRAS

neuroblastoma RAS viral (v-ras) oncogene homolog

1

115052679

115060304

1.000

702

1462

YRI

NA18523

TRI3

tripartite motif-containing 33

1

114741793

114808533

1.000

2610

5225

TSI

NA20533

TRPV3

transient receptor potential cation channel, subfamily V, member 3

17

3363961

3404894

1.000

3365

5275

TSI

NA20774

PTMAP1

prothymosin, alpha pseudogene 1 (gene sequence 26)

6

30725671

30728671

1.000

132

260

TSI

NA20796

SNRNP27

small nuclear ribonucleoprotein 27 kDa (U4/U6.U5)

2

69974621

69977184

0.998

105

194

TSI

NA20807

HIST1H2BC

histone cluster 1, H2bc

6

26231731

26232111

0.998

42

90

TSI

NA20772

ULBP1

UL16 binding protein 1

6

150331436

150332954

0.997

104

205

TSI

NA20807

CYP2A13

cytochrome P450, family 2, subfamily A, polypeptide 13

19

46291375

46293686

0.996

126

204

YRI

NA18508

PTMAP1

prothymosin, alpha pseudogene 1 (gene sequence 26)

6

30725671

30728671

0.992

145

230

CEU

NA07000

PSG8

pregnancy specific beta-1-glycoprotein 8

19

47950287

47960273

0.990

29

70

CEU

NA11893

PSG8

pregnancy specific beta-1-glycoprotein 8

19

47950287

47960273

0.985

43

86

TSI

NA20771

PTMAP1

prothymosin, alpha pseudogene 1 (gene sequence 26)

6

30725671

30728671

0.980

533

862

TSI

NA20773

CCK

cholecystokinin

3

42274594

42280126

0.971

282

474

CEU

NA07000

HMGN4

high mobility group nucleosomal binding domain 4

6

26653414

26653686

0.966

68

132

CEU

NA12749

HMGN4

high mobility group nucleosomal binding domain 4

6

26653414

26653686

0.966

156

286

TSI

NA20772

AIF1

allograft inflammatory factor 1

6

31692086

31692262

0.964

51

124

CEU

NA12348

DUSP10

dual specificity phosphatase 10

1

219942377

219946216

0.962

155

242

YRI

NA18508

ULBP1

UL16 binding protein 1

6

150331436

150332954

0.941

40

79

YRI

NA18523

PPM1J

protein phosphatase, Mg2+/Mn2+ dependent, 1 J

1

113056116

113057756

0.891

560

924

TSI

NA20807

POU5F1

POU class 5 homeobox 1

6

31240884

31241803

0.891

124

193

TSI

NA20772

SERPINA11

serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 11

14

93978696

93984864

0.889

786

1243

CEU

NA07000

KRT18P19

keratin 18 pseudogene 19

12

51630379

51632393

0.887

85

174

CEU

NA12348

ULBP1

UL16 binding protein 1

6

150331436

150332954

0.879

49

88

YRI

NA18523

RHOC

ras homolog gene family, member C

1

113054308

113055529

0.867

557

955

CEU

NA12348

STBD1

starch binding domain 1

4

77446947

77450177

0.839

246

395

CEU

NA07000

POU5F1

POU class 5 homeobox 1

6

31240884

31241803

0.823

106

169

CEU

NA12749

SNRNP27

small nuclear ribonucleoprotein 27 kDa (U4/U6.U5)

2

69974621

69977184

0.775

142

216

TSI

NA20752

POU5F1

POU class 5 homeobox 1

6

31240884

31241803

0.723

76

142

TSI

NA20807

HIST1H2BO

histone cluster 1, H2bo

6

27969220

27969600

0.723

48

88

TSI

NA20589

POU5F1

POU class 5 homeobox 1

6

31240884

31241803

0.697

61

117

TSI

NA20786

NPSR1

neuropeptide S receptor 1

7

34884213

34884321

0.678

51

88

Table 5

Gene deletion calls in the BI dataset

Population

Sample

Gene name

Gene function

Chr

Start [bp]

End [bp]

