Open Access

Number of rare germline CNVs and TP53 mutation types

  • Amanda G Silva1,
  • Maria Isabel Waddington Achatz1,
  • Ana CV Krepischi1,
  • Peter L Pearson2 and
  • Carla Rosenberg2Email author
Orphanet Journal of Rare Diseases20127:101

https://doi.org/10.1186/1750-1172-7-101

Received: 11 May 2012

Accepted: 11 December 2012

Published: 21 December 2012

Abstract

Background

The Li-Fraumeni syndrome (LFS), an inherited rare cancer predisposition syndrome characterized by a variety of early-onset tumors, is caused by different highly penetrant germline mutations in the TP53 gene; each separate mutation has dissimilar functional and phenotypic effects, which partially clarifies the reported heterogeneity between LFS families. Increases in copy number variation (CNV) have been reported in TP53 mutated individuals, and are also postulated to contribute to LFS phenotypic variability. The Brazilian p.R337H TP53 mutation has particular functional and regulatory properties that differ from most other common LFS TP53 mutations, by conferring a strikingly milder phenotype.

Methods

We compared the CNV profiles of controls, and LFS individuals carrying either p.R337H or DNA binding domain (DBD) TP53 mutations by high resolution array-CGH.

Results

Although we did not find any significant difference in the frequency of CNVs between LFS patients and controls, our data indicated an increased proportion of rare CNVs per genome in patients carrying DBD mutations compared to both controls (p=0.0002***) and p.R337H (0.0156*) mutants.

Conclusions

The larger accumulation of rare CNVs in DBD mutants may contribute to the reported anticipation and severity of the syndrome; likewise the fact that p.R337H individuals do not present the same magnitude of rare CNV accumulation may also explain the maintenance of this mutation at relatively high frequency in some populations.

Keywords

TP53 R337H Li-Fraumeni syndrome CNV Penetrance

Background

Li-Fraumeni syndrome (LFS; OMIM #151623) is an autosomal dominant highly penetrant cancer predisposition syndrome characterized by a variety of early onset tumors [1, 2]. LFS arises from germline mutations in the TP53 gene, which codes for a transcription factor implicated in cell proliferation, apoptosis, and genomic stability [3, 4]. Although decreases in the age of cancer onset and increases in cancer incidence over successive generations have been documented in LFS [5, 6], the molecular basis of this anticipation is still a matter of debate.

Most mutations in the TP53 gene affect the DNA binding domain (DBD) and result in missense substitutions giving rise to altered proteins [7] that have a considerable longer half-life than the wild-type protein, resulting in accumulation of the mutant protein, at least in transfected or neoplastic cells [8].

It is known that different mutant p53 proteins may have diverse functional and biological effects, which could partially explain the heterogeneity reported between Li-Fraumeni families [9]. Not only the type of TP53 mutation determines the tumor spectrum and severity of phenotype, but other germline genetic factors such as polymorphisms for TP53 and MDM2[10] and telomere length [11, 12] are assumed to modulate the cancer phenotype. It has been more recently observed that individuals carrying TP53 mutations show a ~3-fold increase in DNA copy number variation (CNV) compared to controls, suggesting that CNVs make an additive contribution to cancer risk [13].

In Brazil, the Li-Fraumeni syndrome also occurs as a frequent cancer predisposition alteration due to a germline mutation at codon 337 (c.1010G4A, p.R337H) that has been identified in families matching LFS definitions [14, 15]. The frequency of p.R337H in the population of Southern Brazil is about 1:280 individuals, about 300 times higher than any other single germline TP53 mutation [16]. Analysis of 28 polymorphic markers showed p.R337H to have arisen from a founder mutation that has been spreading in the Brazilian population since the XVIII century [17, 18]. This mildly pathogenic mutation shows a penetrance for cancer of less than 20% by age 30, compared to about 50% in classic LFS [18]: this provides more opportunity for transmitting the mutation to the next generations, which explains, at least in part, the maintenance in the Brazilian population.

Structural and functional studies have demonstrated that the p.R337H protein displays a pH-dependence, rendering the protein inactive only under conditions of increased intracellular pH, and the majority of the time behaves normally, which could explain its lower overall malignant potential [19].

It is accepted that differences in severity and age of onset of cancer manifestation linked to various germline TP53 mutations not only result from the TP53 mutations themselves, but also from their interaction with other genetic variants, of which CNVs are now becoming recognized as a significant contributant (reviewed in [20]). Based on the foregoing considerations we hypothesize that different types of TP53 mutations exhibit different CNV profiles reflecting the genotype- phenotype correlation and also possibly playing a role in the anticipation observed in LFS families.