Posterior probability

RD observed

RD expected

CHD

NA18695

TPM3

tropomyosin 3

1

152396739

152422219

1.000

166

337

JPT

NA19066

TPM3

tropomyosin 3

1

152396739

152422219

1.000

169

288

CHD

NA18687

RPL27A

ribosomal protein L27a

11

8661325

8663929

1.000

93

182

JPT

NA18983

POU5F1

POU class 5 homeobox 1

6

31240357

31241803

1.000

122

256

JPT

NA19066

POU5F1

POU class 5 homeobox 1

6

31240357

31241803

1.000

166

318

JPT

NA19066

RPL27A

ribosomal protein L27a

11

8661325

8663929

1.000

106

203

CHD

NA18687

TPM3

tropomyosin 3

1

152396739

152422219

1.000

155

258

CHD

NA18687

POU5F1

POU class 5 homeobox 1

6

31240357

31241803

1.000

156

285

JPT

NA19054

TPM3

tropomyosin 3

1

152396739

152422219

1.000

135

230

CHD

NA18695

POU5F1

POU class 5 homeobox 1

6

31240357

31241803

1.000

194

371

JPT

NA18960

SETD8

SET domain containing (lysine methyltransferase) 8

12

122441130

122455574

1.000

221

347

CHD

NA18164

RPL27A

ribosomal protein L27a

11

8661325

8663929

1.000

129

223

JPT

NA19054

POU5F1

POU class 5 homeobox 1

6

31240357

31241803

1.000

130

254

CHD

NA18695

SETD8

SET domain containing (lysine methyltransferase) 8

12

122441130

122455574

1.000

142

309

CHD

NA18695

RPL27A

ribosomal protein L27a

11

8661325

8663929

1.000

128

238

CHD

NA18695

AKR1B1

aldo-keto reductase family 1, member B1 (aldose reductase)

7

133778020

133787045

1.000

310

554

CHD

NA18164

HAX1

HCLS1 associated protein X-1

1

152512874

152514801

1.000

214

339

CHD

NA18687

SETD8

SET domain containing (lysine methyltransferase) 8

12

122441130

122455574

1.000

125

237

JPT

NA19054

HFE

hemochromatosis

6

26201326

26202433

1.000

56

122

JPT

NA18983

RPL27A

ribosomal protein L27a

11

8661325

8663929

0.990

95

164

JPT

NA18983

TPM3

tropomyosin 3

1

152396739

152422219

0.990

147

232

JPT

NA19561

TRIM55

tripartite motif-containing 55

8

67202058

67209944

0.990

119

193

CHD

NA18687

RBMS1

RNA binding motif, single stranded interacting protein 1

2

160840394

160932124

0.990

334

575

CHB

NA18757

CRIPAK

cysteine-rich PAK1 inhibitor

4

1378300

1379640

0.990

327

669

JPT

NA19054

PSAT1

phosphoserine aminotransferase 1

9

80109471

80113319

0.980

140

253

JPT

NA19066

PSAT1

phosphoserine aminotransferase 1

9

80109471

80113319

0.980

190

317

CHD

NA18164

TPM3

tropomyosin 3

1

152396739

152422219

0.980

209

317

JPT

NA19568

OR8A1

olfactory receptor, family 8, subfamily A, member 1

11

123945175

123946141

0.980

471

764

JPT

NA19066

RAN

RAN, member RAS oncogene family

12

129923334

129926424

0.980

229

462

CHD

NA18695

KLHL12

kelch-like 12

1

201128284

201160913

0.970

767

1358

JPT

NA19066

SETD8

SET domain containing (lysine methyltransferase) 8

12

122441130

122455574

0.970

154

265

JPT

NA19066

RPS15A

ribosomal protein S15a

16

18706886

18707936

0.960

83

161

CHD

NA18695

RPS15A

ribosomal protein S15a

16

18706886

18707936

0.960

88

188

CHD

NA18687

KLHL12

kelch-like 12

1

201128284

201160913

0.960

621

1041

JPT

NA18983

SETD8

SET domain containing (lysine methyltransferase) 8

12

122441130

122455574

0.960

120

213

JPT

NA18983

DCTN5

dynactin 5 (p25)

16

23560365

23585966

0.960

177

298

JPT

NA18983

EIF2B5

eukaryotic translation initiation factor 2B, subunit 5 epsilon, 82 kDa

3

185500333

185509372

0.940

856

1482

CHD

NA18687

ARG2

arginase, type II

14

67187855

67187951

0.940

28

62

CHD

NA18695

PSAT1

phosphoserine aminotransferase 1

9

80109471

80113319

0.930

221

371

CHD

NA18695

RBMS1

RNA binding motif, single stranded interacting protein 1

2

160840394

160932124

0.900

442

750

JPT

NA19561

OR8A1

olfactory receptor, family 8, subfamily A, member 1

11

123945175

123946141

0.890

254

466

YRI

NA19247

TIMM8B

translocase of inner mitochondrial membrane 8 homolog B (yeast)

11

111461229

111462657

0.880

40

89

CHD

NA18164

POU5F1

POU class 5 homeobox 1

6

31240357

31241803

0.850

226

349

CHD

NA18164

KLHL12

kelch-like 12 (Drosophila)

1

201128284

201160913

0.800

803

1276

CHD

NA18164

SETD8

SET domain containing (lysine methyltransferase) 8

12

122441130

122455574

0.790

181

291

CHD

NA18687

RPS15A

ribosomal protein S15a

16

18706886

18707936

0.790

81

144

JPT

NA19066

EIF2B5

eukaryotic translation initiation factor 2B, subunit 5 epsilon, 82 kDa

3

185500333

185509372

0.780

1137

1840

JPT

NA19568

GABARAPL2

GABA(A) receptor-associated protein-like 2

1

157676173

157676631

0.760

254

476

JPT

NA19560

OR8A1

olfactory receptor, family 8, subfamily A, member 1

11

123945175

123946141

0.750

614

1119

JPT

NA19058

RPL27

ribosomal protein L27

17

38404294

38408463

0.730

356

518

CHD

NA18699

SDPR

serum deprivation response

2

192408894

192419896

0.720

524

1033

JPT

NA18983

SPRR2G

small proline-rich protein 2 G

1

151388989

151389210

0.670

81

147

JPT

NA19066

SPRR2G

small proline-rich protein 2 G

1

151388989

151389210

0.670

105

182

JPT

NA19066

RBMS1

RNA binding motif, single stranded interacting protein 1

2

160840394

160932124

0.670

404

642

JPT

NA19054

EIF2B5

eukaryotic translation initiation factor 2B, subunit 5 epsilon, 82 kDa

3

185500333

185509372

0.670

869

1470

CHD

NA18695

RAN

RAN, member RAS oncogene family

12

129923334

129926424

0.660

290

539

Table 6

Gene deletion calls in the BCM dataset

Population

Sample

Gene name

Gene function

Chr

Start [bp]

End [bp]

Posterior probability

RD observed

RD expected

LWK

NA19355

MBD5

methyl-CpG binding domain protein 5

2

148932798

148986980

0.999

618

973

CHD

NA17970

MTERFD2

MTERF domain containing 2

2

241684086

241687982

0.996

255

393

CHB

NA18618

GABARAPL2

GABA(A) receptor-associated protein-like 2

16

74159436

74168768

0.800

58

99

CHD

NA18135

PSMB4

proteasome (prosome, macropain) subunit, beta type, 4

1

149638688

149640929

0.729

390

605

https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-13-305/MediaObjects/12859_2012_Article_5642_Fig3_HTML.jpg
Figure 3

Detected CNV events. A. Top-ranked (by posterior probability) deletion events in the SC dataset. B. Validation results for different callsets (left – without neighboring information, right – with use of neighboring information). Green denotes events positively validated either in our experiments or as known events [17]; red – calls validated negatively in our experiments; yellow – calls without validation status (not submitted for validation or validation experiments without conclusive outcomes). C. Detection sensitivity as a function of number of samples. D. Sensitivity of detecting common CNV as a function of the deleted allele frequency.

When two or more gene deletions are detected in close proximity, it is likely that these events are part of a single, longer genomic deletion spanning the genes. With this in mind, we searched the sequenced genes for deletion events at a lower probability cutoff value (h = 0.1), but required that an immediate neighbor of a candidate gene be located within 3 Mbp and also show evidence for a deletion at the same probability cutoff. This procedure produced 17 heterozygous deletion calls in the SC dataset, 11 calls in the BI dataset (but no such calls were made in the BCM dataset). The union of both callsets (i.e. those made with and without use of neighboring information) resulted in a total of 107 unique deletion events (41 in SC dataset, 62 in BI, and 4 in BCM).

We note that none of the events we detected in our data were at high allele frequency. In fact, even the most “common” events were only present in two samples, as heterozygotes.

Call-set accuracy assessment

To assess the accuracy of deletion calls made in the SC dataset, we performed experimental validations on calls made with posterior probability ≥ 0.65 without neighbor information, using qPCR (see Methods). The validation results are summarized in Figure 3B. Of the 36 calls made, we evaluated 26. All 22 calls with posterior probability ≥ 0.95 and 4 out of 12 calls (randomly selected) with posterior probability between 0.65 and 0.95 were submitted for validation. 6 were considered positively validated as they appeared in an earlier publication [17] and 20 were validated de novo using qPCR. The qPCR validations produced positive results for 12 calls (measured fold change < 0.7) and negative results for 3 calls (measured fold change > 0.8). The validation results for the remaining 5 were inconclusive. All the 17 neighbored calls with posterior probability ≥ 0.1 were selected for validation. 7 were considered valid per previous publication [17], 7 were positively validated de novo and none was found invalid; validation was not obtained for the remaining 3. The union of those two callsets counted 41 calls and 32 of them were evaluated. Among these 32 calls 7 were considered positively validated per previous publication [17], 14 were positively validated de novo, 3 were invalidated, 5 were inconclusive and 3 did not obtain the validation results. The numbers of validated calls are presented in Table 7. The selection procedure for site validation was as follows: (1) We selected sites for validation (in some categories, all candidates, in others, a random selection); (2) we searched the literature, and removed from the validation list events that we found as validated in one of the publications we consulted; (3) events that remained on the list were sent for experimental validation. The overall FDR for the union of calls made with and without neighboring information can be estimated as 12.5% (3/24).
Table 7