Methods

Patients

The patients were seen in the Department of Oncogenetics in the A. C. Camargo Hospital, São Paulo, Brazil. Research protocol approval was provided by the ethics committee of the institution, and informed consent obtained from all subjects and their families. The cohort comprised 21 probands with TP53 germline mutations [14]. The characteristics of the mutations, cancer type, age at onset and clinical classification of the patients are described in Table 1.
Table 1

Clinical data of the patients and characteristics of the detected mutations

ID

Classification

Gender

TP53 mutation

Effect

p53 protein domain function

Tumor type (age)

Y1T0

Chompret

F

R213Q CGA>CAA

Missense

DNA binding

Hydathiform mole (23), Ampulla of Vater (41)

Y33T0

LFL Birch

F

V173M GTG>ATG

Missense

DNA binding

Soft tissue sarcoma (23), Breast (43)

Y53T0

Eeles1

F

G245S GGC>AGC

Missense

DNA binding

Bilateral breast (36), Gall bladder (?)

Y57T0

Eeles1

F

G244D GGC>GAC

Missense

DNA binding

Breast (40), Colorectal (?)

Y65T0

Chompret

F

V197M GTG >ATG

Missense

DNA binding

Colorectal (45), Lung (51), Soft tissue sarcoma (51)

Y79T0

Chompret

F

T125T ACG>ACA

Splice

DNA binding

Adrenal carcinoma (1)

Y87T0

LFL/Chompret

M

S241Y TCC>TAC

Missense

DNA binding

Rhabdomyosarcoma (2), Choroid plexus tumor (7), Liposarcoma (10)

Y97T1

LFS

F

IVS8+1G>A

Splice

DNA binding

Breast (27)

Y103T2

LFL/Chompret

F

H214Q

Missense

DNA binding

Breast (62)

Y12T1

LFS

F

R337H CGC>CAC

Missense

Tetramerisation

Soft tissue sarcoma (58), Breast (59), Thyroid (61), Soft tissue sarcoma (62)

Y15T0

Chompret

F

R337H CGC>CAC

Missense

Tetramerisation

Adrenal carcinoma (6), Kidney (7)

Y27T0

LFL Birch

F

R337H CGC>CAC

Missense

Tetramerisation

Breast (36)

Y49T1

Eeles 1

M

R337H CGC>CAC

Missense

Tetramerisation

Kidney (64)

Y99T0

Chompret

F

R337H CGC>CAC

Missense

Tetramerisation

Breast (32)

Y100T0

LFS

F

R337H CGC>CAC

Missense

Tetramerisation

Breast (46)

Y106T0

Chompret

F

R337H CGC>CAC

Missense

Tetramerisation

Breast (43)

Y107T0

LFS

M

R337H CGC>CAC

Missense

Tetramerisation

Adrenal carcinoma (2)

Y127T0

Chompret

M

R337H CGC>CAC

Missense

Tetramerisation

Adrenal carcinoma (3)

Y131T0

LFS

F

R337H CGC>CAC

Missense

Tetramerisation

Leg synovial sarcoma (32)

Y144T0

Chompret

F

R337H CGC>CAC

Missense

Tetramerisation

Breast (29)

Y154T0

Chompret

F

R337H CGC>CAC

Missense

Tetramerisation

Breast (48)

Controls

The CNV control data used in this study was obtained from a previously reported group of 100 random individuals from the urban area of São Paulo, Brazil [21].

Array-CGH

We performed oligonucleotide microarray-based CGH (array-CGH) using a 4 × 180 K whole-genome platform (design 22060, Agilent Technologies, Santa Clara, USA), which has an average spacing of 18 Kb between probes. Briefly, samples were labeled with Cy3- and Cy5-dCTPs by random priming; purification, hybridization, and washing were carried out as recommended by the manufacturer. Scanned images of the arrays were processed and analyzed using Feature Extraction software and Genomic Workbench software (both from Agilent Technologies), with the statistical algorithm ADM-2, and a sensitivity threshold of 6.7. We applied a′loop design′ in our hybridizations as previously described [22], resulting in two reverse labeling hybridizations per sample. We considered a gain or loss in copy number when the log2 ratio of the Cy3/Cy5 intensities of a given genomic segment was >0.5 or <−0.8, respectively. Alterations had to encompass at least three consecutive probes to be called by the software, and those not detected in both dye-swap experiments of the same sample were excluded from the analysis.