Validation results

 

Posterior ≥ 0.95 without neighbor information

0.65 ≤ Posterior < 0.95 without neighbor information

Posterior ≥ 0.1 with neighbor information

Validated per previous publication

4

2

7

Validated positively de novo

11

1

7

Validated inconclusively de novo (intermediate fold change)

4

1

0

Validated negatively de novo

3

0

0

Submitted for validation but without result or no validation attempt

0

10

3

Total calls

22

14

17

Sensitivity

We performed simulations to assess the detection efficiency of our method, both for individual gene and for pairs of neighboring genes deletions. Specifically, in each sample we randomly selected (a) 5 out of 862 genes in one simulation and (b) 5 pairs of neighboring genes in another simulation. In the selected genes we down-sampled the actual read depth seen in the experimental data by a factor of 2 to simulate a heterozygous deletion. The results of those simulations are presented in Figure 3C. Of the 530 gene deletions, we detected 237 (45%). Of the 530 gene-pair deletions we detected 287 (54%). We also performed simulations on smaller subsets of the original 106 samples to assess the impact of sample size on detection sensitivity. Reduction of sample size did not substantially degrade detection sensitivity as long as the number of samples was >20. Therefore, our detection efficiency is 40-45% without using neighboring information and approximately 50-55% with the use of neighboring information, in the SC dataset.

In addition to simulations, we compared our dataset to a published study [17]. This study reported 12 heterozygous deletion events in samples and genes (in our terminology, GSS) that were part of our analyzed dataset. We detected 6 of these 12 events, which is broadly consistent with our overall sensitivity estimate.

Finally, we investigated our sensitivity to common events (see Methods) using simulations. Figure 3D shows detection sensitivity as a function of gene-level affinity: for a gene affinity value of 1.8 (representing the 75th percentile of our data), sensitivity to common events (allele frequency between 10% and 90%) approaches 40%. Note that the detection efficiency starts to decrease at high allele frequency (> 70%) due to a reduction of the overall read depth because more samples have a deletion and a corresponding depleted read depth signal. We can also see that the median gene affinity is substantially lower than the mean because the distribution of gene affinity has a long tail at the high end (Figure 2D). Since sensitivity is directly related to the gene affinity, the simulated data with the substantially higher mean gene affinity (red) has better sensitivity than with the substantially lower median gene affinity (green).

The number of CNV events in the samples

We estimated the total number of gene deletions in the SC dataset from the number of detected events (41), the FDR (12.5%) and the detection efficiency (45%), as ~66, or a nominal 0.62 deletions per sample. By projecting the per-sample number, corresponding to 3.9% of the exome (862 genes of 21,999), onto the whole exome, our estimate for the average number of genic deletion events is 16 ± 4 per sample. This estimation is representative for the whole-exome sequencing data since the 1000 Genomes Exon Pilot Project randomly selected all the exon targets from the CCDS collection. Our gene set is therefore a quasi-random sampling of known human genes, with no intentional enrichment for any given gene family. Figure 4A and B show the distributions of exon length in the gene list used for our analysis and the full human exome. There is no significant difference between these two distributions: the median and the standard deviation of the exon length for our study are 125 bp and 236 bp, whereas the corresponding values for the whole exome are 127 bp and 264 bp. The similarity of these two distributions suggests that our estimation of the number of events per sample is unbiased and is representative for a whole-exome analysis.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-13-305/MediaObjects/12859_2012_Article_5642_Fig4_HTML.jpg
Figure 4

Exon length distribution. A. Exon length distribution in the gene list used for our analysis. (median: 125 bp, standard deviation: 236 bp) B. Exon length distribution of the whole exome. (median: 127 bp, standard deviation: 264 bp) These two distributions are very similar to each other, suggesting our estimation of the number of events per sample is unbiased and is representative for a whole-exome study.

Detection efficiency as a function of data quantity and data quality

As discussed earlier, our algorithm’s sensitivity was ~45% at ~87.5% accuracy. Both sensitivity and accuracy are considerably lower than achievable for SNP detection in the same datasets [16]. This poses the more general question of how detection efficiency is influenced by sample size, data quantity, and data quality. Our simulations show that sensitivity only modestly depends on sample size, above approximately 20 samples (Figure 3C).

We found that the primary factors that determine detection efficiency are (i) sequence coverage, or more precisely, RD (higher RD supplies more statistical power to detect systematic changes in coverage); (ii) the level of over-dispersion of the RD distribution for individual genes (the more the RD distribution departs from an expected Poisson distribution, the less one can rely on the statistics); and (iii) the shape of the distribution of RD across all genes in the dataset, determined by the gene affinities (uneven distribution means that detection power is low in a high fraction of the genes, but this effect is not compensated by the extra coverage in other, “over-sequenced” genes where detection efficiency is already high, see Figure 5A below. Favorable scenarios therefore involve distributions in which all or most genes have sufficient RD for detection).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-13-305/MediaObjects/12859_2012_Article_5642_Fig5_HTML.jpg
Figure 5

Detection efficiency. A. Distributions of the detection efficiency estimated from the quality index for each gene-sample site. B. Theoretical detection efficiency (at posterior probability cutoff h = 0.65) as a function of expected read depth, plotted for various values of the over-dispersion factor. C. Histograms of the quality index (QI) distribution in the three datasets. Overall, QI was highest in SC: 9.4±8.8 (median 6.6); second highest in BI: QI = 7.6 ± 5.6 (median 6.2); and lowest in BCM: QI = 5.5 ± 2.3 (median 5.0).

For each gene, we compute a quality index (QI) taking into account the variance of the expected read depth for that gene (assuming the ideal, Poisson distribution), RDexp, and a over-dispersion factor, ODF, that quantifies the over-dispersion of RD relative to the Poisson expectation:
Q I = R D expected O D F
(1)
QI is directly related to detection sensitivity (see Additional file 1 for the exact formula and its derivation), as shown in Figure 5B. According to our power calculations, for the posterior detection threshold value we used in this study (h = 0.65), sensitivity is completely diminished for genes with QI < 5.1. QI ≥ 7.2 is required to achieve 50% sensitivity, and QI ≥ 9.5 to achieve 90% sensitivity. This estimated sensitivity from QI is made only for heterozygous deletions. To achieve the same sensitivity for detecting higher copy number variation (CN≥3), higher QI value will be required since the difference of prior probability between higher copy and normal copy (CN=2) is greater than that between heterozygous deletion and normal copy (Table 8).
Table 8

Nominal prior probabilities corresponding to the range of gene region copy numbers derived from Conrad et al. [[17]]

Copy number

Prior probability per gene

0

6.34·10-4

1

2.11·10-3

2

9.96·10-1

3

5.38·10-4

4

6.68·10-4

5

3.57·10-5

6

7.52·10-6

7

1.39·10-6

8

3.61·10-7

9

4.37·10-8

The distributions of QI values in our three datasets are shown in Figure 5C. Overall, QI was highest in SC: 9.4 ± 8.8 (median 6.6); second highest in BI: QI = 7.6 ± 5.6 (median 6.2); and lowest in BCM: QI = 5.5 ± 2.3 (median 5.0). The corresponding distributions of detection efficiency values are shown in Figure 5A. Because detection efficiency increases abruptly from 0 to almost 1 over a narrow range of QI values (note the mapping between the vertical axes in Figure 5B), the distribution of detection sensitivity (Figure 5A) is strongly bimodal, with the vast majority of GSS having either close to zero or close to 100% sensitivity. Even in the SC dataset with the highest overall QI values, in less than half of the GSS does the quantity and quality of the data support >80% detection efficiency. There was also very substantial variation across samples: only 15 of the 106 SC samples had sufficiently high coverage to support ≥ 90% overall sensitivity, and in 22 samples overall sensitivity was below 10%.

Given that QI improves only with the square root of RD, over-dispersion can profoundly influence detection performance, as shown in Figure 5B. The ODF values we chose for this figure correspond to the 25th, 50th and 75th percentile, and the mean values (ODF=3, 5.5, 10, and 8, respectively) in the SC dataset. Using the observed distribution of QI in the SC dataset, we predict ~46% sensitivity, in good agreement with our estimate based on simulations.

The QI formulation permits one to estimate CNV (or specifically in our case, heterozygous deletion) detection power in any given exon capture dataset, based on the read mappings. One can also use the formulation to calculate the amount of base coverage required for a given level of desired power, to guide data collection. For example, using the distributions of QI values in the SC dataset, one would need to collect an overall 110× coverage, assuming 36 bp reads, to achieve 60% detection power, and 320× coverage to achieve 80% detection power. However, if DNA capture methods improved to support a median ODF=3, assuming an accordingly scaled version of the observed distribution of QI in the SC dataset, one would only need to collect 33× coverage for 60% power, and 96× for 80% power. It is important to also point out that, in the case of whole-exome data, sensitivity would also improve just by virtue of the higher density of targeted genes, if one were to integrate in one’s pipeline neighbor-gene based detection.