Analysis

The detected copy number variations were compared to CNVs reported in the Database of Genomic Variants (DGV; http://projects.tcag.ca/variation/; freeze December, 2011). We classified the CNVs into ″rare″ and ″common″, considering as ″rare″ those CNVs where the imbalances encompassed coding sequences which were never or only once reported in DGV. Mann–Whitney and Fisher-exact tests were used to evaluate CNVs regarding proportions of total and rare CNVs, frequency of deletions and duplications and size of the variation.

The frequencies were then calculated for each rare variant in the total population described in the DGV studies. For estimating the total population in DGV, BAC array publications have been excluded because they are known to overestimate the CNV boundaries; the remaining 22 studies comprised a total of 8058 individuals investigated. The frequencies of these rare variants were than cross-checked with a study comprising 8148 controls [23], which is not documented in DGV but is registered in dbVar (http://www.ncbi.nlm.nih.gov/dbvar).

Results

The CNV data of this study are summarized in Table 2; a full description of the CNVs identified in the patients can be found in Additional file 1. The overall frequency of CNVs per genome did not differ either between patients and controls or between p.R33H and DBD mutants (Mann–Whitney test). No significant differences were detected between the groups regarding CNV size or frequency of deletions and duplications.
Table 2

Copy number variation data in controls and LFS/LFL patients classified according to TP53 mutation type

 

Controls

p.R337H carriers

DBD mutation carriers

Number of individuals

100

12

9

Number of CNVs

702

92

66

Common

679

89

56

Rare

23

3

10

Average number of all CNVs per diploid genome

7.0±3.1

7.7±2.2

7.3±2.5

Average number of rare CNVs per diploid genome

0.2±0.4

0.2±0.6

1.1±1.3 a

Proportion of rare/common CNVs

0.034

0.034 b

0.179 c

a. difference between controls and DBD mutation carriers (p=0.0026**).

b. difference between p.R337H and DBD mutation carriers (p=0.0156*).

c. difference between controls and DBD mutation carriers (p=0.0002***).

However, with regards to rarity, we found 13 rare CNVs among all LFS patients, including 9 deletions and 4 duplications. Additional file 2 describes the rare CNVs, the studies in which they have been observed and their corresponding frequencies in the combined individuals of DGV and dbVar. The calculated frequencies of these rare variants in normal populations as estimated based both on DGV and dbVar were < 0.1%, a value much inferior to the 1% threshold often associated with polimorphisms.

In comparison to our control group, TP53 mutated individuals (p.R337H and DBD) did not exhibit differences in the total number of CNVs/genome (Figure 1A). However, we did find a highly significant increase in the rare/common CNV ratio in the LFS patients carrying mutations in the TP53 DBD when compared to both controls (p=0.0002) and to patients carrying the p.R337H mutation (p=0.0156; Fisher-exact test) (Figure 1B). Interestingly, no significant difference was found between controls and p.R337H patients.
Figure 1

Distribution of CNVs according to TP53 mutation status. The graphs show no differences in the frequency of total CNVs but an increased frequency of rare CNVs in DBD mutation carriers. (A) Frequency of total CNVs in LFS patients and controls; (B) Rare/common CNV ratio in LFS patients and controls. White bar represents the control individuals, grey bar represents p.R337H mutated carriers and dark black bar the TP53 DBD mutated carriers. Fisher-exact test; *p=0.0156 and ***p=0.0002.

Discussion

The vast majority of TP53 mutations lies within the DNA binding domain and target critical sites that either make contact with DNA or are required to maintain appropriate p53 protein conformation. Such alterations lead to a significant loss of TP53 transcription and tumor suppressive activity, with great impact on genomic stability and apoptosis [2426]. In contrast, the p.R337H mutation, which affects a residue of the oligomerization domain of the p53 protein, has a markedly less severe impact on tumor predisposition, which correlates with its remaining p53 activity [19, 27].