Methodology comparison with CoNIFER

Krumm and his colleagues recently published a method, CoNIFER [18], that also used read-depth signal to detect CNV in the exome capturing sequencing data. Like our method, CoNIFER normalizes the read depth signal in order to discover the CNV. However, it is quite different for these two algorithms in the approach of calling samples copy number variants on the basis that they present aberrant read depth. As we mentioned previously, our method deploys specific models for copy numbers 0, 1, 2, and is capable of detecting both rare, intermediate frequency, and common CNV events. On the other hand, CoNIFER deploys singular value decomposition (SVD) to remove noise from the read depth data, and interprets the first “k” singular values as noise in the data. This approach may identify systematic variance in the data caused by a high-frequency CNV event as noise and removes it. Therefore CoNIFER has limited power for detecting common CNV events. On the other hand, our method is capable of detecting CNV events on the entire frequency spectrum, and is therefore more generally applicable.

Conclusions

We have developed a novel, Bayesian method to identify CNVs in exon-capture data. We applied this method (and a simple extension using neighbor-gene information) to the 1000 Genomes Project Exon Sequencing Pilot dataset. We were able to achieve reasonable sensitivity and specificity in a dataset that was optimized for SNP discovery and, as discussed above, is far from ideal for CNV detection. The main accomplishment of this work is that we provide a statistically rigorous algorithm for CNV detection in exon capture data, backed by experimental validations, that can be applied to the thousands of exomes sequenced to date in various medical projects, and to nascent and ongoing projects targeting increasingly higher numbers of samples. Our formulation allows investigators to assess detection power in existing datasets and to take into account CNV detection power during experimental design for future datasets. We also uncovered >100 heterozygous deletion events in the 1000 Genomes samples we examined, allowing us to estimate the average number of heterozygous deletions per exome (as ~16 events per exome). Because we focused on algorithm development functional assessment of these sites is beyond the scope of this study. Nevertheless, these and other gene deletions that will be found using our methods are very likely to uncover events with strong functional significance.

Methods

The overall detection workflow (shown in Figure 1) consists of three main steps. (1) We tabulate the observed read depth for every GSS. (2) We determine whether the distribution of read depth for a specific gene distribute across samples should be modeled using simple uni-linear fit or using a more sophisticated tri-linear fit. (3) If the simple uni-linear fit is found suitable, we determine an expected read depth for every GSS under a null hypothesis of a normal copy number, using a simple linear fit model. (4) Subsequently, we compare the observed read depth for a GSS to the corresponding expectation and calculate a Bayesian posterior probability for each copy number considered (CN=0-9), threshold these, and report events with a non-normal CN. (5) If data do not allow for modeling using a simple uni-linear fit model, we perform a more sophisticated tri-linear fit. The tri-linear fit directly assigns copy number to every sample.

Observed read depth

Capture sequencing reads from the 1000 Genomes Project Exon Sequencing Pilot Project were downloaded, in FASTQ format, from the 1000 Genomes Project DCC site: http://1000genomes.org. The reads were mapped using the MOSAIK read mapping program, to the NCBI build 36.3 human reference genome. The resulting read alignments (in BAM format) were further processed to remove duplicate reads, and reads with low mapping quality (<20).

Gene target regions were also downloaded from the 1000 Genomes Project site. For each GSS, we determined RD as the number of distinct reads that had their first (5’) base uniquely mapped within an exon of that gene. This resulted in a matrix of RD observations (illustrated in Figure 1C left).

Data filtering

We discarded all duplicate reads and all reads with mapping quality less than 20. We also discarded all the targets with median RD less than 30. Similarly, we discarded all the samples with median RD less than 30. In 454-sequenced data, this led to discarding almost all targets and samples; therefore we relaxed those criteria to 5 and 1, respectively. Additionally, we discarded all the genes that failed to exhibit correlation between observed RD and MRD at r2 ≥ 0.7.

Expected read depth based on uni-linear fit and tri-linear fit

In the first attempt, we use the simple uni-linear fit; we calculate the expected read depth for normal copy number (CN=2) as the product of a gene-specific capture affinity value, αg, and a sample-specific measure of read coverage, the median of read depths, MRDs, across all genes for that sample:
R D gs = α g . M R D s
(2)

The gene-specific capture affinity (αg) is determined as the slope of a least-squares zero-intercept linear fit between the gene-specific read depth (RDgs) and the median read depth (MRDs) for all samples (illustrated in Figure 1B). This procedure resulted in a matrix of RD expectations (Figure 1C right).

The afore-mentioned procedure requires a single-line linear fit between RDgs and MRDs. The quality of such a fit is evaluated by comparing r2 against a predetermined threshold (≥ 0.7 as described before). When this indicates poor quality of the single-line linear fit, we attempt to perform a tri-linear fit.

Briefly, we attempted to minimize error function
erro r g = s min R D g , s α g M R D s 2 , R D g , s α g 2 M R D s 2 , R D g , s 0 . M R D s 2 ,

where s iterates over samples and g indicates the gene in question. Note that the tri-linear fit directly assigns copy number to each GSS. Please see Common CNVs for more detail.

Copy number probabilities

We used a Bayesian scheme to calculate the probability P(CN gs |RD gs of a given copy number at a given GSS, based on the observed read depth. We only considered CN=0-9 i.e. homozygous deletion (CN=0), heterozygous deletion (CN=1), normal copy number (CN=2), and amplifications of various magnitudes (CN>2). We assigned prior probabilities P(CNgs) to each copy number based on CNV events reported in an earlier study [17] (Table 8). We assumed that, for each distinct CN, the observed RD obeys an over-dispersed Poisson distribution. Its mean value for normal copy number (CN=2) is calculated according to (Eq. 2) and for other copy numbers it is proportionally scaled. The standard deviation of the distribution includes an over-dispersion factor (ODF) in the range of 1 to 20 to account for over-dispersion (variance beyond the level of Poisson fluctuations, see Additional file 1).

Briefly, to account for over-Poisson dispersion, we used observed RDgs and calculated corresponding z-score under an assumption of an ideal Poisson distribution at every GSS. Subsequently, we calculated a sample-specific standard deviation of that z-score for every sample and annotated it as sample over-dispersion factor. Similarly, we calculated a gene-specific standard deviation of z-score for every gene and annotated it as the gene-specific over-dispersion factor. If the assumption of an ideal Poisson distribution were true, those sample- and gene-specific standard deviations should equal 1. Subsequently, we calculated the over-dispersion factor for every GSS as a product of respective sample- and gene-specific ODFs. The ODF was then normalized and assigned to 1 if less than 1.

We used the over-dispersed Poisson distributions to calculate the data likelihoods P(RDgs|CN) for all considered CN values. Finally, we used Bayes’ method to estimate the posteriors for each considered CN. A CNV event is reported the posterior probability of a non-normal copy number is above a pre-defined threshold value, h.

Neighboring gene deletions

A simple extension of the algorithm used neighboring gene deletion events as part of the detection method. For the purpose of our algorithm, the genes were deemed “neighboring” if they were located on the same chromosome, the segment between those genes was no longer than 3 Mbp and no gene was sequenced in between. In principle, when a gene has a deleted neighbor, we should assume a higher prior probability of a deletion in the gene in question. Since the posterior probability usually scales monotonically with the prior, for practical reasons we assumed a lower Bayesian posterior probability threshold (h = 0.1) to produce a preliminary list of candidate events. Events on this list for which at least one of the two immediate neighbor genes was also on the list were retained.

Sensitivity estimation

We carried out sensitivity estimation in the SC dataset, using simple simulations. In each simulation cycle, we drew 5 genes randomly from every sample, and downscaled the observed RD for those genes by a factor of 2, to emulate heterozygous deletions. We then applied our standard detection procedure to this “spiked” dataset, and tabulated the fraction of simulated events that were detected by the algorithm.

Common CNVs

We evaluated all genes that failed to achieve r2 ≥ 0.7 using the linear fit model from Figure 1B. The results of that evaluation are shown in Figure 6. The last row describes result for gene RNF150 that achieved the worst r2 of 0.48. The histogram shown in the left columns demonstrates distribution of observed RD to MRD (taken as from Figure 1B), In case of a rare CNV (or lack of CNVs at all), one would expect a unimodal distribution centered around that gene affinity. For a common CNV, one additional peak corresponding to CN=1 centered around half of that gene affinity, and another peak corresponding to homozygous deletion (CN=0) around 0, should be visible. However, the data shown do not allow identifying such a pattern of either bi- or tri-modal distribution.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-13-305/MediaObjects/12859_2012_Article_5642_Fig6_HTML.jpg
Figure 6

Analysis of genes that failed simple linear fit. Each row describes a different gene. Left panels – distribution of the ratio of RD at the GSS sites to the sample MRD. Right panels – distribution of the quality index for that gene. The non-multimodal distributions and the low quality-index values of these genes suggest that there are no common CNV events on these loci.