Shlien et al. [13] reported a ~3-fold increase in the frequency of CNVs in TP53 germline mutation carriers compared to normal individuals, and proposed that this increase might result from enhanced genome instability in the presence of TP53 mutations. In our study, we found no difference between the total number of germline CNVs present in LFS patients and controls; however, we noted a highly significant increase (>5 fold) in the rare CNVs in TP53 DBD mutation carriers as compared both to controls and to p.R337H carriers. A difference between Shlien et al. [13] data and ours refers to the controls samples: in their work data previously assembled in other studies were used to establish a baseline CNV control frequency, whereas our control and patient samples have been investigated in parallel in the same laboratory. We should also point out that the studies were carried out using different microarray technologies, namely array-CGH and SNP platforms. In fact, a large study comparing different platforms found that different analytic tools typically yield CNV calls with <50% concordance, overall regarding smaller CNVs [28].

An increase of only rare germline CNVs in the DBD TP53 carriers, rather than an overall increase in CNVs, suggests that a selective mechanism would be involved in this event. Under the assumption that pathogenic variants would only remain in the population at low frequency, the selection against new pathogenic CNVs (in the context of studies on schizophrenia and autism, respectively) is extremely high [29, 30]. The finding of an increased proportion of rare CNVs, potentially pathogenic, among TP53 DBD mutants may be the result of an inefficient selection against new CNVs (deficient apoptosis). This hypothesis could be tested by determining the pattern of inheritance of the CNVs [13], but unfortunately parents were not available for investigation.

Anticipation, characterized by an increase of penetrance and earlier age of onset over subsequent generations, has been well documented for LFS [5, 6]. An accumulation of germline CNVs impacting cancer predisposition in LFS provides a logical explanation for anticipation. How CNVs and other genetic modifiers interact and modulate TP53 tumor suppressor activities remains to be determined. Elucidating these mechanisms may hold the key to defining evidence-based strategies for diagnosis, counseling, follow-up and therapeutic care based on a detailed understanding of the nature of combined risk between TP53 and other variants, including CNVs.

Conclusions

In this work we show a highly significant increase of rare CNVs in patients with mutations in the TP53 DNA binding domain site (DBD) relative both to controls and to carriers of the mild TP53 Brazilian variant. An accumulation of rare CNVs over generations in the presence of penetrant TP53 mutation provides a plausible explanation for anticipation in LFS: in contrast, lack of this increase in rare CNVs combined with a longer lifespan and likelihood of reproduction probably contributes to the frequency of the p.R337H mutation being ~300 xs larger than reported for any other TP53 mutation.

Availability of supporting data

The data set supporting the results of this article is included within the article and its additional files.

Abbreviations

LFS: 

Li-Fraumeni syndrome

LFL: 

Li-Fraumeni like syndrome

CNV: 

Copy Number Variation

DBD: 

DNA binding domain.

Declarations

Acknowledgements

We thank all patients for their participation to this study. We also thank the Brazilian National Institute of Science and Technology in Oncogenomics and FAPESP for financial support. Amanda G. Silva and Maria Isabel W Achatz are the first 2 authors that should be regarded as joint First Authors.

Authors’ Affiliations

(1)
International Center for Research and Training, A. C. Camargo Hospital
(2)
Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo

References

  1. Li FP, Fraumeni JF, Mulvihill JJ, Blattner WA, Dreyfus MG, Tucker MA, Miller RW: A cancer family syndrome in twenty-four kindreds. Cancer Res. 1988, 48 (18): 5358-5362.PubMedGoogle Scholar
  2. Birch JM, Alston RD, McNally RJ, Evans DG, Kelsey AM, Harris M, Eden OB, Varley JM: Relative frequency and morphology of cancers in carriers of germline TP53 mutations. Oncogene. 2001, 20 (34): 4621-4628. 10.1038/sj.onc.1204621.View ArticlePubMedGoogle Scholar
  3. Malkin D, Li FP, Strong LC, Fraumeni JF, Nelson CE, Kim DH, Kassel J, Gryka MA, Bischoff FZ, Tainsky MA: Germ line p53 mutations in a familial syndrome of breast cancer, sarcomas, and other neoplasms. Science. 1990, 250 (4985): 1233-1238. 10.1126/science.1978757.View ArticlePubMedGoogle Scholar
  4. Eeles RA: Germline mutations in the TP53 gene. Cancer Surv. 1995, 25: 101-124.PubMedGoogle Scholar
  5. Trkova M, Hladikova M, Kasal P, Goetz P, Sedlacek Z: Is there anticipation in the age at onset of cancer in families with Li-Fraumeni syndrome?. J Hum Genet. 2002, 47 (8): 381-386. 10.1007/s100380200055.View ArticlePubMedGoogle Scholar
  6. Brown BW, Costello TJ, Hwang SJ, Strong LC: Generation or birth cohort effect on cancer risk in Li-Fraumeni syndrome. Hum Genet. 2005, 118 (3–4): 489-498.View ArticlePubMedGoogle Scholar
  7. Levine AJ, Momand J, Finlay CA: The p53 tumour suppressor gene. Nature. 1991, 351 (6326): 453-456. 10.1038/351453a0.View ArticlePubMedGoogle Scholar
  8. Andersen TI, Holm R, Nesland JM, Heimdal KR, Ottestad L, Børresen AL: Prognostic significance of TP53 alterations in breast carcinoma. Br J Cancer. 1993, 68 (3): 540-548. 10.1038/bjc.1993.383.PubMed CentralView ArticlePubMedGoogle Scholar
  9. Petitjean A, Achatz MI, Borresen-Dale AL, Hainaut P, Olivier M: TP53 mutations in human cancers: functional selection and impact on cancer prognosis and outcomes. Oncogene. 2007, 26 (15): 2157-2165. 10.1038/sj.onc.1210302.View ArticlePubMedGoogle Scholar
  10. Marcel V, Palmero EI, Falagan-Lotsch P, Martel-Planche G, Ashton-Prolla P, Olivier M, Brentani RR, Hainaut P, Achatz MI: TP53 PIN3 and MDM2 SNP309 polymorphisms as genetic modifiers in the Li-Fraumeni syndrome: impact on age at first diagnosis. J Med Genet. 2009, 46 (11): 766-772. 10.1136/jmg.2009.066704.View ArticlePubMedGoogle Scholar
  11. Tabori U, Nanda S, Druker H, Lees J, Malkin D: Younger age of cancer initiation is associated with shorter telomere length in Li-Fraumeni syndrome. Cancer Res. 2007, 67 (4): 1415-1418. 10.1158/0008-5472.CAN-06-3682.View ArticlePubMedGoogle Scholar
  12. Trkova M, Prochazkova K, Krutilkova V, Sumerauer D, Sedlacek Z: Telomere length in peripheral blood cells of germline TP53 mutation carriers is shorter than that of normal individuals of corresponding age. Cancer. 2007, 110 (3): 694-702. 10.1002/cncr.22834.View ArticlePubMedGoogle Scholar
  13. Shlien A, Tabori U, Marshall CR, Pienkowska M, Feuk L, Novokmet A, Nanda S, Druker H, Scherer SW, Malkin D: Excessive genomic DNA copy number variation in the Li-Fraumeni cancer predisposition syndrome. Proc Natl Acad Sci USA. 2008, 105 (32): 11264-11269. 10.1073/pnas.0802970105.PubMed CentralView ArticlePubMedGoogle Scholar
  14. Achatz MI, Olivier M, Le Calvez F, Martel-Planche G, Lopes A, Rossi BM, Ashton-Prolla P, Giugliani R, Palmero EI, Vargas FR, et al: The TP53 mutation, R337H, is associated with Li-Fraumeni and Li-Fraumeni-like syndromes in Brazilian families. Cancer Lett. 2007, 245 (1–2): 96-102.View ArticlePubMedGoogle Scholar
  15. Palmero EI, Schüler-Faccini L, Caleffi M, Achatz MI, Olivier M, Martel-Planche G, Marcel V, Aguiar E, Giacomazzi J, Ewald IP, et al: Detection of R337H, a germline TP53 mutation predisposing to multiple cancers, in asymptomatic women participating in a breast cancer screening program in Southern Brazil. Cancer Lett. 2008, 261 (1): 21-25. 10.1016/j.canlet.2007.10.044.View ArticlePubMedGoogle Scholar
  16. Achatz MI, Hainaut P, Ashton-Prolla P: Highly prevalent TP53 mutation predisposing to many cancers in the Brazilian population: a case for newborn screening?. Lancet Oncol. 2009, 10 (9): 920-925. 10.1016/S1470-2045(09)70089-0.View ArticlePubMedGoogle Scholar