Additionally, the histogram of quality index calculated for that gene is presented in the right column. The low values of quality index further corroborate the conclusion that the absence of a call in that locus is due to lack of high quality data rather than due to a hypothetical common CNV event. Careful inspection of the graphs calculated for all 69 genes the failed simple linear fit reveals lack of evidence for a common CNV in any of them. Notably, in the SC dataset only 28% of GSS in genes with r2 < 0.7 were potentially detectable vs. 62% in genes with r2 ≥ 0.7.

With no common CNV present in the experimental data, we tested the sensitivity of our algorithm using simulated deletions. We used realistic gene affinities (mean and three quartiles from Figure 2B) and the empirical MRDs for 106 samples. We assumed frequency of the deleted allele among 106 samples varying from 0 to 100% in 10% increments; we allowed for random segregation, so that both homo- and heterozygous deletions were introduced. Then for each sample we calculated the expected read depth as a product of MRD and affinity; however in the samples drawn for a heterozygous deletion we used halves of the nominal affinities and in the samples drawn for a homozygous deletion, we multiplied the MRD by 0.01 to account for reads erroneously mapped into that region. Having an expected read depth m for each sample, we drew a random read depth using a normal distribution N m , O D F m 2 , where ODF was assumed 8.

In Figure 7B and C we show the results of analysis performed on simulated common CNV events. Panel B shows r2 values obtained from the simple linear fit (as in Figure 1B) and panel C shows the r2 values obtained from the tri-linear fit (as in Figure 7A). The uni-linear r2 values deteriorate with the increase of the deleted allele frequency. To the contrary, the tri-linear r2 values stay relatively high over wide range of the allele frequency.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-13-305/MediaObjects/12859_2012_Article_5642_Fig7_HTML.jpg
Figure 7

Simulated Common CNVs. A. If a simple linear fit fails, the gene affinity is estimated for each gene as the slope of the least-square-error tri-linear fit between MRD and RD for that gene. B and C r2 values of a simple linear fit (B) and a tri-linear fit (C) as a function of the deleted allele frequency.

Finally, Figure 3D demonstrates that the sensitivity of the algorithm to the common CNVs remains relatively stable over wide range of the deleted allele frequency (up to 90%).

Validation experiments

All primers were designed using primer3 (http://fokker.wi.mit.edu/primer3/) with default settings to obtain a desired PCR amplicon size between 200 bp and 250 bp. All primers were checked with BLAT (http://genome.ucsc.edu/) to avoid known SNPs that could influence primer hybridization. PCR products were run on an agarose gel to make sure they gave no additional bands besides the expected amplicon.

Primer efficiencies were determined by calculating the standard curve of a serial dilution (4 times, 10-fold) of pooled genomic DNA (Promega, Madison, WI). All experiments were performed in triplicates on the Roche LightCycler 480 platform with LightCycler 480 SYBR Green I Master (cat# 04707516001). The volume of each reaction was 20 μl with final primer concentrations of 400 nM. The PCR was performed according to the following protocol: 5 min at 95°C, 2. 45 cycles of 5 s at 95°C, 10s at 60°C, 30s at 72°C. To determine the copy number state of an event locus, we used the Delta-Delta-Ct-Method (2-ΔΔCt) for each event locus compared to a reference locus in the sample and a control pool of seven individuals (Promega, Madison, WI), respectively. This reference locus was not previously known to show any copy number variation.

Among the calls made without neighboring information, we exhaustively validated all the calls with posterior probability of 0.95 or more (4 coincided with known events [17]; we experimentally validated the remaining 18 events). Additionally, we performed qPCR validations for 4 events randomly selected from those with posterior probability between 0.65 and 0.95 (2 coincided with known events [17]; we experimentally validated the remaining 2 events).

Of the calls made with the neighboring information, we deemed 7 calls coincided with known events [17]; 7 out of 10 remaining calls were submitted for qPCR validation. For the purpose of validation, the fold change for a given gene < 0.7 was classified as a positive validation, > 0.8 as a negative validation and in the intermediate range as inconclusive.

Notes

Abbreviations

CNV: 

Copy Number Variation

FDR: 

False Discovery Rate

RP: 

Read Pair

SR: 

Split Read

RD: 

Read Depth

AS: 

Assembly

GSS: 

Gene-Sample Site

WU: 

Washington University

SC: 

Wellcome Trust Sanger Institute

BI: 

Broad Institute

BCM: 

Baylor College of Medicine

MRD: 

Median Read Depth

CN: 

Copy Number

ODF: 

Over-Dispersion Factor

QI: 

Quality Index.

Declarations

Acknowledgements

This work was supported by National Human Genome Research Institute grants R01-HG004719 and RC2-HG005552.

Authors’ Affiliations

(1)
Boston College
(2)
Stanford University School of Medicine

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© Wu et al.; licensee BioMed Central Ltd. 2012

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