  17. Pinto EM, Billerbeck AE, Villares MC, Domenice S, Mendonça BB, Latronico AC: Founder effect for the highly prevalent R337H mutation of tumor suppressor p53 in Brazilian patients with adrenocortical tumors. Arq Bras Endocrinol Metabol. 2004, 48 (5): 647-650.View ArticlePubMedGoogle Scholar
  18. Garritano S, Gemignani F, Palmero EI, Olivier M, Martel-Planche G, Le Calvez-Kelm F, Brugiéres L, Vargas FR, Brentani RR, Ashton-Prolla P, et al: Detailed haplotype analysis at the TP53 locus in p.R337H mutation carriers in the population of Southern Brazil: evidence for a founder effect. Hum Mutat. 2010, 31 (2): 143-150. 10.1002/humu.21151.View ArticlePubMedGoogle Scholar
  19. DiGiammarino EL, Lee AS, Cadwell C, Zhang W, Bothner B, Ribeiro RC, Zambetti G, Kriwacki RW: A novel mechanism of tumorigenesis involving pH-dependent destabilization of a mutant p53 tetramer. Nat Struct Biol. 2002, 9 (1): 12-16. 10.1038/nsb730.View ArticlePubMedGoogle Scholar
  20. Kuiper RP, Ligtenberg MJ, Hoogerbrugge N, Geurts Van Kessel A: Germline copy number variation and cancer risk. Curr Opin Genet Dev. 2010, 20 (3): 282-289. 10.1016/j.gde.2010.03.005.View ArticlePubMedGoogle Scholar
  21. Krepischi AC, Achatz MI, Santos EM, Costa SS, Lisboa BC, Brentani H, Santos TM, Goncalves A, Nobrega AF, Pearson PL, et al: Germline DNA copy number variation in familial and early-onset breast cancer. Breast Cancer Res. 2012, 14 (1): R24-10.1186/bcr3109.PubMed CentralView ArticlePubMedGoogle Scholar
  22. Allemeersch J, Van Vooren S, Hannes F, De Moor B, Vermeesch JR, Moreau Y: An experimental loop design for the detection of constitutional chromosomal aberrations by array CGH. BMC Bioinformatics. 2009, 10: 380-10.1186/1471-2105-10-380.PubMed CentralView ArticlePubMedGoogle Scholar
  23. Xu H, Poh WT, Sim X, Ong RT, Suo C, Tay WT, Khor CC, Seielstad M, Liu J, Aung T, et al: SgD-CNV, a database for common and rare copy number variants in three Asian populations. Hum Mutat. 2011, 32 (12): 1341-1349. 10.1002/humu.21601.View ArticlePubMedGoogle Scholar
  24. Camplejohn RS, Perry P, Hodgson SV, Turner G, Williams A, Upton C, MacGeoch C, Mohammed S, Barnes DM: A possible screening test for inherited p53-related defects based on the apoptotic response of peripheral blood lymphocytes to DNA damage. Br J Cancer. 1995, 72 (3): 654-662. 10.1038/bjc.1995.390.PubMed CentralView ArticlePubMedGoogle Scholar
  25. Goi K, Takagi M, Iwata S, Delia D, Asada M, Donghi R, Tsunematsu Y, Nakazawa S, Yamamoto H, Yokota J, et al: DNA damage-associated dysregulation of the cell cycle and apoptosis control in cells with germ-line p53 mutation. Cancer Res. 1997, 57 (10): 1895-1902.PubMedGoogle Scholar
  26. Malkin D: Li-fraumeni syndrome. Genes Cancer. 2011, 2 (4): 475-484. 10.1177/1947601911413466.PubMed CentralView ArticlePubMedGoogle Scholar
  27. Zambetti GP: The p53 mutation ″gradient effect″ and its clinical implications. J Cell Physiol. 2007, 213 (2): 370-373. 10.1002/jcp.21217.View ArticlePubMedGoogle Scholar
  28. Pinto D, Darvishi K, Shi X, Rajan D, Rigler D, Fitzgerald T, Lionel AC, Thiruvahindrapuram B, Macdonald JR, Mills R, et al: Comprehensive assessment of array-based platforms and calling algorithms for detection of copy number variants. Nat Biotechnol. 2011, 29 (6): 512-520. 10.1038/nbt.1852.PubMed CentralView ArticlePubMedGoogle Scholar
  29. Rees E, Moskvina V, Owen MJ, O′Donovan MC, Kirov G: De novo rates and selection of schizophrenia-associated copy number variants. Biol Psychiatry. 2011, 70 (12): 1109-1114. 10.1016/j.biopsych.2011.07.011.View ArticlePubMedGoogle Scholar
  30. Itsara A, Wu H, Smith JD, Nickerson DA, Romieu I, London SJ, Eichler EE: De novo rates and selection of large copy number variation. Genome Res. 2010, 20 (11): 1469-1481. 10.1101/gr.107680.110.PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

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

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.